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    Factors influencing the outcome of lower-extremity diabetic ulcers treated with hyperbaric oxygen therapy. Fife Caroline E,Buyukcakir Cem,Otto Gordon,Sheffield Paul,Love Tommy,Warriner Robert Wound repair and regeneration : official publication of the Wound Healing Society [and] the European Tissue Repair Society The objectives of this study were to report outcomes of a large number of patients receiving hyperbaric oxygen therapy (HBO(2)T) for diabetic lower-extremity ulcers, and to identify likely outcome predictors. Five hyperbaric facilities supplied data on 1,006 patients. A sixth clinic served as a validation sample for the regression-based prediction model, and later additional data from Memorial Hermann Hospital were added. The severity of lower-extremity lesions was assessed upon initiation of HBO(2)T using the Modified Wagner scale, and the outcome described as healed, partially healed, not improved, amputated, or died. Overall, 73.8% of patients improved (granulated or healed). Factors significantly related to outcome included renal failure, pack-year smoking history, transcutaneous oximetry, number of HBO(2)T treatments, and interruption of treatment regimen. Number of treatments per week and treatment pressure (2.0 vs. 2.4 atmospheres absolute) were not significant factors in outcome. Concomitant administration of autologous growth factor gel did not improve outcome. A multiple regression model was fitted to the data that can be used to predict the outcome of diabetic patients undergoing HBO(2)T. Given the high cost of amputation and rehabilitation, these data suggest that hyperbaric oxygen treatment should be an important adjunctive therapy to heal lower-extremity lesions, especially those with a Wagner grade of 3 or higher. 10.1111/j.1524-475X.2007.00234.x
    An inverse finite-element model of heel-pad indentation. Erdemir Ahmet,Viveiros Meredith L,Ulbrecht Jan S,Cavanagh Peter R Journal of biomechanics A numerical-experimental approach has been developed to characterize heel-pad deformation at the material level. Left and right heels of 20 diabetic subjects and 20 nondiabetic subjects matched for age, gender and body mass index were indented using force-controlled ultrasound. Initial tissue thickness and deformation were measured using M-mode ultrasound; indentation forces were recorded simultaneously. An inverse finite-element analysis of the indentation protocol using axisymmetric models adjusted to reflect individual heel thickness was used to extract nonlinear material properties describing the hyperelastic behavior of each heel. Student's t-tests revealed that heel pads of diabetic subjects were not significantly different in initial thickness nor were they stiffer than those from nondiabetic subjects. Another heel-pad model with anatomically realistic surface representations of the calcaneus and soft tissue was developed to estimate peak pressure prediction errors when average rather than individualized material properties were used. Root-mean-square errors of up to 7% were calculated, indicating the importance of subject-specific modeling of the nonlinear elastic behavior of the heel pad. Indentation systems combined with the presented numerical approach can provide this information for further analysis of patient-specific foot pathologies and therapeutic footwear designs. 10.1016/j.jbiomech.2005.03.007
    Short-term prediction of major lower-limb amputation based on clinical indicators on admission: a single institutional experience in a developing country. Seuc A H,López M,Rodríguez L,Montequín J F International angiology : a journal of the International Union of Angiology AIM:The aim of this study was to analyze the possibility of predicting short-term major lower-limb amputation (SMLA) in patients with vascular diagnoses, based only on clinical variables measured on admission. METHODS:A longitudinal, retrospective cohort study of patients with peripheral vascular diagnoses admitted at our Institute was carried out. A stratified sample of 463 patients admitted during 1997, 2000 and 2003, was studied. Logistic regression was used to identify significant predictors of amputation; twelve clinical variables measured on admission were considered as potential predictors. RESULTS:Of the 463 patients, 93 (20%) were amputated. Significant predictors of amputation identified by the logistic regression analysis were ''type of lesion'' (none; neuro-infectious; ischemic; mixture), ''initial diagnosis'' (phlebolymphopathies, acute arterial insufficiency, chronic arterial insufficiency, diabetic foot, others), ''plantar region lesion'' (no; yes), ''diabetes'' (no; yes), ''number of toes affected'' (none; 1-2; 3 or more), and ''area of leg affected'' (none; lower third; + lower third). More than 80% of patients were correctly classified with the final model: sensitivity was 42% and specificity 96%. CONCLUSIONS:It seems that SMLA in patients with vascular diagnoses can be predicted reasonably well using as predictors only clinical variables measured on admission. This is a potentially useful result for Angiology Services located in developing/poor communities. The amputation probability for each patient obtained from the logistic regression model can be used in several ways: 1) the medical care of patients can be customized so that the amputation rate of the whole Service can be reduced, and 2) the amputation probability of the statistical model can be used as an estimation of the severity of the disease in each patient, which in turn can be used to standardize the amputation rates computed on different years; this would allow a better assessment of the Institutional performance over time.
    [Optimization of amoxicillin/clavulanate therapy based on pharmacokinetic/pharmacodynamic parameters in patients with diabetic foot infection]. Sedivý Josef,Petkov Vladimír,Jirkovská Alexandra,Stambergová Alexandra,Ulbrichová Zorka,Lupínková Jana,Fejfarová Vladimíra,Bém Robert Klinicka mikrobiologie a infekcni lekarstvi AIM OF THE STUDY:Individualized optimization of amoxicillin/clavulanate (AMC) antimicrobial therapy in diabetic foot infection. METHODS:Pharmacokinetic analysis of individual steady-state plasma amoxicillin concentrations was done both in the i.v. infusion phase and in the oral phase of AMC, administered on the basis of the quantitative susceptibility of the detected microbe(s). The in vitro growth/killing dynamic parameters on model of Staphylococcus aureus as the most frequent isolate were evaluated. Therapeutic protocol optimization, leading to prediction of the earliest time to reduce the number of viable bacteria to 10-6 as a surrogate criterion of efficacy, was performed. RESULTS:Based on individual plasma amoxicillin oscillations in 17 patients suffering from infected diabetic foot ulcers and the model microbial dynamic parameters, the reduction of the number of viable bacteria was reached significantly earlier after the administration of continuous i.v. AMC infusion than after the same daily AMC dose administered intermittently. In case of highly susceptible staphylococcal strain, highly frequent oral therapy of AMC (not longer than 8 hrs dosing interval) was also sufficiently effective. Decreasing plasma amoxicillin concentrations exponentially extended the time required for effective reduction of microbes. CONCLUSION:Individualized optimization of amoxicillin/clavulanate dosage on the basis of growth/killing microbial dynamic parameters and antibiotic concentration/time fluctuations may enhance the antimicrobial effect and the treatment of infected non-critical ischemic diabetic foot ulcers.
    Evaluation of machine learning methodology for the prediction of healthcare resource utilization and healthcare costs in patients with critical limb ischemia-is preventive and personalized approach on the horizon? The EPMA journal BACKGROUND:Critical limb ischemia (CLI) is a severe stage of peripheral arterial disease and has a substantial disease and economic burden not only to patients and families, but also to the society and healthcare systems. We aim to develop a personalized prediction model that utilizes baseline patient characteristics prior to CLI diagnosis to predict subsequent 1-year all-cause hospitalizations and total annual healthcare cost, using a novel Bayesian machine learning platform, Reverse Engineering Forward Simulation™ (REFS™), to support a paradigm shift from reactive healthcare to Predictive Preventive and Personalized Medicine (PPPM)-driven healthcare. METHODS:Patients ≥ 50 years with CLI plus clinical activity for a 6-month pre-index and a 12-month post-index period or death during the post-index period were included in this retrospective cohort of the linked Optum-Humedica databases. REFS™ built an ensemble of 256 predictive models to identify predictors of all-cause hospitalizations and total annual all-cause healthcare costs during the 12-month post-index interval. RESULTS:The mean age of 3189 eligible patients was 71.9 years. The most common CLI-related comorbidities were hypertension (79.5%), dyslipidemia (61.4%), coronary atherosclerosis and other heart disease (42.3%), and type 2 diabetes (39.2%). Post-index CLI-related healthcare utilization included inpatient services (14.6%) and ≥ 1 outpatient visits (32.1%). Median annual all-cause and CLI-related costs per patient were $30,514 and $2196, respectively. REFS™ identified diagnosis of skin and subcutaneous tissue infections, cellulitis and abscess, use of nonselective beta-blockers, other aftercare, and osteoarthritis as high confidence predictors of all-cause hospitalizations. The leading predictors for total all-cause costs included region of residence and comorbid health conditions including other diseases of kidney and ureters, blindness of vision defects, chronic ulcer of skin, and chronic ulcer of leg or foot. CONCLUSIONS:REFS™ identified baseline predictors of subsequent healthcare resource utilization and costs in CLI patients. Machine learning and model-based, data-driven medicine may complement physicians' evidence-based medical services. These findings also support the PPPM framework that a paradigm shift from post-diagnosis disease care to early management of comorbidities and targeted prevention is warranted to deliver a cost-effective medical services and desirable healthcare economy. 10.1007/s13167-019-00196-9
    Predictive Model of Type 2 Diabetes Remission after Metabolic Surgery in Chinese Patients. Luo Yufang,Guo Zi,He Honghui,Yang Youbo,Zhao Shaoli,Mo Zhaohui International journal of endocrinology Introduction:Metabolic surgery is an effective treatment for type 2 diabetes (T2D). At present, there is no authoritative standard for predicting postoperative T2D remission in clinical use. In general, East Asian patients with T2D have a lower body mass index and worse islet function than westerners. We aimed to look for clinical predictors of T2D remission after metabolic surgery in Chinese patients, which may provide insights for patient selection. Methods:Patients with T2D who underwent metabolic surgery at the Third Xiangya Hospital between October 2008 and March 2017 were enrolled. T2D remission was defined as an HbA1c level below 6.5% and an FPG concentration below 7.1 mmol/L for at least one year in the absence of antidiabetic medications. Results:(1) Independent predictors of short-term T2D remission (1-2 years) were age and C-peptide area under the curve (C-peptide AUC); independent predictors of long-term T2D remission (4-6 years) were C-peptide AUC and fasting plasma glucose (FPG). (2) The optimal cutoff value for C-peptide AUC in predicting T2D remission was 30.93 ng/ml, with a specificity of 67.3% and sensitivity of 75.8% in the short term and with a specificity of 61.9% and sensitivity of 81.5% in the long term, respectively. The areas under the ROC curves are 0.674 and 0.623 in the short term and long term, respectively. (3) We used three variables (age, C-peptide AUC, and FPG) to construct a remission prediction score (ACF), a multidimensional 9-point scale, along which greater scores indicate a better chance of T2D remission. We compared our scoring system with other reported models (ABCD, DiaRem, and IMS). The ACF scoring system had the best distribution of patients and prognostic significance according to the ROC curves. Conclusion:Presurgery age, C-peptide AUC, and FPG are independent predictors of T2D remission after metabolic surgery. Among these, C-peptide AUC plays a decisive role in both short- and long-term remission prediction, and the optimal cutoff value for C-peptide AUC in predicting T2D remission was 30.93 ng/ml, with moderate predictive values. The ACF score is a simple reliable system that can predict T2D remission among Chinese patients. 10.1155/2020/2965175
    Numerical simulation of the plantar pressure distribution in the diabetic foot during the push-off stance. Actis Ricardo L,Ventura Liliana B,Smith Kirk E,Commean Paul K,Lott Donovan J,Pilgram Thomas K,Mueller Michael J Medical & biological engineering & computing The primary objective of conservative care for the diabetic foot is to protect the foot from excessive pressures. Pressure reduction and redistribution may be achieved by designing and fabricating orthotic devices based on foot structure, tissue mechanics, and external loads on the diabetic foot. The purpose of this paper is to describe the process used for the development of patient-specific mathematical models of the second and third rays of the foot, their solution by the finite element method, and their sensitivity to model parameters and assumptions. We hypothesized that the least complex model to capture the pressure distribution in the region of the metatarsal heads would include the bony structure segmented as toe, metatarsal and support, with cartilage between the bones, plantar fascia and soft tissue. To check the hypothesis, several models were constructed with different levels of details. The process of numerical simulation is comprised of three constituent parts: model definition, numerical solution and prediction. In this paper the main considerations relating model selection and computation of approximate solutions by the finite element method are considered. The fit of forefoot plantar pressures estimated using the FEA models and those explicitly tested were good as evidenced by high Pearson correlations (r=0.70-0.98) and small bias and dispersion. We concluded that incorporating bone support, metatarsal and toes with linear material properties, tendon and fascia with linear material properties, soft tissue with nonlinear material properties, is sufficient for the determination of the pressure distribution in the metatarsal head region in the push-off position, both barefoot and with shoe and total contact insert. Patient-specific examples are presented. 10.1007/s11517-006-0078-5
    Monitoring temporal development and healing of diabetic foot ulceration using hyperspectral imaging. Yudovsky Dmitry,Nouvong Aksone,Schomacker Kevin,Pilon Laurent Journal of biophotonics This study combines non-invasive hyperspectral imaging with an experimentally validated skin optical model and inverse algorithm to monitor diabetic feet of two representative patients. It aims to observe temporal changes in local epidermal thickness and oxyhemoglobin concentration and to gain insight into the progression of foot ulcer formation and healing. Foot ulceration is a debilitating comorbidity of diabetes that may result in loss of mobility and amputation. Inflammation and necrosis preempt ulceration and can result in changes in the skin prior to ulceration and during ulcer healing that affect oxygen delivery and consumption. Previous studies estimated oxyhemoglobin and deoxyhemoglobin concentrations around pre-ulcerative and ulcer sites on the diabetic foot using commercially available hyperspectral imaging systems. These measurements were successfully used to detect tissue at risk of ulceration and predict the healing potential of ulcers. The present study shows epidermal thickening and decrease in oxyhemoglobin concentration can also be detected prior to ulceration at pre-ulcerative sites. The algorithm was also able to observe reduction in the epidermal thickness combined with an increase in oxyhemoglobin concentration around the ulcer as it healed and closed. This methodology can be used for early prediction of diabetic foot ulceration in a clinical setting. 10.1002/jbio.201000117
    Risk stratification systems for diabetic foot ulcers: a systematic review. Monteiro-Soares M,Boyko E J,Ribeiro J,Ribeiro I,Dinis-Ribeiro M Diabetologia AIMS/HYPOTHESIS:Several risk stratification systems have been proposed for predicting development of diabetic foot ulcer. However, little has been published that assesses their similarities and disparities, diagnostic accuracy and evidence level. Consequently, we conducted a systematic review of the existing stratification systems. METHODS:We searched the MEDLINE database for studies (published until April 2010) describing the creation and validation of risk stratification systems for prediction of diabetic foot ulcer development. RESULTS:We included 13 studies describing or evaluating the following different risk degree stratification systems: University of Texas; International Working Group on Diabetic Foot; Scottish Intercollegiate Guideline Network (SIGN); American Diabetes Association; and Boyko and colleagues. We confirmed that five variables were included in almost all the systems: diabetic neuropathy, peripheral vascular disease, foot deformity, and previous foot ulcer and amputation. The number of variables included ranged from four to eight and the number of risk groups from two to six. Only four studies reported or allowed the calculation of diagnostic accuracy measures. The SIGN system showed some higher diagnostic accuracy values, particularly positive likelihood ratio, while predictive ability was confirmed through external validation only in the system of Boyko et al. CONCLUSIONS/INTERPRETATION:Foot ulcer risk stratification systems are a much needed tool for screening patients with diabetes. The core variables of various systems are very similar, but the number of included variables in each model and risk groups varied greatly. Overall, the quality of evidence for these systems is low, as little validation of their predictive ability has been done. 10.1007/s00125-010-2030-3
    Predictive model of short-term amputation during hospitalization of patients due to acute diabetic foot infections. Barberán José,Granizo Juan-José,Aguilar Lorenzo,Alguacil Rafael,Sainz Felipe,Menéndez Maria-Antonia,Giménez Maria-José,Martínez David,Prieto José Enfermedades infecciosas y microbiologia clinica INTRODUCTION:Factors predicting short-term amputation during hospital treatment of patients admitted for acute diabetic foot infections are of interest for clinicians managing the acute episode. METHODS:A retrospective clinical records analysis of 78 consecutive patients hospitalized for acute diabetic foot infections was performed to identify predictive factors for short-term amputation by comparing the data of patients who ultimately required amputation and those who did not. Clinical/epidemiological, laboratory, imaging, and treatment variables were comparatively analyzed. A logistic regression model was performed, with amputation as the dependent variable and factors showing significant differences in the bivariate analysis as independent variables. A prediction score was calculated (and validated by ROC curve analysis) using beta coefficients for significant variables in the regression analysis to predict amputation. RESULTS:Of the 78 patients (70.5% with peripheral vasculopathy) included, 26 ultimately required amputation. In the bivariate analysis, white blood cell count, previous homolateral lesions, odor, lesion depth, sedimentation rate, Wagner ulcer grade, and arterial obstruction on Doppler study were significantly higher in patients ending in amputation. In the multivariate analysis, the risk of amputation was increased only by Wagner grade 4 or 5 (20-fold higher), obstruction (12.5-fold higher), and elevated sedimentation rate (6% higher per unit). Logistic regression predicted outcome in 76.9% of patients who underwent amputation and 92.3% of those who did not. CONCLUSION:The score calculated using beta coefficients for significant variables in the regression model (Wagner grades 4 and 5, obstruction on Doppler, and elevated sedimentation rate for the clinical, imaging, and laboratory data, respectively) correctly predicted amputation during hospital management of acute diabetic foot infections. 10.1016/j.eimc.2009.12.017
    Diagnostic Accuracy of Oxygen Desaturation Index for Sleep-Disordered Breathing in Patients With Diabetes. Chen Lihong,Tang Weiwei,Wang Chun,Chen Dawei,Gao Yun,Ma Wanxia,Zha Panpan,Lei Fei,Tang Xiangdong,Ran Xingwu Frontiers in endocrinology Background:Polysomnography (PSG) is the gold standard for diagnosis of sleep-disordered breathing (SDB). But it is impractical to perform PSG in all patients with diabetes. The objective was to develop a clinically easy-to-use prediction model to diagnosis SDB in patients with diabetes. Methods:A total of 440 patients with diabetes were recruited and underwent overnight PSG at West China Hospital. Prediction algorithms were based on oxygen desaturation index (ODI) and other variables, including sex, age, body mass index, Epworth score, mean oxygen saturation, and total sleep time. Two phase approach was employed to derivate and validate the models. Results:ODI was strongly correlated with apnea-hypopnea index (AHI) (r = 0.941). In the derivation phase, the single cutoff model with ODI was selected, with area under the receiver operating characteristic curve (AUC) of 0.956 (95%CI 0.917-0.994), 0.962 (95%CI 0.943-0.981), and 0.976 (95%CI 0.956-0.996) for predicting AHI ≥5/h, ≥15/h, and ≥30/h, respectively. We identified the cutoff of ODI 5/h, 15/h, and 25/h, as having important predictive value for AHI ≥5/h, ≥15/h, and ≥30/h, respectively. In the validation phase, the AUC of ODI was 0.941 (95%CI 0.904-0.978), 0.969 (95%CI 0.969-0.991), and 0.949 (95%CI 0.915-0.983) for predicting AHI ≥5/h, ≥15/h, and ≥30/h, respectively. The sensitivity of ODI ≥5/h, ≥15/h, and ≥25/h was 92%, 90%, and 93%, respectively, while the specificity was 73%, 89%, and 85%, respectively. Conclusions:ODI is a sensitive and specific tool to predict SDB in patients with diabetes. 10.3389/fendo.2021.598470
    ELAV-like RNA binding protein 1 regulates osteogenesis in diabetic osteoporosis: Involvement of divalent metal transporter 1. Molecular and cellular endocrinology Diabetic osteoporosis (DOP) is a complication of diabetes mellitus (DM) and occurs due to alterations in bone metabolism under hyperglycemic condition. ELAV-like RNA binding protein 1 (ELAVL1) is abnormally up-regulated in diabetes-related diseases. Bioinformatics prediction indicates that divalent metal transporter 1 (DMT1) is a potential target of ELAVL1. To explore the role of ELAVL1 and the involvement of ELAVL1/DMT1 axis in DOP, we established a mouse model of DM by administration of high-fat diet and intraperitoneal injection with streptozotocin (STZ). The expression of ELAVL1 and DMT1 was increased in the bone tissues of DM mice. Knockdown of ELAVL1 reduced iron level and oxidative stress, promoted osteogensis, and prevented bone mass loss, thus mitigating DOP in DM mice. In vitro, mouse pre-osteoblast MC3T3-E1 cells were treated with high glucose (25 mM) and ferric ammonium citrate (FAC, 200 μM). The inhibitory effects of ELAVL1 knockdown on iron accumulation and oxidative stress were evidenced in MC3T3-E1 cells. Knockdown of ELAVL1 enhanced osteoblast viability, differentiation and mineralization. Notably, the expression of DMT1 was positively correlated with that of ELAVL1 in vivo and in vitro. Overexpression of DMT1 abolished the effect of ELAVL1 knockdown on the behaviors of MC3T3-E1 cells, suggesting that ELAVL1 might function through regulating DMT1. In conclusion, knockdown of ELAVL1 likely alleviated DOP by inhibiting iron overload and oxidative stress and promoting osteogenesis, and DMT1 might be involved in this process. These findings provide insights into the pathogenesis of DOP and suggest a potential therapeutic target for DOP treatment. 10.1016/j.mce.2022.111559
    Outcome predictors for wound healing in patients with a diabetic foot ulcer. Mohammad Zadeh Maryam,Lingsma Hester,van Neck Johan W,Vasilic Dalibor,van Dishoeck Anne-Margreet International wound journal The aim of this study was to identify diabetic foot ulcer (DFU) patients at risk for the development of a hard-to-heal wound. This is a post-hoc analysis of a prospective cohort study including a total of 208 patients with a DFU. The primary endpoints were time to healing and the development of a hard-to-heal-wound. Univariable and multivariable logistic and Cox regression analysis were used to study the associations of patient characteristics with the primary endpoints. The number of previous DFUs [odds ratio (OR): 1.42, 95% confidence interval (CI): 1.01-1.99, P = .04], University of Texas (UT) classification grade 2 (OR: 2.93, 95% CI: 1.27-6.72, P = .01), UT classification grade 3 (OR: 2.80, 95% CI: 1.17-6.71, P = .02), and a diagnosis of foot stand deformation (OR: 1.54, 95% CI: 0.77-3.08, P = .05) were significantly associated with the development of a hard-to-heal wound. Only UT classification grade 3 (HR: 0.61, 95% CI: 0.41-0.90, P = .01) was associated with time to healing. The number of previous DFUs, UT classification grade, and a diagnosis of foot deformation are significantly associated with development of a hard-to-heal wound in patients with a DFU. The only predictor significantly associated with time to healing was UT classification grade 3. These patient characteristics can be used to identify patients at risk for the development of hard-to-heal wounds, who might need an early intervention to prevent wound problems. 10.1111/iwj.13194
    Mortality prediction following non-traumatic amputation of the lower extremity. Norvell D C,Thompson M L,Boyko E J,Landry G,Littman A J,Henderson W G,Turner A P,Maynard C,Moore K P,Czerniecki J M The British journal of surgery BACKGROUND:Patients who undergo lower extremity amputation secondary to the complications of diabetes or peripheral artery disease have poor long-term survival. Providing patients and surgeons with individual-patient, rather than population, survival estimates provides them with important information to make individualized treatment decisions. METHODS:Patients with peripheral artery disease and/or diabetes undergoing their first unilateral transmetatarsal, transtibial or transfemoral amputation were identified in the Veterans Affairs Surgical Quality Improvement Program (VASQIP) database. Stepdown logistic regression was used to develop a 1-year mortality risk prediction model from a list of 33 candidate predictors using data from three of five Department of Veterans Affairs national geographical regions. External geographical validation was performed using data from the remaining two regions. Calibration and discrimination were assessed in the development and validation samples. RESULTS:The development sample included 5028 patients and the validation sample 2140. The final mortality prediction model (AMPREDICT-Mortality) included amputation level, age, BMI, race, functional status, congestive heart failure, dialysis, blood urea nitrogen level, and white blood cell and platelet counts. The model fit in the validation sample was good. The area under the receiver operating characteristic (ROC) curve for the validation sample was 0·76 and Cox calibration regression indicated excellent calibration (slope 0·96, 95 per cent c.i. 0·85 to 1·06; intercept 0·02, 95 per cent c.i. -0·12 to 0·17). Given the external validation characteristics, the development and validation samples were combined, giving a total sample of 7168. CONCLUSION:The AMPREDICT-Mortality prediction model is a validated parsimonious model that can be used to inform the 1-year mortality risk following non-traumatic lower extremity amputation of patients with peripheral artery disease or diabetes. 10.1002/bjs.11124
    Migratory activity of circulating mononuclear cells is associated with cardiovascular mortality in type 2 diabetic patients with critical limb ischemia. Spinetti Gaia,Specchia Claudia,Fortunato Orazio,Sangalli Elena,Clerici Giacomo,Caminiti Maurizio,Airoldi Flavio,Losa Sergio,Emanueli Costanza,Faglia Ezio,Madeddu Paolo Diabetes care OBJECTIVE:Prediction of clinical outcome in diabetic patients with critical limb ischemia (CLI) is unsatisfactory. This prospective study investigates if the abundance and migratory activity of a subpopulation of circulating mononuclear cells, namely, CD45(dim)CD34(pos)CXCR4(pos)KDR(pos) cells, predict major amputation and cardiovascular death in type 2 diabetic patients undergoing percutaneous transluminal angioplasty for CLI. RESEARCH DESIGN AND METHODS:A consecutive series of 119 type 2 diabetic patients with CLI was enrolled. CD45(dim)CD34(pos)CXCR4(pos)KDR(pos) cells were assessed by flow cytometry upon isolation and also after spontaneous or stromal cell-derived factor 1α-directed migration in an in vitro assay. The association between basal cell counts and migratory activity and the risk of an event at 18-month follow-up was evaluated in a multivariable regression analysis. RESULTS:Time-to-event analysis of amputation (n = 13) showed no association with the candidate predictors. Sixteen cardiovascular deaths occurred during 18 months of follow-up. Abundance of CD45(dim)CD34(pos)CXCR4(pos)KDR(pos) cells was not associated with cardiovascular mortality. Interestingly, in vitro migration of CD45(dim)CD34(pos)CXCR4(pos)KDR(pos) cells was higher in patients with cardiovascular death compared with event-free subjects (percentage of migrated cells median value and interquartile range, 0.03 [0.02-0.07] vs. 0.01 [0.01-0.03]; P = 0.0095). Multivariable regression model analysis showed that cell migration forecasts cardiovascular mortality independently of other validated predictors, such as age, diagnosed coronary artery disease, serum C-reactive protein, and estimated glomerular filtration rate. In this model, doubling of migrated cell counts increases the cardiovascular death hazard by 100% (P < 0.0001). CONCLUSIONS:The new predictor could aid in the identification of high-risk patients with type 2 diabetes requiring special diagnostic and therapeutic care after revascularization. 10.2337/dc13-2084
    Causation Research on Diabetic Foot Complications-What I Learned From Roger Pecoraro: The 2021 Roger E. Pecoraro Award Lecture. Boyko Edward J Diabetes care Roger Pecoraro made important contributions to diabetic foot research and is primarily responsible for instilling in me an interest in these complications. Our collaboration in the final years of his life led to the development of the Seattle Diabetic Foot Study. At the time it began, the Seattle Diabetic Foot Study was perhaps unique in being a prospective study of diabetic foot ulcer conducted in a nonspecialty primary care population of patients with diabetes and without foot ulcer. Important findings from this research include the demonstration that neurovascular measurements, diabetes characteristics, past history of ulcer or amputation, body weight, and poor vision all significantly and independently predict foot ulcer risk. A prediction model from this research that included only readily available clinical information showed excellent ability to discriminate between patients who did and did not develop ulcer during follow-up (area under the receiver operating characteristic curve [AUROC] 0.81 at 1 year). Identification of limb-specific amputation risk factors showed considerable overlap with those risk factors identified for foot ulcer but suggested arterial perfusion as playing a more important role. Risk of foot ulcer in relation to peak plantar pressure estimated at the site of the pressure measurement showed a significant association over the metatarsal heads, but not other foot locations, suggesting that the association between pressure and this outcome may differ by foot location. The Seattle Diabetic Foot Study has helped to expand our knowledge base on risk factors and potential causes of foot complications. Translating this information into preventive interventions remains a continuing challenge. 10.2337/dci21-0026
    A predictive model of anxiety and depression symptoms after a lower limb amputation. Pedras Susana,Carvalho Rui,Pereira M Graça Disability and health journal BACKGROUND:Patients with Diabetic Foot Ulcer (DFU) show high levels of depression and anxiety symptoms. The loss of a limb is undoubtedly a devastating experience and several studies have shown that anxiety and depression symptoms are a common reaction after a lower limb amputation (LLA). However, no study has focused on the immediate emotional reactions to LLA as a personal factor based on the ICF Model. OBJECTIVE:This study focused on the characterization of anxiety and depression levels, before and after surgery, differences in levels of depression and anxiety before and after surgery and the predictors of anxiety and depression one month after surgery, in a sample of patients with DFU. METHODS:This was a longitudinal study with 179 patients with Diabetes Mellitus Type 2 and DFU indicated for amputation, screened for the presence of anxiety and depression symptoms during the hospitalization that preceded amputation and one month after surgery, during a follow-up consultation. RESULTS:The results showed a significant effect of anxiety and depression symptoms at pre-surgery in the prediction of anxiety and depression symptoms one month after LLA. Patients showed higher levels of anxiety than depression symptoms at pre-surgery, although anxiety significantly decreased on month after surgery. Both anxiety and depression symptoms contributed to depression after LLA, although anxiety at pre-surgery was the only predictor of anxiety at post-surgery. CONCLUSIONS:Tailored multidisciplinary interventions need to be developed providing support before and after an amputation surgery, in order to reduce anxiety and depression symptoms and promote psychological adjustment to limb loss. 10.1016/j.dhjo.2017.03.013
    Toe Pressure in Predicting Diabetic Foot Ulcer Healing: A Systematic Review and Meta-analysis. Tay Wei Ling,Lo Zhiwen Joseph,Hong Qiantai,Yong Enming,Chandrasekar Sadhana,Tan Glenn Wei Leong Annals of vascular surgery BACKGROUND:Foot ulceration is the most frequently recognized lower extremity complication in diabetic patients. Predicting wound healing is an essential step in the management of diabetic foot ulcers (DFUs), as it is estimated that early detection and appropriate treatments may prevent up to 85% of amputations. Toe systolic blood pressure (TBP) is a quick and portable bedside assessment and is less affected by medial sclerosis of arteries present in the diabetic population compared to other measurements like ankle-brachial index. This systematic review seeks to evaluate the sensitivity and specificity of toe pressure in prediction of DFU wound healing. METHODS:PubMed/MEDLINE and EMBASE databases were systematically searched up to September 20, 2017 in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. All randomized control, prospective and retrospective trials were considered for inclusion if they reported healing rates of DFUs with respect to different TBP readings. Healing was defined to be intact skin for at least 6 months or at time of death. Quality assessment of articles was performed using the RevMan Quality Assessment. Information on healing rates with respect to different TBP values was extracted. Summary estimates of sensitivity and specificity of TBP in predicting healing of DFU wounds were obtained using a bivariate model. RESULTS:A total of 580 articles were screened. Eight studies (6 prospective and 2 retrospective) inclusive of 909 patients were eligible for inclusion. It was found that a TBP of more than 30 mm Hg is associated with a sensitivity and specificity of 0.86 and 0.58 respectively for healing of DFUs. CONCLUSIONS:A TBP of more than 30 mm Hg is sensitive but not specific in the prediction of healing of DFUs. Due to its portability and quick analysis, TBP may be used as a bedside assessment to complement current clinical parameters to aid in predicting the healing of diabetic foot ulcers. 10.1016/j.avsg.2019.04.011
    Prediction of diabetic foot ulcer occurrence using commonly available clinical information: the Seattle Diabetic Foot Study. Boyko Edward J,Ahroni Jessie H,Cohen Victoria,Nelson Karin M,Heagerty Patrick J Diabetes care OBJECTIVE:The ability of readily available clinical information to predict the occurrence of diabetic foot ulcer has not been extensively studied. We conducted a prospective study of the individual and combined effects of commonly available clinical information in the prediction of diabetic foot ulcer occurrence. RESEARCH DESIGN AND METHODS:We followed 1,285 diabetic veterans without foot ulcer for this outcome with annual clinical evaluations and quarterly mailed questionnaires to identify foot problems. At baseline we assessed age; race; weight; current smoking; diabetes duration and treatment; HbA(1c) (A1C); visual acuity; history of laser photocoagulation treatment, foot ulcer, and amputation; foot shape; claudication; foot insensitivity to the 10-g monofilament; foot callus; pedal edema; hallux limitus; tinea pedis; and onychomycosis. Cox proportional hazards modeling was used with backwards stepwise elimination to develop a prediction model for the first foot ulcer occurrence after the baseline examination. RESULTS:At baseline, subjects were 62.4 years of age on average and 98% male. Mean follow-up duration was 3.38 years, during which time 216 foot ulcers occurred, for an incidence of 5.0/100 person-years. Significant predictors (P </= 0.05) of foot ulcer in the final model (hazard ratio, 95% CI) included A1C (1.10, 1.06-1.15), impaired vision (1.48, 1.00-2.18), prior foot ulcer (2.18, 1.61-2.95), prior amputation (2.57, 1.60-4.12), monofilament insensitivity (2.03, 1.50-2.76), tinea pedis (0.73, 0.54-0.98), and onychomycosis (1.58, 1.16-2.16). Area under the receiver operating characteristic curve was 0.81 at 1 year and 0.76 at 5 years. CONCLUSIONS:Readily available clinical information has substantial predictive power for the development of diabetic foot ulcer and may help in accurately targeting persons at high risk of this outcome for preventive interventions. 10.2337/dc05-2031
    Robust Methods for Real-Time Diabetic Foot Ulcer Detection and Localization on Mobile Devices. Goyal Manu,Reeves Neil D,Rajbhandari Satyan,Yap Moi Hoon IEEE journal of biomedical and health informatics Current practice for diabetic foot ulcers (DFU) screening involves detection and localization by podiatrists. Existing automated solutions either focus on segmentation or classification. In this work, we design deep learning methods for real-time DFU localization. To produce a robust deep learning model, we collected an extensive database of 1775 images of DFU. Two medical experts produced the ground truths of this data set by outlining the region of interest of DFU with an annotator software. Using five-fold cross-validation, overall, faster R-CNN with InceptionV2 model using two-tier transfer learning achieved a mean average precision of 91.8%, the speed of 48 ms for inferencing a single image and with a model size of 57.2 MB. To demonstrate the robustness and practicality of our solution to real-time prediction, we evaluated the performance of the models on a NVIDIA Jetson TX2 and a smartphone app. This work demonstrates the capability of deep learning in real-time localization of DFU, which can be further improved with a more extensive data set. 10.1109/JBHI.2018.2868656
    Prediction of plantar soft tissue stiffness based on sex, age, bodyweight, height and body mass index. Teoh Jee Chin,Lee Taeyong Journal of the mechanical behavior of biomedical materials 15% of Diabetes Mellitus (DM) patients suffer high risk of ulceration and 85% of the amputation involving DM population is caused by non-healing ulcers. These findings elucidate the fact that foot ulcer can result in major amputation especially to the DM and elderly population. Therefore, early diagnosis of abnormally stiffened plantar soft tissue is needed to prevent the catastrophic tissue damage. In order to differentiate between normal and pathological tissues, a threshold reference value that defines healthy tissue is required. The objective of this study is to perform a multivariate analysis to estimate the healthy plantar tissue stiffness values based on the individuals physical attributes such as bodyweight (BW), height and body mass index (BMI) as well as their age and sex. 100 healthy subjects were recruited. Indentation was performed on 2nd metatarsal head pad at 3 different dorsiflexion angles of 0°, 20°, 40° and the hallux and heel at 0°. The results showed the important influences of BW, height and BMI in determining the plantar tissue stiffness. On the other hand, age and sex only play minimal roles. The study can be further extended to increase the reliability and accuracy of the proposed predictive model by evaluating several other related parameters such as body fat content, footwear usage, frequency of sports participation, etc. 10.1016/j.jmbbm.2015.09.015
    Designing a Logistic Regression Model for a Dataset to Predict Diabetic Foot Ulcer in Diabetic Patients: High-Density Lipoprotein (HDL) Cholesterol Was the Negative Predictor. Ahmadi Seyyed Amir Yasin,Shirzadegan Razieh,Mousavi Nazanin,Farokhi Ermia,Soleimaninejad Maryam,Jafarzadeh Mehrzad Journal of diabetes research Objectives:Although the risk factors for diabetic neuropathy and diabetic foot ulcer have been detected, there was no practical modeling for their prediction. We aimed to design a logistic regression model on an Iranian dataset to predict the probability of experiencing diabetic foot ulcers up to a considered age in diabetic patients. Methods:The present study was a statistical modeling on a previously published dataset. The covariates were sex, age, body mass index (BMI), fasting blood sugar (FBS), hemoglobin A1C (HbA1C), low-density lipoprotein (LDL), high-density lipoprotein (HDL), triglyceride (TG), insulin dependency, and statin use. The final model of logistic regression was designed through a manual stepwise method. To study the performance of the model, an area under receiver operating characteristic (AUC) curve was reported. A scoring system was defined according to the coefficients to be used in logistic function for calculation of the probability. Results:The pretest probability for the outcome was 30.83%. The final model consisted of age (1 = 0.133), BMI (2 = 0.194), FBS (3 = 0.011), HDL (4 = -0.118), and insulin dependency (5 = 0.986) ( < 0.1). The performance of the model was definitely acceptable (AUC = 0.914). Conclusion:This model can be used clinically for consulting the patients. The only negative predictor of the risk is HDL cholesterol. Keeping the HDL level more than 50 (mg/dl) is strongly suggested. Logistic regression modeling is a simple and practical method to be used in the clinic. 10.1155/2021/5521493
    Predicting Future Geographic Hotspots of Potentially Preventable Hospitalisations Using All Subset Model Selection and Repeated K-Fold Cross-Validation. Tuson Matthew,Turlach Berwin,Murray Kevin,Kok Mei Ruu,Vickery Alistair,Whyatt David International journal of environmental research and public health Long-term future prediction of geographic areas with high rates of potentially preventable hospitalisations (PPHs) among residents, or "hotspots", is critical to ensure the effective location of place-based health service interventions. This is because such interventions are typically expensive and take time to develop, implement, and take effect, and hotspots often regress to the mean. Using spatially aggregated, longitudinal administrative health data, we introduce a method to make such predictions. The proposed method combines all subset model selection with a novel formulation of repeated k-fold cross-validation in developing optimal models. We illustrate its application predicting three-year future hotspots for four PPHs in an Australian context: type II diabetes mellitus, heart failure, chronic obstructive pulmonary disease, and "high risk foot". In these examples, optimal models are selected through maximising positive predictive value while maintaining sensitivity above a user-specified minimum threshold. We compare the model's performance to that of two alternative methods commonly used in practice, i.e., prediction of future hotspots based on either: (i) current hotspots, or (ii) past persistent hotspots. In doing so, we demonstrate favourable performance of our method, including with respect to its ability to flexibly optimise various different metrics. Accordingly, we suggest that our method might effectively be used to assist health planners predict excess future demand of health services and prioritise placement of interventions. Furthermore, it could be used to predict future hotspots of non-health events, e.g., in criminology. 10.3390/ijerph181910253
    A methodological framework for detecting ulcers' risk in diabetic foot subjects by combining gait analysis, a new musculoskeletal foot model and a foot finite element model. Scarton Alessandra,Guiotto Annamaria,Malaquias Tiago,Spolaor Fabiola,Sinigaglia Giacomo,Cobelli Claudio,Jonkers Ilse,Sawacha Zimi Gait & posture Diabetic foot is one of the most debilitating complications of diabetes and may lead to plantar ulcers. In the last decade, gait analysis, musculoskeletal modelling (MSM) and finite element modelling (FEM) have shown their ability to contribute to diabetic foot prevention and suggested that the origin of the plantar ulcers is in deeper tissue layers rather than on the plantar surface. Hence the aim of the current work is to develop a methodology that improves FEM-derived foot internal stresses prediction, for diabetic foot prevention applications. A 3D foot FEM was combined with MSM derived force to predict the sites of excessive internal stresses on the foot. In vivo gait analysis data, and an MRI scan of a foot from a healthy subject were acquired and used to develop a six degrees of freedom (6 DOF) foot MSM and a 3D subject-specific foot FEM. Ankle kinematics were applied as boundary conditions to the FEM together with: 1. only Ground Reaction Forces (GRFs); 2. OpenSim derived extrinsic muscles forces estimated with a standard OpenSim MSM; 3. extrinsic muscle forces derived through the (6 DOF) foot MSM; 4. intrinsic and extrinsic muscles forces derived through the 6 DOF foot MSM. For model validation purposes, simulated peak pressures were extracted and compared with those measured experimentally. The importance of foot muscles in controlling plantar pressure distribution and internal stresses is confirmed by the improved accuracy in the estimation of the peak pressures obtained with the inclusion of intrinsic and extrinsic muscle forces. 10.1016/j.gaitpost.2017.08.036
    Prediction of post-interventional physical function in diabetic foot ulcer patients using patient reported outcome measurement information system (PROMIS). Hao Stephanie P,Houck Jeff R,Waldman Olivia V,Baumhauer Judith F,Oh Irvin Foot and ankle surgery : official journal of the European Society of Foot and Ankle Surgeons BACKGROUND:Infected diabetic foot ulcer (DFU) patients present with an impaired baseline physical function (PF) that can be further compromised by surgical intervention to treat the infection. The impact of surgical interventions on Patient Reported Outcomes Measurement Information System (PROMIS) PF within the DFU population has not been investigated. We hypothesize that preoperative PROMIS scores (PF, Pain Interference (PI), Depression) in combination with relevant clinical factors can be utilized to predict postoperative PF in DFU patients. METHODS:DFU patients from a single academic physician's practice between February 2015 and November 2018 were identified (n = 240). Ninety-two patients met inclusion criteria with complete follow-up and PROMIS computer adaptive testing records. Demographic and clinical factors, procedure performed, and wound healing status were collected. Spearman's rank correlation coefficient, Chi-Squared tests and multidimensional modelling were applied to all variables' pre- and postoperative values to assess patients' postoperative PF. RESULTS:The mean age was 60.5 (33-96) years and mean follow-up was 4.7 (3-12) months. Over 70 % of the patients' initial PF were 2-3 standard deviations below the US population (n = 49; 28). Preoperative PF (p <  0.01), PI (p < 0.01), Depression (p <  0.01), CRF (p <  0.02) and amputation level (p <  0.04) showed significant univariate correlation with postoperative PF. Multivariate model (r = 0.55) showed that the initial PF (p =  0.004), amputation level (p =  0.008), and wound healing status (p =  0.001) predicted postoperative PF. CONCLUSIONS:Majority of DFU patients present with poor baseline PF. Preoperative PROMIS scores (PF, PI, Depression) are predictive of postoperative PROMIS PF in DFU patients. Postoperative patient's physical function can be assessed by PF = 29.42 + 0.34 (PF) - 5.87 (Not Healed) - 2.63 (Amputation Category). This algorithm can serve as a valuable tool for predicting post-operative physical function and setting expectations. 10.1016/j.fas.2020.04.009
    Factors influencing lower extremity amputation outcomes in people with active foot ulceration in regional Australia: A retrospective cohort study. Tehan Peta Ellen,Hawes Morgan Brian,Hurst Joanne,Sebastian Mathew,Peterson Benjamin John,Chuter Vivienne Helaine Wound repair and regeneration : official publication of the Wound Healing Society [and] the European Tissue Repair Society Australia has the second highest rate of non-traumatic lower extremity amputation (LEA) globally. Australia's large geographical size is one of the biggest challenges facing limb preservation services and may be contributing to LEA. The aim of this study was to determine what factors contribute to the likelihood of LEA in people with active foot ulceration in regional Australia. This retrospective cohort study audited patients with active foot ulceration in a multidisciplinary high risk foot service (HRFS) in regional Australia. Neurological, vascular and wound characteristics were systematically extracted, along with demographic information. Participants were followed for at least 12 months until healing or LEA occurred. Correlations between LEA and clinical and demographic characteristics were assessed using the Pearson's product moment correlation coefficient and chi squared test for independence. Significant variables (p < 0.05) were included in the model. Direct logistic regression assessed the independent contribution of significantly correlated variables on the likelihood of LEA. Of note, 1876 records were hand screened with 476 participants (25%) meeting the inclusion criteria. Geographical distance from the HRFS, toe systolic pressure (TSP), diabetes and infection were all significantly correlated with LEA and included in the logistic regression model. TSP decrease of 1 mmHg (OR 1.02, 95% CI 1.01-1.03), increased geographical distance (1 km) from HRFS (OR 1.006, 95% CI 1.001-1.01) infection (OR 2.08, 95% CI 1.06-4.07) and presence of diabetes (OR 3.77, 95% CI 1.12-12.65) were all significantly associated with increased likelihood of LEA. HRFS should account for the disparity in outcomes between patients living in close proximity to their service, compared to those in rural areas. Optimal management of diabetes, vascular perfusion and control of infection may also contribute to preventing LEA in people with active foot ulceration. 10.1111/wrr.12978
    Machine learning algorithm to evaluate risk factors of diabetic foot ulcers and its severity. Medical & biological engineering & computing Early identification of the risk factors associated with development of diabetic foot ulcer (DFU) can be facilitated using machine learning techniques. The aim of this study is to find out the association of various clinical and biochemical risk factors with DFU and develop a prediction model using different machine learning algorithms. Eighty each of type 2 diabetes mellitus (T2DM) with DFU and (T2DM) without DFU were enrolled for this observational study. Clinical and laboratory data were analysed using different machine learning algorithms: Support vector machines (SVM-Poly K), Naive Bayes (NB), K-nearest neighbour (KNN), random forest (RF) and three ensemble learners: Stacking C, Bagging and AdaBoost for constructing prediction models for discriminating between the two groups (stage I classification) and ulcer type classification (stage II classification). Ensemble learning performed better than individual classifiers in terms of various performance evaluation metrics. New risk factors like ApoA1 and IL-10 for development of DFU in diabetes mellitus were identified. IL-10 along with uric acid could discriminate the grades of ulcers according to its severity. Decision fusion strategy using Stacking C algorithm resulted in enhanced prediction accuracy for both the stages of classification which can be used as a complementary method for computational screening for DFU and its subtypes. Current methodology for T2DM with DFU/T2DM without DFU and ulcer type classification. 10.1007/s11517-022-02617-w
    Can plantar soft tissue mechanics enhance prognosis of diabetic foot ulcer? Naemi R,Chatzistergos P,Suresh S,Sundar L,Chockalingam N,Ramachandran A Diabetes research and clinical practice AIM:To investigate if the assessment of the mechanical properties of plantar soft tissue can increase the accuracy of predicting Diabetic Foot Ulceration (DFU). METHODS:40 patients with diabetic neuropathy and no DFU were recruited. Commonly assessed clinical parameters along with plantar soft tissue stiffness and thickness were measured at baseline using ultrasound elastography technique. 7 patients developed foot ulceration during a 12months follow-up. Logistic regression was used to identify parameters that contribute to predicting the DFU incidence. The effect of using parameters related to the mechanical behaviour of plantar soft tissue on the specificity, sensitivity, prediction strength and accuracy of the predicting models for DFU was assessed. RESULTS:Patients with higher plantar soft tissue thickness and lower stiffness at the 1st Metatarsal head area showed an increased risk of DFU. Adding plantar soft tissue stiffness and thickness to the model improved its specificity (by 3%), sensitivity (by 14%), prediction accuracy (by 5%) and prognosis strength (by 1%). The model containing all predictors was able to effectively (χ (8, N=40)=17.55, P<0.05) distinguish between the patients with and without DFU incidence. CONCLUSION:The mechanical properties of plantar soft tissue can be used to improve the predictability of DFU in moderate/high risk patients. 10.1016/j.diabres.2017.02.002
    Protocol for a systematic review and individual patient data meta-analysis of prognostic factors of foot ulceration in people with diabetes: the international research collaboration for the prediction of diabetic foot ulcerations (PODUS). Crawford Fay,Anandan Chantelle,Chappell Francesca M,Murray Gordon D,Price Jacqueline F,Sheikh Aziz,Simpson Colin R,Maxwell Martin,Stansby Gerard P,Young Matthew J,Abbott Caroline A,Boulton Andrew J M,Boyko Edward J,Kastenbauer Thomas,Leese Graham P,Monami Matteo,Monteiro-Soares Matilde,Rith-Najarian Stephen J,Veves Aristidis,Coates Nikki,Jeffcoate William J,Leech Nicola,Fahey Tom,Tierney Jayne BMC medical research methodology BACKGROUND:Diabetes-related lower limb amputations are associated with considerable morbidity and mortality and are usually preceded by foot ulceration. The available systematic reviews of aggregate data are compromised because the primary studies report both adjusted and unadjusted estimates. As adjusted meta-analyses of aggregate data can be challenging, the best way to standardise the analytical approach is to conduct a meta-analysis based on individual patient data (IPD).There are however many challenges and fundamental methodological omissions are common; protocols are rare and the assessment of the risk of bias arising from the conduct of individual studies is frequently not performed, largely because of the absence of widely agreed criteria for assessing the risk of bias in this type of review. In this protocol we propose key methodological approaches to underpin our IPD systematic review of prognostic factors of foot ulceration in diabetes.Review questions;1. What are the most highly prognostic factors for foot ulceration (i.e. symptoms, signs, diagnostic tests) in people with diabetes?2. Can the data from each study be adjusted for a consistent set of adjustment factors?3. Does the model accuracy change when patient populations are stratified according to demographic and/or clinical characteristics? METHODS:MEDLINE and EMBASE databases from their inception until early 2012 were searched and the corresponding authors of all eligible primary studies invited to contribute their raw data. We developed relevant quality assurance items likely to identify occasions when study validity may have been compromised from several sources. A confidentiality agreement, arrangements for communication and reporting as well as ethical and governance considerations are explained.We have agreement from the corresponding authors of all studies which meet the eligibility criteria and they collectively possess data from more than 17000 patients. We propose, as a provisional analysis plan, to use a multi-level mixed model, using "study" as one of the levels. Such a model can also allow for the within-patient clustering that occurs if a patient contributes data from both feet, although to aid interpretation, we prefer to use patients rather than feet as the unit of analysis. We intend to only attempt this analysis if the results of the investigation of heterogeneity do not rule it out and the model diagnostics are acceptable. DISCUSSION:This review is central to the development of a global evidence-based strategy for the risk assessment of the foot in patients with diabetes, ensuring future recommendations are valid and can reliably inform international clinical guidelines. 10.1186/1471-2288-13-22
    A new diabetic foot risk assessment tool: DIAFORA. Monteiro-Soares M,Dinis-Ribeiro M Diabetes/metabolism research and reviews AIMS:This study aimed to derive a new model to classify subjects with diabetes and active diabetic foot ulcer by their risk of lower extremity amputation. METHODS:A prospective cohort study was conducted that included all subjects with diabetic foot ulcer attending our Hospital Diabetic Foot Clinic from 2010 to 2013. Variables were collected at baseline. Subjects were followed up until healing, lower extremity amputation, death or for at least 3 months. Logistic regression was used to derive the new model, and the area under the receiver operating characteristic curve was assessed to propose the model with the greatest discrimination. RESULTS:A total of 293 participants were included and followed for a median of 91 days. In 23.2% amputation was required, 5.1% died and 3.1% were lost. Our final model included the variables most commonly used in clinical practice for diabetic foot risk assessment (presence of neuropathy, foot deformity, peripheral arterial disease and previous foot complications) in addition to multiple diabetic foot ulcer, infection, gangrene and bone involvement. This model had an area under the receiver operating characteristic curve of 0.91 [95% confidence interval (CI) 0.87-0.95] and as classification of 0.89 (95% CI 0.84-0.93) for lower extremity amputation prediction. The high-risk group presented a positive likelihood ratio of 5 (95% CI 3-8) and predictive value of 58 (46-71). Only one minor lower extremity amputation occurred in the low-risk group. CONCLUSIONS:We propose a new classification: diabetic foot risk assessment (DIAFORA). This classification was equally or more accurate for lower extremity amputation prediction in diabetic foot ulcer patients when compared with the existing ones. 10.1002/dmrr.2785
    Predicting the amputation risk for patients with diabetic foot ulceration - a Bayesian decision support tool. Hüsers Jens,Hafer Guido,Heggemann Jan,Wiemeyer Stefan,John Swen Malte,Hübner Ursula BMC medical informatics and decision making BACKGROUND:Diabetes mellitus is a major global health issue with a growing prevalence. In this context, the number of diabetic complications is also on the rise, such as diabetic foot ulcers (DFU), which are closely linked to the risk of lower extremity amputation (LEA). Statistical prediction tools may support clinicians to initiate early tertiary LEA prevention for DFU patients. Thus, we designed Bayesian prediction models, as they produce transparent decision rules, quantify uncertainty intuitively and acknowledge prior available scientific knowledge. METHOD:A logistic regression using observational collected according to the standardised PEDIS classification was utilised to compute the six-month amputation risk of DFU patients for two types of LEA: 1.) any-amputation and 2.) major-amputation. Being able to incorporate information which is available before the analysis, the Bayesian models were fitted following a twofold strategy. First, the designed prediction models waive the available information and, second, we incorporated the a priori available scientific knowledge into our models. Then, we evaluated each model with respect to the effect of the predictors and validity of the models. Next, we compared the performance of both models with respect to the incorporation of prior knowledge. RESULTS:This study included 237 patients. The mean age was 65.9 (SD 12.3), and 83.5% were male. Concerning the outcome, 31.6% underwent any- and 12.2% underwent a major-amputation procedure. The risk factors of perfusion, ulcer extent and depth revealed an impact on the outcomes, whereas the infection status and sensation did not. The major-amputation model using prior information outperformed the uninformed counterpart (AUC 0.765 vs AUC 0.790, Cohen's d 2.21). In contrast, the models predicting any-amputation performed similarly (0.793 vs 0.790, Cohen's d 0.22). CONCLUSIONS:Both of the Bayesian amputation risk models showed acceptable prognostic values, and the major-amputation model benefitted from incorporating a priori information from a previous study. Thus, PEDIS serves as a valid foundation for a clinical decision support tool for the prediction of the amputation risk in DFU patients. Furthermore, we demonstrated the use of the available prior scientific information within a Bayesian framework to establish chains of knowledge. 10.1186/s12911-020-01195-x
    Single snapshot spatial frequency domain imaging for risk stratification of diabetes and diabetic foot. Li Ying,Guo Mingrou,Qian Xiafei,Lin Weihao,Zheng Yang,Yu Kangyuan,Zeng Bixin,Xu Zhang,Zheng Chao,Xu M Biomedical optics express Diabetic foot is one of the major complications of diabetes. In this work, a real-time Single Snapshot Multiple-frequency Demodulation (SSMD) - Spatial Frequency Domain Imaging (SFDI) system was used to image the forefoot of healthy volunteers, diabetes, and diabetic foot patients. A layered skin model was used to obtain the 2D maps of optical and physiological parameters, including cutaneous hemoglobin concentration, oxygen saturation, scattering properties, melanin content, and epidermal thickness, from every single snapshot. We observed a strong correlation between the measured optical and physiological parameters and the degree of diabetes. The cutaneous hemoglobin concentration, oxygen saturation, and epidermal thickness decrease, whereas the melanin content increases with the progress of diabetes. The melanin content further increases, and the reduced scattering coefficient and scattering power are lower for diabetic foot patients than those of both healthy and diabetic subjects. High accuracies (AUC) of 97.2% (distinguishing the diabetic foot patients among all subjects), 95.2% (separating healthy subjects from the diabetes patients), and 87.8% (classifying mild vs severe diabetes), respectively, are achieved in binary classifications in sequence using the SSMD-SFDI system, demonstrating its applicability to risk stratification of diabetes and diabetic foot. The prognostic value of the SSMD-SFDI system in the prediction of the occurrence of the diabetic foot and other applications in monitoring tissue microcirculation and peripheral vascular disease are also addressed. 10.1364/BOE.394929
    A two-phase model of plantar tissue: a step toward prediction of diabetic foot ulceration. Sciumè G,Boso D P,Gray W G,Cobelli C,Schrefler B A International journal for numerical methods in biomedical engineering A new computational model, based on the thermodynamically constrained averaging theory, has been recently proposed to predict tumor initiation and proliferation. A similar mathematical approach is proposed here as an aid in diabetic ulcer prevention. The common aspects at the continuum level are the macroscopic balance equations governing the flow of the fluid phase, diffusion of chemical species, tissue mechanics, and some of the constitutive equations. The soft plantar tissue is modeled as a two-phase system: a solid phase consisting of the tissue cells and their extracellular matrix, and a fluid one (interstitial fluid and dissolved chemical species). The solid phase may become necrotic depending on the stress level and on the oxygen availability in the tissue. Actually, in diabetic patients, peripheral vascular disease impacts tissue necrosis; this is considered in the model via the introduction of an effective diffusion coefficient that governs transport of nutrients within the microvasculature. The governing equations of the mathematical model are discretized in space by the finite element method and in time domain using the θ-Wilson Method. While the full mathematical model is developed in this paper, the example is limited to the simulation of several gait cycles of a healthy foot. 10.1002/cnm.2650
    CT-Angiography-Based Outcome Prediction on Diabetic Foot Ulcer Patients: A Statistical Learning Approach. Diagnostics (Basel, Switzerland) The purpose of our study is to predict the occurrence and prognosis of diabetic foot ulcers (DFUs) by clinical and lower extremity computed tomography angiography (CTA) data of patients using the artificial neural networks (ANN) model. DFU is a common complication of diabetes that severely affects the quality of life of patients, leading to amputation and even death. There are a lack of valid predictive techniques for the prognosis of DFU. In clinical practice, the use of scales alone has a large subjective component, leading to significant bias and heterogeneity. Currently, there is a lack of evidence-based support for patients to develop clinical strategies before reaching end-stage outcomes. The present study provides a novel technical tool for predicting the prognosis of DFU. After screening the data, 203 patients with diabetic foot ulcers (DFUs) were analyzed and divided into two subgroups based on their Wagner Score (138 patients in the low Wagner Score group and 65 patients in the high Wagner Score group). Based on clinical and lower extremity CTA data, 10 predictive factors were selected for inclusion in the model. The total dataset was randomly divided into the training sample, testing sample and holdout sample in ratio of 3:1:1. After the training sample and testing sample developing the ANN model, the holdout sample was utilized to assess the accuracy of the model. ANN model analysis shows that the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and area under the curve (AUC) of the overall ANN model were 92.3%, 93.5%, 87.0%, 94.2% and 0.955, respectively. We observed that the proposed model performed superbly on the prediction of DFU with a 91.6% accuracy. Evaluated with the holdout sample, the model accuracy, sensitivity, specificity, PPV and NPV were 88.9%, 90.0%, 88.5%, 75.0% and 95.8%, respectively. By contrast, the logistic regression model was inferior to the ANN model. The ANN model can accurately and reliably predict the occurrence and prognosis of a DFU according to clinical and lower extremity CTA data. We provided clinicians with a novel technical tool to develop clinical strategies before end-stage outcomes. 10.3390/diagnostics12051076
    Deep Personal Multitask Prediction of Diabetes Complication with Attentive Interactions Predicting Diabetes Complications by Multitask-Learning. Journal of healthcare engineering Objective:Diabetic complications have brought a tremendous burden for diabetic patients, but the problem of predicting diabetic complications is still unresolved. Our aim is to explore the relationship between hemoglobin A1C (HbA1c), insulin (INS), and glucose (GLU) and diabetic complications in combination with individual factors and to effectively predict multiple complications of diabetes. Methods:This was a real-world study. Data were collected from 40,913 participants with an average age of 48 years from the Department of Endocrinology of Ruijin Hospital in Shanghai. We proposed deep personal multitask prediction of diabetes complication with attentive interactions (DPMP-DC) to predict the five complication models of diabetes, including diabetic retinopathy, diabetic nephropathy, diabetic peripheral neuropathy, diabetic foot disease, and diabetic cardiovascular disease. Results:Our model has an accuracy rate of 88.01% for diabetic retinopathy, 89.58% for diabetic nephropathy, 85.77% for diabetic neuropathy, 80.56% for diabetic foot disease, and 82.48% for diabetic cardiovascular disease. The multitasking accuracy of multiple complications is 84.67%, and the missed diagnosis rate is 9.07%. Conclusion:We put forward the method of interactive integration with individual factors of patients for the first time in diabetic complications, which reflect the differences between individuals. Our multitask model using the hard sharing mechanism provides better prediction than prior single prediction models. 10.1155/2022/5129125
    Existing predictive methods applied to gait analysis of patients with diabetes: study protocol for a systematic review. BMJ open INTRODUCTION:Type 2 diabetes can lead to gait abnormalities, including a longer stance phase, shorter steps and improper foot pressure distribution. Quantitative data from objective methods for evaluating gait patterns are accurate and cost-effective. In addition, it can also help predictive methods to forecast complications and develop early strategies to guide treatments. To date, no research has systematically summarised the predictive methods used to assess type 2 diabetic gait. Therefore, this protocol aims to identify which predictive methods have been employed to assess the diabetic gait. METHODS AND ANALYSIS:This protocol will follow the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocol (PRISMA-P) statement. Electronic searches of articles from inception to January 2022 will be performed, from May 2021 to 31 January 2022, in the Web of Science, MEDLINE, Embase, IEEE Xplore Digital Library, Scopus, CINAHL, Google Scholar, APA PsycInfo, the Cochrane Library and in references of key articles and grey literature without language restrictions. We will include studies that examined the development and/or validation of predictive methods to assess type 2 diabetic gait in adults aged >18 years without amputations, use of assistive devices, ulcers or neuropathic pain. Two independent reviewers will screen the included studies and extract the data using a customised charting form. A third reviewer will resolve any disagreements. A narrative synthesis will be performed for the included studies. Risk of bias and quality of evidence will be assessed using the Prediction Model Risk of Bias Assessment Tool and the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis. ETHICS AND DISSEMINATION:Ethical approval is not required because only available secondary published data will be analysed. The findings will be disseminated through peer-reviewed journals and/or presentations at relevant conferences and other media platforms. PROSPERO REGISTRATION NUMBER:CDR42020199495. 10.1136/bmjopen-2021-051981
    The independent contribution of diabetic foot ulcer on lower extremity amputation and mortality risk. Martins-Mendes Daniela,Monteiro-Soares Matilde,Boyko Edward John,Ribeiro Manuela,Barata Pedro,Lima Jorge,Soares Raquel Journal of diabetes and its complications AIMS:To estimate 3-year risk for diabetic foot ulcer (DFU), lower extremity amputation (LEA) and death; determine predictive variables and assess derived models accuracy. MATERIAL AND METHODS:Retrospective cohort study including all subjects with diabetes enrolled in our diabetic foot outpatient clinic from beginning 2002 until middle 2010. Data were collected from clinical records. RESULTS:644 subjects with mean age of 65.1 (±11.2) and diabetes duration of 16.1 (±10.8) years. Cumulative incidence was 26.6% for DFU, 5.8% for LEA and 14.0% for death. In multivariate analysis, physical impairment, peripheral arterial disease complication history, complication count and previous DFU were associated with DFU; complication count, foot pulses and previous DFU with LEA and age, complication count and previous DFU with death. Predictive models' areas under the ROC curves ranged from 0.80 to 0.83. A simplified model including previous DFU and complication count presented high accuracy. Previous DFU was associated with all outcomes, even when adjusted for complication count, in addition to more complex models. CONCLUSIONS:DFU seems more than a marker of complication status, having independent impact on LEA and mortality risk. Proposed models may be applicable in healthcare settings to identify patients at higher risk of DFU, LEA and death. 10.1016/j.jdiacomp.2014.04.011
    Prediction of Diabetic Foot Ulceration: The Value of Using Microclimate Sensor Arrays. Jones Petra,Bibb Richard,Davies Melanie,Khunti Kamlesh,McCarthy Matthew,Webb David,Zaccardi Francesco Journal of diabetes science and technology BACKGROUND:Accurately predicting the risk of diabetic foot ulceration (DFU) could dramatically reduce the enormous burden of chronic wound management and amputation. Yet, the current prognostic models are unable to precisely predict DFU events. Typically, efforts have focused on individual factors like temperature, pressure, or shear rather than the overall foot microclimate. METHODS:A systematic review was conducted by searching PubMed reports with no restrictions on start date covering the literature published until February 20, 2019 using relevant keywords, including temperature, pressure, shear, and relative humidity. We review the use of these variables as predictors of DFU, highlighting gaps in our current understanding and suggesting which specific features should be combined to develop a real-time microclimate prognostic model. RESULTS:The current prognostic models rely either solely on contralateral temperature, pressure, or shear measurement; these parameters, however, rarely reach 50% specificity in relation to DFU. There is also considerable variation in methodological investigation, anatomical sensor configuration, and resting time prior to temperature measurements (5-20 minutes). Few studies have considered relative humidity and mean skin resistance. CONCLUSION:Very limited evidence supports the use of single clinical parameters in predicting the risk of DFU. We suggest that the microclimate as a whole should be considered to predict DFU more effectively and suggest nine specific features which appear to be implicated for further investigation. Technology supports real-time in-shoe data collection and wireless transmission, providing a potentially rich source of data to better predict the risk of DFU. 10.1177/1932296819877194
    The development and validation of a multivariable prognostic model to predict foot ulceration in diabetes using a systematic review and individual patient data meta-analyses. Crawford F,Cezard G,Chappell F M, Diabetic medicine : a journal of the British Diabetic Association AIMS:Diabetes guidelines recommend screening for the risk of foot ulceration but vary substantially in the underlying evidence base. Our purpose was to derive and validate a prognostic model of independent risk factors for foot ulceration in diabetes using all available individual patient data from cohort studies conducted worldwide. METHODS:We conducted a systematic review and meta-analysis of individual patient data from 10 cohort studies of risk factors in the prediction of foot ulceration in diabetes. Predictors were selected for plausibility, availability and low heterogeneity. Logistic regression produced adjusted odds ratios (ORs) for foot ulceration by ulceration history, monofilament insensitivity, any absent pedal pulse, age, sex and diabetes duration. RESULTS:The 10 studies contained data from 16 385 participants. A history of foot ulceration produced the largest OR [6.59 (95% CI 2.49 to 17.45)], insensitivity to a 10 g monofilament [3.18 (95% CI 2.65 to 3.82)] and any absent pedal pulse [1.97 (95% CI 1.62 to 2.39)] were consistently, independently predictive. Combining three predictors produced sensitivities between 90.0% (95% CI 69.9% to 97.2%) and 95.3% (95% CI 84.5% to 98.7%); the corresponding specificities were between 12.1% (95% CI 8.2% to 17.3%) and 63.9% (95% CI 61.1% to 66.6%). CONCLUSIONS:This prognostic model of only three risk factors, a history of foot ulceration, an inability to feel a 10 g monofilament and the absence of any pedal pulse, compares favourably with more complex approaches to foot risk assessment recommended in clinical diabetes guidelines. 10.1111/dme.13797
    Biomarker Prediction of Postoperative Healing of Diabetic Foot Ulcers: A Retrospective Observational Study of Serum Albumin. Cheng Pu,Dong Yunxian,Hu Zhicheng,Huang Shaobin,Cao Xiaoling,Wang Peng,Xu Hailin,Zhu Jiayuan,Tang Bing Journal of wound, ostomy, and continence nursing : official publication of The Wound, Ostomy and Continence Nurses Society PURPOSE:The purpose of this study was to investigate the relationship and to determine potential usefulness of serum albumin as a biomarker for predicting postoperative diabetic foot ulcer (DFU) healing. DESIGN:A retrospective study. SUBJECTS AND SETTING:The sample comprised 266 inpatients with type 2 diabetes receiving care in The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China. Among them, 174 had DFUs and underwent surgery for foot DFUs including amputation, skin grafting, and flap procedures. A comparison group consisted of 92 inpatients without a DFU or surgery. METHODS:The association between healing and preoperative albumin levels was analyzed via a logistic regression model and receiver operating characteristic (ROC) curve. RESULTS:The albumin value of patients with DFU grade 3 or more (3.23 ± 0.58 g/dL) was lower than that of patients with DFU grade 1-2 (3.58 ± 0.5 g/dL), and both were lower than that of the comparison group (3.89 ± 0.3 g/dL). Patients with a DFU with hypoalbuminemia (<3.5 g/dL) had a 2.5-fold higher risk of nonhealing at postoperative 28 days than patients with normal levels (odds ratio = 3.51; 95% confidence interval, 1.75-7.06; P < .001). For patients with a DFU overall, the ROC curve showed a preoperative albumin cutoff of 3.44 g/dL for DFU wound healing. CONCLUSIONS:For patients with a DFU undergoing surgery, preoperative serum albumin may be used as a biomarker for predicting postoperative healing. 10.1097/WON.0000000000000780
    Construction of a knowledge graph for diabetes complications from expert-reviewed clinical evidences. Wang Lei,Xie Huimin,Han Wentao,Yang Xiao,Shi Lili,Dong Jiancheng,Jiang Kui,Wu Huiqun Computer assisted surgery (Abingdon, England) A knowledge graph is a structured representation of data that can express entity and relational knowledge. More attention has been paid to the study of a clinical knowledge graph, especially in the field of chronic diseases. However, knowledge graph construction is based mainly on electronic medical records and other data sources, and the authority of the constructed knowledge graph presents some problems. Therefore, regarding the quality of evidence, this study, in combination with experimental research on system evaluation and meta-analysis presents some new information, On the basis of evidence-based medicine (EBM), the secondary results of systematic evaluation and meta-analyses of social, psychological, and behavioral aspects were extracted as data for the core nodes and edges of a knowledge graph to construct a graph of type 2 diabetes (T2D) and its complications. In this study, relevant life-style evidence that are factors for the risk of diabetic retinopathy (DR), diabetic nephropathy (DN), diabetic foot (DF), and diabetic depression (DD), and the results of several of the relevant clinical test, including bariatric surgery, myopia, lipid-lowering drugs, lipid-lowering drug duration, blood glucose control, disease course, glycosylated hemoglobin, fasting blood glucose, hypertension, sex, smoking and other common lifestyle characteristics were finally extracted. The evidence-based knowledge graph of the DM complications was constructed by extracting relevant disease, risk factors, risk outcomes, and other diabetes entities and the strength of the data for the odds ratio (OR) or relative risk (RR) correlations from clinical evidence. Moreover, the risk prediction models constructed using a logistic model were incorporated into the knowledge graph to visualize the risk score of DM complications for each user. In short, the EBM-powered construction of the knowledge graph could provide high-quality information to support decisions for the prevention and control of diabetes and its complications. 10.1080/24699322.2020.1850866
    An explainable machine learning model for predicting in-hospital amputation rate of patients with diabetic foot ulcer. International wound journal Diabetic foot ulcer (DFU) is one of the most serious and alarming diabetic complications, which often leads to high amputation rates in diabetic patients. Machine learning is a part of the field of artificial intelligence, which can automatically learn models from data and better inform clinical decision-making. We aimed to develop an accurate and explainable prediction model to estimate the risk of in-hospital amputation in patients with DFU. A total of 618 hospitalised patients with DFU were included in this study. The patients were divided into non-amputation, minor amputation or major amputation group. Light Gradient Boosting Machine (LightGBM) and 5-fold cross-validation tools were used to construct a multi-class classification model to predict the three outcomes of interest. In addition, we used the SHapley Additive exPlanations (SHAP) algorithm to interpret the predictions of the model. Our area under the receiver-operating-characteristic curve (AUC) demonstrated a 0.90, 0.85 and 0.86 predictive ability for non-amputation, minor amputation and major amputation outcomes, respectively. Taken together, our data demonstrated that the developed explainable machine learning model provided accurate estimates of the amputation rate in patients with DFU during hospitalisation. Besides, the model could inform individualised analyses of the patients' risk factors. 10.1111/iwj.13691
    External validation and optimisation of a model for predicting foot ulcers in patients with diabetes. Monteiro-Soares M,Dinis-Ribeiro M Diabetologia AIMS/HYPOTHESIS:In 2006 a risk stratification model was developed by Boyko et al. to predict foot ulceration in patients with diabetes, using seven commonly available clinical variables. We sought to validate and optimise this clinical prediction rule in a different setting. METHODS:A retrospective cohort study was conducted on all patients with diabetes attending the podiatry section of a diabetic foot clinic at a tertiary hospital in Portugal (n = 360). Assessment at baseline included variables evaluated in the previous study and other relevant variables. RESULTS:Type 2 diabetes was present in 98% of patients, 45% were men and (at baseline) the median age was 65 years. Median follow-up was 25 months (range 3-86), during which 94 patients (26%) developed a foot ulcer. Boyko's model had an area under the receiver operating curve of 0.83 (95% CI 0.78-0.88). The corresponding value for the optimised model, which included the footwear risk variable, was 0.88 (95% CI 0.84-0.91). Both models had high classification accuracy for prediction of foot ulceration. However, the optimised model tended to produce higher specificity and positive likelihood ratio values at all levels. CONCLUSIONS/INTERPRETATION:This study confirmed that Boyko's proposed model has a high capacity to predict foot ulceration in diabetes patients of both sexes. Our results suggest that the inclusion of a further footwear variable could improve the model. Nevertheless, prospective validation in a larger population is still necessary. 10.1007/s00125-010-1731-y
    Risk score for prediction of 10 year dementia risk in individuals with type 2 diabetes: a cohort study. Exalto Lieza G,Biessels Geert Jan,Karter Andrew J,Huang Elbert S,Katon Wayne J,Minkoff Jerome R,Whitmer Rachel A The lancet. Diabetes & endocrinology BACKGROUND:Although patients with type 2 diabetes are twice as likely to develop dementia as those without this disease, prediction of who has the highest future risk is difficult. We therefore created and validated a practical summary risk score that can be used to provide an estimate of the 10 year dementia risk for individuals with type 2 diabetes. METHODS:Using data from two longitudinal cohorts of patients with type 2 diabetes (aged ≥60 years) with 10 years of follow-up, we created (n=29,961) and validated (n=2413) the risk score. We built our prediction model by evaluating 45 candidate predictors using Cox proportional hazard models and developed a point system for the risk score based on the size of the predictor's β coefficient. Model prediction was tested by discrimination and calibration methods. Dementia risk per sum score was calculated with Kaplan-Meier estimates. FINDINGS:Microvascular disease, diabetic foot, cerebrovascular disease, cardiovascular disease, acute metabolic events, depression, age, and education were most strongly predictive of dementia and constituted the risk score (C statistic 0·736 for creation cohort and 0·746 for validation cohort). The dementia risk was 5·3% (95% CI 4·2-6·3) for the lowest score (-1) and 73·3% (64·8-81·8) for the highest (12-19) sum scores. INTERPRETATION:To the best of our knowledge, this is the first risk score for the prediction of 10 year dementia risk in patients with type 2 diabetes mellitus. The risk score can be used to increase vigilance for cognitive deterioration and for selection of high-risk patients for participation in clinical trials. FUNDING:Kaiser Permanente Community Benefit, National Institute of Health, Utrecht University, ZonMw, and Fulbright. 10.1016/S2213-8587(13)70048-2
    Neutrophil to Lymphocyte Ratio and Platelet to Lymphocyte Ratio are Associated with Lower Extremity Vascular Lesions in Chinese Patients with Type 2 Diabetes. Liu Nina,Sheng Jianlong,Pan Tianrong,Wang Youmin Clinical laboratory BACKGROUND:Neutrophil to lymphocyte ratio (NLR) and platelet to lymphocyte ratio (PLR) are inflammatory markers used for prediction of chronic complications of diabetes. Lower extremity arterial disease (LEAD) is one of chronic complications of type 2 diabetes mellitus (T2DM). The correlation between NLR, PLR, and lower extremity vascular lesions was investigated in subjects with T2DM to determine the best predictive marker for LEAD. METHODS:Three hundred thirty-five patients with T2DM (199 males and 136 females; age 54.12 ± 14.07 years) were enrolled. Blood differential counts and anklebrachial index (ABI) were assessed. Patients were divided into the LEAD group (ABI ≤ 0.9, n = 236) and non-LEAD group (ABI > 0.9, n = 99), and NLR and PLR were compared between the two groups. The independent risk factors for LEAD were analyzed using a logistic regression model. Receiver operating characteristic (ROC) curve analysis was used to assess the optimal cutoff values of PLR and NLR for prediction of LEAD. RESULTS:NLR and PLR in the LEAD group were significantly increased compared to non-LEAD group patients. Univariate analyses identified that NLR was positively correlated with age, glycosylated hemoglobin (HbA1c), triglycerides (TG), total cholesterol (TC), low-density lipoprotein (LDL), and 2-hours postprandial glucose levels. PLR was positively correlated with age, duration of T2DM, HbA1c, TG, TC, and LDL, but negatively correlated with diastolic blood pressure and fasting C-peptide levels. Binary logistic regression analysis identified that age, total number of white blood cells, PLR, and TC were significant and independent factors of diabetic LEAD. Moreover, ROC curve analysis showed that NLR and PLR were both predictive markers of LEAD in diabetes, and that the area under the PLR curve was larger than NLR. CONCLUSIONS:NLR and PLR are positively correlated with LEAD in diabetes. PLR was superior to NLR as a predictor of LEAD in diabetes. 10.7754/Clin.Lab.2018.180804
    Application of three statistical models for predicting the risk of diabetes. Liu Siyu,Gao Yue,Shen Yuhang,Zhang Min,Li Jingjing,Sun Pinghui BMC endocrine disorders BACKGROUND:At present, the proportion of undiagnosed diabetes in Chinese adults is as high as 15.5%. People with diabetes who are not treated and controlled in time may have various complications, such as cardiovascular and cerebrovascular diseases and diabetic foot disorders, which not only seriously affect the quality of life of people with diabetes but also impose a heavy burden on families and society. Therefore, prevention and control of type 2 diabetes is of great significance. METHODS:We constructed a logistic regression model, a neural network model and a decision tree model to analyse the risk factors for type 2 diabetes and then compared the prediction accuracy of the different models by calculating the area under the relative operating characteristic (ROC) curve and back-inputting the data into the model. RESULTS:The prevalence of type 2 diabetes in 4177 subjects who were not diagnosed with type 2 diabetes was 9.31%. The most influential factors associated with type 2 diabetes were triglyceride (TG) ≥ 1.17 mmol/L (odds ratio (OR) =2.233), age ≥ 70 years (OR = 1.734), hypertension (OR = 1.703), alcohol consumption (OR = 1.674), and total cholesterol≥5.2 mmol/L (TC) (OR = 1.463). The prediction accuracies of the three prediction models were 90.8, 91.2, and 90.7%, respectively, and the areas under curve (AUCs) were 0.711, 0.780, and 0.698, respectively. The differences in the AUCs after back propagation (BP) of the neural network model, logistic regression model and decision tree model were statistically significant (P < 0.05). CONCLUSION:BP neural networks have a higher predictive power for identifying the associated risk factors of type 2 diabetes than the other two models, but it is necessary to select a suitable model for specific situations. 10.1186/s12902-019-0456-2
    Predicting major adverse limb events in individuals with type 2 diabetes: Insights from the EXSCEL trial. Weissler E Hope,Clare Robert M,Lokhnygina Yuliya,Buse John B,Goodman Shaun G,Katona Brian,Iqbal Nayyar,Pagidipati Neha J,Sattar Naveed,Holman Rury R,Hernandez Adrian F,Mentz Robert J,Patel Manesh R,Jones W Schuyler Diabetic medicine : a journal of the British Diabetic Association AIMS:Although models exist to predict amputation among people with type 2 diabetes with foot ulceration or infection, we aimed to develop a prediction model for a broader range of major adverse limb events (MALE)-including gangrene, revascularization and amputation-among individuals with type 2 diabetes. METHODS:In a post-hoc analysis of data from the Exenatide Study of Cardiovascular Event Lowering (EXSCEL) trial, we compared participants who experienced MALE with those who did not. A multivariable model was constructed and translated into a risk score. RESULTS:Among the 14,752 participants with type 2 diabetes in EXSCEL, 3.6% experienced MALE. Characteristics associated with increased risk of MALE were peripheral artery disease (PAD) (HR 4.83, 95% CI: 3.94-5.92), prior foot ulcer (HR 2.16, 95% CI: 1.63-2.87), prior amputation (HR 2.00, 95% CI: 1.53-2.64), current smoking (HR 2.00, 95% CI: 1.54-2.61), insulin use (HR 1.86, 95% CI: 1.52-2.27), coronary artery disease (HR 1.67, 95% CI: 1.38-2.03) and male sex (HR 1.64, 95% CI: 1.31-2.06). Cerebrovascular disease, former smoking, age, glycated haemoglobin, race and neuropathy were also associated significantly with MALE after adjustment. A risk score ranging from 6 to 96 points was constructed, with a C-statistic of 0.822 (95% CI: 0.803-0.841). CONCLUSIONS:The majority of MALE occurred among participants with PAD, but participants without a history of PAD also experienced MALE. A risk score with good performance was generated. Although it requires validation in an external dataset, this risk score may be valuable in identifying patients requiring more intensive care and closer follow-up. 10.1111/dme.14552
    Development of Predictive Nomograms for Clinical Use to Quantify the Risk of Amputation in Patients with Diabetic Foot Ulcer. Peng Bocheng,Min Rui,Liao Yiqin,Yu Aixi Journal of diabetes research Objective:To determine the novel proposed nomogram model accuracy in the prediction of the lower-extremity amputations (LEA) risk in diabetic foot ulcer (DFU). Methods and Materials:In this retrospective study, data of 125 patients with diabetic foot ulcer who met the research criteria in Zhongnan Hospital of Wuhan University from January 2015 to December 2019 were collected by filling in the clinical investigation case report form. Firstly, univariate analysis was used to find the primary predictive factors of amputation in patients with diabetic foot ulcer. Secondly, single factor and multiple factor logistic regression analysis were employed to screen the independent influencing factors of amputation introducing the primary predictive factors selected from the univariate analysis. Thirdly, the independent influencing factors were applied to build a prediction model of amputation risk in patients with diabetic foot ulcer by using R4.3; then, the nomogram was established according to the selected variables visually. Finally, the performance of the prediction model was evaluated and verified by receiver working characteristic (ROC) curve, corrected calibration curve, and clinical decision curve. Results:7 primary predictive factors were selected by univariate analysis from 21 variables, including the course of diabetes, peripheral angiopathy of diabetic (PAD), glycosylated hemoglobin A1c (HbA1c), white blood cells (WBC), albumin (ALB), blood uric acid (BUA), and fibrinogen (FIB); single factor logistic regression analysis showed that albumin was a protective factor for amputation in patients with diabetic foot ulcer, and the other six factors were risk factors. Multivariate logical regression analysis illustrated that only five factors (the course of diabetes, PAD, HbA1c, WBC, and FIB) were independent risk factors for amputation in patients with diabetic foot ulcer. According to the area under curve (AUC) of ROC was 0.876 and corrected calibration curve of the nomogram displayed good fitting ability, the model established by these 5 independent risk factors exhibited good ability to predict the risk of amputation. The decision analysis curve (DCA) indicated that the nomogram model was more practical and accurate when the risk threshold was between 6% and 91%. Conclusion:Our novel proposed nomogram showed that the course of diabetes, PAD, HbA1c, WBC, and FIB are the independent risk factors of amputation in patients with DFU. This prediction model was well developed and behaved a great accurate value for LEA so as to provide a useful tool for screening LEA risk and preventing DFU from developing into amputation. 10.1155/2021/6621035
    Individualised screening of diabetic foot: creation of a prediction model based on penalised regression and assessment of theoretical efficacy. Štotl Iztok,Blagus Rok,Urbančič-Rovan Vilma Diabetologia AIMS/HYPOTHESIS:A large proportion of people with diabetes do not receive proper foot screening due to insufficiencies in healthcare systems. Introducing an effective risk prediction model into the screening protocol would potentially reduce the required screening frequency for those considered at low risk for diabetic foot complications. The main aim of the study was to investigate the value of individualised risk assignment for foot complications for optimisation of screening. METHODS:From 2015 to 2020, 11,878 routine follow-up foot investigations were performed in the tertiary diabetes clinic. From these, 4282 screening investigations with complete data containing all of 18 designated variables collected at regular clinical and foot screening visits were selected for the study sample. Penalised logistic regression models for the prediction of loss of protective sensation (LOPS) and loss of peripheral pulses (LPP) were developed and evaluated. RESULTS:Using leave-one-out cross validation (LOOCV), the penalised regression model showed an AUC of 0.84 (95% CI 0.82, 0.85) for prediction of LOPS and 0.80 (95% CI 0.78, 0.83) for prediction of LPP. Calibration analysis (based on LOOCV) presented consistent recall of probabilities, with a Brier score of 0.08 (intercept 0.01 [95% CI -0.09, 0.12], slope 1.00 [95% CI 0.92, 1.09]) for LOPS and a Brier score of 0.05 (intercept 0.01 [95% CI -0.12, 0.14], slope 1.09 [95% CI 0.95, 1.22]) for LPP. In a hypothetical follow-up period of 2 years, the regular screening interval was increased from 1 year to 2 years for individuals at low risk. In individuals with an International Working Group on the Diabetic Foot (IWGDF) risk 0, we could show a 40.5% reduction in the absolute number of screening examinations (3614 instead of 6074 screenings) when a 10% risk cut-off was used and a 26.5% reduction (4463 instead of 6074 screenings) when the risk cut-off was set to 5%. CONCLUSIONS/INTERPRETATION:Enhancement of the protocol for diabetic foot screening by inclusion of a prediction model allows differentiation of individuals with diabetes based on the likelihood of complications. This could potentially reduce the number of screenings needed in those considered at low risk of diabetic foot complications. The proposed model requires further refinement and external validation, but it shows the potential for improving compliance with screening guidelines. 10.1007/s00125-021-05604-2
    Prediction of peak plantar pressure for diabetic foot: The regressional model. Hazari Animesh,Maiya Arun,Agouris Ioannis,Monteiro Ashma,Shivashankara Foot (Edinburgh, Scotland) BACKGROUND:The increase in peak plantar pressure could be the most important etiological factor for pathogenesis of a diabetic foot. Thus the fate of a diabetic foot syndrome which is a clinical triad of neurological, vascular and musculoskeletal changes could be biomechanically predictive and preventive using clinical parameters. In the presence of peripheral neuropathy, certain clinical parameters could be severely altered resulting into increased peak plantar pressure. Therefore the aim of the study was to identify the most important clinical parameters for the prediction of peak plantar pressure between neuropathy and non-neuropathy type 2 diabetes mellitus participants. METHODOLOGY:A total of 380 participants were recruited under the study and divided into two groups (190 each group). The cross-sectional study was conducted at Kasturba Hosipal, Manipal, India. Multiple regression analysis was performed to find the hyperplane of best fit. Stepwise regression was performed with (α entry=0.15 and α removal=0.2) to select the best subset of predictors. RESULTS:Adjusted R2 of the final model which included the predictors showed 90.8% variability for the dependent variable. CONCLUSION:The findings from the regression analysis suggested model was found to be strongly significant in predicting the peak plantar pressure between neuropathy and non-neuropathy type 2 diabetes mellitus participants. Since higher values of peak plantar pressure is strongly associated with risk for future diabetic foot complications, it could be suggested that these clinical parameters could be very useful to assess and should be used in routine clinical practice very effectively. 10.1016/j.foot.2019.06.001
    Utilization of smartphone and tablet camera photographs to predict healing of diabetes-related foot ulcers. Computers in biology and medicine The objective of this study was to build a machine learning model that can predict healing of diabetes-related foot ulcers, using both clinical attributes extracted from electronic health records (EHR) and image features extracted from photographs. The clinical information and photographs were collected at an academic podiatry wound clinic over a three-year period. Both hand-crafted color and texture features and deep learning-based features from the global average pooling layer of ResNet-50 were extracted from the wound photographs. Random Forest (RF) and Support Vector Machine (SVM) models were then trained for prediction. For prediction of eventual wound healing, the models built with hand-crafted imaging features alone outperformed models built with clinical or deep-learning features alone. Models trained with all features performed comparatively against models trained with hand-crafted imaging features. Utilization of smartphone and tablet photographs taken outside of research settings hold promise for predicting prognosis of diabetes-related foot ulcers. 10.1016/j.compbiomed.2020.104042
    A systematic review and individual patient data meta-analysis of prognostic factors for foot ulceration in people with diabetes: the international research collaboration for the prediction of diabetic foot ulcerations (PODUS). Crawford Fay,Cezard Genevieve,Chappell Francesca M,Murray Gordon D,Price Jacqueline F,Sheikh Aziz,Simpson Colin R,Stansby Gerard P,Young Matthew J Health technology assessment (Winchester, England) BACKGROUND:Annual foot risk assessment of people with diabetes is recommended in national and international clinical guidelines. At present, these are consensus based and use only a proportion of the available evidence. OBJECTIVES:We undertook a systematic review of individual patient data (IPD) to identify the most highly prognostic factors for foot ulceration (i.e. symptoms, signs, diagnostic tests) in people with diabetes. DATA SOURCES:Studies were identified from searches of MEDLINE and EMBASE. REVIEW METHODS:The electronic search strategies for MEDLINE and EMBASE databases created during an aggregate systematic review of predictive factors for foot ulceration in diabetes were updated and rerun to January 2013. One reviewer applied the IPD review eligibility criteria to the full-text articles of the studies identified in our literature search and also to all studies excluded from our aggregate systematic review to ensure that we did not miss eligible IPD. A second reviewer applied the eligibility criteria to a 10% random sample of the abstract search yield to check that no relevant material was missed. This review includes exposure variables (risk factors) only from individuals who were free of foot ulceration at the time of study entry and who had a diagnosis of diabetes mellitus (either type 1 or type 2). The outcome variable was incident ulceration. RESULTS:Our search identified 16 cohort studies and we obtained anonymised IPD for 10. These data were collected from more than 16,000 people with diabetes worldwide and reanalysed by us. One data set was kept for independent validation. The data sets contributing IPD covered a range of temporal, geographical and clinical settings. We therefore selected random-effects meta-analysis, which assumes not that all the estimates from each study are estimates of the same underlying true value, but rather that the estimates belong to the same distribution. We selected candidate variables for meta-analysis using specific criteria. After univariate meta-analyses, the most clinically important predictors were identified by an international steering committee for inclusion in the primary, multivariable meta-analysis. Age, sex, duration of diabetes, monofilaments and pulses were considered most prognostically important. Meta-analyses based on data from the entire IPD population found that an inability to feel a 10-g monofilament [odds ratio (OR) 3.184, 95% confidence interval (CI) 2.654 to 3.82], at least one absent pedal pulse (OR 1.968, 95% CI 1.624 to 2.386), a longer duration of a diagnosis of diabetes (OR 1.024, 95% CI 1.011 to 1.036) and a previous history of ulceration (OR 6.589, 95% CI 2.488 to 17.45) were all predictive of risk. Female sex was protective (OR 0.743, 95% CI 0.598 to 0.922). LIMITATIONS:It was not possible to perform a meta-analysis using a one-step approach because we were unable to procure copies of one of the data sets and instead accessed data via Safe Haven. CONCLUSIONS:The findings from this review identify risk assessment procedures that can reliably inform national and international diabetes clinical guideline foot risk assessment procedures. The evidence from a large sample of patients in worldwide settings show that the use of a 10-g monofilament or one absent pedal pulse will identify those at moderate or intermediate risk of foot ulceration, and a history of foot ulcers or lower-extremity amputation is sufficient to identify those at high risk. We propose the development of a clinical prediction rule (CPR) from our existing model using the following predictor variables: insensitivity to a 10-g monofilament, absent pedal pulses and a history of ulceration or lower-extremities amputations. This CPR could replace the many tests, signs and symptoms that patients currently have measured using equipment that is either costly or difficult to use. STUDY REGISTRATION:This study is registered as PROSPERO CRD42011001841. FUNDING:The National Institute for Health Research Health Technology Assessment programme. 10.3310/hta19570
    Derivation and validation of a clinical prediction model for assessing the risk of lower extremity amputation in patients with type 2 diabetes. Li Chia-Ing,Lin Cheng-Chieh,Cheng Hui-Man,Liu Chiu-Shong,Lin Chih-Hsueh,Lin Wen-Yuan,Wang Mu-Cyun,Yang Shing-Yu,Li Tsai-Chung Diabetes research and clinical practice AIM:This study aims to develop and validate a lower extremity amputation (LEA) risk score system in persons with type 2 diabetes. METHODS:A retrospective population-based cohort study was conducted among eligible 21,484 participants in the derivation set and 10,742 participants in the validation set who were enrolled in the Taiwan National Diabetes Care Management Program. The risk score system was developed following the steps proposed by the Framingham Heart Study with a Cox proportional hazards model algorithm. Discrimination ability was assessed by the receiver operating characteristic curve, and calibration was performed by Hosmer-Lemeshow test. RESULTS:A total of 504 patients developed LEA at an average follow-up of 7.4 years. The point scores were derived from 15 predictors as follows: age, gender, duration of type 2 diabetes, body mass index, HbA1c, triglyceride, eGFR, variation of fasting blood glucose, comorbidities of stroke, diabetes retinopathy, hypoglycemia and foot ulcer, anti-diabetes medication, and use of diuretics and nitrates. The c-statistics for predicting 3-, 5-, and 8-year LEA risks were 0.80 [95% confidence interval (CI) 0.76-0.83], 0.78 (0.75-0.81), and 0.76 (0.74-0.79) in the derivation set, respectively, and 0.81 (0.76-0.85), 0.77 (0.73-0.81), and 0.74 (0.71-0.77) in the validation set, respectively. CONCLUSIONS:A new risk score for LEA was developed and validated in the clinical setting with good discriminatory ability. Poor glycemic control, glucose variation, comorbidities, and medication use were identified as predictive factors for LEA in patients with type 2 diabetes. 10.1016/j.diabres.2020.108231
    Prediction of diabetic foot ulcer progression: a computational study. Gupta Shubham,Singh Gurpreet,Chanda Arnab Biomedical physics & engineering express The development of foot ulcers is a common consequence of severe diabetes. Due to vascular disorders and impeded healing caused by the disease, most foot ulcers have been reported to be affected by body weight and progress with time. Also, abnormal distribution of plantar pressures has been observed to cause the formation of additional ulcers, which may collectively lead to traumatic amputations. While a study of such pathophysiology is not possible through experiments, a few computational modelling works have investigated diabetic foot ulcers. To date, ulcers with a few sizes and locations have been studied, and their effect on the plantar stresses has been quantified. In this work, we have attempted to study the effect of all possible ulcer locations on the generated plantar peak stresses and peak stress locations where additional ulcers may form. Also, the effect of ulcer location on the possible ulcer growth was investigated. A full-scale foot model was developed and a total of 52 ulcer locations were simulated separately, with standing and walking loads. The generated stresses were normalised with the foot size and statistically analysed to develop novel formulations for predicting peak plantar stresses and their locations for any known ulcer location. The results from this study are anticipated to provide important guidelines to doctors and medical practitioners for predicting foot ulcer progression in diabetic patients with existing ulcers and allow the administration of timely preventive interventions. 10.1088/2057-1976/ac29f3
    Assessment of Simple Bedside Wound Characteristics for a Prediction Model for Diabetic Foot Ulcer Outcomes. Journal of diabetes science and technology BACKGROUND:Evidence-based learning systems built on prediction models can support wound care community nurses (WCCNs) during diabetic foot ulcer care sessions. Several prediction models in the area of diabetic foot ulcer healing have been developed, most built on cardiovascular measurement data. Two other data types are patient information (i.e. sex and hemoglobin A1c) and wound characteristics (i.e. wound area and wound duration); these data relate to the status of the diabetic foot ulcer and are easily accessible for WCCNs. The aim of the study was to assess simple bedside wound characteristics for a prediction model for diabetic foot ulcer outcomes. METHOD:Twenty predictor variables were tested. A pattern prediction model was used to forecast whether a given diabetic foot ulcer would (i) increase in size (or not) or (ii) decrease in size. Sensitivity, specificity, and area under the curve (AUC) in a receiver-operating characteristics curve were calculated. RESULTS:A total of 162 diabetic foot ulcers were included. In combination, the predictor variables necrosis, wound size, granulation, fibrin, dry skin, and age were most informative, in total an AUC of 0.77. CONCLUSIONS:Wound characteristics have potential to predict wound outcome. Future research should investigate implementation of the prediction model in an evidence-based learning system. 10.1177/1932296820942307
    [Development of a Diabetic Foot Ulceration Prediction Model and Nomogram]. Lee Eun Joo,Jeong Ihn Sook,Woo Seung Hun,Jung Hyuk Jae,Han Eun Jin,Kang Chang Wan,Hyun Sookyung Journal of Korean Academy of Nursing PURPOSE:This study aimed to identify the risk factors for diabetic foot ulceration (DFU) to develop and evaluate the performance of a DFU prediction model and nomogram among people with diabetes mellitus (DM). METHODS:This unmatched case-control study was conducted with 379 adult patients (118 patients with DM and 261 controls) from four general hospitals in South Korea. Data were collected through a structured questionnaire, foot examination, and review of patients' electronic health records. Multiple logistic regression analysis was performed to build the DFU prediction model and nomogram. Further, their performance was analyzed using the Lemeshow-Hosmer test, concordance statistic (C-statistic), and sensitivity/specificity analyses in training and test samples. RESULTS:The prediction model was based on risk factors including previous foot ulcer or amputation, peripheral vascular disease, peripheral neuropathy, current smoking, and chronic kidney disease. The calibration of the DFU nomogram was appropriate (χ² = 5.85, = .321). The C-statistic of the DFU nomogram was .95 (95% confidence interval .93~.97) for both the training and test samples. For clinical usefulness, the sensitivity and specificity obtained were 88.5% and 85.7%, respectively at 110 points in the training sample. The performance of the nomogram was better in male patients or those having DM for more than 10 years. CONCLUSION:The nomogram of the DFU prediction model shows good performance, and is thereby recommended for monitoring the risk of DFU and preventing the occurrence of DFU in people with DM. 10.4040/jkan.20257
    Multiple factors predict longer and shorter time-to-ulcer-free in people with diabetes-related foot ulcers: Survival analyses of a large prospective cohort followed-up for 24-months. Diabetes research and clinical practice AIMS:To investigate factors independently associated with time-to-(being)-ulcer-free, time-varying effects and predict adjusted ulcer-free probabilities, in a large prospective cohort with diabetes-related foot ulcers (DFU) followed-up for 24 months. METHODS:Patients presenting with DFU(s) to 65 Diabetic Foot Services across Queensland, Australia, between July-2011 and December-2017 were included. Demographic, comorbidity, limb, ulcer, and treatment factors were captured at presentation. Patients were followed-up until ulcer-free (all DFU(s) healed), amputation, death or two years. Factors associated with time-to-ulcer-free were investigated using both Cox proportional hazards and flexible parametric survival models to explore time-varying effects and plot predicted adjusted ulcer-free probability graphs. RESULTS:Of 4,709 included patients (median age 63 years, 69.5% male), median time-to-ulcer-free was 112 days (IQR:40->730), with 68.4% ulcer-free within two years. Factors independently associated with longer time-to-ulcer-free were each year of age younger than 60 years, living in a regional or remote area, smoking, neuropathy, peripheral artery disease (PAD), ulcer size >1 cm, deep ulcer and mild infection (all p < 0.05). Time-varying effects were found for PAD and ulcer size limiting their association to six months only. Shorter time-to-ulcer-free was associated with recent DFU treatment by a podiatrist and receiving knee-high offloading treatment (both p < 0.05). Predicted adjusted ulcer-free probability graphs reported largest differences in time-to-ulcer-free over 24-months for geographical remoteness and PAD factors. CONCLUSIONS:Multiple factors predicted longer and shorter time-to-ulcer-free in people presenting with DFUs. Considering these factors, their time-varying effects and adjusted ulcer-free probability graphs, should aid the prediction of the likely time-to-(being)-ulcer-free for DFU patients. 10.1016/j.diabres.2022.109239
    Development of a prediction model for foot ulcer recurrence in people with diabetes using easy-to-obtain clinical variables. Aan de Stegge Wouter B,Schut Martijn C,Abu-Hanna Ameen,van Baal Jeff G,van Netten Jaap J,Bus Sicco A BMJ open diabetes research & care INTRODUCTION:We aimed to develop a prediction model for foot ulcer recurrence in people with diabetes using easy-to-obtain clinical variables and to validate its predictive performance in order to help risk assessment in this high-risk group. RESEARCH DESIGN AND METHODS:We used data from a prospective analysis of 304 people with foot ulcer history who had 18-month follow-up for ulcer outcome. Demographic, disease-related and organization-of-care variables were included as potential predictors. Two logistic regression prediction models were created: model 1 for all recurrent foot ulcers (n=126 events) and model 2 for recurrent plantar foot ulcers (n=70 events). We used 10-fold cross-validation, each including five multiple imputation sets for internal validation. Performance was assessed in terms of discrimination using area under the receiver operating characteristic curve (AUC) (0-1, 1=perfect discrimination), and calibration with the Brier Score (0-1, 0=complete concordance predicted vs observed values) and calibration graphs. RESULTS:Predictors in model 1 were: a younger age, more severe peripheral sensory neuropathy, fewer months since healing of previous ulcer, presence of a minor lesion, use of a walking aid and not monitoring foot temperatures at home. Mean AUC for model 1 was 0.69 (2SD 0.040) and mean Brier Score was 0.22 (2SD 0.011). Predictors in model 2 were: a younger age, plantar location of previous ulcer, fewer months since healing of previous ulcer, presence of a minor lesion, consumption of alcohol, use of a walking aid, and foot care received in a university medical center. Mean AUC for model 2 was 0.66 (2SD 0.023) and mean Brier Score was 0.16 (2SD 0.0048). CONCLUSIONS:These internally validated prediction models predict with reasonable to good calibration and fair discrimination who is at highest risk of ulcer recurrence. The people at highest risk should be monitored more carefully and treated more intensively than others. TRIAL REGISTRATION NUMBER:NTR5403. 10.1136/bmjdrc-2021-002257
    Development of a multivariable prediction model for plantar foot ulcer recurrence in high-risk people with diabetes. Aan de Stegge Wouter B,Abu-Hanna Ameen,Bus Sicco A BMJ open diabetes research & care INTRODUCTION:Forty per cent of people with diabetes who heal from a foot ulcer recur within 1 year. The aim was to develop a prediction model for plantar foot ulcer recurrence and to validate its predictive performance. RESEARCH DESIGN AND METHODS:Data were retrieved from a prospective analysis of 171 high-risk patients with 18 months follow-up. Demographic, disease-related, biomechanical and behavioral factors were included as potential predictors. Two logistic regression models were created. Model 1 for all recurrent plantar foot ulcers (71 cases) and model 2 for those ulcers indicated to be the result of unrecognized repetitive stress (41 cases). Ten-fold cross-validation, each including five multiple imputation sets, was used to internally validate the prediction strategy; model performance was assessed in terms of discrimination and calibration. RESULTS:The presence of a minor lesion, living alone, increased barefoot peak plantar pressure, longer duration of having a previous foot ulcer and less variation in daily stride count were predictors of the first model. The area under the receiver operating curve was 0.68 (IQR 0.61-0.80) and the Brier score was 0.24 (IQR 0.20-0.28). The predictors of the second model were presence of a minor lesion, longer duration of having a previous foot ulcer and location of the previous foot ulcer. The area under the receiver operating curve was 0.76 (IQR 0.66-0.87) and the Brier score was 0.17 (IQR 0.15-0.18). CONCLUSIONS:These validated prediction models help identify those patients that are at increased risk of plantar foot ulcer recurrence and for that reason should be monitored more carefully and treated more intensively. 10.1136/bmjdrc-2020-001207
    Risk assessments and structured care interventions for prevention of foot ulceration in diabetes: development and validation of a prognostic model. Crawford Fay,Chappell Francesca M,Lewsey James,Riley Richard,Hawkins Neil,Nicolson Donald,Heggie Robert,Smith Marie,Horne Margaret,Amanna Aparna,Martin Angela,Gupta Saket,Gray Karen,Weller David,Brittenden Julie,Leese Graham Health technology assessment (Winchester, England) BACKGROUND:Diabetes-related foot ulcers give rise to considerable morbidity, generate a high monetary cost for health and social care services and precede the majority of diabetes-related lower extremity amputations. There are many clinical prediction rules in existence to assess risk of foot ulceration but few have been subject to validation. OBJECTIVES:Our objectives were to produce an evidence-based clinical pathway for risk assessment and management of the foot in people with diabetes mellitus to estimate cost-effective monitoring intervals and to perform cost-effectiveness analyses and a value-of-information analysis. DESIGN:We developed and validated a prognostic model using predictive modelling, calibration and discrimination techniques. An overview of systematic reviews already completed was followed by a review of randomised controlled trials of interventions to prevent foot ulceration in diabetes mellitus. A review of the health economic literature was followed by the construction of an economic model, an analysis of the transitional probability of moving from one foot risk state to another, an assessment of cost-effectiveness and a value-of-information analysis. INTERVENTIONS:The effects of simple and complex interventions and different monitoring intervals for the clinical prediction rules were evaluated. MAIN OUTCOME MEASURE:The main outcome was the incidence of foot ulceration. We compared the new clinical prediction rules in conjunction with the most effective preventative interventions at different monitoring intervals with a 'treat-all' strategy. DATA SOURCES:Data from an electronic health record for 26,154 people with diabetes mellitus in one Scottish health board were used to estimate the monitoring interval. The Prediction Of Diabetic foot UlcerationS (PODUS) data set was used to develop and validate the clinical prediction rule. REVIEW METHODS:We searched for eligible randomised controlled trials of interventions using search strategies created for Ovid (Wolters Kluwer, Alphen aan den Rijn, the Netherlands), MEDLINE, EMBASE and the Cochrane Central Register of Controlled Trials. Randomised controlled trials in progress were identified via the International Standard Randomised Controlled Trial Number Registry and systematic reviews were identified via PROSPERO. Databases were searched from inception to February 2019. RESULTS:The clinical prediction rule was found to accurately assess the risk of foot ulceration. Digital infrared thermometry, complex interventions and therapeutic footwear with offloading devices were found to be effective in preventing foot ulcers. The risk of developing a foot ulcer did not change over time for most people. We found that interventions to prevent foot ulceration may be cost-effective but there is uncertainty about this. Digital infrared thermometry and therapeutic footwear with offloading devices may be cost-effective when used to treat all people with diabetes mellitus regardless of their ulcer risk. LIMITATIONS:The threats to the validity of the results in some randomised controlled trials in the review and the large number of missing data in the electronic health record mean that there is uncertainty in our estimates. CONCLUSIONS:There is evidence that interventions to prevent foot ulceration are effective but it is not clear who would benefit most from receiving the interventions. The ulceration risk does not change over an 8-year period for most people with diabetes mellitus. A change in the monitoring interval from annually to every 2 years for those at low risk would be acceptable. FUTURE WORK RECOMMENDATIONS:Improving the completeness of electronic health records and sharing data would help improve our knowledge about the most clinically effective and cost-effective approaches to prevent foot ulceration in diabetes mellitus. STUDY REGISTRATION:This study is registered as PROSPERO CRD42016052324. FUNDING:This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in ; Vol. 24, No. 62. See the NIHR Journals Library website for further project information. 10.3310/hta24620
    Development and validation of an incidence risk prediction model for early foot ulcer in diabetes based on a high evidence systematic review and meta-analysis. Chen Dong,Wang Meijun,Shang Xin,Liu Xixi,Liu Xinbang,Ge Tiantian,Ren Qiuyue,Ren Xiaoxia,Song Xin,Xu Hongmei,Sun Mingyan,Zhou Hongmei,Chang Bai Diabetes research and clinical practice OBJECTIVES:To develop and validate a model for predicting the risk of early diabetic foot ulcer (DFU) based on systematic review and meta-analysis. METHODS:Data were analyzed from the risk factors of DFU with their corresponding risk ratio (RR) by meta-analysis. The DFU prediction model included statistically significant risk factors from the meta-analysis, all of which were scored by its weightings, and the prediction model was externally validated using a validation cohort from China. The occurrence of early DFU was defined as patients with type 2 diabetes who were free of DFU at baseline and diagnosed with DFU at follow-up. Evaluation of model performance was based on the area under the discrimination receiver operating characteristic curve (ROC), with optimal cutoff point determined by calculation of sensitivity and specificity. Kaplan-Meier curve were performed tocompare the cumulative risk of different groups. RESULTS:Our meta-analysis confirmed a cumulative incidence of approximately 6.0% in 46,521 patients with diabetes. The final risk prediction model included Sex, BMI, HbA1c, Smoker, DN, DR, DPN, Intermittent Claudication, Foot care, and their RRs were 1.87, 1.08, 1.21, 1.77, 2.97, 2.98, 2.76, 3.77, 0.38, respectively. The total score of all risk factors was 80 points according to their weightings. The prediction model showed good discrimination with AUC = 0.798 (95 %CI 0.738-0.858). At the optimal cut-off value of 46.5 points, the sensitivity, specificity and Youden index were 0.769, 0.798 and 0.567, respectively. The final model stratified the validation cohort into low, low-intermediate, high-intermediate and high-risk groups; Compared with low-risk group, the RR with 95 %CI of developing DFU in high-intermediate and high-risk group were 17.23 (5.12-58.02), p < 0.01 and 46.11 (5.16-91.74), p < 0.01, respectively. CONCLUSION:We have developed a simple tool to facilitates early identification of patients with diabetes at high risk of developing DFU based on scores. This simple tool may improve clinical decision-making and potentially guide early intervention. 10.1016/j.diabres.2021.109040
    Study on Risk Factors of Peripheral Neuropathy in Type 2 Diabetes Mellitus and Establishment of Prediction Model. Wu Birong,Niu Zheyun,Hu Fan Diabetes & metabolism journal BACKGROUND:Diabetic peripheral neuropathy (DPN) is one of the most serious complications of type 2 diabetes mellitus (T2DM). DPN increases the risk of ulcers, foot infections, and noninvasive amputations, ultimately leading to long-term disability. METHODS:Seven hundred patients with T2DM were investigated from 2013 to 2017 in the Sanlin community by obtaining basic data from the electronic medical record system (EMRS). From September 2018 to July 2019, 681 patients (19 missing) were investigated using a questionnaire, physical examination, biochemical index test, and follow-up Toronto clinical scoring system (TCSS) test. Patients with a TCSS score ≥6 points were diagnosed with DPN. After removing missing values, 612 patients were divided into groups in a 3:1 ratio for external validation. Using different Lasso analyses (misclassification error, mean squared error, -2log-likelihood, and area under curve) and a logistic regression analysis of the training set, models A, B, C, and D were established. The receiver operating characteristic (ROC) curve, calibration plot, dynamic component analysis (DCA) measurements, net classification improvement (NRI) and integrated discrimination improvement (IDI) were used to validate discrimination and clinical practicality of the model. RESULTS:Through data analysis, model A (containing four factors), model B (containing five factors), model C (containing seven factors), and model D (containing seven factors) were built. After calibration, ROC curve, DCA, NRI and IDI, models C and D exhibited better accuracy and greater predictive power. CONCLUSION:Four prediction models were established to assist with the early screening of DPN in patients with T2DM. The influencing factors in model C and D are more important factors for patients with T2DM diagnosed with DPN. 10.4093/dmj.2020.0100