Palliative Radiotherapy During the Last Month of Life: Have COVID-19 Recommendations Led to Reduced Utilization?
Nieder Carsten,Haukland Ellinor C,Mannsaker Bard,Yobuta Rosalba
In vivo (Athens, Greece)
BACKGROUND/AIM:The study aimed to evaluate practice changes in the time period of the early wave of the COVID-19 pandemic. PATIENTS AND METHODS:This was a retrospective single institution study. We defined palliative radiotherapy (PRT) initiated before Saturday, March 14 as pre-COVID and PRT initiated later as during-COVID (through June 30). RESULTS:National COVID-19 recommendations led to a significant decrease in PRT with 10 or more fractions, while re-irradiation and radiotherapy during the final 30 days of life were equally common before and after these recommendations had been issued in March 2020. CONCLUSION:Rapid adoption of modified PRT regimens was feasible. However, the challenge of overtreatment in the final phase of the disease, due to inaccurate survival prediction, persisted.
10.21873/invivo.12304
Hypoalbuminemia predicts the outcome of COVID-19 independent of age and co-morbidity.
Journal of medical virology
The coronavirus disease 2019 (COVID-19) has evolved into a pandemic rapidly. Most of the literature show that the elevated liver enzymes in COVID-19 are of little clinical significance. Lower albumin level is seen in severe COVID-19 and is not parallel to the changes in alanine aminotransferase and aspartate aminotransferase levels. We aimed to explore the impact of hypoalbuminemia in COVID-19. This retrospective cohort study included adult patients with confirmed COVID-19. The relationship between hypoalbuminemia and death was studied using binary logistic analysis. A total of 299 adult patients were included, 160 (53.5%) were males and the average age was 53.4 ± 16.7 years. The median time from the onset of illness to admission was 3 days (interquartile ranges, 2-5). Approximately one-third of the patients had comorbidities. Hypoalbuminemia (<35 g/L) was found in 106 (35.5%) patients. The difference in albumin was considerable between survivors and non-survivors (37.6 ± 6.2 vs 30.5 ± 4.0, P < .001). Serum albumin level was inversely correlated to white blood cell (r = -.149, P = .01) and neutrophil to lymphocyte ratio (r = -.298, P < .001). Multivariate analysis showed the presence of comorbidities (OR, 6.816; 95% CI, 1.361-34.133), lymphopenia (OR, 13.130; 95% CI, 1.632-105.658) and hypoalbuminemia (OR, 6.394; 95% CI, 1.315-31.092) were independent predictive factors for mortality. In conclusion, hypoalbuminemia is associated with the outcome of COVID-19. The potential therapeutic value of albumin infusion in COVID-19 should be further explored at the earliest.
10.1002/jmv.26003
Prognosis of 18 H7N9 avian influenza patients in Shanghai.
Lu Shuihua,Li Tao,Xi Xiuhong,Chen Qingguo,Liu Xuhui,Zhang Binxing,Ou Jiaxian,Liu Jie,Wang Qin,Zhu Biao,Liu Xinian,Bai Chunxue,Qu Jieming,Lu Hongzhou,Zhang Zhiyong,Song Yuanlin
PloS one
PURPOSE:To provide prognosis of an 18 patient cohort who were confirmed to have H7N9 lung infection in Shanghai. METHODS:Patients' history, clinical manifestation, laboratory test, treatment strategy and mortality were followed and recorded for data analysis. RESULTS:A total of 18 patients had been admitted into Shanghai Public Health Clinical Center from April 8th to July 29, 2013. 22.2% of the patients were found to have live poultry contact history and 80% were aged male patients with multiple co-morbidities including diabetes, hypertension and/or chronic obstructive pulmonary disease (COPD). This group of patients was admitted to the clinical center around 10 days after disease onset. According to laboratory examinations, increased C reactive protein (CRP), Procalcitonin (PCT), Plasma thromboplastin antecedent (PTA) and virus positive time (days) were indicative of patients' mortality. After multivariate analysis, only CRP level showed significant prediction of mortality (P = 0.013) while results of prothrombin time (PT) analysis almost reached statistical significance (P = 0.056). CONCLUSIONS:H7N9 infection induced pneumonia of different severity ranging from mild to severe pneumonia or acute lung injury/acute respiratory distress syndrome to multiple organ failure. Certain laboratory parameters such as plasma CRP, PCT, PTA and virus positive days predicted mortality of H7N9 infection and plasma CRP is an independent predictor of mortality in these patients.
10.1371/journal.pone.0088728
Utilization of machine-learning models to accurately predict the risk for critical COVID-19.
Assaf Dan,Gutman Ya'ara,Neuman Yair,Segal Gad,Amit Sharon,Gefen-Halevi Shiraz,Shilo Noya,Epstein Avi,Mor-Cohen Ronit,Biber Asaf,Rahav Galia,Levy Itzchak,Tirosh Amit
Internal and emergency medicine
Among patients with Coronavirus disease (COVID-19), the ability to identify patients at risk for deterioration during their hospital stay is essential for effective patient allocation and management. To predict patient risk for critical COVID-19 based on status at admission using machine-learning models. Retrospective study based on a database of tertiary medical center with designated departments for patients with COVID-19. Patients with severe COVID-19 at admission, based on low oxygen saturation, low partial arterial oxygen pressure, were excluded. The primary outcome was risk for critical disease, defined as mechanical ventilation, multi-organ failure, admission to the ICU, and/or death. Three different machine-learning models were used to predict patient deterioration and compared to currently suggested predictors and to the APACHEII risk-prediction score. Among 6995 patients evaluated, 162 were hospitalized with non-severe COVID-19, of them, 25 (15.4%) patients deteriorated to critical COVID-19. Machine-learning models outperformed the all other parameters, including the APACHE II score (ROC AUC of 0.92 vs. 0.79, respectively), reaching 88.0% sensitivity, 92.7% specificity and 92.0% accuracy in predicting critical COVID-19. The most contributory variables to the models were APACHE II score, white blood cell count, time from symptoms to admission, oxygen saturation and blood lymphocytes count. Machine-learning models demonstrated high efficacy in predicting critical COVID-19 compared to the most efficacious tools available. Hence, artificial intelligence may be applied for accurate risk prediction of patients with COVID-19, to optimize patients triage and in-hospital allocation, better prioritization of medical resources and improved overall management of the COVID-19 pandemic.
10.1007/s11739-020-02475-0
Establishing a model for predicting the outcome of COVID-19 based on combination of laboratory tests.
Wang Feng,Hou Hongyan,Wang Ting,Luo Ying,Tang Guoxing,Wu Shiji,Zhou Hongmin,Sun Ziyong
Travel medicine and infectious disease
INTRODUCTION:There are currently no satisfactory methods for predicting the outcome of Coronavirus Disease-2019 (COVID-19). The aim of this study is to establish a model for predicting the prognosis of the disease. METHODS:The laboratory results were collected from 54 deceased COVID-19 patients on admission and before death. Another 54 recovered COVID-19 patients were enrolled as control cases. RESULTS:Many laboratory indicators, such as neutrophils, AST, γ-GT, ALP, LDH, NT-proBNP, Hs-cTnT, PT, APTT, D-dimer, IL-2R, IL-6, IL-8, IL-10, TNF-α, CRP, ferritin and procalcitonin, were all significantly increased in deceased patients compared with recovered patients on admission. In contrast, other indicators such as lymphocytes, platelets, total protein and albumin were significantly decreased in deceased patients on admission. Some indicators such as neutrophils and procalcitonin, others such as lymphocytes and platelets, continuously increased or decreased from admission to death in deceased patients respectively. Using these indicators alone had moderate performance in differentiating between recovered and deceased COVID-19 patients. A model based on combination of four indicators (P = 1/[1 + e]) showed good performance in predicting the death of COVID-19 patients. When cutoff value of 0.572 was used, the sensitivity and specificity of the prediction model were 90.74% and 94.44%, respectively. CONCLUSIONS:Using the current indicators alone is of modest value in differentiating between recovered and deceased COVID-19 patients. A prediction model based on combination of neutrophils, lymphocytes, platelets and IL-2R shows good performance in predicting the outcome of COVID-19.
10.1016/j.tmaid.2020.101782
Validation of the American Thoracic Society-Infectious Diseases Society of America guidelines for hospital-acquired pneumonia in the intensive care unit.
Ferrer Miquel,Liapikou Adamantia,Valencia Mauricio,Esperatti Mariano,Theessen Anna,Antonio Martinez Jose,Mensa Jose,Torres Antoni
Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
BACKGROUND:The 2005 guidelines of the American Thoracic Society-Infectious Diseases Society of America Guidelines for Hospital for managing hospital-acquired pneumonia classified patients according to time of onset and risk factors for potentially drug-resistant microorganisms to select the empirical antimicrobial treatment. We assessed the microbial prediction and validated the adequacy of these guidelines for antibiotic strategy. METHODS:We prospectively observed 276 patients with intensive care unit-acquired pneumonia. We classified patients into group 1 (early onset without risk factors for potentially drug-resistant microorganisms; 38 patients) and group 2 (late onset or risk factors for potentially drug-resistant microorganisms; 238 patients). We determined the accuracy of guidelines to predict causative microorganisms and the influence of guidelines adherence in patients' outcome. RESULTS:Microbial prediction was lower in group 1 than in group 2 (12 [50%] of 24 vs 119 [92%] of 129; P < .001) mainly because of potentially drug-resistant microorganisms in 10 patients (26%) from group 1. Guideline adherence was higher in group 2 (153 [64%] vs 7 [18%]; P < .001). Guideline adherence resulted in more treatment adequacy than did nonadherence (69 [83%] vs 45 [64%]; P = .013) and a trend toward better response to empirical treatment in group 2 only but did not influence mortality. Reclassifying patients according to the risk factors for potentially drug-resistant microorganisms of the former 1996 American Thoracic Society guidelines increased microbial prediction in group 1 to 21 (88%; P = .014); all except 1 patient with potentially drug-resistant microorganisms were correctly identified by these guidelines. CONCLUSIONS:The 2005 guidelines predict potentially drug-resistant microorganisms worse than the 1996 guidelines. Adherence to guidelines resulted in more adequate treatment and a trend to a better clinical response in group 2, but it did not influence mortality.
10.1086/651075
Correlation between the variables collected at admission and progression to severe cases during hospitalization among patients with COVID-19 in Chongqing.
Duan Jun,Wang Xiaohui,Chi Jing,Chen Hong,Bai Linfu,Hu Qianfang,Han Xiaoli,Hu Wenhui,Zhu Linxiao,Wang Xue,Li You,Zhou Chenmei,Mou Huaming,Yan Xiaofeng,Guo Shuliang
Journal of medical virology
Mortality is high among severe patients with 2019 novel coronavirus-infected disease (COVID-19). Early prediction of progression to severe cases is needed. We retrospectively collected patients with COVID-19 in two hospital of Chongqing from 1st January to 29th February 2020. At admission, we collected the demographics and laboratory tests to predict whether the patient would progress to severe cases in hospitalization. Severe case was confirmed when one of the following criteria occurred: (a) dyspnea, respiratory rate ≥30 breaths/min, (b) blood oxygen saturation ≤93%, and (c) PaO /FiO ≤ 300 mm Hg. At admission, 348 mild cases were enrolled in this study. Of them, 20 (5.7%) patients progressed to severe cases after median 4.0 days (interquartile range: 2.3-6.0). Pulmonary inflammation index, platelet counts, sodium, C-reactive protein, prealbumin, and PaCO showed good distinguishing power to predict progression to severe cases (each area under the curve of receiver operating characteristics [AUC] ≥ 0.8). Age, heart rate, chlorine, alanine aminotransferase, aspartate aminotransferase, procalcitonin, creatine kinase, pH, CD3 counts, and CD4 counts showed moderate distinguishing power (each AUC between 0.7-0.8). And potassium, creatinine, temperature, and D-dimer showed mild distinguishing power (each AUC between 0.6-0.7). In addition, higher C-reactive protein was associated with shorter time to progress to severe cases (r = -0.62). Several easily obtained variables at admission are associated with progression to severe cases during hospitalization. These variables provide a reference for the medical staffs when they manage the patients with COVID-19.
10.1002/jmv.26082
Usefulness of biological markers in the early prediction of corona virus disease-2019 severity.
Bennouar Salam,Bachir Cherif Abdelghani,Kessira Amel,Hamel Hadjer,Boudahdir Adel,Bouamra Abderrazek,Bennouar Djameleddine,Abdi Samia
Scandinavian journal of clinical and laboratory investigation
Coronavirus Disease 2019 is a very fast-spreading infectious disease. Severe forms are marked by a high mortality rate. The objective of this study is to identify routine biomarkers that can serve as early predictors of the disease progression. This is a prospective, single-center, cohort study involving 330 SARS-CoV-2 infected patients who were admitted at the University Hospital of Blida, Algeria in the period between the 27th of March and 22nd of April 2020. The ROC curve was used to evaluate the predictive performance of biomarkers, assessed at admission, in the early warning of progression toward severity. Multivariate logistic regression was used to quantify the independent risk for each marker. After an average follow-up period of 13.9 ± 3.5 days, 143 patients (43.3%) were classified as severe cases. Six biological abnormalities were identified as potential risk markers independently related to the severity: elevated urea nitrogen (>8.0 mmol/L, OR = 9.3 [2.7-31.7], < .00001), elevated CRP (>42mg/L, OR = 7.5 [2.4-23.3], = .001), decreased natremia (<133. 6 mmol/L, OR = 6.0 [2.0-17.4], = .001), decreased albumin (<33.5 g/L, OR = 5.2 [1.7-16.6], = .003), elevated LDH (>367 IU/L, OR = 4.9 [1.7-14.2], = .003) and elevated neutrophil to lymphocyte ratio (>7.99, OR = 4.2, [1.4-12.2], = .009). These easy-to-measure, time-saving and very low-cost parameters have been shown to be effective in the early prediction of the COVID-19 severity. Their use at the early admission stage can improve the risk stratification and management of medical care resources in order to reduce the mortality rate.
10.1080/00365513.2020.1821396
Real-world extravascular lung water index measurements in critically ill patients : Pulse index continuous cardiac output measurements: time course analysis and association with clinical characteristics.
Werner Matthias,Wernly Bernhard,Lichtenauer Michael,Franz Marcus,Kabisch Bjoern,Muessig Johanna M,Masyuk Maryna,Schulze Paul Christian,Hoppe Uta C,Kelm Malte,Lauten Alexander,Jung Christian
Wiener klinische Wochenschrift
BACKGROUND:Pulse index continuous cardiac output (PiCCO) is used for hemodynamic assessment. This study describes real world extravascular lung water index (EVLWI) measurements at three time points and relates them to other hemodynamic parameters and mortality in critically ill patients admitted to a medical intensive care unit (ICU). METHODS:A total of 198 patients admitted to a tertiary medical university hospital between February 2004 and December 2010 were included in this retrospective analysis. Patients were admitted for various diseases such as sepsis (n = 22), myocardial infarction (n = 53), pulmonary embolism (n = 3), cardiopulmonary resuscitation (n = 15), acute heart failure (AHF; n = 21) and pneumonia (n = 25). RESULTS:Patients included in this analysis were severely ill as represented by the high simplified acute physiology score 2 (SAPS2, 42 ± 18) and acute physiology and chronic health evaluation score 2 (APACHE2' 22 ± 9). Real-world values at three time points are provided. Intra-ICU mortality rates did not differ between the EVLWI > 7 vs. the ELVWI < 7 groups (15% vs. 13%; p = 0.82) and no association between hemodynamic measurements obtained by PiCCO with long-term mortality could be shown. CONCLUSION:There were no associations of any PiCCO measurements with mortality most probably due to selection bias towards severely ill patients. Future prospective studies with predefined inclusion criteria and treatment algorithms are necessary to evaluate the value of PiCCO for prediction of mortality against simple clinical tools such as jugular venous pressure, edema and auscultation.
10.1007/s00508-019-1501-x
Failure of CRP decline within three days of hospitalization is associated with poor prognosis of Community-acquired Pneumonia.
Andersen Stine Bang,Baunbæk Egelund Gertrud,Jensen Andreas Vestergaard,Petersen Pelle Trier,Rohde Gernot,Ravn Pernille
Infectious diseases (London, England)
BACKGROUND:C-reactive protein (CRP) is a well-known acute phase protein used to monitor the patient's response during treatment in infectious diseases. Mortality from Community-acquired Pneumonia (CAP) remains high, particularly in hospitalized patients. Better risk prediction during hospitalization could improve management and ultimately reduce mortality levels. The aim of this study was to evaluate CRP on the 3rd day (CRP3) of hospitalization as a predictor for 30 days mortality. METHODS:A retrospective multicentre cohort study of adult patients admitted with CAP at three Danish hospitals. Predictive associations of CRP3 (absolute levels and relative decline) and 30 days mortality were analysed using receiver operating characteristics and logistic regression. RESULTS:Eight hundred and fourteen patients were included and 90 (11%) died within 30 days. The area under the curve for CRP3 level and decline for predicting 30 days mortality were 0.64 (0.57-0.70) and 0.71 (0.65-0.76). Risk of death was increased in patients with CRP3 level >75 mg/l (OR 2.44; 95%CI 1.36-4.37) and in patients with a CRP3 decline <50% (OR 4.25; 95%CI 2.30-7.83). In the multivariate analysis, the highest mortality risk was seen in patients who failed to decline by 50%, irrespective of the actual level of CRP (OR 7.8; 95%CI 3.2-19.3). Mortality risk increased significantly according to CRP decline for all strata of CURB-65 score. CONCLUSIONS:CRP responses day 3 is a valuable predictor of 30 days mortality in hospitalized CAP patients. Failure to decline in CRP was associated with a poor prognosis irrespective of the actual level of CRP or CURB-65.
10.1080/23744235.2016.1253860
Prediction of Ischemic Stroke-Associated Pneumonia: A Comparison between 3 Scores.
Helmy Tamer Abdallah,Abd-Elhady Mohamed Abd-Elalim,Abdou Mohammed
Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
BACKGROUND:Stroke is a leading cause of death and disability worldwide. Among all poststroke complications, pneumonia constitutes a major complication with a strong impact on morbidity and mortality. To identify patients at high risk of stroke-associated pneumonia (SAP) and to tailor a prophylactic approach, a reliable scoring model for prediction may be useful in daily stroke care. OBJECTIVES:This study aimed to compare the performance of the Age, Atrial fibrillation, Dysphagia, Sex, Stroke Severity (ADS) score, the acute ischemic stroke-associated pneumonia score (AIS-APS), and the Preventive ANtibacterial THERapy in acute Ischemic Stroke (PANTHERIS) score in predicting SAP. METHODS:Seventy consecutive patients with ischemic stroke admitted to the Critical Care Medicine Department of Alexandria Main University Hospital were included. Patients were prospectively followed up for primary outcome of pneumonia within the first 7 days after admission diagnosed by the Centers for Disease Control and Prevention criteria. Accuracy in predicting outcome measures was assessed by calculating the area under receiver operating characteristic curve (AUC). RESULTS:Twenty-six (37.1%) patients developed pneumonia by the seventh day; the ADS score AUC was .847 (95% CI: .741-.922), and the AIS-APS AUC was .798 (95% CI: .685-.884). The PANTHERIS score AUC was .715 (95% CI: .595-.817). The ADS score AUC was significantly higher than the AIS-APS and the PANTHERIS score AUCs (P = .048 and P = .009 respectively), and the AIS-APS AUC was significantly higher than the PANTHERIS score AUC (P = .044). CONCLUSIONS:The ADS score is a valid tool for the prediction of SAP based on routinely collected data, and among the 3 studied scores, it shows the best performance in predicting SAP.
10.1016/j.jstrokecerebrovasdis.2016.07.030
Association of Frailty With 30-Day Outcomes for Acute Myocardial Infarction, Heart Failure, and Pneumonia Among Elderly Adults.
JAMA cardiology
Importance:The addition of a claims-based frailty metric to traditional comorbidity-based risk-adjustment models for acute myocardial infarction (AMI), heart failure (HF), and pneumonia improves the prediction of 30-day mortality and readmission. This may have important implications for hospitals that tend to care for frail populations and participate in Centers for Medicare & Medicaid Services value-based payment programs, which use these risk-adjusted metrics to determine reimbursement. Objective:To determine whether the addition of frailty measures to traditional comorbidity-based risk-adjustment models improved prediction of outcomes for patients with AMI, HF, and pneumonia. Design, Setting, and Participants:A nationwide cohort study included Medicare fee-for-service beneficiaries 65 years and older in the United States between January 1 and December 1, 2016. Analysis began August 2018. Main Outcomes and Measures:Rates of mortality within 30 days of admission and 30 days of discharge, as well as 30-day readmission rates by frailty group. We evaluated the incremental effect of adding the Hospital Frailty Risk Score (HFRS) to current comorbidity-based risk-adjustment models for 30-day outcomes across all conditions. Results:For 785 127 participants, there were 166 200 hospitalizations [21.2%] for AMI, 348 619 [44.4%] for HF, and 270 308 [34.4%] for pneumonia. The mean (SD) age at the time of hospitalization was 79.2 (8.9) years; 656 315 (83.6%) were white and 402 639 (51.3%) were women. The mean (SD) HFRS was 7.3 (7.4) for patients with AMI, 10.8 (8.3) for patients with HF, and 8.2 (5.7) for patients with pneumonia. Among patients hospitalized for AMI, an HFRS more than 15 (compared with an HFRS <5) was associated with a higher risk of 30-day postadmission mortality (adjusted odds ratio [aOR], 3.6; 95% CI, 3.4-3.8), 30-day postdischarge mortality (aOR, 4.0; 95% CI, 3.7-4.3), and 30-day readmission (aOR, 3.0; 95% CI, 2.9-3.1) after multivariable adjustment for age, sex, race, and comorbidities. Similar patterns were observed for patients hospitalized with HF (30-day postadmission mortality: aOR, 3.5; 95% CI, 3.4-3.7; 30-day postdischarge mortality: aOR, 3.5; 95% CI, 3.3-3.6; and 30-day readmission: aOR, 2.9; 95% CI, 2.8-3.0) and among patients with pneumonia (30-day postadmission mortality: aOR, 2.5; 95% CI, 2.3-2.6; 30-day postdischarge mortality: aOR, 3.0; 95% CI, 2.9-3.2; and 30-day readmission: aOR, 2.8; 95% CI, 2.7-2.9). The addition of HFRS to traditional comorbidity-based risk-prediction models improved discrimination to predict outcomes for all 3 conditions. Conclusions and Relevance:Among Medicare fee-for-service beneficiaries, frailty as measured by the HFRS was associated with mortality and readmissions among patients hospitalized for AMI, HF, or pneumonia. The addition of HFRS to traditional comorbidity-based risk-prediction models improved the prediction of outcomes for all 3 conditions.
10.1001/jamacardio.2019.3511
Comparison between the Identification of Seniors at Risk and Triage Risk Screening Tool in predicting mortality of older adults visiting the emergency department: Results from Indonesia.
Rizka Aulia,Harimurti Kuntjoro,Pitoyo Ceva W,Koesnoe Sukamto
Geriatrics & gerontology international
AIM:Among others, the Identification of Seniors at Risk (ISAR) and Triage Risk Screening Tool (TRST) are widely used screening instruments for risk stratification of older adults visiting the emergency department (ED). In developing countries, such as Indonesia, older patients often present with acute and severe conditions, leading to a high mortality rate, in which the performance of these two instruments have not been studied. This study aimed to measure the performance of the ISAR and TRST to predict 1- and 3-month mortality in older patients visiting the ED in Indonesia. METHODS:This was a prospective cohort study of older patients consecutively visiting the ED of Cipto Mangunkusumo Hospital, a national referral hospital in Jakarta, Indonesia, from January to July 2017. The area of under the curve (AUC) of the ISAR and TRST in predicting 1- and 3-month mortality was measured. RESULTS:Of 771 participants, 400 (52.8%) were men. The 1 month-mortality incidence was 22.8% (95% CI 21.3-24.8), and 3-month mortality was 31.2% (95% CI 29.3-33.8). For 1-month mortality, the ISAR showed AN AUC of 0.62 (95% CI 0.57-0.68), whereas the TRST showed an AUC of 0.58 (95% CI 0.52-0.64). For 3-month mortality, the ISAR showed an AUC of 0.60 (95% CI 0.54-0.65), whereas the TRST showed an AUC of 0.57 (95% CI 0.51-0.62). CONCLUSIONS:Both instruments showed moderate predictive ability, but the ISAR showed better performance in predicting 1- and 3-month mortality of older patients visiting the ED in Indonesia. Geriatr Gerontol Int 2020; 20: 47-51.
10.1111/ggi.13817
Serum Prealbumin Improves the Sensitivity of Pneumonia Severity Index in Predicting 30-day Mortality of CAP Patients.
Zhang Hai F,Li Li Q,Ge Yan L,Zhang Jia B,Fu Ai S,Liu Cong H,Shao Dong F,Bai Jing,Zhu Xiao Y
Clinical laboratory
BACKGROUND:Serum prealbumin (PAB) is an effective tool to evaluate patients with malnutrition. In recent years, studies have shown that PAB is statistically reduced during the course of disease infection. The pneumonia severity index (PSI) scoring system is one of the most widely used scoring tools to evaluate the condition and prognosis of community acquired pneumonia (CAP) patients. However, few studies have reported on PSI combined with blood indicators to predict the prognosis of pneumonia. The aim of this study was to investigate the prognostic value of PAB combined with PSI in patients with CAP. METHODS:We retrospectively analyzed the data of 400 patients who met the inclusion criteria. Death and survival were selected as prognostic indicators of pneumonia. On the first day after admission, venous blood samples were taken to test PAB and PSI scores. Subject operating characteristic curve (ROC) was used to evaluate PSI, PAB, and PSI combined with PAB to predict 30-day mortality of CAP patients. RESULTS:The 30-day mortality rate of CAP patients was 10.5% (42/400). PAB and PSI score were independent risk factors for 30-day mortality in CAP patients. The sensitivity, specificity, positive predictive value, and negative predictive value of PAB predicting the death of CAP patients were 86.3%, 79%, 50.74%, and 95.83%, respectively. The sensitivity, specificity, positive predictive value, and negative predictive value of PSI predicting the death of CAP patients were 74.80%, 63%, 33.71%, and 90.99%, respectively. The sensitivity, specificity, positive predictive value, and negative predictive value of the combined index predicting the death of CAP patients were 95.20%, 77.80%, 51.70% and 98.41%, respectively. CONCLUSIONS:Serum prealbumin is a relatively simple acquired index and an independent risk factor for death in CAP patients. Serum prealbumin improves the sensitivity of pneumonia severity index in predicting 30-day mortality of CAP patients.
10.7754/Clin.Lab.2019.190929
Performance of pneumonia severity index and CURB-65 in predicting 30-day mortality in patients with COVID-19.
International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases
OBJECTIVE:The aim of the study was to analyze the usefulness of CURB-65 and the pneumonia severity index (PSI) in predicting 30-day mortality in patients with COVID-19, and to identify other factors associated with higher mortality. METHODS:A retrospective study was performed in a pandemic hospital in Istanbul, Turkey, which included 681 laboratory-confirmed patients with COVID-19. Data on characteristics, vital signs, and laboratory parameters were recorded from electronic medical records. Receiver operating characteristic analysis was used to quantify the discriminatory abilities of the prognostic scales. Univariate and multivariate logistic regression analyses were performed to identify other predictors of mortality. RESULTS:Higher CRP levels were associated with an increased risk for mortality (OR: 1.015, 95% CI: 1.008-1.021; p < 0.001). The PSI performed significantly better than CURB-65 (AUC: 0.91, 95% CI: 0.88-0.93 vs AUC: 0.88, 95% CI: 0.85-0.90; p = 0.01), and the addition of CRP levels to PSI did not improve the performance of PSI in predicting mortality (AUC: 0.91, 95% CI: 0.88-0.93 vs AUC: 0.92, 95% CI: 0.89-0.94; p = 0.29). CONCLUSION:In a large group of hospitalized patients with COVID-19, we found that PSI performed better than CURB-65 in predicting mortality. Adding CRP levels to PSI did not improve the 30-day mortality prediction.
10.1016/j.ijid.2020.06.038
Sarcopenia for predicting mortality among elderly nursing home residents: SARC-F versus SARC-CalF.
Medicine
Little is known about the prognostic value of the strength, assistance walking, rise from a chair, climb stairs, and falls questionnaire (SARC-F) and SARC-F combined with calf circumference (SARC-CalF) among elderly nursing home residents.To compare the prognostic value of SARC-F and SARC-CalF for mortality in this population.We conducted a prospective study in four nursing homes in western China. Sarcopenia was estimated using SARC-F and SARC-CalF, respectively. Nutrition status, activities of daily living, and other covariates were evaluated. The survival status was collected via medical records and telephone interviews at the 12th month after the baseline investigation. We used multivariate Cox proportional-hazard models to calculate the hazard ratio (HR) and 95% confidence interval (CI) for 1-year all-cause mortality by SARC-F-defined sarcopenia and SARC-CalF-defined sarcopenia, separately.We included 329 participants (median age: 85 years). The prevalences of SARC-F-defined sarcopenia and SARC-CalF-defined sarcopenia were 39.8% and 46.8%, respectively. During the 1-year follow-up period, 73 participants (22.7%) died. The mortality was 29.0% and 18.3% in the participants with or without SARC-F-defined sarcopenia, respectively (P = .025). The mortality was 26.6% and 19.0% in the participants with or without SARC-CalF-defined sarcopenia, respectively (P = .105). After adjusted for the relevant confounders including malnutrition, SARC-F-defined sarcopenia was independently associated with an increased risk of 1-year mortality (adjusted HR: 2.08; 95% CI: 1.27-3.42). However, SARC-CalF-defined sarcopenia was not an independent predictor of 1-year mortality (adjusted HR: 1.54; 95% CI: 0.95-2.47).Sarcopenia is highly prevalent in Chinese elderly nursing home residents according to SARC-F or SARC-CalF. SARC-F-defined sarcopenia appears to be better for predicting the 1-year mortality of Chinese nursing home residents than SARC-CalF-defined sarcopenia.
10.1097/MD.0000000000014546
Morbidity Measures Predicting Mortality in Inpatients: A Systematic Review.
Soh Cheng Hwee,Ul Hassan Syed Wajih,Sacre Julian,Maier Andrea B
Journal of the American Medical Directors Association
OBJECTIVES:Morbidity is an important risk factor for mortality and a variety of morbidity measures have been developed to predict patients' health outcomes. The objective of this systematic review was to compare the capacity of morbidity measures in predicting mortality among inpatients admitted to internal medicine, geriatric, or all hospital wards. DESIGN:A systematic literature search was conducted from inception to March 6, 2019 using 4 databases: Medline, Embase, Cochrane, and CINAHL. Articles were included if morbidity measures were used to predict mortality (registration CRD42019126674). SETTING AND PARTICIPANTS:Inpatients with a mean or median age ≥65 years. MEASUREMENTS:Morbidity measures predicting mortality. RESULTS:Of the 12,800 articles retrieved from the databases, a total of 34 articles were included reporting on inpatients admitted to internal medicine, geriatric, or all hospital wards. The Charlson Comorbidity Index (CCI) was reported most frequently and a higher CCI score was associated with greater mortality risk, primarily at longer follow-up periods. Articles comparing morbidity measures revealed that the Geriatric Index of Comorbidity was better predicting mortality risk than the CCI, Cumulative Illness Rating Scale, Index of Coexistent Disease, and disease count. CONCLUSIONS AND IMPLICATIONS:Higher morbidity measure scores are better in predicting mortality at longer follow-up period. The Geriatric Index of Comorbidity was best in predicting mortality and should be used more often in clinical practice to assist clinical decision making.
10.1016/j.jamda.2019.12.001