Assessing Nutritional Status in Gastric Cancer Patients after Total versus Subtotal Gastrectomy: Cross-Sectional Study.
Nutrients
Gastric cancer (GC) remains a significant global health concern, ranking as the third leading cause of cancer-related deaths. Malnutrition is common in GC patients and can negatively impact prognosis and quality of life. Understanding nutritional issues and their management is crucial for improving patient outcomes. This cross-sectional study included 51 GC patients who underwent curative surgery, either total or subtotal gastrectomy. Various nutritional assessments were conducted, including anthropometric measurements, laboratory tests, and scoring systems such as Eastern Cooperative Oncology Group/World Health Organization Performance Status (ECOG/WHO PS), Observer-Reported Dysphagia (ORD), Nutritional Risk Screening-2002 (NRS-2002), Patient-Generated Subjective Global Assessment (PG-SGA), and Simplified Nutritional Appetite Questionnaire (SNAQ). Serum carcinoembryonic antigen (CEA) levels were significantly higher in the subtotal gastrectomy group. Nutritional assessments indicated a higher risk of malnutrition in patients who underwent total gastrectomy, as evidenced by higher scores on ORD, NRS-2002, and PG-SGA. While total gastrectomy was associated with a higher risk of malnutrition, no single nutritional parameter emerged as a strong predictor of surgical approach. PG-SGA predominantly identified malnutrition, with its occurrence linked to demographic factors such as female gender and age exceeding 65 years.
10.3390/nu16101485
Automated Screening of COVID-19-Based Tongue Image on Chinese Medicine.
BioMed research international
Objective:Artificial intelligence-powered screening systems of coronavirus disease 2019 (COVID-19) are urgently demanding since the ongoing outbreak of SARS-CoV-2 worldwide. Chest CT or X-ray is not sufficient to support the large-scale screening of COVID-19 because mildly-infected patients do not have imaging features on these images. Therefore, it is imperative to exploit supplementary medical imaging strategies. Traditional Chinese medicine has played an essential role in the fight against COVID-19. Methods:In this paper, we conduct two kinds of verification experiments based on a newly-collected multi-modality dataset, which consists of three types of modalities: tongue images, chest CT scans, and X-ray images. First, we study a binary classification experiment on tongue images to verify the discriminative ability between COVID-19 and non-COVID-19. Second, we design extensive multimodality experiments to validate whether introducing tongue image can improve the screening accuracy of COVID-19 based on chest CT or X-ray images. Results:Tongue image screening of COVID-19 showed that the accuracy (ACC), sensitivity (SEN), specificity (SPEC), and Matthew correlation coefficient (MCC) of the improved AlexNet and Googlenet both reached 98.39%, 98.97%, 96.67%, and 99.11%. The fusion of chest CT and tongue images used a tandem multimodal classifier fusion strategy to achieve optimal classification, and the results and screening accuracy of COVID-19 reached 98.98%, resulting in a significant improvement of 4.75% the highest accuracy in 375 years compared with the single-modality model. The fusion of chest x-rays and tongue images also had good classification accuracy. Conclusions:Both experimental results demonstrate that tongue image not only has an excellent discriminative ability for screening COVID-19 but also can improve the screening accuracy based on chest CT or X-rays. To the best of our knowledge, it is the first work that verifies the effectiveness of tongue image on screening COVID-19. This paper provides a new perspective and a novel solution that contributes to large-scale screening toward fast stopping the pandemic of COVID-19.
10.1155/2022/6825576
Multi-hypothesis tracking of the tongue surface in ultrasound video recordings of normal and impaired speech.
Laporte Catherine,Ménard Lucie
Medical image analysis
Characterizing tongue shape and motion, as they appear in real-time ultrasound (US) images, is of interest to the study of healthy and impaired speech production. Quantitative anlaysis of tongue shape and motion requires that the tongue surface be extracted in each frame of US speech recordings. While the literature proposes several automated methods for this purpose, these either require large or very well matched training sets, or lack robustness in the presence of rapid tongue motion. This paper presents a new robust method for tongue tracking in US images that combines simple tongue shape and motion models derived from a small training data set with a highly flexible active contour (snake) representation and maintains multiple possible hypotheses as to the correct tongue contour via a particle filtering algorithm. The method was tested on a database of large free speech recordings from healthy and impaired speakers and its accuracy was measured against the manual segmentations obtained for every image in the database. The proposed method achieved mean sum of distances errors of 1.69 ± 1.10 mm, and its accuracy was not highly sensitive to training set composition. Furthermore, the proposed method showed improved accuracy, both in terms of mean sum of distances error and in terms of linguistically meaningful shape indices, compared to the three publicly available tongue tracking software packages Edgetrak, TongueTrack and Autotrace.
10.1016/j.media.2017.12.003
A comparison of ultrasound echo intensity to magnetic resonance imaging as a metric for tongue fat evaluation.
Sleep
STUDY OBJECTIVES:Tongue fat is associated with obstructive sleep apnea (OSA). Magnetic resonance imaging (MRI) is the standard for quantifying tongue fat. Ultrasound echo intensity has been shown to correlate to the fat content in skeletal muscles but has yet to be studied in the tongue. The objective of this study is to evaluate the relationship between ultrasound echo intensity and tongue fat. METHODS:Ultrasound coronal cross-sections of ex-vivo cow tongues were recorded at baseline and following three 1 mL serial injections of fat into the tongue. In humans, adults with and without OSA had submental ultrasound coronal cross-sections of their posterior tongue. The average echo intensity of the tongues (cow/human) was calculated in ImageJ software. Head and neck MRIs were obtained on human subjects to quantify tongue fat volume. Echo intensity was compared to injected fat volume or MRI-derived tongue fat percentage. RESULTS:Echo intensity in cow tongues showed a positive correlation to injected fat volume (rho = 0.93, p < .001). In human subjects, echo intensity of the tongue base strongly correlated with MRI-calculated fat percentage for both the posterior tongue (rho = 0.95, p < .001) and entire tongue (rho = 0.62, p < .001). Larger tongue fat percentages (rho = 0.38, p = .001) and higher echo intensity (rho = 0.27, p = .024) were associated with more severe apnea-hypopnea index, adjusted for age, body mass index, sex, and race. CONCLUSIONS:Ultrasound echo intensity is a viable surrogate measure for tongue fat volume and may provide a convenient modality to characterize tongue fat in OSA.
10.1093/sleep/zsab295
Automatic Construction of Chinese Herbal Prescriptions From Tongue Images Using CNNs and Auxiliary Latent Therapy Topics.
Hu Yang,Wen Guihua,Liao Huiqiang,Wang Changjun,Dai Dan,Yu Zhiwen
IEEE transactions on cybernetics
The tongue image provides important physical information of humans. It is of great importance for diagnoses and treatments in clinical medicine. Herbal prescriptions are simple, noninvasive, and have low side effects. Thus, they are widely applied in China. Studies on the automatic construction technology of herbal prescriptions based on tongue images have great significance for deep learning to explore the relevance of tongue images for herbal prescriptions, it can be applied to healthcare services in mobile medical systems. In order to adapt to the tongue image in a variety of photographic environments and construct herbal prescriptions, a neural network framework for prescription construction is designed. It includes single/double convolution channels and fully connected layers. Furthermore, it proposes the auxiliary therapy topic loss mechanism to model the therapy of Chinese doctors and alleviate the interference of sparse output labels on the diversity of results. The experiment use the real-world tongue images and the corresponding prescriptions and the results can generate prescriptions that are close to the real samples, which verifies the feasibility of the proposed method for the automatic construction of herbal prescriptions from tongue images. Also, it provides a reference for automatic herbal prescription construction from more physical information.
10.1109/TCYB.2019.2909925
Segmentation of tongue muscles from super-resolution magnetic resonance images.
Ibragimov Bulat,Prince Jerry L,Murano Emi Z,Woo Jonghye,Stone Maureen,Likar Boštjan,Pernuš Franjo,Vrtovec Tomaž
Medical image analysis
Imaging and quantification of tongue anatomy is helpful in surgical planning, post-operative rehabilitation of tongue cancer patients, and studying of how humans adapt and learn new strategies for breathing, swallowing and speaking to compensate for changes in function caused by disease, medical interventions or aging. In vivo acquisition of high-resolution three-dimensional (3D) magnetic resonance (MR) images with clearly visible tongue muscles is currently not feasible because of breathing and involuntary swallowing motions that occur over lengthy imaging times. However, recent advances in image reconstruction now allow the generation of super-resolution 3D MR images from sets of orthogonal images, acquired at a high in-plane resolution and combined using super-resolution techniques. This paper presents, to the best of our knowledge, the first attempt towards automatic tongue muscle segmentation from MR images. We devised a database of ten super-resolution 3D MR images, in which the genioglossus and inferior longitudinalis tongue muscles were manually segmented and annotated with landmarks. We demonstrate the feasibility of segmenting the muscles of interest automatically by applying the landmark-based game-theoretic framework (GTF), where a landmark detector based on Haar-like features and an optimal assignment-based shape representation were integrated. The obtained segmentation results were validated against an independent manual segmentation performed by a second observer, as well as against B-splines and demons atlasing approaches. The segmentation performance resulted in mean Dice coefficients of 85.3%, 81.8%, 78.8% and 75.8% for the second observer, GTF, B-splines atlasing and demons atlasing, respectively. The obtained level of segmentation accuracy indicates that computerized tongue muscle segmentation may be used in surgical planning and treatment outcome analysis of tongue cancer patients, and in studies of normal subjects and subjects with speech and swallowing problems.
10.1016/j.media.2014.11.006
Multiple color representation and fusion for diabetes mellitus diagnosis based on back tongue images.
Computers in biology and medicine
Tongue images have been proved to be effective in diabetes mellitus (DM) diagnosis. Without requirement of collecting blood sample, tongue image based diagnosis approach is non-invasive and convenient for the patients. Meanwhile, the colors of tongues play an important in aiding accurate diagnosis. However, the tongues' colors fall on a small color gamut that makes it difficult for the existing color descripts to identify and distinguish the tiny difference of the tongues. To tackle this problem, we introduce a novel color descriptor by representing the colors with the clustering centers, namely color centroid points, of the color points sampled from tongue images. In order to boost the capacity of the descriptor, we extend it into three color spaces, i.e., RGB, HSV and LAB to mine a rich set of color information and exploit the complementary information among the three spaces. Since there exist correlation and complementarity among the features extracted from the three color spaces, we propose a novel multiple color features fusion method for DM diagnosis. Particularly, two projections are learned to project the multiple features to their corresponding shared and specific subspaces, in which their similarity and diversity are firstly measured by the Euclidean Distance and Hilbert Schmidt Independence Criterion (HSIC), respectively. To fully exploit the similar and complementary information, the two components are jointly transformed to their label vector, efficiently embedding the discriminant prior into the model, leading to significant improvement in the diagnosis outcomes. Experimental results on clinical tongue dataset substantiated the effectiveness of our proposed clustering-based color descriptor and the proposed multiple colors fusion approach. Overall, the proposed pipeline for the diagnosis of DM using back tongue images, achieved an average accuracy of up to 93.38%, indicating its potential toward realization of a clinical diagnostic tool for DM. Without loss generality, we also assessed the performance of the novel multiple features fusion method on two public datasets. The experiments prove the superiority of our multiple features learning model on general real-life application.
10.1016/j.compbiomed.2023.106652
A non-invasive measurement of tongue surface temperature.
Lv Cong,Wang Xinmiao,Chen Jianshe,Yang Ni,Fisk Ian
Food research international (Ottawa, Ont.)
Oral temperature, tongue specifically, is a key factor affecting oral sensation and perception of food flavour and texture. It is therefore very important to know how the tongue temperature is affected by food consumption. Unfortunately, traditional methods such as clinical thermometers and thermocouples for oral temperature measurement are not most applicable during food oral consumption due to its invasive nature and interference with food. In this study, infrared thermal (IRT) imager was investigated for its feasibility for the measurement of tongue surface temperature. The IRT technique was firstly calibrated using a digital thermometer (DT). The technique was then used to measure tongue surface temperature after tongue was stimulated by (1) water rinsing at different temperatures (0-45 °C); and (2) treated with capsaicin solutions (5, 10, and 20 ppm). For both cases, tongue surface temperature showed significant changes as a result of the physical and chemical stimulation. Results confirm that IRT is feasible for tongue temperature measurement and could be a useful supporting tool in future for the study of food oral processing and sensory perception.
10.1016/j.foodres.2018.08.066
Application of computer tongue image analysis technology in the diagnosis of NAFLD.
Jiang Tao,Guo Xiao-Jing,Tu Li-Ping,Lu Zhou,Cui Ji,Ma Xu-Xiang,Hu Xiao-Juan,Yao Xing-Hua,Cui Long-Tao,Li Yong-Zhi,Huang Jing-Bin,Xu Jia-Tuo
Computers in biology and medicine
Nonalcoholic fatty liver disease (NAFLD), a leading cause of chronic hepatic disease, can progress to liver fibrosis, cirrhosis, and hepatocellular carcinoma. Therefore, it is extremely important to explore early diagnosis and screening methods. In this study, we developed models based on computer tongue image analysis technology to observe the tongue characteristics of 1778 participants (831 cases of NAFLD and 947 cases of non-NAFLD). Combining quantitative tongue image features, basic information, and serological indexes, including the hepatic steatosis index (HSI) and fatty liver index (FLI), we utilized machine learning methods, including Logistic Regression, Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Adaptive Boosting Algorithm (AdaBoost), Naïve Bayes, and Neural Network for NAFLD diagnosis. The best fusion model for diagnosing NAFLD by Logistic Regression, which contained the tongue image parameters, waist circumference, BMI, GGT, TG, and ALT/AST, achieved an AUC of 0.897 (95% CI, 0.882-0.911), an accuracy of 81.70% with a sensitivity of 77.62% and a specificity of 85.22%; in addition, the positive likelihood ratio and negative likelihood ratio were 5.25 and 0.26, respectively. The application of computer intelligent tongue diagnosis technology can improve the accuracy of NAFLD diagnosis and may provide a convenient technical reference for the establishment of early screening methods for NAFLD, which is worth further research and verification.
10.1016/j.compbiomed.2021.104622
Developing tongue coating status assessment using image recognition with deep learning.
Journal of prosthodontic research
PURPOSE:To build an image recognition network to evaluate tongue coating status. METHODS:Two image recognition networks were built: one for tongue detection and another for tongue coating classification. Digital tongue photographs were used to develop both networks; images from 251 (178 women, 74.7±6.6 years) and 144 older adults (83 women, 73.8±7.3 years) who volunteered to participate were used for the tongue detection network and coating classification network, respectively. The learning objective of the tongue detection network is to extract a rectangular region that includes the tongue. You-Only-Look-Once (YOLO) v2 was used as the detection network, and transfer learning was performed using ResNet-50. The accuracy was evaluated by calculating the intersection over the union. For tongue coating classification, the rectangular area including the tongue was divided into a grid of 7×7. Five experienced panelists scored the tongue coating in each area using one of five grades, and the tongue coating index (TCI) was calculated. Transfer learning for tongue coating grades was performed using ResNet-18, and the TCI was calculated. Agreement between the panelists and network for the tongue coating grades in each area and TCI was evaluated using the kappa coefficient and intraclass correlation, respectively. RESULTS:The tongue detection network recognized the tongue with a high intersection over union (0.885±0.081). The tongue coating classification network showed high agreement with tongue coating grades and TCI, with a kappa coefficient of 0.826 and an intraclass correlation coefficient of 0.807, respectively. CONCLUSIONS:Image recognition enables simple and detailed assessment of tongue coating status.
10.2186/jpr.JPR_D_23_00117
Pre-Operative Malnutrition in Patients with Ovarian Cancer: What Are the Clinical Implications? Results of a Prospective Study.
Cancers
BACKGROUND:Malnutrition was associated with worse survival outcomes, impaired quality of life, and deteriorated performance status across various cancer types. We aimed to identify risk factors for malnutrition in patients with epithelial ovarian cancer (EOC) and impact on survival. METHODS:In our prospective observational monocentric study, we included the patients with primary and recurrent EOC, tubal or peritoneal cancer conducted. We assessed serum laboratory parameters, body mass index, nutritional risk index, nutritional risk screening score (NRS-2002), and bio-electrical impedance analysis. RESULTS:We recruited a total of 152 patients. Patients > 65 years-old, with ascites of >500 mL, or with platinum-resistant EOC showed statistically significant increased risk of malnutrition when evaluated using NRS-2002 (-values= 0.014, 0.001, and 0.007, respectively). NRS-2002 < 3 was an independent predictive factor for complete tumor resectability ( = 0.009). The patients with NRS-2002 ≥ 3 had a median overall survival (OS) of seven months (95% CI = 0-24 months), as compared to the patients with NRS-2002 < 3, where median OS was forty-six months ( = 0.001). A phase angle (PhAα) ≤ 4.5 was the strongest predictor of OS. CONCLUSIONS:In our study, we found malnutrition to be an independent predictor of incomplete cytoreduction and independent prognostic factor for poor OS. Preoperative nutritional assessment is an effective tool in the identification of high-risk EOC groups characterized by poor clinical outcome.
10.3390/cancers16030622
Prognostic value of nutritional risk screening 2002 scale in nasopharyngeal carcinoma: A large-scale cohort study.
Cancer science
Little is known about the value of the nutritional risk screening 2002 (NRS2002) scale in nasopharyngeal carcinoma (NPC). We conducted a large-scale study to address this issue. We employed a big-data intelligence database platform at our center and identified 3232 eligible patients treated between 2009 and 2013. Of the 3232 (12.9% of 24 986) eligible patients, 469 (14.5%), 13 (0.4%), 953 (29.5%), 1762 (54.5%) and 35 (1.1%) had NRS2002 scores of 1, 2, 3, 4 and 5, respectively. Survival outcomes were comparable between patients with NRS2002 <3 and ≥3 (original scale). However, patients with NRS2002 ≤3 vs >3 (regrouping scale) had significantly different 5-year disease-free survival (DFS; 82.7% vs 75.0%, P < .001), overall survival (OS; 88.8% vs 84.1%, P = .001), distant metastasis-free survival (DMFS; 90.2% vs 85.9%, P = .001) and locoregional relapse-free survival (LRRFS; 91.6% vs 87.2%, P = .001). Therefore, we proposed a revised NRS2002 scale, and found that it provides a better risk stratification than the original or regrouping scales for predicting DFS (area under the curve [AUC] = 0.530 vs 0.554 vs 0.577; P < .05), OS (AUC = 0.534 vs 0.563 vs 0.582; P < .05), DMFS (AUC = 0.531 vs 0.567 vs 0.590; P < .05) and LRRFS (AUC = 0.529 vs 0.542 vs 0.564; P < .05 except scale A vs B). Our proposed NRS2002 scale represents a simple, clinically useful tool for nutritional risk screening in NPC.
10.1111/cas.13603
Application of NRS2002 in Preoperative Nutritional Screening for Patients with Liver Cancer.
Journal of oncology
OBJECTIVE:To explore the application of NRS2002 in preoperative nutritional screening of patients with liver cancer (LC). METHODS:60 LC patients treated in the First Affiliated Hospital of Gannan Medical University (January 2018-May 2021) were chosen as the research objects, and split into group J without nutritional risk and group Q with nutritional risk according to the results of NRS2002 to compare the preoperative situation, surgery-related indexes, hematological indexes, postoperative recovery, and incidence of complications between the two groups. RESULTS:Group J ( = 28) and group Q ( = 32) showed no obvious difference in preoperative situation, and patients' liver function indexes were within the normal range. The duration of surgery in group J was notably shorter compared with group Q ( < 0.05). Alanine aminotransferase (ALT), aspartate aminotransferase (AST), direct bilirubin (DBIL), and albumin in group J were notably different from those of group Q ( < 0.001) at 1 day after surgery. ALT and AST in group J were notably different from those of group Q at 3 days after surgery ( < 0.001). No obvious differences were observed in the hematological indexes between the two groups at 5 days after surgery ( > 0.05). The total amount of albumin infusion, postoperative hospitalization time, and hospitalization cost in group J were notably lower compared with group Q ( < 0.001). The incidence of complications in group J was notably lower compared with group Q ( < 0.05). CONCLUSION:Postoperative recovery of LC patients is closely related to their preoperative nutritional status, and those with poor nutritional status have a high incidence of postoperative complications and long recovery time. NRS2002 can effectively screen the nutritional status of patients and provide reference for prognosis evaluation.
10.1155/2021/8943353
The association between nutrition risk status assessment and hospital mortality in Chinese older inpatients: a retrospective study.
Journal of health, population, and nutrition
PURPOSE:The association between nutritional risk status assessment and hospital mortality in older patients remains controversial. The aim of this study was to assess the relationship between nutritional risk on admission and in-hospital mortality, and explore the best Nutritional Risk Status Screening 2002 (NRS2002) threshold for predicting in-hospital mortality of older inpatients in China. METHOD:The elderly inpatients were recruited from a hospital in Hunan Province, China. Nutritional risk was screened and assessed using the NRS2002. Logistic regression was used to analyze whether NRS2002 scores were independently associated with hospital mortality, and the results were expressed as odds ratios (OR) and 95% confidence intervals (CIs). Receiver operating characteristic curve (ROC) was used to determine the best NRS2002 threshold for predicting in-hospital mortality in elderly inpatients. And 500 bootstrap re-samplings were performed for ROC analysis. RESULT:In total, 464 elderly inpatients completed the survey (15 of whom died, 205 males and 259 females, mean age = 72.284 ± 5.626 years). Multifactorial analysis revealed that age, the NRS2002 score, and length of hospital stay significantly influenced in-hospital mortality among older inpatients (P < 0.05). The results also showed that higher NRS2002 scores were associated with an increased risk of in-hospital mortality in both the unadjusted (OR = 1.731,95%CI = 1.362-2.20, P < 0.0001), adjusted model I (OR = 1.736, 95% CI = 1.354-2.206, P < 0.0001) and model II (OR = 1.602, 95% CI = 1.734-2.488, P = 0.0005). The optimal NRS2002 threshold for predicting in-hospital mortality in older inpatients was 3.5, with the largest ROC area of 0.84. CONCLUSION:Our findings indicated that nutritional risk was an independent predictor of in-hospital mortality, with a cut-off value of 3.50 for the NRS2002 nutritional risk assessment being more appropriate than a cut-off value of 3.0.
10.1186/s41043-024-00726-w
Comparison of different nutritional screening tools in nutritional screening of patients with cirrhosis: A cross-sectional observational study.
Heliyon
Aims:The Royal Free Hospital Nutritional Prioritizing Tool (RFH-NPT), the Liver Disease Undernutrition Screening Tool (LDUST) and Nutritional Risk Screening 2002 (NRS2002) were used by nurses to screen, compare, and analyze the nutritional status of patients with liver cirrhosis. The application value of different screening tools was summarized in the nutritional screening of patients with liver cirrhosis. Methods:In this study, LDUST, RFH-NPT, and NRS2002 were used by nurses to screen the nutritional status of hospitalized patients with liver cirrhosis within 24-48 h after admission. The study calculated validity indicators such as sensitivity, specificity, the area under the receiver operating curve (AUC), and reliability indicators such as the Kappa coefficient. The efficacy of these screening tools in the nutritional screening of patients with liver cirrhosis was compared. Results:Among the 207 patients, LDUST and NRS2002 identified 72.9 % and 23.7 % as undernourished, respectively. The sensitivity of LDUST and NRS2002 were 92.1 % and 30.0 %, respectively. The Kappa value of LDUST and RFH-NPT was 0.620, and the Kappa value of LDUST compared with NRS2002 was 0.144. Conclusion:This study shows that the Liver Disease Undernutrition Screening Tool, a special screening tool for patients with liver cirrhosis, has a more reliable screening effect and higher sensitivity than NRS2002. The Liver Disease Undernutrition Screening Tool is recommended for nutritional screening in patients with liver cirrhosis.
10.1016/j.heliyon.2024.e30339
Differences in nutritional risk assessment between NRS2002, RFH-NPT and LDUST in cirrhotic patients.
Scientific reports
Nutritional status is an independent predictor of outcome in cirrhosis patients. Nutritional Risk Screening 2002 (NRS2002), Royal Free Hospital-Nutritional Prioritizing Tool (RFH-NPT), and Liver Disease Undernutrition Screening Tool (LDUST) were employed to detect cirrhosis with malnutrition risk in this work. Meanwhile, their diagnostic performances were compared to find the best screening method. This work aimed to establish the sarcopenia cut-off value of the transversal psoas thickness index (TPTI), and identify the risk factors for malnutrition. Cirrhosis patients who were admitted to Heibei Gerneral hospital from April 2021 to October 2021 and underwent abdominal CT examination were enrolled. 78 patients were assessed by NRS2002, RFH-NPT, and LDUST. The Global Leadership Initiative for Malnutrition (GLIM) criteria were selected as the gold standard for the diagnosis of malnutrition. Meanwhile the cut-off value of sarcopenia was established based on the TPTI of malnourished patients. Logistic regression analysis was adopted to assess the influencing factors of malnutrition risk and malnutrition. The prevalence of malnutrition was 42.31%. The prevalence of malnutrition risk was 32.1%, 61.5%, and 62.8% with NRS2002, RFH-NPT, and LDUST, respectively. NRS2002 presented the best specificity compared with the other methods, while RFH-NPT showed the highest sensitivity. The optimal gender-specific TPTI cut-off value for diagnosing sarcopenia was determined as TPTI < 14.56 mm/m (male) and TPTI < 8.34 mm/m (female). In the multivariate analysis, ascites was associated with malnutrition risk, while sarcopenia showed a significant risk for malnutrition. NRS2002 and RFH-NPT were superior to LDUST at detecting the malnutrition in cirrhosis patients diagnosed according to GLIM criteria. The gender-specific TPTI cut-off value was TPTI < 14.56 mm/m (male) and TPTI < 8.34 mm/m (female). Malnutrition risk should be screened for patients with ascites as soon as possible. In addition, it was important to evaluate malnutrition in sarcopenia patients in time.
10.1038/s41598-023-30031-1