Development of Taiwan Risk Score for Sarcopenia (TRSS) for Sarcopenia Screening among Community-Dwelling Older Adults

Tseng, Tzyy-Guey; Lu, Chun-Kuan; Hsiao, Yu-Han; Pan, Shu-Chuan; Tai, Chi-Jung; Lee, Meng-Chih

doi:10.3390/ijerph17082859

Cited by 9 publications

(4 citation statements)

References 39 publications

(46 reference statements)

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“…Previous studies on sarcopenia prediction or identification models usually do not consider sex as a very important grouping indicator, but as a risk factor. Therefore, males and females used the same risk factors in one model, and the classification performance may be not satisfactory [ 20 ]. However, studies have shown that sex-specific aging patterns involve muscle mass and quality changes [ 21 ].…”

Section: Discussionmentioning

confidence: 99%

Use machine learning to help identify possible sarcopenia cases in maintenance hemodialysis patients

et al. 2023

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Background Maintenance hemodialysis (MHD) patients often suffer from sarcopenia, which is strongly associated with their long-term mortality. The diagnosis and treatment of sarcopenia, especially possible sarcopenia for MHD patients are of great importance. This study aims to use machine learning and medical data to develop two simple sarcopenia identification assistant tools for MHD patients and focuses on sex specificity. Methods Data were retrospectively collected from patients undergoing MHD and included patients’ basic information, body measurement results and laboratory findings. The 2019 consensus update by Asian working group for sarcopenia was used to assess whether a MHD patient had sarcopenia. Finally, 140 male (58 with possible sarcopenia or sarcopenia) and 102 female (65 with possible sarcopenia or sarcopenia) patients’ data were collected. Participants were divided into sarcopenia and control groups for each sex to develop binary classifiers. After statistical analysis and feature selection, stratified shuffle split and Synthetic Minority Oversampling Technique were conducted and voting classifiers were developed. Results After eliminating handgrip strength, 6-m walk, and skeletal muscle index, the best three features for sarcopenia identification of male patients are age, fasting blood glucose, and parathyroid hormone. Meanwhile, age, arm without vascular access, total bilirubin, and post-dialysis creatinine are the best four features for females. After abandoning models with overfitting or bad performance, voting classifiers achieved good sarcopenia classification performance for both sexes (For males: sensitivity: 77.50% ± 11.21%, specificity: 83.13% ± 9.70%, F1 score: 77.32% ± 5.36%, the area under the receiver operating characteristic curves (AUC): 87.40% ± 4.41%. For females: sensitivity: 76.15% ± 13.95%, specificity: 71.25% ± 15.86%, F1 score: 78.04% ± 8.85%, AUC: 77.69% ± 7.92%). Conclusions Two simple sex-specific sarcopenia identification tools for MHD patients were developed. They performed well on the case finding of sarcopenia, especially possible sarcopenia.

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Section: Discussionmentioning

confidence: 99%

Use machine learning to help identify possible sarcopenia cases in maintenance hemodialysis patients

et al. 2023

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“…Therefore, we performed a bootstrapping approach as a sensitivity analysis. Initially, using the SURVEYSELECT procedure, with an unrestricted random sampling method in SAS, we generated 1,000 bootstrap samples 26 . Then, the bootstrap adjHRs and 95% CIs were estimated from bootstrap samples using Cox regression and subdistribution proportional hazard regression 27 .…”

Section: Methodsmentioning

confidence: 99%

The Impact of Urate‐Lowering Therapy in Post‐Myocardial Infarction Patients: Insights From a Population‐Based, Propensity Score‐Matched Analysis

Tai

Lee

et al. 2021

Clin Pharma and Therapeutics

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The role of urate‐lowering therapy (ULT) for the primary prevention of cardiovascular (CV) events has been widely discussed, but its evidence for the secondary prevention of myocardial infarction (MI) is limited. Therefore, we conduct a population‐based, propensity score‐matched cohort study to investigate the CV outcomes among patients with post‐MI with and without ULT. A total of 19,042 newly diagnosed in‐hospital patients with MI were selected using the Taiwan National Health Insurance Database between January 1, 2005, and December 31, 2016. After 1:1 propensity score matching with covariates, patients with MI with (n = 963) and without (n = 963) ULT were selected for further analysis. The primary outcome was the all‐cause mortality and the secondary outcomes were composite CV outcomes, including hospitalization for recurrent MI, stroke, heart failure, and cardiac arrhythmias. ULT users were associated with lower all‐cause mortality (adjusted hazard ratio (adjHR), 0.67; 95% confidence interval (CI), 0.51–0.87) compared to the ULT nonusers. In addition, ULT users had a significantly lower risk of recurrent MI, which needed revascularization by percutaneous coronary intervention or coronary artery bypass grafting (adjHR, 0.67; 95% CI, 0.53–0.86) than the ULT nonusers. The primary and secondary outcomes were not different between patients with post‐MI who received uricosuric agents and xanthine oxidase inhibitors. The anti‐inflammatory effect of ULT plays an essential role in MI management. From a real‐world setting, this study shows that ULT is associated with the lower risk of all‐cause mortality in patients with post‐MI. In addition, the result shows the possible lower incidence of repeat revascularization procedures in the ULT users.

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“…Taiwan risk score for sarcopenia Taiwan Risk Score for Sarcopenia (TRSS) is a recently developed screening tool in community-dwelling older adults [32]. It is based on a prediction model including a large set of parameters related to sarcopenia, i.e., age, female, low BMI, receiving social assistance pension, absence of exercise, abnormal fasting glucose and creatinine levels.…”

Section: New Tests Proposed For Screening Sarcopeniamentioning

confidence: 99%

“…Defining sarcopenia by AWGS but without its muscle mass component, TRSS displayed a high sensitivity and specificity (both about 71%). Hence, TRSS can find sarcopenic cases by applying a different approach than most of the usual screening tools [32]. Yet, it seems to demand more time and tests than the commonly used tools.…”

Section: New Tests Proposed For Screening Sarcopeniamentioning

confidence: 99%

SARC-F and other screening tests for sarcopenia

Bahat

Erdoğan

İlhan

2021

Current Opinion in Clinical Nutrition &Amp; Metabolic Care

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SARC-F and other screening tests for sarcopenia G€ ulistan Bahat a , Tug ˘ba Erdog ˘an a , and Birkan I ˙lhan bPurpose of review Sarcopenia screening tools can enable clinicians to select individuals for more demanding evaluations, and hence, may facilitate its timely diagnosis and management. The most common recommended screening test is SARC-F, whereas many others are proposed. We aimed to summarize the recent studies and evidence performed on SARC-F and other sarcopenia screening tools. Recent findingsMeta-analysis studies reported that despite having moderate-high specificity, SARC-F has low-moderate sensitivity to detect sarcopenia, which would cause a significant number of individuals having sarcopenia to be unrecognized. Several recent studies aimed to increase sensitivity and utility of SARC-F as a screening tool by i.e., application of lower cut-offs, adding extra-items, and combining with other screening tests. Some of these approaches increased its screening efficacy significantly. In line with its previous studies, SARC-F showed success to predict adverse outcomes in the latest studies as well. Recently, it has also been suggested as a reasonable screening test for frailty. In addition to the long-standing screening tests i.e., anthropometric measures, Ishii Test and Mini Sarcopenia Risk Assessment (MSRA) Questionnaire; new tests, i.e., Taiwan Risk Score for Sarcopenia, Sarcopenia Scoring Assessment Model (SarSA-Mod) and re-purposed tests, i.e., SARQoL questionnaire and fracture risk assessment tool have been investigated as potential screening tests for sarcopenia. Some of these tests performed as well as or superior to standard SARC-F.

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Development of Taiwan Risk Score for Sarcopenia (TRSS) for Sarcopenia Screening among Community-Dwelling Older Adults

Cited by 9 publications

References 39 publications

Use machine learning to help identify possible sarcopenia cases in maintenance hemodialysis patients

Use machine learning to help identify possible sarcopenia cases in maintenance hemodialysis patients

The Impact of Urate‐Lowering Therapy in Post‐Myocardial Infarction Patients: Insights From a Population‐Based, Propensity Score‐Matched Analysis

SARC-F and other screening tests for sarcopenia

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