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Updated: May 9, 2026
Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos
Published on: March 21, 2021
Risk prediction model performances for sarcopenia in middle-aged and elderly people: a systematic review and
Kaixin Lei1,2, Yitang Chen2, Wen Guo3
1The Center of Gerontology and Geriatrics, Sichuan University West China Hospital, No.17, Section No. 37 Guo Xue Xiang, Chengdu, Sichuan Province, 610041, China.
Purpose:
As a common disease in the elderly, sarcopenia often causes complications such as falls and affects the quality of life of the elderly. Therefore, early detection and early prevention of sarcopenia are of great significance in improving the long-term prognosis of the elderly.
Methods:
We selected studies from Embase, Ovid Medline, Cochrane library and Web of Science until August 15, 2025. Data from selected studies were extracted, including author, country, study design, participants, data source, main outcome and its definition, cases/sample size, predictors, model development and performance. PROBAST (Prediction Model Risk of Bias Assessment Tool) were utilized for risks assessments.
Results:
We screened 8201 publications and included 23 studies with 41,083 participants. 15 researches collected their data from community dwellers and the other 8 research items were carried out in the inpatient department. 21 studies utilized logistic regression to establish sarcopenia prediction models. The most frequently used predictors were BMI, age and gender. The reported area under the curve (AUC) ranged from 0.710-0.974. Only 3 studies were low risk of bias, 6 studies were judged as having an unclear risk of bias, and 14 studies were high risk of bias. The pooled AUC value of 19 developing models was 0.88 (0.85-0.91).
Conclusions:
In conclusion, many current models demonstrated reliable predictability for sarcopenia with advancing age and BMI as most commly used predictive factors. However, these results ought to be interpenetrated and extrapolated with caution due to absence of external validation, heterogeneous populations and predictor selections.
