肌萎缩
医学
接收机工作特性
逻辑回归
人口
队列
风险评估
队列研究
老年学
内科学
环境卫生
计算机科学
计算机安全
作者
Taiping Lin,Rui Liang,Quhong Song,Hualong Liao,Miao Dai,Tingting Jiang,Xiangping Tu,Xiaoyu Shu,Xiaotao Huang,Ning Ge,Ke Wan,Jirong Yue
标识
DOI:10.1016/j.jamda.2024.105128
摘要
Objective Reliable identification of high-risk older adults who are likely to develop sarcopenia is essential to implement targeted preventive measures and follow-up. However, no sarcopenia prediction model is currently available for community use. Our objective was to develop and validate a risk prediction model for calculating the 1-year absolute risk of developing sarcopenia in an aging population. Methods One prospective population-based cohort of non-sarcopenic individuals aged 60 years or older were used for the development of a sarcopenia risk prediction model and model validation. Sarcopenia was defined according to the 2019 Asian Working Group for Sarcopenia consensus. Stepwise logistic regression was used to identify risk factors for sarcopenia incidence within a 1-year follow-up. Model performance was evaluated using the area under the receiver operating characteristics curve (AUROC) and calibration plot, respectively. Results The development cohort included 1042 older adults, among whom 87 participants developed sarcopenia during a 1-year follow-up. The PRE-SARC (PREdiction of SARCopenia Risk in community older adults) model can accurately predict the 1-year risk of sarcopenia by using 7 easily accessible community-based predictors. The PRE-SARC model performed well in predicting sarcopenia, with an AUROC of 87% (95% CI, 0.83-0.90) and good calibration. Internal validation showed minimal optimism, with an adjusted AUROC of 0.85. The prediction score was categorized into 4 risk groups: low (0%-10%), moderate (>10%-20%), high (>20%-40%), and very high (>40%). The PRE-SARC model has been incorporated into an online risk calculator, which is freely accessible for daily clinical applications (https://sarcopeniariskprediction.shinyapps.io/dynnomapp/). Conclusions In community-dwelling individuals, the PRE-SARC model can accurately predict 1-year sarcopenia incidence. This model serves as a readily available and free accessible tool to identify older adults at high risk of sarcopenia, thereby facilitating personalized early preventive approaches and optimizing the utilization of health care resources.
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