A relatively accurate prediction model for the risk of developing mild cognitive impairment in patients with sarcopenia: Evidence from the CHARLS

Background Sarcopenia significantly raises the risk of cognitive impairments in older adults. Early warning of mild cognitive impairment (MCI) in those with sarcopenia is crucial for timely intervention. Aims To construct an accurate prediction model for screening MCI in sarcopenia population. Methods We combined machine learning and deep learning techniques to analyze data from 597 sarcopenia patients in the China Health and Retirement Longitudinal Study (CHARLS). Our model predicts MCI incidence over the next four years, categorizing patients into low, intermediate, and high-risk groups based on their risk levels. Results The model was constructed using CHARLS data from 2011–2015 and included seven validated variables. It performed superior to logistic regression, achieving an Area Under the Curve (AUC) of 0.808 (95% CI: 0.704–0.899) for the test set and accurately classifying patients' risk in the training set. The deep learning model demonstrated a low false-positive rate of 1.63% for MCI in higher-risk groups. Independent validation using 2015–2018 CHARLS data confirmed the model’s efficacy, with an AUC of 0.76 (95% CI: 0.67–0.83). A convenient online tool to implement the model was developed at http://47.115.214.16:5000/. Conclusions This deep learning model effectively predicts MCI risk in sarcopenia patients, supporting early interventions. Its accuracy helps identify high-risk individuals, potentially improving patient care.