Abstract
BACKGROUND: Sarcopenia (SP) is a progressive, age-related disease that may result in various adverse health outcomes and even mortality in older adults. Accurately predicting the mortality risk of older adults with SP is essential for informed clinical decision-making. This study aims to utilize machine learning techniques that incorporate sociodemographic factors, health-related metrics, lifestyle variables, and biomarker data to improve risk stratification and management in older adults with SP. METHODS: We analyzed data from the NHANES from 1999-2006 and 2010-2018, including a total of 1,619 older adult patients with SP, with a 10-year follow-up period for this population, during which 541 (33%) patients died and 1,078 (67%) survived. This study extracted 36 clinical variables for each patient, encompassing sociodemographic factors, health-related metrics, and biochemical markers. Feature selection was performed using Lasso Regression, XGBoost, and Random Forest machine learning algorithms, and a nomogram model was developed using univariate and multivariate Cox regression analyses, with validation of its accuracy, concordance, and clinical applicability. RESULTS: A total of 12 feature variables were identified through the combined use of three machine learning methods. Univariate and multivariate Cox regression analyses identified Age, Height, Neutrophil count (NENO), The ratio of hemoglobin to red cell distribution width (HRR), Uric Acid (UA), and Creatinine as significant predictors of mortality in older adults with SP, and a nomogram model was constructed based on these feature variables, with model performance assessed through discrimination, calibration curves, and clinical utility evaluation. The model achieved AUC values of 0.753, 0.773, 0.782, and 0.800 at 1, 3, 5, and 10 years, respectively, demonstrating good concordance and adequate calibration. Decision curve analysis (DCA) indicated that the model had broad applicability in predicting short-term and long-term outcomes in older adult patients with SP. Finally, based on the nomogram risk score, patients were stratified into risk groups and survival curves were plotted, illustrating a significantly lower survival probability in the high-risk group compared to the low-risk group (p < 0.0001). CONCLUSION: Utilizing advanced statistical and machine learning techniques, we developed and validated a prognostic model for SP in the older adult that integrates multimodal data, enhancing predictive accuracy and reliability. This model provides valuable insights for clinicians, facilitates risk stratification, and provides personalized interventions for older adults with SP.