Association of sarcopenia with all-cause and cause-specific mortality in cancer patients: development and validation of a 3-year and 5-year survival prediction model.

BACKGROUND

Sarcopenia is a clinicopathological condition characterized by a decrease in muscle strength and muscle mass, playing a crucial role in the prognosis of cancer. Therefore, this study aims to investigate the association between sarcopenia and both all-cause mortality and cancer-specific mortality among cancer patients. Furthermore, we plan to develop risk prediction models using machine learning algorithms to predict 3-year and 5-year survival rates in cancer patients.

METHOD: This study included 1095 cancer patients from the National Health and Nutrition Examination Survey (NHANES) cohorts spanning 1999-2006 and 2011-2014. Initially, we used the Least Absolute Shrinkage and Selection Operator (LASSO)-Cox regression models for feature selection. Subsequently, we employed multivariable Cox regression models to investigate the association between sarcopenia and all-cause and cancer-specific mortality in cancer patients. We developed five machine learning algorithms, including Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), LightGBM, and XGBoost, to predict 3-year and 5-year survival rates and to perform risk stratification.

RESULTS

The multivariable COX regression model showed sarcopenia significantly increases the risk of all-cause mortality (HR = 1.33, 95%CI:1.05, 1.70, P = 0.0194) and cancer-specific mortality (HR = 1.67, 95%CI:1.09, 2.55, P = 0.0176) in cancer patients. Among the five machine learning algorithms developed, the LightGBM model demonstrated strong performance in the 3-year and 5-year survival prediction tasks, making it the optimal model selection. Decision curve analysis and Kaplan-Meier curves further confirmed our model's ability to identify high-risk individuals effectively.

CONCLUSIONS

Sarcopenia significantly increases the risk of mortality in cancer patients. We developed a survival prediction model for cancer patients that effectively identifies high-risk individuals, thereby providing a foundation for personalized survival assessment.