Establishing of a risk prediction model for metabolic dysfunction-associated steatotic liver disease: a retrospective cohort study - Nan Li; Chenbing Liu; Zhangfan Lu; Wenjian Wu; Feng Zhang; Lihong Qiu; Chao Shen; Di Sheng; Zhong Liu (BMC Gastroenterology)
Abstract Objectives Over 30% of people worldwide suffer from metabolic dysfunction-associated steatotic liver disease (MASLD), a significant global health issue. Identifying and preventing high-risk individuals for MASLD early is crucial. The purpose of our study is to investigate the factors related to the development of MASLD and develop a risk prediction model for its occurrence. Methods The study included 5107 subjects, divided into training and validation groups in a 7:3 ratio using a random number table method. Collinearity diagnosis and Cox regression were used to identify factors associated with MASLD incidence, and a risk prediction model was created. The model’s accuracy, reliability, and clinical applicability were assessed. Results Our study indicated that male, body mass index (BMI), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), fasting plasma glucose (FPG), serum uric acid to creatinine ratio (SUA/Cr) and white blood cell (WBC) were associated with MASLD incidence. The elements were determined to be crucial for creating a risk prediction model. The model showed strong discriminative potential with a C-index of 0.783 and the time-dependent AUCs of 0.781, 0.789, 0.814 and 0.796 for 1–4 years in the training group, and a C-index of 0.788 and the time-dependent AUCs of 0.798, 0.782, 0.787 and 0.825 for 1–4 years in validation. Calibration curves confirmed the model’s accuracy, and decision curve analysis (DCA) validated its clinical utility. Conclusions The model may provide clinical physicians with a reliable method for identifying high-risk populations for MASLD and serve as a guide for developing prediction models for other diseases.