BMC gastroenterology · 2025
Establishing of a risk prediction model for metabolic dysfunction-associated steatotic liver disease: a retrospective cohort study.
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Li Nan, Liu Chenbing, Lu Zhangfan, Wu Wenjian, Zhang Feng, Qiu Lihong, Shen Chao, Sheng Di, Liu Zhong
Аннотация
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.
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.