Journal of affective disorders · 2024
Development and external validation of a risk prediction model for depression in patients with coronary heart disease.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод в очереди
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Hou Xin-Zheng, Wu Qian, Lv Qian-Yu, Yang Ying-Tian, Li Lan-Lan, Ye Xue-Jiao, Yang Chen-Yan, Lv Yan-Fei, Wang Shi-Han
Аннотация
BACKGROUND: Depression is an independent risk factor for adverse outcomes of coronary heart disease (CHD). This study aimed to develop a depression risk prediction model for CHD patients.
METHODS: This study utilized data from the National Health and Nutrition Examination Survey (NHANES). In the training set, reference literature, logistic regression, LASSO regression, optimal subset algorithm, and machine learning random forest algorithm were employed to screen prediction variables, respectively. The optimal prediction model was selected based on the C-index, Net Reclassification Improvement (NRI), and Integrated Discrimination Improvement (IDI). A nomogram for the optimal prediction model was constructed. 3 external validations were performed.
RESULTS: The training set comprised 1375 participants, with a depressive symptoms prevalence of 15.2 %. The optimal prediction model was constructed using predictors obtained from optimal subsets algorithm (C-index = 0.774, sensitivity = 0.751, specificity = 0.685). The model includes age, gender, education, marriage, diabetes, tobacco use, antihypertensive drugs, high-density lipoprotein cholesterol (HDLC), and aspartate aminotransferase (AST). The model demonstrated consistent discrimination ability, accuracy, and clinical utility across the 3 external validations.
LIMITATIONS: The applicable population of the model is CHD patients. And the clinical benefits of interventions based on the prediction results are still unknown.
CONCLUSION: We developed a depression risk prediction model for CHD patients, which was presented in the form of a nomogram for clinical application.
METHODS: This study utilized data from the National Health and Nutrition Examination Survey (NHANES). In the training set, reference literature, logistic regression, LASSO regression, optimal subset algorithm, and machine learning random forest algorithm were employed to screen prediction variables, respectively. The optimal prediction model was selected based on the C-index, Net Reclassification Improvement (NRI), and Integrated Discrimination Improvement (IDI). A nomogram for the optimal prediction model was constructed. 3 external validations were performed.
RESULTS: The training set comprised 1375 participants, with a depressive symptoms prevalence of 15.2 %. The optimal prediction model was constructed using predictors obtained from optimal subsets algorithm (C-index = 0.774, sensitivity = 0.751, specificity = 0.685). The model includes age, gender, education, marriage, diabetes, tobacco use, antihypertensive drugs, high-density lipoprotein cholesterol (HDLC), and aspartate aminotransferase (AST). The model demonstrated consistent discrimination ability, accuracy, and clinical utility across the 3 external validations.
LIMITATIONS: The applicable population of the model is CHD patients. And the clinical benefits of interventions based on the prediction results are still unknown.
CONCLUSION: We developed a depression risk prediction model for CHD patients, which was presented in the form of a nomogram for clinical application.