Journal of psychosomatic research · 2024
Estimating cardiovascular mortality in patients with hypertension using machine learning: The role of depression classification based on lifestyle and physical activity.
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Liu Xingyu, Luo Zeyu, Jing Fengshi, Ren Hao, Li Changjin, Wang Lei, Chen Tao
Аннотация
PURPOSE: This study aims to harness machine learning techniques, particularly the Random Survival Forest (RSF) model, to assess the impact of depression on cardiovascular disease (CVD) mortality among hypertensive patients. A key objective is to elucidate the interplay between mental health, lifestyle, and physical activity while comparing the effectiveness of the RSF model against the traditional Cox proportional hazards model in predicting CVD mortality.
METHODS: Data from the National Health and Nutrition Examination Survey (NHANES) spanning 2007 to 2014 were used for comprehensive depression screening. The Patient Health Questionnaire-9 (PHQ-9) was employed to categorize depression severity levels among participants. The final cohort included 9271 participants, selected after excluding those with incomplete data. Participants were followed up for a median of 7.1 years, and cardiovascular mortality was assessed up to December 31, 2019. We employed the RSF model to predict cardiovascular mortality with high effectiveness and precision. And to ensure comparability, we developed the traditional Cox proportional hazards model using the same set of predictors.
RESULTS: RSF model outperformed the Cox proportional hazards model in predicting cardiovascular mortality among hypertensive patients with varying depression levels. The RSF model's integrated area under the curve (iAUC) scores were 0.842, 0.893, and 0.760 for none, mild, and severe depression, respectively, surpassing the Cox model's scores of 0.826, 0.805, and 0.746.
CONCLUSION: The RSF model provides a more accurate prediction of CVD mortality among hypertensive patients with varying degrees of depression, offering a valuable tool for personalized patient care. Its ability to stratify patients into risk categories can assist healthcare professionals in making informed decisions, underscoring the potential of machine learning in public health and clinical settings. This model demonstrates particular utility in settings where detailed, patient-specific risk assessments are critical for managing long-term health outcomes. Future research should focus on external validation and integration of more diverse variables to enhance predictive power.
METHODS: Data from the National Health and Nutrition Examination Survey (NHANES) spanning 2007 to 2014 were used for comprehensive depression screening. The Patient Health Questionnaire-9 (PHQ-9) was employed to categorize depression severity levels among participants. The final cohort included 9271 participants, selected after excluding those with incomplete data. Participants were followed up for a median of 7.1 years, and cardiovascular mortality was assessed up to December 31, 2019. We employed the RSF model to predict cardiovascular mortality with high effectiveness and precision. And to ensure comparability, we developed the traditional Cox proportional hazards model using the same set of predictors.
RESULTS: RSF model outperformed the Cox proportional hazards model in predicting cardiovascular mortality among hypertensive patients with varying depression levels. The RSF model's integrated area under the curve (iAUC) scores were 0.842, 0.893, and 0.760 for none, mild, and severe depression, respectively, surpassing the Cox model's scores of 0.826, 0.805, and 0.746.
CONCLUSION: The RSF model provides a more accurate prediction of CVD mortality among hypertensive patients with varying degrees of depression, offering a valuable tool for personalized patient care. Its ability to stratify patients into risk categories can assist healthcare professionals in making informed decisions, underscoring the potential of machine learning in public health and clinical settings. This model demonstrates particular utility in settings where detailed, patient-specific risk assessments are critical for managing long-term health outcomes. Future research should focus on external validation and integration of more diverse variables to enhance predictive power.