Liu Xingyu, Luo Zeyu, Jing Fengshi, Ren Hao, Li Changjin, Wang Lei, Chen Tao
Badminton Technical and Tactical Analysis and Diagnostic Laboratory, National Academy of Badminton, Guangzhou Sport University, Guangzhou 510500, China.
Faculty of Data Science, City University of Macau, Taipa 999078, Macao SAR, China.
J Psychosom Res. 2025 Feb;189:112030. doi: 10.1016/j.jpsychores.2024.112030. Epub 2024 Dec 29.
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.
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.
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.
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.
本研究旨在利用机器学习技术，特别是随机生存森林（RSF）模型，评估抑郁症对高血压患者心血管疾病（CVD）死亡率的影响。一个关键目标是阐明心理健康、生活方式和身体活动之间的相互作用，同时比较RSF模型与传统Cox比例风险模型在预测CVD死亡率方面的有效性。
使用2007年至2014年国家健康与营养检查调查（NHANES）的数据进行全面的抑郁症筛查。采用患者健康问卷-9（PHQ-9）对参与者的抑郁症严重程度进行分类。最终队列包括9271名参与者，这些参与者是在排除数据不完整的个体后挑选出来的。对参与者进行了中位数为7.1年的随访，并评估截至2019年12月31日的心血管死亡率。我们使用RSF模型以高效和精确的方式预测心血管死亡率。为确保可比性，我们使用相同的一组预测变量开发了传统的Cox比例风险模型。
在预测不同抑郁水平的高血压患者的心血管死亡率方面，RSF模型优于Cox比例风险模型。RSF模型的曲线下综合面积（iAUC）得分在无抑郁、轻度抑郁和重度抑郁时分别为0.842、0.893和0.760，超过了Cox模型的0.826、0.805和0.746得分。
RSF模型能更准确地预测不同抑郁程度的高血压患者的CVD死亡率，为个性化患者护理提供了有价值的工具。它将患者分层为风险类别的能力可帮助医疗保健专业人员做出明智决策，凸显了机器学习在公共卫生和临床环境中的潜力。该模型在详细的、针对患者的风险评估对管理长期健康结果至关重要的环境中显示出特别的效用。未来的研究应侧重于外部验证以及整合更多不同变量以增强预测能力。
