医学
心力衰竭
射血分数
萧条(经济学)
内科学
比例危险模型
病人健康调查表
心脏病学
抑郁症状
精神科
焦虑
宏观经济学
经济
作者
F Xing,Min Gao,Yuzhong Wu,Weihao Liang,Jingzhou Jiang,Yugang Dong,Yi Li,Bin Dong,Chen Liu
出处
期刊:Heart
[BMJ]
日期:2025-03-15
卷期号:111 (15): 733-740
被引量:4
标识
DOI:10.1136/heartjnl-2024-324505
摘要
Background Long-term patterns of depressive symptoms among patients with heart failure, specifically those with a preserved ejection fraction (HFpEF), and their relationship with prognoses are not well studied. Methods This analysis included 609 participants from the TOPCAT (Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist) trial. Depressive symptoms were assessed using the Patient Health Questionnaire-9 (PHQ-9) at baseline and at 1-year, 2-year and 3-year intervals. Individual trajectory patterns based on PHQ-9 scores during the first 3 years were identified using latent class trajectory models, and their associations with clinical outcomes were evaluated using Cox regression models. Results Among the 609 participants, 316 (51.9%) were female, with a median age of 74 years (IQR: 66, 80). Four distinct depression trajectory patterns were identified: low (consistently low scores; 349, 57.3%), mild (sustained mild elevation; 110, 18.1%), high (sustained moderate–severe elevation; 52, 8.5%) and recurrent deterioration (high baseline scores, remission, then escalation; 98, 16.1%). According to the multivariate Cox model, recurrent deterioration was associated with a significantly greater risk of all-cause mortality (HR: 2.05; 95% CI 1.16, 3.64) than the low trajectory pattern. No significant differences were found among the low, mild and high trajectory groups. Conclusions Four distinct depression trajectory patterns were identified among patients with HFpEF. Notably, patients who experienced a recurrent deterioration trajectory presented a significantly increased risk of all-cause mortality. Our findings highlight the importance of monitoring patients’ depressive symptoms over time rather than focusing on a single timepoint. Trial registration number NCT00094302 .
科研通智能强力驱动
Strongly Powered by AbleSci AI