Underestimated prognostic value of depression in patients with obstructive coronary artery disease - Liu, Quanjun; Yin, Han; Jiang, Cheng; Xu, Mingyu; Liu, Yuting; Liu, Anbang; Wang, Haochen; Bai, Bingqing; Liu, Fengyao; Guo, Lan; Ma, Huan; Geng, Qingshan (Frontiers in Cardiovascular Medicine, 2022)
Objective: The aim of this study was to explore the different predictive values of depression among patients with different cardiac systolic function levels. Methods: 483 consecutive patients with obstructive coronary artery disease (CAD) were included The depressive state was assessed using the Chinese version of the Patient Health Questionnaire 9 (PHQ-9). Depression was defined as have depressive symptoms with a PHQ-9 score ≥5. The level of cardiac systolic function was classified as left ventricular ejection fraction (LVEF) ≥50% and <50%. Results: Over a median of 26.2 months, 421 patients completed the follow-up and experienced 101 major adverse cardiovascular events (MACEs), 45 noncardiac rehospitalizations, and 17 deaths. Predictors for clinical outcomes in patients with different cardiac systolic function levels were not the same. For participants with preserved LVEF, depression was associated with increased risks for cardiovascular events and composite outcomes. However, when focusing the whole population, predictive values of depression for MACEs, noncardiac rehospitalizations and composite endpoints all dropped. Receiver operating characteristic (ROC) analyses further confirmed that depression was the one of the main predictors for all clinical outcomes. With the combination of other simple features, area under curve (AUC) could reach 0.64-0.67. Conclusions: Inconsistent with the general impression, depression is found to have a closer linkage with clinical outcomes in CAD patients with preserved LVEF rather than in those with decreased LVEF. These findings appeal for more attention on CAD patients with depressive symptoms and comparatively normal LVEF. Including psychological factors may be a good attempt when constructing risk prediction models.