O'Neil Adrienne, Fisher Aaron J, Kibbey Katherine J, Jacka Felice N, Kotowicz Mark A, Williams Lana J, Stuart Amanda L, Berk Michael, Lewandowski Paul A, Atherton John J, Taylor Craig B, Pasco Julie A
Melbourne School of Population & Global Health, University of Melbourne, Carlton, VIC, Australia; School of Medicine, Deakin University, Geelong, VIC, Australia; School of Public Health and Preventive Medicine, Monash University, Prahran, VIC, Australia.
Department of Psychology, University of California Berkeley, CA, USA.
Prev Med. 2016 Jun;87:115-120. doi: 10.1016/j.ypmed.2016.02.028. Epub 2016 Feb 22.
Depression is widely considered to be an independent and robust predictor of Coronary Heart Disease (CHD), however is seldom considered in the context of formal risk assessment. We assessed whether the addition of depression to the Framingham Risk Equation (FRE) improved accuracy for predicting 10-year CHD in a sample of women.
A prospective, longitudinal design comprising an age-stratified, population-based sample of Australian women collected between 1993 and 2011 (n=862).
Clinical depressive disorder was assessed using the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders (SCID-I/NP), using retrospective age-of-onset data. A composite measure of CHD included non-fatal myocardial infarction, unstable angina coronary intervention or cardiac death. Cox proportional-hazards regression models were conducted and overall accuracy assessed using area under receiver operating characteristic (ROC) curve analysis.
ROC curve analyses revealed that the addition of baseline depression status to the FRE model improved its overall accuracy (AUC:0.77, Specificity:0.70, Sensitivity:0.75) when compared to the original FRE model (AUC:0.75, Specificity:0.73, Sensitivity:0.67). However, when calibrated against the original model, the predicted number of events generated by the augmented version marginally over-estimated the true number observed.
The addition of a depression variable to the FRE equation improves the overall accuracy of the model for predicting 10-year CHD events in women, however may over-estimate the number of events that actually occur. This model now requires validation in larger samples as it could form a new CHD risk equation for women.
抑郁症被广泛认为是冠心病（CHD）的一个独立且有力的预测指标，但在正式的风险评估中很少被考虑。我们评估了在弗明汉姆风险方程（FRE）中加入抑郁症因素是否能提高对一组女性样本中10年冠心病发病风险的预测准确性。
一项前瞻性纵向研究，样本为1993年至2011年间收集的澳大利亚女性，按年龄分层，基于人群（n = 862）。
使用《精神疾病诊断与统计手册》结构化临床访谈（SCID - I/NP），依据回顾性发病年龄数据评估临床抑郁症。冠心病的综合指标包括非致命性心肌梗死、不稳定型心绞痛、冠状动脉介入治疗或心源性死亡。进行Cox比例风险回归模型分析，并使用受试者操作特征（ROC）曲线下面积分析评估总体准确性。
ROC曲线分析显示，与原始FRE模型（AUC：0.75，特异性：0.73，敏感性：0.67）相比，在FRE模型中加入基线抑郁状态可提高其总体准确性（AUC：0.77，特异性：0.70，敏感性：0.75）。然而，与原始模型校准后，增强版模型预测的事件数略高于实际观察到的真实事件数。
在FRE方程中加入抑郁症变量可提高该模型对女性10年冠心病事件的预测总体准确性，但可能高估实际发生的事件数。该模型目前需要在更大样本中进行验证，因为它可能构成一个新的女性冠心病风险方程。
