Marrero-Polanco Jean, Suarez Pardo Laura, Niazi Shehzad K, Smith Daniel G, Stoppel Cynthia J, Moose Candace, Athreya Arjun P, Cooper Leslie T, Bobo William V
Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, United States.
Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, United States.
Front Psychiatry. 2025 Apr 28;16:1581314. doi: 10.3389/fpsyt.2025.1581314. eCollection 2025.
The frequency and impact of depressive symptoms in myocarditis survivors are poorly understood.
We conducted a cross-sectional study to identify risk factors and the relative importance of each for predicting clinically significant depressive symptomatology in myocarditis survivors.
Participants completed an electronic survey assessing sociodemographic, general health, and myocarditis-related variables, as well as self-reported cardiac symptoms and personal and family mental health history. Participants also completed the Center for Epidemiologic Studies Depression Scale (CES-D), Beck Anxiety Inventory (BAI), revised Impact of Events Scale (IES-R), and other validated measures of social support, quality of life, resiliency, childhood adversity, treatment distress, and somatic symptom burden. Clinically significant depressive symptomatology was defined as a CES-D total score ≥ 16. We used supervised machine learning to examine which and how well psychosocial and other types of variables predicted clinically significant depressive symptomatology in myocarditis survivors. Finally, we calculated the variable importance for each variable from the trained models and examined the rank ordering of predictors.
Ninety-six of 113 respondents (85.0%) with complete survey data were included in the analyses. Forty-three (44.8%) respondents had clinically significant depressive symptomatology. When predicting depressive symptomatology, random forests achieved a mean AUC of 0.91 (95% CI 0.87-0.95) and a significantly higher accuracy than that of the null information rate (0.84 vs 0.55, < 0.005), with correspondingly high sensitivity (0.84) and specificity (0.85). Emotional wellbeing, quality of life, history of depression, anxiety, and resilience were the top predictors in variable importance analyses, ahead of self-reported cardiovascular symptoms, other myocarditis-related variables, and family history of depression.
Myocarditis survivors are at high risk for clinically significant depressive symptomatology. Psychosocial factors that are measurable in routine practice may be more predictive of significant depressive symptomatology than demographics, family history, or self-reported cardiovascular symptoms.
心肌炎幸存者中抑郁症状的发生率及其影响尚不清楚。
我们进行了一项横断面研究，以确定预测心肌炎幸存者临床上显著抑郁症状的危险因素及其相对重要性。
参与者完成了一项电子调查，评估社会人口统计学、一般健康状况、与心肌炎相关的变量，以及自我报告的心脏症状和个人及家族心理健康史。参与者还完成了流行病学研究中心抑郁量表（CES-D）、贝克焦虑量表（BAI）、事件影响量表修订版（IES-R），以及其他经过验证的社会支持、生活质量、心理韧性、童年逆境、治疗困扰和躯体症状负担的测量。临床上显著的抑郁症状定义为CES-D总分≥16分。我们使用监督机器学习来检验哪些心理社会变量和其他类型的变量能够预测心肌炎幸存者临床上显著的抑郁症状，以及预测效果如何。最后，我们从训练模型中计算每个变量的重要性，并检查预测因素的排名顺序。
113名有完整调查数据受访者中的96名（85.0%）纳入分析。43名（44.8%）受访者有临床上显著的抑郁症状。在预测抑郁症状时，随机森林的平均AUC为0.91（95%CI 0.87-0.95），准确率显著高于零信息率（0.84对0.55，<0.005），相应地具有较高的敏感性（0.84）和特异性（0.85）。在变量重要性分析中，情绪健康、生活质量、抑郁史、焦虑史和心理韧性是首要预测因素，排在自我报告的心血管症状、其他与心肌炎相关的变量以及抑郁家族史之前。
心肌炎幸存者有临床上显著抑郁症状的风险很高。在常规实践中可测量的心理社会因素可能比人口统计学、家族史或自我报告的心血管症状更能预测显著的抑郁症状。
