ArticleFrontiers in psychiatry2025
The occurrence of and risk factors for depressive symptomatology in myocarditis survivors: a cross-sectional survey-based study using machine learning.
Article in Frontiers in psychiatry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- "Bridging the clinical, molecular and genetic perspectives on myocarditis in post-COVID-19 era".International journal of cardiology. Cardiovascular risk and prevention · 2026Review
- WASOG Clinical Trial Endpoint Task Force: Executive Summary.Sarcoidosis, vasculitis, and diffuse lung diseases : official journal of WASOG · 2025Article
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Authors and funding
9 authors.
Funding
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Abstract
Background: The frequency and impact of depressive symptoms in myocarditis survivors are poorly understood. Objectives: 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. Methods: 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. Results: 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, Conclusions: 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.
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