Characteristics of Cardiovascular Disease Prediction Models Considering Mental Disorders: A Systematic Review

BACKGROUND: Prognostic models for cardiovascular disease (CVD) risk have commonly included predictors such as cholesterol levels. Mental disorders are robust predictors of CVD and associated mortality, leading to approximately 2-fold increases in risk. This systematic review aimed to narratively summarize the key characteristics, strengths, and limitations of all CVD prediction models that consider mental disorders. METHODS: A literature search with medical subject headings/key terms related to CVD, mental disorders and prognostic modeling was conducted in Medline and EMBASE. Included studies were: cohort studies of CVD prediction model development, validation, or recalibration that included mental disorders as prognostic factors/covariate(s), or the population of interest. All studies were screened by 2 independent reviewers, followed by data extraction. The Prediction Model Risk Of Bias Assessment Tool was used to critically appraise bias. A narrative synthesis was used to summarize mental disorder and sociodemographic factor/intersectionality inclusion. RESULTS: There were 31 unique models identified (n=35 records including external validations). Considering these, 77% included mental disorders as a covariate, whereby depression and/or anxiety were the most considered (71%, n=22 studies). Most models were published within the last 5 years, included measures of socioeconomic status; however, many models lacked intersectionality considerations. Only one study was identified with a low risk of bias, while the majority had analytic concerns. CONCLUSIONS: Depression and/or anxiety were the most commonly considered mental disorders in modeling, despite larger associations with CVD for other disorders. Further CVD prediction modeling should consider a broader array of mental disorders, and limit methodological biases.