Key result
Most CVD prediction models incorporate mental disorders as covariates, led by depression and anxiety in ~71%.
Why the study?
Mental disorders are robust predictors of cardiovascular disease risk, but the characteristics, strengths, and limitations of CVD prediction models considering them have not been comprehensively synthesized.
Systematic Review (n=31)
Most cardiovascular disease prediction models that incorporate mental disorders focus narrowly on depression and anxiety and are limited by significant methodological biases.
Most CVD prediction models now incorporate mental disorders; extends psychosocial risk integration but methodological biases limit clinical adoption.
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.
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Siddiqi et al. (2026) conducted a systematic review in Cardiovascular disease (n=31). Mental disorders (including depression, anxiety, severe mental illness) vs. Individuals without mental disorders was evaluated on Proportion of models including mental disorders as a covariate. In a systematic review of 31 unique cardiovascular disease prediction models, 77% included mental disorders as covariates, with depression and/or anxiety being the most commonly considered (71%).
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