ArticlePloS one2019
Prediction of cardiovascular disease risk among people with severe mental illness: A cohort study.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Characteristics of Cardiovascular Disease Prediction Models Considering Mental Disorders: A Systematic Review.Journal of the American Heart Association · 2026Pooled it
- Beyond Traditional Risk Factors: The Role of Mental Health and Intersectionality in Cardiovascular Disease Risk Prediction Models.Journal of the American Heart Association · 2026Article
- A five-year risk prediction model of cardiovascular disease in individuals with bipolar disorder: a nationwide register study from Sweden.Molecular psychiatry · 2026Article
- External validation and recalibration of the psychosis metabolic risk calculator (PsyMetRiC) in young adults with chronic psychotic disorders in the Netherlands.European psychiatry : the journal of the Association of European Psychiatrists · 2026Article
- Development and external validation of machine learning approaches for risk prediction of cardiovascular disease in individuals with schizophrenia: a nationwide Swedish and Danish study.BMJ mental health · 2026Article
- Mental health disorders and their impact on cardiovascular health disparities.The Lancet regional health. Europe · 2025Review
- Risk of Cardiovascular Disease Associated with Serious Mental Illness among People of African Ancestry.Ethnicity & disease · 2025Article
- Sex-Specific Association Between Genetic Risk of Psychiatric Disorders and Cardiovascular Diseases.Circulation. Genomic and precision medicine · 2024Article
- 2024 Guidelines of the Taiwan Society of Cardiology on the Primary Prevention of Atherosclerotic Cardiovascular Disease --- Part I.Acta Cardiologica Sinica · 2024Article
- Improving Cardiovascular Disease Prediction With Machine Learning Using Mental Health Data: A Prospective UK Biobank Study.JACC. Advances · 2024Article
- Mortality and adverse events associated with statin use in primary care patients with depression: a real-world, population-based cohort study.BMJ mental health · 2024Article
- Assessing, communicating and managing cardiovascular disease risk: a practical summary of the 2023 guideline.Australian prescriber · 2024Review
- Severe mental illness: cardiovascular risk assessment and management.European heart journal · 2024Article
- Development of machine learning-based models to predict 10-year risk of cardiovascular disease: a prospective cohort study.Stroke and vascular neurology · 2023Article
- Psychiatric polygenic risk as a predictor of COVID-19 risk and severity: insight into the genetic overlap between schizophrenia and COVID-19.Translational psychiatry · 2023Article
- Developing a System of Health Support for Young People Experiencing First-Episode Psychosis: Protocol for a Co-design Process.JMIR research protocols · 2023Article
- Cardiovascular disease in older people with serious mental illness: Current challenges and future directions.Frontiers in psychiatry · 2023Article
- The Potential Impact of a Public Health Approach to Improving the Physical Health of People Living with Mental Illness.International journal of environmental research and public health · 2022Article
- The development and validation of a medicines optimisation tool to protect the physical health of people with severe mental illness (OPTIMISE).BMC psychiatry · 2022Review
- Disparities in cardiovascular outcomes among emergency department patients with mental illness.The American journal of emergency medicine · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo determine whether contemporary sex-specific cardiovascular disease (CVD) risk prediction equations underestimate CVD risk in people with severe mental illness from the cohort in which the equations were derived.
methodsWe identified people with severe mental illness using information on prior specialist mental health treatment. This group were identified from the PREDICT study, a prospective cohort study of 495,388 primary care patients aged 30 to 74 years without prior CVD that was recently used to derive new CVD risk prediction equations. CVD risk was calculated in participants with and without severe mental illness using the new equations and the predicted CVD risk was compared with observed risk in the two participant groups using survival methods.
results28,734 people with a history of recent contact with specialist mental health services, including those without a diagnosis of a psychotic disorder, were identified in the PREDICT cohort. They had a higher observed rate of CVD events compared to those without such a history. The PREDICT equations underestimated the risk for this group, with a mean observed:predicted risk ratio of 1.29 in men and 1.64 in women. In contrast the PREDICT algorithm performed well for those without mental illness.
conclusionsClinicians using CVD risk assessment tools that do not include severe mental illness as a predictor could by underestimating CVD risk by about one-third in men and two-thirds in women in this patient group. All CVD risk prediction equations should be updated to include mental illness indicators.
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