Severe mental illness: cardiovascular risk assessment and management
Bibliographic record
Abstract
Patients with severe mental illness (SMI) including schizophrenia and bipolar disorder die on average 15-20 years earlier than the general population often due to sudden death that, in most cases, is caused by cardiovascular disease. This state-of-the-art review aims to address the complex association between SMI and cardiovascular risk, explore disparities in cardiovascular care pathways, describe how to adequately predict cardiovascular outcomes, and propose targeted interventions to improve cardiovascular health in patients with SMI. These patients have an adverse cardiovascular risk factor profile due to an interplay between biological factors such as chronic inflammation, patient factors such as excessive smoking, and healthcare system factors such as stigma and discrimination. Several disparities in cardiovascular care pathways have been demonstrated in patients with SMI, resulting in a 47% lower likelihood of undergoing invasive coronary procedures and substantially lower rates of prescribed standard secondary prevention medications compared with the general population. Although early cardiovascular risk prediction is important, conventional risk prediction models do not accurately predict long-term cardiovascular outcomes as cardiovascular disease and mortality are only partly driven by traditional risk factors in this patient group. As such, SMI-specific risk prediction models and clinical tools such as the electrocardiogram and echocardiogram are necessary when assessing and managing cardiovascular risk associated with SMI. In conclusion, there is a necessity for differentiated cardiovascular care in patients with SMI. By addressing factors involved in the excess cardiovascular risk, reconsidering risk stratification approaches, and implementing multidisciplinary care models, clinicians can take steps towards improving cardiovascular health and long-term outcomes in patients with SMI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".