006 Interventions to address risk-treatment paradox in patients at risk of cardiovascular events: a systematic review
Bibliographic record
Abstract
Introduction Cardiovascular (CV) disease is a leading cause of mortality worldwide. In the U.S., it accounts for one out of every three deaths, translating to over 700,000 deaths each year.1–3 The risk-treatment paradox, where individuals at lower risk receive more interventions than those at higher risk, is a significant concern in CV disease management4 that exposes low-risk individuals to unnecessary treatment side effects and costs, while high-risk individuals often remain undertreated. Addressing this paradox is crucial to optimize care and improve outcomes in patients at risk of CV events. Objective To identify, assess, and synthesize interventions designed to mitigate the risk-treatment paradox in the context of CV care, focusing on the enhancement of risk-concordant therapy. Methods We reviewed interventions targeting adult patients at risk of CV events. Our primary outcome was the effectiveness of these interventions in balancing treatment rates based on patient risk profiles. Results An initial search yielded 913 papers, reduced to 114 after screening and 9 following full-text review. These studies spanned from 2003 to 2022 across the USA and Australia, used randomized trial and observational study designs, and were in varied healthcare environments. We assessed interventions including electronic support for clinicians and educational programs for adult patients. Results were mixed; certain strategies notably enhanced medication prescription and guideline adherence, while others showed minimal impact, reflecting the nuanced efficacy of such interventions in different clinical contexts. Discussion Addressing the risk-treatment paradox is imperative not only for clinical appropriateness but for aligning care with patient needs. Our review is poised to provide insights into interventions that can rectify this imbalance, promoting a patient-centered approach in CV healthcare. Conclusion This systematic review is an essential step in understanding how to better align CV treatments with patient risk profiles, thereby enhancing clinical efficacy and patient-centric care. References Go AS, et al. Heart disease and stroke statistics—2013 update. 2013. Mahmood SS, et al. The framingham heart study and the epidemiology of cardiovascular disease: a historical perspective. 2014. Murphy SL, et al. Mortality in the United States, 2020. 2021. McAlister FA, Oreopoulos A, Norris CM, Graham MM, Tsuyuki RT, Knudtson M, Ghali WA, Alberta provincial project for outcome assessment in coronary heart disease (APPROACH) investigators exploring the treatment- risk paradox in coronary disease. Arch Intern Med. 2007;167(10):1019–1025. doi: 10.1001/archinte.167.10.1019.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".