Effectiveness of Interventions Aimed at Increasing Statin-Prescribing Rates in Primary Cardiovascular Disease Prevention
Bibliographic record
Abstract
Importance: Statins are a cornerstone medication in cardiovascular disease prevention, but their use in clinical practice remains suboptimal, with less than half of people who are indicated for statins actually taking the medication. Objective: To perform a systematic review and synthesis of the literature on patient-oriented and physician-oriented interventions aimed at increasing statin-prescribing rates in adults without a history of cardiovascular disease. Evidence Review: PubMed, Embase, and the Cochrane Library were searched for randomized clinical trials published between January 2000 and May 2019. Data abstraction was performed using the Cochrane Public Health Review Group's data collection template, and a narrative synthesis of study results was conducted. The risk of bias in each study was qualitatively assessed, and a funnel plot was created to further evaluate the risk of publication bias. Findings: Among 7948 citations and 128 full-text articles reviewed, 20 studies (of 109 807 patients) were included in the review. Eight trials reported a statistically significant increases in statin-prescribing rates. Among the effective trials, absolute effect sizes ranged from 4.2% (95% CI, 2.2%-6.4%) to 23% (95% CI, 7.3%-38.9%) and odds ratios from 1.29 (95% CI, 1.01-1.66) to 11.8 (95% CI, 8.8-15.9). Patient-education initiatives were the most commonly effective intervention, with 4 of 7 trials indicating increases in statin-prescribing rates. Two trials combined electronic decision-support tools with audit-and-feedback systems, both of which were effective overall. Physician-education programs without dynamic input regarding patient risk or updated treatment recommendations were generally found to be less effective. Conclusions and Relevance: While heterogeneous in their interventions and outcomes, a number of interventions have demonstrated increases in statin-prescribing rates, with patient-education initiatives demonstrating more promising results than those focused on physician education alone. As opposed to more education about generic recommendations, tailored patient-focused and physician-focused interventions were more effective when they provided personalized cardiovascular risk information, dynamic decision-support tools, or audit-and-feedback reports in a multicomponent program. There are a number of modestly successful approaches to implement increases in rates of statin prescribing, a proven yet underused cardiovascular disease prevention class of therapy.
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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 | 0.027 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".