Trends in Performance and Opportunities for Improvement on a Composite Measure of Acute Myocardial Infarction Care
Bibliographic record
Abstract
Background Despite improvements on individual process of care measures for acute myocardial infarction (AMI), little is known about performance on a composite measure of AMI care that assesses the delivery of many components of high-quality AMI care. We sought to examine trends in patient- and hospital-level performance on a composite defect-free care measure, identify disparities in the performance across sociodemographic groups, and identify opportunities to further improve quality and outcomes. Methods and Results We calculated the proportion of patients in the National Cardiovascular Data Registry-Acute Coronary Treatment and Intervention Outcomes Network Registry-Get With The Guidelines (now known as the Chest Pain - Myocardial Infarction Registry) between January 1, 2010, and December 31, 2017, receiving defect-free AMI care including guideline-recommended pharmacotherapy, timely provision of medical and reperfusion therapy, assessment of ventricular function, referral to cardiac rehabilitation, and smoking cessation counseling for patients with AMI. A total of 522 800 patients at 222 hospitals were included. Overall, the proportion of patients receiving defect-free care significantly increased from 66.0% in 2010 to 77.1% in 2017 ( P<0.001). Improvements in performance were observed across all sociodemographic subgroups, with the greatest absolute improvement observed for black and Hispanic patients ( P<0.001). However, absolute performance was consistently lower among older patients, women, black and Hispanic patients, and those with government insurance in 2017 ( P<0.001 for all). Improvements in care and reduced variation in performance were observed at the hospital level overall (2010, median [IQR] 67.2% [40.7%-76.3%]; 2017, median [IQR] 80.7% [73.1%-88.1%]; P<0.001) as well as across region, safety net status, teaching status, and proportion of patients who are nonwhite and have Medicaid insurance coverage ( P<0.001 for all). Conclusions Despite improvements in the proportion of patients with AMI receiving defect-free care overall and across sociodemographic groups, nearly 1 in 4 patients in 2017 still did not receive optimal care and absolute performance was consistently lower among older patients, women, black, and Hispanic patients. Composite measures of cardiovascular care, which assess the delivery of several evidence-based processes of care, can illuminate opportunities to improve the quality of care beyond that provided by conventional process measures.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".