15-Year trends, predictors, and outcomes of heart failure hospitalization complicating first acute myocardial infarction in the modern percutaneous coronary intervention era
Why this work is in the frame
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Bibliographic record
Abstract
Aims: Heart failure (HF) following acute myocardial infarction (AMI) is a global health concern, but data on risk factors associated with HF hospitalization post-AMI are limited. Methods and results: We analysed data from the Myocardial Ischaemia National Audit Project, including patients admitted with AMI from 1 January 2006 to 31 March 2019. Data linkage with Hospital Episode Statistics Admitted Patient Care and the Office for National Statistics facilitated a longitudinal analysis. High-risk patients were identified using dapagliflozin in patients without diabetes mellitus with acute myocardial infarction (DAPA-MI) and EMPAgliflozin on Hospitalization for Heart Failure and Mortality in Patients With aCuTe Myocardial Infarction (EMPACT-MI) criteria. We assessed clinical outcomes, adherence to European Society of Cardiology quality indicators, and predictors of HF-related hospitalizations. Out of 1 046 480 AMI patients, 9.1% overall, 17.2% in the DAPA-MI cohort, and 16.6% in the EMPACT-MI cohort experienced HF hospitalization within a year post-AMI. High-risk patients, defined by the presence of five risk factors, had nearly one in four hospitalizations with HF at 1-year follow-up. The predicted adjusted incidence rate for heart failure within 1 year almost doubled from 64.5 cases per 1000 person-years [95% confidence interval (CI): 51.1 to 78.0] in 2005, to 118.2 cases per 1000 person-years in 2019 (95% CI: 115.0 to 121.5). Heart failure hospitalization was associated with a three-fold increase in 1-year mortality (hazard ratio 3.01, 95% CI 2.95-3.13). Conclusion: One in 10 AMI patients experienced HF hospitalization within the first-year post-AMI, with rising trends in high-risk subgroups. These findings highlight the need for targeted post-AMI care strategies to improve outcomes and address the increasing burden of HF in the modern percutaneous coronary intervention era.
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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) | 0.000 | 0.000 |
| 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 it