Abstract 14508: Development of a Risk Prediction Model for 1-year Readmission Among Younger Adults Hospitalized for Acute Myocardial Infarction
Bibliographic record
Abstract
Introduction: Although readmission over the first year following hospitalization for acute myocardial infarction (AMI) is common among younger adults (18-55 yrs), there is no available risk prediction model for this age group. Existing risk models have been developed in older populations, have modest predictive ability, and exhibit methodological drawbacks. We developed a risk prediction model that considered a broad range of demographic, clinical, and psychosocial factors for readmission within 1-year of hospitalization for AMI among young adults. Methods: Young AMI adults (18-55 yrs) were enrolled from the prospective observational VIRGO study (2008-2012) of 3,572 patients. Data were obtained from medical record abstraction, interviews, and adjudicated hospitalization records. The outcome was all-cause readmission within 1-year. We used a two-stage selection process (LASSO followed by Bayesian Model Averaging) to develop a risk model. Results: The median age was 48 years (IQR: 44,52), 67.1% were women, and 20.1% were Non-white or Hispanic. Within 1-year, 906 patients (25.3%) were readmitted. Patients who were readmitted were more likely to be female, black, and had a clustering of adverse risk factors and co-morbidities. From 61 original variables considered, the final multivariable model of readmission within 1-year of discharge consisted of 14 predictors (Figure) . The model was well calibrated (Hosmer-Lemeshow P >0.05) with moderate discrimination (C statistic over 33 imputations: 0.69 development cohort). Conclusion: Adverse clinical risk factors such as diabetes, hypertension and prior AMI, but also female sex, access to specialist care, and major depression were associated with a higher risk of readmission at 1-year post AMI. This information is important to inform the development of interventions to reduce readmissions in young patients with AMI.
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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 | 0.000 | 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 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".