Forecasting mortality: dynamic assessment of risk in ST-segment elevation acute myocardial infarction
Bibliographic record
Abstract
AIMS: To demonstrate the feasibility and clinical utility of developing dynamic risk assessment models for ST-segment elevation myocardial infarction (STEMI) patients. METHODS AND RESULTS: In 6066 STEMI patients enrolled in the Assessment of the Safety and Efficacy of a New Thrombolytic-3 (ASSENT-3) trial with complete electrocardiographic data, we assessed the probability of 30-day mortality over the following forecasting periods beginning at day 0 (baseline), 3 h, day 2, and day 5 using multiple-logistic regression. These models were validated and simplified in independent samples of 1622 similar fibrinolytic-treated patients from the ASSENT-3 PLUS trial and in 814 STEMI patients undergoing primary percutaneous coronary intervention in the COMplement inhibition in Myocardial infarction treated with Angioplasty (COMMA) trial. The discriminatory power of these predictive models, from baseline to day 5, was excellent (c-statistics 0.80 to 0.87); and their predictive ability was supported by strong gradients in mortality outcomes as the risk score increased. Dynamic modelling also provided information on the change in prognosis over time which may be used to advise more appropriate therapeutic decisions, e.g. the identification of high-risk patients for possible co-interventions. CONCLUSION: Dynamic modelling for STEMI patients enhances the risk assessment and stratification and should provide valuable ongoing guidance for their management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".