Ethnicity-dependent performance of the Global Registry of Acute Coronary Events risk score for prediction of non-ST-segment elevation myocardial infarction in-hospital mortality: nationwide cohort study
Bibliographic record
Abstract
Abstract Aims The Global Registry of Acute Coronary Events (GRACE) score was developed to evaluate risk in patients with the acute coronary syndrome with or without ST-segment elevation. Little is known about its performance at predicting in-hospital mortality for ethnic minority patients. Methods and results We identified 326 160 admissions with non-ST-segment elevation myocardial infarction (NSTEMI) in the Myocardial Infarction National Audit Project (MINAP), 2010–17, including White (n = 299 184) and ethnic minorities (excluding White minorities) (n = 26 976). We calculated the GRACE score for in-hospital mortality and assessed ethnic group baseline characteristics by low, intermediate and high risk. The performance of the GRACE risk score was estimated by discrimination [area under the receiver operating characteristic curve (AUC)] and calibration (calibration plots). Ethnic minorities presented younger and had increased prevalence of cardiometabolic risk factors in all GRACE risk groups. The GRACE risk score for White [AUC 0.87, 95% confidence interval (CI) 0.86–0.87] and ethnic minority (AUC 0.87, 95% CI 0.86–0.88) patients had good discrimination. However, whilst the GRACE risk model was well calibrated in White patients (expected to observed (E : O) in-hospital death rate ratio 0.99; slope 1.00), it overestimated risk in ethnic minority patients (E : O ratio 1.29; slope: 0.94). Conclusion The GRACE risk score provided good discrimination overall for in-hospital mortality, but was not well calibrated and overestimated risk for ethnic minorities with NSTEMI. Key question Does the performance of the Global Registry of Acute Coronary Events (GRACE) (v2.0) score in predicting in-hospital mortality for non-ST-segment elevation myocardial infarction (NSTEMI) differ by ethnicity? Key finding The GRACE risk score provided good discrimination overall for in-hospital mortality but was not well calibrated and overestimated risk for ethnic minority patients with NSTEMI. Take-home message Ethnicity or race should be considered during the development of risk scoring systems. Existing systems can be recalibrated in the population they serve to better address risk.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".