Validation and comparison of six risk scores for post acute myocardial infarction infection
Bibliographic record
Abstract
Abstract Background Various risk scores have been proven to predict outcomes in patients with ST-segment elevation myocardial infarction (STEMI) undergoing percutaneous coronary intervention (PCI). However, few of them were validated and compared the difference of the prediction of infection during hospitalization in such patients. Aim We aimed to validate and compare the discriminatory value of different risk scores for predicting infection. Methods Patients who were diagnosed with STEMI treated with PCI were enrolled from January 2010 to May 2018. The six risk scores included the Age, Serum Creatinine (SCr), or Glomerular Filtration Rate, and Ejection Fraction (ACEF or AGEF) score, Canada Acute Coronary Syndrome Risk Score (CACS score), CHADS2 score, Global Registry for Acute Coronary Events (GRACE) score and Mehran score. The primary end point was infection during hospitalization. The secondary endpoint was major adverse clinical events including all cause death, stroke and any bleeding. The prognostic accuracy of the six scores was assessed using the c statistic for discrimination and the Hosmer-Lemeshow test for calibration. Results A total of 2260 eligible patients were enrolled (62.32±12.36 year, 81.3% of males). A significant gradient of risk with respect to infection and in hospital major adverse clinical events (MACE) was observed with increasing all six risk scores. Other than the CHADS2 score (AUC: 0.682; 95% CI, 0.652–0.712), other five risk scores showed the good discrimination for predicting infection, with the GRACE score being the best (AUC: 0.791; 95% CI, 0.765–0.817). In addition, all risk scores showed best calibration for infection, but good calibration for CACS risk score (calibration slope: 0.77, 95% CI: 0.18–1.35) (Figure 1). Furthermore, each score showed a best discrimination for in hospital MACE, with AUCs ranging from 0.761 to 0.786, other than CACS risk score and CHADS2 risk score with AUC of 0.700 and 0.696, respectively. All risk scores showed best calibration for in hospital MACE. Conclusions In patients with STEMI undergoing PCI, these risk scores (ACEF, AGEF, CACS, GRACE and Mehran) showed good discrimination and calibration to predict infection and MACE. The CACS score was recommended for clinical use as its clinical variables were simple and practical. Figure 1 Funding Acknowledgement Type of funding source: Public Institution(s). Main funding source(s): National Science Foundation for Young Scientists of China
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".