Abstract 11849: Validating and Updating the Toronto In-Hospital Mortality Risk Score After Percutaneous Coronary Interventions in Brazil
Bibliographic record
Abstract
Background: Estimating percutaneous coronary intervention (PCI) mortality risk by a clinical prediction model is imperative to help physicians, patients and family members make informed clinical decisions and optimize participation in the consent process, reducing anxiety and improving quality of care. At a healthcare system level, risk prediction scores are essential to measure and benchmark performance. Hypothesis: The Toronto PCI mortality risk score is accurate and precise in predicting death in a Brazilian population. Methods: Between 2009 and 2013, a cohort of 4,806 patients from the ICP-BR registry, treated with PCI in eight tertiary referral medical centers, was included in the analysis. This population was compared to 10,694 patients of the derivation dataset from the Toronto study. To assess predictive performance, an update of the model was performed by three different methods, which were compared by discrimination, calculating the area under the receiver operating characteristic curve (AUC), and by calibration, assessed through Hosmer-Lemeshow (H-L) test and graphical analysis. The score included the following predictors: age - 40-49 (1), 50-59 (2), 60-69 (3), 70-79 (4) and ≥80 (5); diabetes (2); renal failure (2), NYHA class IV heart failure symptoms (3); severe myocardial dysfunction (3); multivessel disease (1), left main disease (2); recent myocardial infarction (3); early PCI after thrombolysis (3); primary PCI (4); cardiogenic shock (6). After summing the score values we apply the equation: 1/(1+e^(- (-7.448 + risk score x 0.352))). We sought to re-divide the integer score according to the predictive risk in low ( 5% - score ≥13) risk groups. Results: Death occurred in 2.6% of patients in the ICP-BR registry and in 1.3% in the Toronto cohort. The median age was 64 and 63 years, 23.8 and 32.8% were female, 28.6 and 32.3% were diabetics, respectively. Through recalibration of intercept and slope (AUC= 0.8790; H-L p value= 0.3132), we achieved a well-calibrated and well-discriminative model. Conclusions: After updating to our dataset, we demonstrated that the Toronto PCI in-hospital mortality risk score has a good performance and discrimination in Brazilian hospitals.
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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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".