Percutaneous coronary intervention and 30‐day mortality: The British Columbia PCI risk score
Bibliographic record
Abstract
OBJECTIVES: To construct a calculator to assess the risk of 30-day mortality following PCI. BACKGROUND: Predictors of 30-day mortality are commonly used to aid management decisions for cardiac surgical patients. There is a need for an equivalent risk-score for 30-day mortality for percutaneous coronary intervention (PCI) as many patients are suitable for both procedures. METHODS: The British Columbia Cardiac Registry (BCCR) is a population-based registry that collects information on all PCI procedures performed in British Columbia (BC). We used data from the BCCR to identify risk factors for mortality in PCI patients and construct a calculator that predicts 30-day mortality. RESULTS: Patients (total n = 32,899) were divided into a training set (n = 26,350, PCI between 2000 and 2004) and validation set (n = 6,549, PCI in 2005). Univariate predictors of mortality were identified. Multivariable logistic regression analysis was performed on the training set to develop a statistical model for prediction of 30-day mortality. This model was tested in the validation set. Variables that were objective and available before PCI were included in the final risk score calculator. The 30-day mortality for the overall population was 1.5% (n = 500). Area under the ROC curve was 90.2% for the training set and 91.1% for the validation set indicating that the model also performed well in this group. CONCLUSIONS: We describe a large, contemporary cohort of patients undergoing PCI with complete follow-up for 30-day mortality. A robust, validated model of 30-day mortality after PCI was used to construct a risk calculator, the BC-PCI risk score, which can be accessed at www.bcpci.org.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.002 | 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".