Simple Laboratory Test-Based Risk Scores in Coronary Catheterization: Development, Validation, and Comparison to Conventional Risk Factors
Bibliographic record
Abstract
BACKGROUND: We developed and validated laboratory test-based risk scores (i.e., lab risk scores) to reclassify mortality risk among patients undergoing their first coronary catheterization. METHODS: Patients were catheterized between 2009 and 2015 in Calgary, Alberta, Canada (n = 14 135, derivation cohort), and in Edmonton, Alberta, Canada (n = 12 143, validation cohort). Logistic regression with group LASSO (least absolute shrinkage and selection operator) penalty was used to select quintiles of the last laboratory tests (red blood cell count, mean corpuscular hemoglobin concentration, mean corpuscular hemoglobin, mean corpuscular volume, red cell distribution width, platelet count, total white blood cell count, plasma sodium, potassium, chloride, CO2, international normalized ratio, estimated glomerular filtration rate) performed <30 days before catheterization and by age and sex that were significantly associated with death ≤60 and >60 days after catheterization. Follow-up was until 2016. Risk scores were developed from significant tests, internally validated in Calgary among bootstrap samples and externally validated in Edmonton after recalibration using coefficients developed in Calgary. Interaction tests were performed, and net reclassification improvement vs conventional demographic and clinical risk factors was determined. RESULTS: Lab risk scores were strongly associated with mortality (29-40× for top vs bottom quintile, P for trends <0.01), had good discrimination and were well calibrated in Calgary (C = 0.80-0.85, slope = 0.99-1.01) and Edmonton (C = 0.80-0.82; slope = 1.02-1.05)-similar to demographic and clinical risk factors alone. Associations were attenuated by several comorbidities; however, scores appropriately reclassified 11%-20% of deaths (both follow-up periods) and 6%-9% of survivors (>60 days) after catheterization vs demographic and clinical risk factors. CONCLUSIONS: In 2 populations of patients undergoing their first coronary catheterization, risk scores based on simple laboratory tests were as powerful as a combination of demographic and clinical risk factors in predicting mortality. Lab risk scores should be used for patients undergoing coronary catheterization.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".