External Validation of Models to Predict Risk of Major Cardiovascular Events and Death for People with Kidney Failure Having Noncardiac Surgery
Bibliographic record
Abstract
Background: Patients with kidney failure undergoing non-cardiac surgery have a significantly higher risk of adverse cardiovascular events and mortality compared to people with normal kidney function. Existing tools for perioperative risk stratification are not valid for patients with kidney failure. Recently, three risk prediction models were developed from a population-based cohort of people with kidney failure in Alberta, Canada. We externally validated the established Alberta models for major cardiovascular events and mortality in patients with kidney failure within 30 days of non-cardiac surgery in Manitoba, Canada. Methods: Data was sourced from the Manitoba Centre for Health Policy. The cohort included adults (≥ 18 years) with pre-existing kidney failure (estimated glomerular filtration rate < 15 mL/min/1.73m2 or on maintenance dialysis) undergoing non-cardiac surgery procedures between April 1, 2007, and December 31, 2019. The primary outcome of this study was a composite of acute myocardial infarction, cardiac arrest, ventricular arrhythmia, and all-cause mortality. The models performance was evaluated using C-statistics, Brier scores, and calibration on Manitoba data using two approaches: 1. Model deployment: Used coefficients from the Alberta models to predict outcomes on Manitoba data. 2. Model refitting: Re-estimated model coefficients using logistic regression on Manitoba data while maintaining the same variables as the Alberta models. Results: We identified 12,082 surgeries and 569 outcomes (5%). All three models performed well with both approaches, with C-statistics ranging from 0.82 for model 1 to 0.87 for model 3 in the first approach. The calibration slopes for models 1, 2, and 3 were 1.3, 1.4, and 1.2, respectively. Once refit, discrimination remained strong with C-statistics ranging from 0.83 (model 1) to 0.86 (model 3). Calibration slopes were 1 across all the models. Brier scores were consistently low at 0.04 for all the models in both approaches. Conclusion: Our external validation study confirms the original Alberta models' robustness in a geographically distinct Canadian population. Future research should test the impact of these models in clinical care. Funding: Government Support – Non-U.S.
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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 | 0.058 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".