Using the Revised Cardiac Risk Index to Predict Major Postoperative Events for People With Kidney Failure: An External Validation and Update
Bibliographic record
Abstract
Background: People with kidney failure have high risk of postoperative morbidity and mortality. Although the revised cardiac risk index (RCRI) is used to estimate the risk of major postoperative events, it has not been validated in this population. We aimed to externally validate the RCRI and determine whether updating the model improved predictions for people with kidney failure. Methods: ) who had surgery in Alberta, Canada between 2005 and 2019. We categorized participants based on RCRI variables and assigned risk estimates of death or major cardiac events, and then estimated predictive performance. We re-estimated the coefficients for each RCRI variable and internally validated the updated model. Net benefit was estimated with decision curve analysis. Results: After 38,541 surgeries, 1204 events (3.1%) occurred. The estimated C-statistic for the original RCRI was 0.64 (95% confidence interval: 0.62, 0.65). Examination of calibration revealed significant risk overestimation. In the re-estimated RCRI model, discrimination was marginally different (C-statistic 0.67 [95% confidence interval: 0.66, 0.69]), though calibration was improved. No net benefit was observed when the data were examined with decision curve analysis, whereas the original RCRI was associated with harm. Conclusions: The RCRI performed poorly in a Canadian kidney failure cohort and significantly overestimated risk, suggesting that RCRI use in similar kidney failure populations should be limited. A re-estimated kidney failure-specific RCRI may be promising but needs external validation. Novel perioperative models for this population are urgently needed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".