Development and Validation of Models to Predict Major Adverse Cardiovascular Events in Chronic Kidney Disease
Bibliographic record
Abstract
Background: Accurate cardiovascular (CV) risk prediction tools may heighten awareness and monitoring, improve the use of evidence-based therapies and help inform shared decision making for patients with chronic kidney disease (CKD). The purpose of this study was to develop and externally validate a risk prediction model for incident and recurrent CV events across all stages of CKD using commonly available demographics and laboratory data. Methods: A series of models were developed using administrative and laboratory data (n=36,317) from Manitoba, Canada, between April 1, 2006, and December 31, 2018, with external validation in health system's data from Alberta, Canada (n=95,191), and Stockholm, Sweden (n=83,000). Adults with incident CKD stages G1-G4 were followed for the occurrence of major adverse cardiovascular events (MACE) (myocardial infraction, stroke, and CV death), and MACE including hospitalization for heart failure (MACE+). Discrimination and calibration were evaluated using the area under the receiver operating characteristic curve (AUC), Brier scores, and plots of observed vs predicted risk, and the models were compared to an existing model from the Chronic Renal Insufficiency Cohort (CRIC). Results: In the Alberta cohort, the AUCs for predicting MACE and MACE+ were 0.77 (0.77-0.77) and 0.80 (0.79-0.80), respectively. In the Stockholm cohort, the model achieved an AUC of 0.87 (0.86-0.87) for predicting MACE and 0.88 (0.88-0.88) for MACE+. Overall performance was improved relative to CRIC. Conclusions: A model including commonly available administrative data and laboratory results can predict the risk of MACE and MACE+ outcomes among individuals with CKD.
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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 | 0.026 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".