External Validation of a Machine Learning Model for Progression of CKD in the CREDENCE and CANVAS Trials
Bibliographic record
Abstract
Background: Sodium glucose cotransporter 2 inhibitors (SGLT2i) are indicated for slowing progression of chronic kidney disease (CKD). A previously validated machine learning model (Klinrisk model) accurately predicts 40% decline in eGFR or kidney failure using routinely collected laboratory data. We sought to validate this model in the pooled CANVAS/CREDENCE trials. Methods: The CANVAS/CREDENCE trials evaluated the effects of the SGLT2i canagliflozin on cardiorenal outcomes in patients with type 2 diabetes at high cardiovascular risk or with CKD. We validated the Klinrisk model for prediction of CKD progression, defined as greater than 40% decline in eGFR or kidney failure. The model applies results from complete blood cell counts, chemistry panels, comprehensive metabolic panels, and urinalysis. Model performance was assessed up to 3 years (median follow up 2.4 years) with the area under the receiver characteristic operating curve (AUC), Brier scores, and calibration plots of observed and predicted risks. We compared performance of the model to standard of care using eGFR (G1-G4) and urine ACR (A1-A3) KDIGO heatmap categories. Results: Among 14,464 patients in CANVAS/CREDENCE, we found the Klinrisk model provided excellent discrimination for CKD progression (696 events at 2 years), with an AUC of 0.81 (95% confidence interval 0.78 - 0.83) for prediction of the outcome at 1 year, increasing to 0.88 (0.86 - 0.89) at 3 years. Brier scores were 0.020 (0.018 - 0.022) at 1 year, increasing to 0.056 (0.052 - 0.059) at 3 years. Calibration was satisfactory, with minor overprediction in patients randomized to canagliflozin. Compared to the KDIGO heatmap, the Klinrisk model had improved performance at every interval (Table 1). Table 1. - Results of model performance Klinrisk model Klinrisk model eGFR and ACR categories (KDIGO heatmap) eGFR and ACR categories (KDIGO heatmap) Time frame, years AUC (95% CI) Brier score (95% CI) AUC (95% CI) Brier score (95% CI) 1 0.81 (0.78 - 0.83) 0.020 (0.018 - 0.022) 0.74 (0.71 - 0.76) 0.021 (0.018 - 0.023) 2 0.85 (0.84 - 0.87) 0.042 (0.039 - 0.046) 0.79 (0.78 - 0.81) 0.046 (0.042 - 0.050) 3 0.88 (0.86 - 0.89) 0.056 (0.052 - 0.059) 0.83 (0.81 - 0.84) 0.063 (0.059 - 0.067) Brier scores range from 0 to 1, with lower values representing higher accuracy. Conclusions: The Klinrisk machine learning model using routinely collected laboratory features was highly accurate in its prediction of CKD progression in the CANVAS and CREDENCE trials.
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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 | 0.157 | 0.135 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".