Validation of the Klinrisk Machine Learning Model for CKD Progression in a Large Representative US Population
Bibliographic record
Abstract
Key Points The Klinrisk model predicted risk of 2-year CKD progression with area under the receiver operating characteristic curves of 0.80–0.87. When compared with standard of care, the Kidney Disease Improving Global Outcomes (KDIGO) heatmap-based staging system, the Klinrisk model achieved superior predictive capacity. The Klinrisk machine learning model predicted CKD progression in US commercial, Medicare, and Medicaid insured adults. Background Early identification of high-risk CKD can facilitate optimal medical management and improve outcomes. We aimed to validate the Klinrisk machine learning model for prediction of CKD progression in large US commercial, Medicare, and Medicaid populations. Methods We developed three cohorts, consisting of insured adults enrolled in ( 1 ) commercial, ( 2 ) Medicare, and ( 3 ) Medicaid plans between January 1, 2007, and December 31, 2020, with ≥1 serum creatinine test, an eGFR between 15 and 180 ml/min per 1.73 m 2 , and ≥7 of the 19 other laboratory analytes available. Two primary subcohorts were evaluated within each insurer: ( 1 ) all patients with ≥7 laboratory analytes and ( 2 ) patients in ( 1 ) with available urinalysis results. Disease progression was defined as the composite outcome of a sustained 40% decline in eGFR or kidney failure. Discrimination, accuracy, and calibration were assessed using the area under the receiver operating characteristic curve (AUC), Brier scores, and calibration plots. Results In the commercial cohort, the Klinrisk model achieved AUCs ranging from 0.83 (95% confidence interval, 0.82 to 0.83) to 0.87 (0.86 to 0.87) and a maximum Brier score of 0.005 (0.0005 to 0.005) at 2 years. In Medicare patients, AUCs ranged from 0.80 (0.79 to 0.80) to 0.81 (0.80 to 0.82), with a maximum Brier score of 0.026 (0.025 to 0.027). In Medicaid patients, we found AUCs ranging from 0.82 (0.82 to 0.82) to 0.84 (0.82 to 0.86) and a maximum Brier score of 0.014 (0.012 to 0.015). Conclusions The Klinrisk machine learning model was accurate in predicting CKD progression in 4.8 million US adults across commercial, Medicare, and Medicaid populations.
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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 | 0.018 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".