Validation and Comparison of the Kidney Failure Risk Equation and a Novel Risk Calculator in Advanced CKD
Bibliographic record
Abstract
Background: The Kidney Failure Risk Equation (KFRE) is a widely used clinical tool for predicting risk of CKD G3-5 progressing to end-stage kidney disease (ESKD). A novel calculator (Grams) was developed for G4+ patients to predict ESKD, CVD events and death. The Grams model has not been externally validated. We aimed to assess this new tool in a cohort of advanced CKD patients for ESKD prediction and compare it to the KFRE. Methods: This retrospective cohort study included 444 adult CKD G4+ patients (mean age 73 ± SD 12; mean eGFR 19.6 ± 6.1). The 2- and 5-year KFRE and 2 and 4-year Grams scores were compared in terms of discrimination and calibration (4 ESKD risk intervals <10%,10-20%, 20-40% and >40%). Sensitivity, specificity, positive (PPV) and negative predictive values (NPV) of KFRE-2 and Grams-2 were reported using 10% and 20% thresholds. Results: Both models had similar discrimination for ESKD risk at the 2-(KFRE-2 AUC 0.82, 95% CI 0.80-0.87, Grams-2 AUC 0.80, 95% CI 0.75-0.86), 4-(Grams-4 AUC 0.81, 95% CI 0.77-0.86) and 5-year (KFRE-5 AUC 0.80, 95% CI 0.76-0.84) timepoints. Both were well calibrated with observed risk at predicted intervals of <10% and 10-20% at 2 years and <10%, 10-20% and 20-40% intervals at 4 and 5 years (Figure 1). Grams-2 under-predicted while KFRE-2 over-predicted risk at higher intervals (20-40% and >40%). KFRE-2 and Grams-2 had adequate sensitivity, performing similarly at ESKD risk thresholds of 10% (p=0.71) and 20% (p=0.48) (Table 1). Both had poor specificity and low PPVs at both thresholds.Figure 1Table 1Conclusions: The KFRE and Grams models perform similarly at lower ranges of risk in CKD G4+. The KFRE and Grams models however overestimate and underestimate risk respectively at higher risk intervals.
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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 | 0.023 | 0.034 |
| 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) | 0.001 | 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".