Independent External Validation and Comparison of Death and Kidney Replacement Therapy Prediction Models in Advanced CKD
Bibliographic record
Abstract
Rationale & Objective: The Kidney Failure Risk Equation (KFRE) is widely used to predict the risk of kidney replacement therapy (KRT) initiation in chronic kidney disease (CKD) stages G3-G5. The new Grams calculator developed for advanced CKD (stage G4+) predicts KRT initiation, cardiovascular events, and death by uniquely incorporating the competing risk of death. We aimed to validate this tool in a stage G4+ cohort for death and KRT. Study Design: Retrospective cohort study. Setting & Participants: ) who visited the multidisciplinary CKD clinic at Kingston Health Sciences Center in Ontario, Canada. Outcomes & Analytical Approach: Discrimination and calibration were examined for the outcome of death using the 2- and 4-year Grams scores. The 2- and 5-year KFRE and 2- and 4-year Grams scores were compared in terms of discrimination and calibration for KRT. Results: There were 91, 161, and 206 death events and 90, 145, and 159 KRT events in our cohort at 2, 4, and 5 years, respectively. The Grams model demonstrated modest discrimination for death at 4 years (area under the curve [AUC] 0.70; 95% CI, 0.65-0.75) and performed worse at 2 years (AUC, 0.63; 95% CI, 0.57-0.70). It only overpredicted death by approximately 10% across most of the predicted range. Both models had similar discrimination for KRT at 2 years (KFRE AUC, 0.83; 95% CI, 0.78-0.88 and Grams AUC, 0.8; 95% CI, 0.76-0.87), 4 years (Grams AUC, 0.82; 95% CI, 0.77-0.86), and 5 years (KFRE AUC, 0.81; 95% CI, 0.76-0.85). There was excellent calibration for KRT using the 2-year KFRE and Grams values for predicted risk thresholds of ≤15% and using the 5-year KFRE and 4-year Grams values for predicted risk thresholds of ≤20%. At higher risk ranges, KFRE overpredicts and Grams underpredicts the KRT risk. Limitations: This is a single-center study with a primarily White cohort limited by smaller sample sizes at the higher ranges of the predicted risks, particularly for the Grams calculator. Conclusions: The Grams model provides moderately accurate death predictions, and consideration should be given to its incorporation into patient education and advanced care planning. Both the Grams and KFRE models remain clinically useful for determining KRT risks in advanced 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 | 0.081 | 0.096 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".