The Effect of Age on Performance of the Kidney Failure Risk Equation in Advanced CKD
Bibliographic record
Abstract
INTRODUCTION: The Kidney Failure Risk Equation (KFRE) is a clinical tool widely used to predict progression from chronic kidney disease (CKD) to kidney failure. This study aimed to evaluate the effect of age on KFRE performance in advanced CKD. METHODS: We conducted a retrospective cohort study among 1701 consecutive patients referred to an advanced CKD clinic in Ottawa, Canada, between 2010 and 2018. Patients were categorized by age as follows: <60, 60 to 69, 70 to 79, and ≥80 years. Calibration plots compared the predicted (through the KFRE) and observed incidence of kidney failure. Concordance statistic (C-statistic) evaluated discrimination. Cumulative incidence of kidney failure was compared between models that accounted for the competing risk of death and those that did not. RESULTS: < 0.001). The degree of overestimation in the elderly was most pronounced among those with the highest predicted risks for kidney failure. KFRE discrimination was acceptable (C-statistic 0.70-0.79) across all age categories. The cumulative incidence of kidney failure was overestimated in models that did not account for the competing risk of death, and this overestimation was more pronounced with older age. CONCLUSION: The KFRE overestimates kidney failure risk among elderly patients with advanced CKD. This overestimation relates to the increasing competing risk of death with older age, particularly over longer time horizons.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".