The Effect of Age on Performance of the Kidney Failure Risk Equation in Advanced CKD
Bibliographic record
Abstract
Background: The Kidney Failure Risk Equation (KFRE) is a validated clinical tool used to predict progression from CKD to kidney failure. Concerns over risk overestimation have been raised with prediction models, such as the KFRE, where death is not treated as a competing event. Herein, we evaluated the effect of age (with which the competing risk of death would be anticipated to increase) on KFRE performance in advanced CKD. Methods: All patients referred to the advanced CKD clinic at the Ottawa Hospital from 2010-2018 were divided into age quartiles: <58, 58-67, 68-77, and ≥78 years. Predicted vs observed rates of kidney failure were compared over 2- and 5-years. Predictive performance of the KFRE was determined by ROC curves (discrimination) and calibration plots. Cumulative incidence of kidney failure was compared between models that accounted for the competing risk of death and those that did not. Results: The mean (SD) age and eGFR were 66 (15) years and 17 (6) mL/min/1.73m2. The median (IQR) 2- and 5-year KFRE scores were 41% (22-64%) and 81% (55-96%), respectively. The KFRE overestimated the risk of kidney failure among the oldest age quartile (≥78 years) with absolute differences of 5.8% (P=0.01) and 21.6% (P<0.001) between predicted and observed risks over 2- and 5-years, respectively. The 2-year KFRE discrimination was reduced among patients ≥78 years compared with patients 58-67 years (P=0.03) and 68-77 years (P=0.03) though the difference was non-significant when compared with patients <58 years (P=0.06). The KFRE displayed adequate calibration across all age quartiles. 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 prominent with older age. Conclusions: In older patients with advanced CKD at high risk of kidney failure, the KFRE overestimates risk and this overestimation relates to the increasing competing risk of death with older age.
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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 | 0.020 | 0.046 |
| 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 | 0.000 | 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".