Kidney Failure Risk Equation in vascular access planning: a population-based study supporting value in decision making
Bibliographic record
Abstract
ABSTRACT Background The Kidney Failure Risk Equation (KFRE) can play a better role in vascular access (VA) planning in patients with chronic kidney disease (CKD) requiring hemodialysis (HD). We described the VA creation and utilization pattern under existing estimated glomerular filtration rate (eGFR)-based referral, and investigated the utility of KFRE score as an adjunct variable in VA planning. Methods Patients with CKD aged ≥18 years with eGFR <20 mL/min/1.73 m2 who chose HD as dialysis modality from January 2010 to August 2020 were included from a population-based database in British Columbia, Canada. Modality selection date was the index date. Exposures were categorized as (i) current eGFR-based referral, (ii) eGFR-based referral plus KRFE 2-year risk score on index date (KFRE-2) >40% and (iii) eGFR-based referral plus KFRE-2 ≤40%. We estimated the proportion of patients who started HD on arteriovenous fistula/graft (AVF/G) within 2 years, indicating timely pre-emptive creation, and the proportion of patients in whom AVF/G was created but did not start HD within 2 years, indicating too-early creation. Results Study included 2581 patients, median age 71 years, 60% male. Overall, 1562(61%) started HD and 276 (11%) experienced death before HD initiation within 2 years. Compared with current referral, the proportion of patients who started HD on AVF/G was significantly higher when KFRE-2 was considered in addition to current referral (49% vs 58%, P-value <.001). Adjunct KFRE-2 significantly reduced too-early creation (31% vs 18%, P-value <.001). Conclusions KFRE in addition to existing eGFR-based referral for VA creation has the potential to improve VA resource utilization by ensuring more patients start HD on AVF/G and may minimize too-early/unnecessary creation. Prospective research is necessary to validate these findings.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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 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".