A Feasibility Study Investigating the Role of Kidney Failure Risk Equation in Optimizing the Timing of Vascular Access Creation
Bibliographic record
Abstract
Background: KFRE 2-year risk threshold of >40% along with existing eGFR based referral for vascular access (VA) creation may improve VA resource utilization. We investigated if KFRE score can play a role in optimizing the timing of VA creation. Methods: We analyzed a cohort of 2,581 patients with CKD who had an eGFR of <20 ml/min/1.73m2 and chose hemodialysis (HD) as their preferred mode of dialysis. We created the cohort using data from PROMIS, a population-level registry database for patients with CKD in BC, Canada. Modality selection date was index date. In step 1, we explored the association between KRFE-2 threshold and timing of HD initiation among patients who reached kidney failure and initiated HD within 24 months from the index date in two ways: First, we categorized the patients based on time to HD initiation into 0-3, 3-6, 6-9, 9-12 and 12-24 months, and investigated the corresponding distribution of KFRE-2 scores at index. Second, we categorized the patients based on index KFRE-2 score into <40%, 40-<50%, 50-<60%, 60-<70%, 70-<80% and >80% and investigated the distribution of time to HD initiation. In step 2, we explored the positive predictive value (PPV) of initiating HD within 6 and 24 months using the aforementioned KFRE-2 thresholds among the entire cohort. Results: Study cohort included 2581 patients, median age 71 years, and 40% female. Of the 1,562 patients who initiated HD within 2 years, a total of 733 (47%) patients initiated HD within 6 months and the median index KFRE-2 was ≥69% (Table 1A). On the other hand, 207 (13%) patients had an index KFRE-2 of 60 - <70% and the median time to initiate HD was 6.4 months (Table 1B). PPV gradually increased with increasing KFRE-2 threshold for both HD initiation within 6 and 24 months (Fig 1). Conclusion: KFRE-2 score has the potential to guide the timing of VA creation. Future research using sophisticated methodology is required to identify an optimal KFRE-2 score for fistula/graft creation.
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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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".