Predicting radiocephalic arteriovenous fistula success with machine learning
Bibliographic record
Abstract
After creation of a new arteriovenous fistula (AVF), assessment of readiness for use is an important clinical task. Accurate prediction of successful use is challenging, and augmentation of the physical exam with ultrasound has become routine. Herein, we propose a point-of-care tool based on machine learning to enhance prediction of successful unassisted radiocephalic arteriovenous fistula (AVF) use. Our analysis includes pooled patient-level data from 704 patients undergoing new radiocephalic AVF creation, eligible for hemodialysis, and enrolled in the 2014-2019 international multicenter PATENCY-1 or PATENCY-2 randomized controlled trials. The primary outcome being predicted is successful unassisted AVF use within 1-year, defined as 2-needle cannulation for hemodialysis for ≥90 days without preceding intervention. Logistic, penalized logistic (lasso and elastic net), decision tree, random forest, and boosted tree classification models were built with a training, tuning, and testing paradigm using a combination of baseline clinical characteristics and 4-6 week ultrasound parameters. Performance assessment includes receiver operating characteristic curves, precision-recall curves, calibration plots, and decision curves. All modeling approaches except the decision tree have similar discrimination performance and comparable net-benefit (area under the ROC curve 0.78-0.81, accuracy 69.1-73.6%). Model performance is superior to Kidney Disease Outcome Quality Initiative and University of Alabama at Birmingham ultrasound threshold criteria. The lasso model is presented as the final model due to its parsimony, retaining only 3 covariates: larger outflow vein diameter, higher flow volume, and absence of >50% luminal stenosis. A point-of-care online calculator is deployed to facilitate AVF assessment in the clinic.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".