Relative risk of home hemodialysis attrition in patients using a telehealth platform
Bibliographic record
Abstract
INTRODUCTION: Home hemodialysis (HHD) facilitates increased treatment frequency, which may improve patient outcomes. However, attrition due to technique failure limits the clinical effectiveness of the modality. Nx2me Connected Health is a telehealth platform that enables ongoing assessment of HHD patients using NxStage equipment, and that may reduce patient burden. We aimed to assess whether use of Nx2me was associated with risk of HHD attrition. METHODS: We compared risks of all-cause attrition, dialysis cessation (i.e., death or transplant), and technique failure in Nx2me users and matched control patients, using a retrospective cohort study. We also compared the likelihood of HHD training graduation in patients who initiated use of Nx2me during training with the likelihood in matched control patients. Matching factors included date of HHD initiation, NxStage treatment duration at initiation of follow-up, and prescribed treatment frequency. We used stratified Fine-Gray and Cox regression to compare risks, with adjustment for demographic factors and vascular access modality, and stratification by matched cluster. FINDINGS: We identified 606 Nx2me users; 49.5% initiated use of Nx2me in <3 months after initiation of HHD with NxStage equipment. Adjusted hazard ratios (AHRs) of all-cause attrition, dialysis cessation, and technique failure were 0.80 (95% confidence interval, 0.68-0.95), 1.10 (0.86-1.41), and 0.71 (0.57-0.87), respectively, for Nx2me users vs. matched controls. AHRs were similar in patients who initiated use of Nx2me in <3 months after initiation of HHD. The AHR of HHD training graduation was 1.61 (1.10-2.36) in patients who initiated use of Nx2me within 2 weeks of training initiation vs. matched controls. DISCUSSION: Use of Nx2me was associated with lower risk of all-cause attrition, lower risk of technique failure, and higher likelihood of HHD training graduation. Further studies are needed to identify the mechanisms by which use of a telehealth platform may improve clinical outcomes and reduce patient burden.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".