Understanding Home Hemodialysis Patient Attrition: A Cohort Study
Bibliographic record
Abstract
BACKGROUND: Home hemodialysis (HHD) offers a flexible, patient-centered modality for patients with kidney failure. Growth in HHD is achieved by increasing the number of patients starting HHD and reducing attrition with strategies to prevent the modifiable reasons for loss. OBJECTIVE: Our primary objective was to describe a Canadian HHD population in terms of technique failure and time to exit from HHD in order to understand reasons for exit. Our secondary objectives include the following: (1) determining reasons for training failure, (2) reasons for early exit from HHD, and (3) timing of program exit. DESIGN: A retrospective cohort study of incident adult HHD patients between January 1, 2013-June 30, 2020. SETTING: Alberta Kidney Care South, AKC-S HHD program. PARTICIPANTS: Patients who started training for HHD in AKC-S. METHODS: A retrospective, cohort study of incident adult HHD patients with primary outcome time on home hemodialysis, secondary outcomes include reason for train failure, time to and reasons for technique failure. Cox-proportional hazard model to determine associations between patient characteristics and technique failure. The cumulative probability of technique failure over time was reported using a competing risks model. RESULTS: < .001). Reasons for HHD exit after training included transplant (35; 21%), death (8; 4.8%), and technique failure (24; 14.4%). Overall, the median time to HHD exit, was 23 months [11, 41] and the median time of technique failure was 17 months [8.9, 36]. Reasons for technique failure included: psychosocial reasons (37%) at a median time 8.9 months [7.7, 13], safety (12.5%) at 19 months [19, 36], and medical (37.5%) at 26 months [11, 50]. LIMITATIONS: Small patient population with quality of data limited by the electronic-based medical record and non-standardized definitions of reasons for exit. CONCLUSIONS: Training failure is a particularly important source of patient loss. Reasons for exit differ according to duration on HHD. Early interventions aimed at reducing train failure and increasing psychosocial supports may help program growth.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".