Home Hemodialysis Patient Loss: A Quality Improvement Initiative to Review Technique Failure in Alberta Kidney Care - South
Bibliographic record
Abstract
Background: The number of dialysis patients has increased 15% over 5 years in Alberta Kidney Care South (AKC-S) with most patients pursuing in-centre hemodialysis. Although home hemodialysis (HHD) offers advantages of improved quality of life for patients and cost savings for programs it has grown at a slower rate. To increase the number of HHD patients, programs need to promote more patients to start on HHD and reduce the number of patients leaving HHD. Understanding the reasons for exit from HHD may lead to strategies to reduce patient loss. Methods: A retrospective cohort study of adult HHD patients who entered training for HHD between January 1 2013 to December 31 2018 in AKC-S, followed until exit/study end date. Reasons for technique failure (TF) identified, with KM estimates used to determine technique survival, and Cox proportional hazard model used to determine risk factors for TF. Results: 147 patients entered the HHD program-48(33%) women; 44(30%) DM, 38(25.9%) CAD, 14(9.5%) CVD, mean age of 54(13) years. 12(8.1%) did not complete training. Overall time in program 28 +/- 20 months, average training time 6.7 +/- 3.3 weeks. Reasons for exit include transplant 24(48%), death 6(4.5%), TF 32(24%). TF reasons include medical 9(39.1%), psychiatric 2(8.7%), social 3(13.0%), safety 4(17.4%), patient request 4(17.45%), change to PD 1(4.3%). Technique survival at 1, 2, and 5 years 91%, 85%, and 63%. Risk factors for TF include DM 2.36(1.06, 5.28) p= 0.036, CVD 4.34(1.8, 10.5) p=0.001 and a longer training time 1.18(1.07, 1.30) p=0.001. Conclusions: We found a high HHD turnover rate with technique survival rates decreasing with time. Risk factors for TF include patients with DM, CVD, longer training time. Improved identification of and education for potential HHD patients could reduce training failure rates. Interventions to provide better support for patients at risk of TF could help keep patients at home longer.Figure 1.: Cumulative Incidence of Competing Risks.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".