Crash Dialysis Starts in Northern Alberta: Demographics, Outcomes, and Uptake of Home Therapies
Bibliographic record
Abstract
Background: “Crash” dialysis starts (i.e. emergency hemodialysis initiation) remain a frequent occurrence, despite guidelines for early referral. They are associated with higher healthcare costs and worse patient outcomes. Better understanding of the crash lander population is key to targeting interventions to reduce crash starts and improve outcomes. Methods: We performed a retrospective chart review of patient initiating hemodialysis (HD) in northern Alberta from 2008-2019. Patients with prior renal replacement therapy or short-term HD were excluded. Patients were categorized into planned HD starts (>3 months Nephrology exposure) and unplanned (<3 months), with subdivisions for late exposure (2 weeks - 3 months) and no exposure (<2 weeks). Demographics, uptake of home therapies, and short-term outcomes were compared between groups using Kruskal-Wallis for continuous variables and χ2 for categorical; p<0.05 indicated significance. Results: 2,685 adult patients were included; of those, 28.1% had an unplanned HD start, and 19.7% started HD with no prior exposure to Nephrology. Crash dialysis patients were more likely to live rurally (17.2% vs 13.7%, p=0.022), and reported fewer instances of comorbid conditions including diabetes, hypertension, cardiovascular disease, and stroke. While percentage conversion to home therapies and listing for renal transplant was similar between groups, there was a higher prevalence of conversion to peritoneal dialysis specifically in the unplanned group at 1 year (9.4% vs 6.4%, p=0.007) and at the end of the study (13.0% vs 8.6%, p<0.001), and a lower prevalence of conversion to home hemodialysis as compared to the planned group. Unplanned HD starts had longer initial hospitalizations, earlier re-hospitalizations, and longer waits to achieve permanent access. Conclusion: Despite optimal referral guidelines, crash dialysis starts remain a significant problem in Alberta and demonstrate worse short-term outcomes than their planned HD counterparts, emphasizing the need for further quality improvement work in this area. Short-term outcomes for unplanned vs planned hemodialysis (HD) patients - Unplanned Planned p-value Mean length of hospital admission for HD initiation (days) 33.9 31.7 0.032 Length of time (days) to obtain permanent access 9.7 3.0 <0.001 Mean time (months) to next all-cause hospitalization 10.9 12.6 <0.001 Conversion to fistula access, N (%) 281 (37.3%) 1,199 (62.2%) <0.001
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".