Rethinking Potentially Preventable Emergency Department Use Among People Receiving Dialysis: A Population-Based Study
Bibliographic record
Abstract
Background: People with kidney failure receiving maintenance dialysis visit the emergency department (ED) 3 times per year on average, which is 3- to 8-fold higher than the general population. Little is known about the clinical and socio-demographic factors that contribute to potentially preventable ED use. Methods: In this retrospective cohort study, we used administrative data to identify adults receiving maintenance dialysis for >3 months between April 1, 2010 and March 31, 2019 in Alberta, Canada. We captured clinical characteristics and rates of ED use and followed patients until death or end of study (March 31, 2019). We determined age- and sex-adjusted rates of all-cause and potentially preventable ED use (defined by kidney disease-specific ambulatory care sensitive conditions: hyperkalemia, heart failure, volume overload, and malignant hypertension). We examined the association between clinical and socio-demographic factors and rates of potentially preventable ED encounters using multivariable negative binomial regression models. Results: Our cohort included 4,402 people with kidney failure (2,781 hemodialysis; 1,621 peritoneal dialysis) followed for a mean of 2.8 years. 3,440 patients had 29,927 all-cause ED encounters (adjusted rate 3,065/1,000 person years). Of these, 654 patients had 1,153 potentially preventable ED encounters (adjusted rate 107/1,000 person years). Potentially preventable ED encounters were more likely in those who were socioeconomically disadvantaged, had higher comorbidity burden, and had longer dialysis vintage. Multivariable regression identified that preventable ED use was significantly higher for younger adults (age <45 years; IRR: 1.37 [95% CI 1.08-1.75]) and those with chronic pain (IRR: 1.33 [95% CI 1.06-1.66]), greatest material deprivation (IRR: 1.39 [95% CI 1.02-1.90]), and a history of hyperkalemia (IRR: 1.34 [95% CI 1.11-1.63]). Conclusions: We identified that potentially preventable ED use among people receiving dialysis is related to both socio-demographic and clinical factors. Our findings underscore the need to implement and test strategies that address social determinants of health to avert potentially preventable ED use in this population.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| 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".