Interhospital Transfer and Outcomes in Patients with AKI: A Population-Based Cohort Study
Bibliographic record
Abstract
Background Patients with AKI may require interhospital transfer to receive RRT. Interhospital transfer may lead to delays in therapy, resulting in poor patient outcomes. There is minimal data comparing outcomes among patients undergoing transfer for RRT versus those who receive RRT at the hospital to which they first present. Methods We conducted a population-based cohort study of all adult patients (≥19 years) who received acute dialysis within 14 days of admission to an acute-care hospital between April 1, 2004 and March 31, 2015. The transferred group included all patients who presented to a hospital without a dialysis program and underwent interhospital transfer (with the start of dialysis ≤3 days of transfer and within 14 days of initial admission). All other patients were considered nontransferred. The primary outcome was time to 90-day all-cause mortality, adjusting for demographics, comorbidities, and measures of acute illness severity. We also assessed chronic dialysis dependence as a secondary outcome, using the Fine and Gray proportional hazards model to account for the competing risks of death. In a secondary post hoc analysis, we assessed these outcomes in a propensity score–matched cohort, matching on age, sex, and prior CKD status. Results We identified 27,270 individuals initiating acute RRT within 14 days of a hospital admission, of whom 2113 underwent interhospital transfer. Interhospital transfer was associated with lower rate of mortality (adjusted hazard ratio [aHR], 0.90; 95% CI, 0.84 to 0.97). Chronic dialysis dependence was not significantly different between groups (aHR, 0.98; 95% CI, 0.91 to 1.06). In the propensity score–matched analysis, interhospital transfer remained associated with a lower risk of death (HR, 0.88; 95% CI, 0.80 to 0.96). Conclusions Interhospital transfer for receipt of RRT does not confer higher mortality or worse kidney outcomes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| 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".