Case Mix, Patterns of Care, and Inpatient Outcomes Among Ontario Kidney Transplant Centers: A Population-Based Study
Bibliographic record
Abstract
Background: Significant variation in both patient case mix and the structure of care in kidney transplantation has been previously described in the United States. Objective: The objective of our study was to characterize patient case mix, patterns of care, and inpatient outcomes across 5 kidney transplant centers in the province of Ontario, Canada. Design: This was a retrospective population-based cohort study using health care administrative databases. Setting: The setting is Ontario, Canada. Patients: We included adult (≥18 years) transplant recipients who received a primary, solitary kidney between January 1, 2000, and December 31, 2013 (N = 5037). Methods: Using linked administrative health care databases, we characterized kidney transplant recipient and donor factors, center characteristics, provider characteristics, and inpatient outcomes across transplant centers in Ontario. To compare case mix–adjusted differences in length of stay across centers, multivariable Cox proportional hazards regression was used to obtain hazard ratios (HRs) for each center relative to the average across all centers. Center volume and provider characteristics were added to the models to examine whether these factors explain differences in length of stay across centers. Results: We noted significant differences across transplant centers in patient race, cause of end-stage renal disease, body mass index, comorbidities, time on dialysis, and donor type. Mean annual transplant center volumes during the study period ranged between 51.5 (9.3) and 101.7 (23.9) transplants/year across centers ( P < .0001). Physician specialty most responsible for in-hospital transplant care varied significantly across centers with the most common combination being nephrologist and urologist. Less than 31 deaths occurred in hospital during the index transplant admission but mortality risk did not differ significantly between centers. Overall, 25.1% of recipients required dialysis in hospital post transplantation (range across centers 18.3%-33.5%, P < .0001) and 24.7% of recipients spent time in the intensive care unit (ICU; range across centers: 5.7%-58.0%, P < .0001). The proportion of participants requiring dialysis did not change with time ( P = .12), whereas the proportion staying in the ICU increased steadily over time ( P < .0001). The median length of stay in hospital after transplantation ranged from 7 to 9 days across centers ( P < .0001) and decreased significantly over time. After adjusting for patient case mix as well as center and provider factors, HRs for length of stay censored at the time of death ranged between 0.75 (95% confidence interval [CI]: 0.69-0.82) and 1.29 (95% CI: 1.20-1.38) across centers. Center volume and provider experience were not independently associated with length of hospital stay. Limitations: Data were missing (0.8%-18.4%) for certain covariates of interest. Conclusions: This study found significant heterogeneity across kidney transplant centers in case mix, practice patterns, and inpatient outcomes. Future studies are needed to examine the influence of length of stay and practice patterns on long-term outcomes such as patient/graft survival and quality of life.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".