Case Mix, Patterns of Care, and Inpatient Outcomes Among Ontario Kidney Transplant Centers: A Population-Based Study
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,004 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».