Using Administrative Health Care Databases to Identify Patients With End-Stage Kidney Disease With No Recorded Contraindication to Receiving a Kidney Transplant
Notice bibliographique
Résumé
Background: Administrative health care databases can be efficiently analyzed to describe the degree to which patients with end-stage kidney disease (ESKD) have access to kidney transplantation. Measures of access to transplantation are better represented when restricting to only those patients eligible to receive a kidney transplant. The way administrative data can be used to assess kidney transplant eligibility in the absence of clinical data has not been well described. Objective: To demonstrate a method that uses administrative health care databases to identify patients with ESKD who have no recorded contraindication to receiving a kidney transplant. Design and setting: Population-based cohort study using linked administrative health care databases in Ontario, Canada. Patients: Adult patients with ESKD approaching the need for dialysis (predialysis) or receiving maintenance dialysis between January 1, 2013 and March 31, 2015 in Ontario, Canada. Measurements: Recipient of a kidney-only or kidney-pancreas transplant. Methods: We assessed more than 80 baseline characteristics, including demographic information, comorbidities, kidney-specific characteristics, and referral and listing criteria for kidney transplantation. We compared these characteristics between patients who did and did not receive a kidney transplant. Results: We included 23 642 patients with ESKD (11 195 who were predialysis and 12 447 receiving maintenance dialysis). Over a median follow-up of 3.2 years (25th, 75th percentile: 1.3, 5.6), 3215 (13.6%) received a kidney-only or kidney-pancreas transplant. Of the studied characteristics available in administrative databases, >97% of patients with one or more of these characteristics did not receive a kidney transplant during follow-up: ESKD-modified Charlson Comorbidity Index score ≥7 (a higher score represents greater comorbidity), home oxygen use, age above 75 years, dementia, living in a long-term care facility, receiving at least one physician house call in the past year, and a combination of select malignancies (ie, lung, lymphoma, cervical, colorectal, liver, active multiple myeloma, and bladder cancer). Using these combined criteria reduced the total number of patients from 23 642 to 12 539 with no recorded contraindications to transplant (a 47% reduction), while the proportion who received a kidney transplant changed from 13.6% (denominator of 23 642) to 24.9% (denominator of 12 539). Limitations: Administrative databases are unable to capture all the complexities of determining transplant eligibility. Conclusion: We identified several criteria available within administrative health care databases that can be used to identify patients with ESKD who have no recorded contraindications to kidney transplant. These criteria could be applied when reporting measures of access to kidney transplantation that require knowledge of transplant eligibility.
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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 | 0,004 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,005 | 0,011 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| 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 ».