Using Administrative Health Care Databases to Identify Patients With End-Stage Kidney Disease With No Recorded Contraindication to Receiving a Kidney Transplant
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".