Use of the Living Kidney Donor Profile Index in the Canadian Kidney Transplant Recipient Population: A Validation Study
Bibliographic record
Abstract
BACKGROUND: The Living Kidney Donor Profile Index (LKDPI) was derived in a cohort of kidney transplant recipients (KTR) from the United States to predict the risk of total graft failure. There are important differences in patient demographics, listing practices, access to transplantation, delivery of care, and posttransplant mortality in Canada as compared with the United States, and the generalizability of the LKDPI in the Canadian context is unknown. OBJECTIVE: The purpose of this study was to externally validate the LKDPI in a large contemporary cohort of Canadian KTR. DESIGN: Retrospective cohort validation study. SETTING: Toronto General Hospital, University Health Network, Toronto, Ontario, Canada. PATIENTS: A total of 645 adult (≥18 years old) living donor KTR between January 1, 2006 and December 31, 2016 with follow-up until December 31, 2017 were included in the study. MEASUREMENTS: The predictive performance of the LKDPI was evaluated. The outcome of interest was total graft failure, defined as the need for chronic dialysis, retransplantation, or death with graft function. METHODS: The Cox proportional hazards model was used to examine the relation between the LKDPI and total graft failure. The Cox proportional hazards model was also used for external validation and performance assessment of the model. Discrimination and calibration were used to assess model performance. Discrimination was assessed using Harrell's C statistic and calibration was assessed graphically, comparing observed versus predicted probabilities of total graft failure. RESULTS: = .02). Discrimination was poor (C statistic = 0.55; 95% CI = 0.48-0.61). Calibration was as good at 1-year posttransplant but suboptimal at 3- and 5-years posttransplant. LIMITATIONS: Limitations include a relatively small sample size, predicted probabilities for assessment of calibration only available for scores of 0 to 100, and some missing data handled by imputation. CONCLUSIONS: In this external validation study, the predictive ability of the LKDPI was modest in a cohort of Canadian KTR. Validation of prediction models is an important step to assess performance in external populations. Potential recalibration of the LKDPI may be useful prior to clinical use in external cohorts.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".