Development of posttransplant diabetes mellitus in US recipients of liver transplant is influenced by OPTN region
Bibliographic record
Abstract
Posttransplant diabetes mellitus (PTDM) is associated with significant morbidity and mortality in liver transplant recipients (LTRs). We used the Organ Procurement and Transplantation Network (OPTN) database to compare the incidence of developing PTDM across the United States and develop a risk prediction model for new-onset PTDM using OPTN region as well as donor-related, recipient-related, and transplant-related factors. All US adult, primary, deceased donor, LTRs between January 1, 2007, and December 31, 2016, with no prior history of diabetes noted , were identified. Kaplan-Meier estimators were used to calculate the cumulative incidence of PTDM, stratified by OPTN region. Multivariable Cox proportional hazards models were fitted to estimate hazards of PTDM in each OPTN region and build a risk prediction model, through backward selection. Cumulative incidence of PTDM at 1 year, 3 years, and 5 years after transplant was 12.0%, 16.1%, and 18.9%, respectively. Region 3, followed by regions 8, 2, and 9, had the highest adjusted hazards of developing PTDM. Inclusion of OPTN region in a risk prediction model for PTDM in LTRs (including recipient age, sex, race, education, insurance coverage, body mass index, primary liver disease, cold ischemia time, and donor history of diabetes) modestly improved performance (C-statistic = 0.60). In patients without pre-existing, confirmed diabetes mellitus, the incidence of PTDM in LTRs varied across OPTN regions, with the highest hazards in region 3, followed by regions 8, 2, and 9. The performance of a novel risk prediction model for PTDM in LTRs has improved performance with the inclusion of the OPTN region. Vigilance is recommended to centers in high-risk regions to identify PTDM and mitigate its development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".