The Impact of Preexisting and Post-transplant Diabetes Mellitus on Outcomes Following Liver Transplantation
Bibliographic record
Abstract
BACKGROUND: Diabetes mellitus (DM) is said to adversely affect transplant outcomes. The aim of this study was to investigate the impact of pre-existing and new-onset DM on liver transplantation (LT) recipients. METHODS: A single-center retrospective analysis of prospectively collected data of LT recipients (1990-2015) was undertaken. RESULTS: Of the 2209 patients, 13% (n = 298) had Pre-DM, 16% (n = 362) developed post-transplant diabetes mellitus (PTDM), 5% (n = 118) developed transient hyperglycemia (t-HG) post-LT, and 65% (n = 1431) never developed DM (no DM). Baseline clinical characteristics of patients with PTDM were similar to that of patients with Pre-DM. Incidence of PTDM peaked during the first year (87%) and plateaued thereafter. On multivariate analysis (Bonferroni-corrected), nonalcoholic fatty liver disease and the use of tacrolimus and sirolimus were independently associated with PTDM development. Both Pre-DM and PTDM patients had satisfactory and comparable glycemic control throughout the follow-up period. Those who developed t-HG seem to have a unique characteristic compared with others. Overall, 9%, 5%, and 8% of patients developed end-stage renal disease (ESRD), major cardiovascular event (mCVE), and de novo cancer, respectively. Both Pre-DM and PTDM did not adversely affect patient survival, retransplantation, or de novo cancer. The risks of ESRD and mCVE were significantly higher in patients with Pre-DM followed by PTDM and no DM. CONCLUSIONS: In this largest nonregistry study, patients with Pre-DM and PTDM share similar baseline clinical characteristics. Pre-DM increases the risk of ESRD and mCVE; however, patient survival was comparable to those with PTDM and without diabetes. Understanding the impact of PTDM would need prolonged follow-up.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".