Predictors of Early and Long-term Readmission of Patients following Liver Transplantation
Bibliographic record
Abstract
Background and objectives: Chronic liver disease (CLD) is on the rise, and accounts for over 2 million deaths annually. Liver transplantation (LT) is the most effective treatment for CLD but has been strongly associated with hospital readmissions and mortality outcomes. While most research focuses on predictors and reasons for readmission within the first 3 months post-LT, very few studies examine long-term readmission outcomes. The objectives of this study are: (1) to compare predictors of early (<30 day), mid-term (30-90 day), and long-term (>90 day) readmissions post-LT, (2) to assess the effects of initial readmission on survival post-LT, (3) to describe reasons for readmission post-LT within the early, mid-term, and long-term period. Methods: This is a retrospective observational study, investigating adult patients older than 18 years who underwent LT between January 1, 2010 – December 31, 2019 at Toronto General Hospital (TGH). The exclusion criteria include patients who died before LT discharge, and patients living outside of the Greater Toronto Area (GTA) at time of LT. Results: 987 patients fulfilled the inclusion criteria. Of those patients, 467 (47.3%) were readmitted at least once, with the median time from transplant discharge to initial readmission being 86 days (IQR 15-279). Of the total cohort, patients readmitted at 30 days, 90 days, and 5 years were 18.5%, 25.3%, and 55.1% respectively. Significant predictors of readmission were BMI > 30kg/m2 (HR=0.64, 95% CI= 0.42-0.98), and autoimmune/cholestatic CLD (HR=1.86, 95% CI= 1.01-3.42) at 30 days, post-LT length of stay (LOS) (HR=1.05, 95% CI= 1.02-1.08), and driving distance (HR=1.07, 95% CI= 1.01-1.14) at 30-90 days, and living donor LT (HR=1.41, 95% CI=1.06-1.89), and driving distance (HR=1.05, 95% CI=1.01-1.09) after 90 days. Readmission was significantly associated with mortality (HR=2.4, 95% CI=1.6-3.6). The main reason for readmission within the early, mid-term, and long-term period was infection, with the incidence of surgical complications (namely incisional hernias) increasing in the long-term period. Conclusion: This large single-centre study provided more information on the short-term as well as long-term predictors of readmission post-LT in a Canadian cohort. Adding to the pre-existing literature, the findings of this study show an association between readmission and mortality. Understanding the predictors of as well as reasons for readmission at specific time intervals may allow healthcare practitioners to mitigate the mortality seen post-LT.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".