Heart transplant outcomes in patients with substance use disorder history: a nationwide cohort study using high-dimensional propensity score matching
Bibliographic record
Abstract
AIMS: History of substance use is assessed in potential heart transplantation (HT) evaluations. The evidence base for this highly consequential practice, linking substance use disorders (SUDs) with poor post-transplantation outcomes, presents methodological limitations. We conducted a retrospective cohort study to address these limitations using high dimensional propensity score matching to compare HT outcomes of patients with and without SUDs. METHODS AND RESULTS: Key outcomes included mortality, hospitalization, and organ rejection rates, controlling for confounders. A national dataset of electronic health records of >120 million patients in the USA (2015-23) was used to identify HT patients with SUDs (n = 808) and controls (n = 7066), matched for medical comorbidities and demographic variables. Only after adjusting for sociodemographic and comorbidities of HT recipients, the results revealed no significant differences between groups with and without SUDs at 1 year in mortality [odds ratio (OR) = 0.96 (95% confidence interval, CI): 0.54, 1.69, P = 0.88], hospitalization [OR = 1.02 (95% CI: 0.83, 1.25), P = 0.840)], organ rejection rates [OR = 0.96 (95% CI: 0.78, 1.18), P = 0.670)], nor at 5 years in mortality [hazard ratio (HR) = 1.15 (95% CI: 0.82, 1.61), P = 0.410] and organ rejection [HR = 0.98 (95% CI: 0.84, 1.14), P = 0.810]. CONCLUSION: Future studies must consider confounding factors when evaluating transplant criteria and outcomes in patients with SUDs.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.001 |
| 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".