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 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".