A289 MAJOR ADVERSE CARDIAC EVENTS (MACE) IN PATIENTS WITH A HISTORY OF LIVER TRANSPLANTATION: A NATIONWIDE ANALYSIS USING THE NATIONAL INPATIENT SAMPLE
Bibliographic record
Abstract
Abstract Background Liver transplantation is the preferred treatment for end-stage liver disease. However, in around one-third of transplant recipients cardiovascular disease remains to be a significant contributor to morbidity and mortality. Aims Using data from the national inpatient sample (NIS), this study aimed to assess the prevalence and patient characteristics of MACE in those who underwent liver transplantation. Methods The study used a cross-sectional design to analyze NIS data, comparing MACE prevalence in hospitalized liver transplant patients. MACE included heart-related events. Patients with MACE and liver transplant history were identified using 2013 NIS data and ICD-9-CM codes. Baseline characteristics were compared, and association strength was assessed via logistic regression. Results Out of 33,725 post-liver transplant patients, 26.1% (8,805) had prior MACE. Those with MACE had higher rates of comorbidities: type 2 diabetes (odds ratio (OR) 1.90, 95% CI 1.69-2.13, p ampersand:003C 0.001), hypertension (OR=2.07, 95% CI 1.83-2.34, pampersand:003C 0.001), dyslipidemia (OR=2.27, 95% CI 1.97-2.60, pampersand:003C 0.001), and morbid obesity (OR=1.69, 95% CI 1.39-2.04, pampersand:003C 0.001). Smoking wasn't significantly linked (p = 0.738). Hospitalization with MACE increased in-hospital mortality (3.25%, p ampersand:003C 0.001). Conclusions These findings highlight the importance of proactive cardiovascular risk factor management to minimize the morbidity and mortality of MACE in this patient population. 1: Baseline characteristics of patients admitted to hospital with or without MACE after liver transplantation. Funding Agencies None
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".