A290 ILLICIT SUBSTANCE USE AND ITS IMPACT ON ALCOHOL-ASSOCIATED HEPATITIS IN LATIN AMERICA
Bibliographic record
Abstract
Abstract Background Concomitant substance use is frequent among patients with alcohol use disorder (AUD), but its impact on alcohol-associated hepatitis (AH) is unknown. Aims To assess the prevalence and impact of substance use in patients hospitalized for AH in a multinational cohort in Latin America. Methods Multicenter prospective cohort study including patients with AH between 2015-2022. We recorded sociodemographic and clinical information, including data on alcohol and drug use. We assessed the impact of substance consumption using competing-risk models. Results We included 405 patients from 24 centers in 8 countries (Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Mexico, and Peru). The mean age was 49.6±12.2 years, 345 (85.4%) were men, 210 (57.5%) had a previous diagnosis of cirrhosis, and the median MELD at diagnosis was 25 [20–31] points. Around 74% of patients fulfilled ACLF criteria (ACLF-1: 11.1%, ACLF-2: 11.6%, ACLF-3: 49.6%). A total of 82 (20.3%) reported active substance use, while 22 (5.4%) were former substance users. The most common drugs used at admission were marijuana (11.1%), cocaine (10.4%), methamphetamine (4.4%), and heroin (0.5%). Out of the total, 35.7% died, and only 2.5% underwent liver transplantation during follow-up. Active substance use was higher in younger patients (users 44.4±16.1 years vs. non-users 51.0±10.6 years; pampersand:003C0.001) and in men compared to women (22.0% vs 10.2%, p=0.036). In a competing-risk model adjusted by age, sex, history of cirrhosis, MELD, and ACLF grade, active substance use was independently associated with mortality (subdistribution Hazard Ratio [sHR] 1.53, 95%CI:1.01–2.32; p=0.043). Also, active cocaine (sHR 1.69, 95%CI:1.07–2.70; p=0.025) and marijuana use (sHR 1.83, 95%CI:1.11–3.04; p=0.018) were independently associated with mortality in adjusted competing-risk analyses. Conclusions Active drug use is common in AH patients. Marijuana and cocaine were the most frequent substances and were independently associated with increased mortality. Substance use should be screened in patients with AUD, and integrated management with addiction specialists and psychiatrists could impact survival in AH. 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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".