Phosphatidylethanol measures in patients with severe <scp>COVID</scp>‐19‐associated respiratory failure identify a subset with alcohol misuse
Bibliographic record
Abstract
BACKGROUND: Clinical trials in patients with COVID-19 have exclusively used self- or proxy-reporting to characterize alcohol consumption. The aim of this study was to measure an objective biomarker of recent alcohol use in patients hospitalized with severe COVID-19-associated respiratory failure who were enrolled in an investigational clinical trial to determine the prevalence of alcohol misuse, and to explore the relationship of alcohol use with outcomes. METHODS: We conducted a substudy of patients enrolled in the multicenter, phase 2, adaptive platform design, Investigation of Serial Studies to Predict Your Therapeutic Response with Imaging And molecular Analysis in COVID-19 trial (ClinicalTrials.gov: NCT04488081), conducted at 20 hospital systems across the United States. Three hundred and fifty-five patients with available red blood cell (RBC) samples and 60-day follow-up assessments were included. RBCs were utilized to measure phosphatidylethanol (PEth). Prespecified thresholds of PEth were utilized to stratify patients into groups: low/no alcohol use (PEth < 20 ng/mL), significant alcohol use (PEth 20-200 ng/mL), and heavy alcohol use (PEth ≥ 200 ng/mL). RESULTS: In this cohort, 17% of patients met criteria for significant alcohol use, while 4% met criteria for heavy alcohol use. Alcohol misuse was associated with diminished odds for home discharge, though this finding did not achieve statistical significance. CONCLUSIONS: In a cohort of patients with severe COVID-19 enrolled in a clinical trial, alcohol consumption of two or more standard drinks per day was present among 21%, approximating the proportion of patients with diabetes, and raising the possibility that alcohol consumption alters risk for severe viral pneumonia. Undetected alcohol misuse among clinical trial participants has the potential to influence study outcomes or contribute to adverse events.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".