Outcomes in Clinical Subgroups of Patients With Alcohol-Related Hospitalizations
Bibliographic record
Abstract
Importance: Alcohol-related hospitalizations are common and associated with significant cost to the health care system. We have a limited understanding of the characteristics of individuals who experience alcohol-related hospitalizations, which limits our capacity to prioritize those at the highest risk of postdischarge harm. Objective: To identify and characterize the clinical subgroups of individuals who are hospitalized for alcohol-related harms. Design, Setting, and Participants: This cohort study used latent class analysis (LCA) to identify clinical subgroups of individuals experiencing alcohol-related hospitalizations in 2 provinces in Canada. All individuals between ages 10 and 105 years who were hospitalized for an alcohol-related harm between January 2017 and December 2018 (ie, the index hospitalization) were eligible. Data were analyzed between June 2023 and August 2023. Exposures: The exposure of interest was the clinical subgroup that an individual belonged to. These subgroups were identified using an LCA based on (1) the characteristics of the index hospitalization and (2) the history of alcohol-related health service use. Main Outcomes and Measures: In-hospital mortality, alcohol-related hospital readmission, and all-cause mortality in the year following discharge from the index hospitalization. The association between subgroup membership and the risk of in-hospital and postdischarge outcomes was evaluated using multivariable regression. Results: A total of 34 043 individuals were included in analysis, 4753 from Manitoba (median [IQR] age, 49 [40-58] years; 1786 female [37.6%]) and 29 290 from Ontario (median [IQR] age, 57 [45-67] years; 8527 female [29.1%]). Seven subgroups were identified following a gradient from low-frequency service use for acute intoxication to high-frequency service use for severe alcohol use disorder and liver disease. In Ontario, there were 4431 individuals in the liver disease subgroup representing 15.5% of the cohort who were at the highest risk of 1-year mortality (1382 [31.2%]) relative to the acute intoxication subgroup (42 [4.0%]) (adjusted hazard ratio [aHR], 3.83; 95% CI, 2.80-5.24). There was also a small subgroup (10.6%) of individuals with high-frequency alcohol-related health service use who had a much higher hazard of readmission following the index hospitalization (1-year readmission: 703 of 1526 [46.1%] vs 104 of 1058 [9.8%] in the acute intoxication subgroup; aHR, 5.09; 95% CI, 4.11-6.31). Conclusions and Relevance: In this population-based cohort study of individuals experiencing alcohol-related hospitalizations, we identified several small, clinically distinct subgroups that were at a disproportionately high risk of readmission and mortality. These groups could merit prioritization in strategies aimed at reducing the risk of adverse outcomes following alcohol-related hospitalizations.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".