Outcomes in Clinical Subgroups of Patients With Alcohol-Related Hospitalizations
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».