Trends of Alcohol Withdrawal Delirium in the Last Decade: Analysis of the Nationwide Inpatient Sample
Bibliographic record
Abstract
Background: Alcohol use disorder, high-risk drinking, and emergency visits for acute and chronic complications of alcohol use have been increasing in the USA recently. Approximately half of patients with alcohol use disorder experience alcohol withdrawal when they reduce or stop drinking. Though alcohol withdrawal is usually mild, 20% of patients experience more severe manifestations such as hallucinations, seizures, and delirium. In this study, we utilized the Nationwide Inpatient Sample to examine the trends of alcohol withdrawal delirium (AWD) in the period 2010 - 2019. Methods: This was a retrospective longitudinal trends study involving hospitalizations with AWD in the USA from 2010 to 2019. We searched the databases for hospitalizations using the International Classification of Diseases (ICD) codes (291.0 and F10231). We involved all hospitalizations complicated by AWD and hospitalizations with AWD as the principal diagnosis for admission. We excluded hospitalizations involving patients under the age of 18. We calculated the crude admission rate and the incidence of AWD per million adult hospitalizations during each calendar year. In addition, we analyzed trends of inpatient mortality, length of stay (LOS), and total hospital charges (THC). Results: The incidence of AWD per million hospitalizations increased from 2,671.8 in 2010 to 3,405.6 in 2019, with an annual percentage change (APC) of 3.1% (P < 0.001). Similarly, AWD admission rate per million hospitalizations increased from 1,030.3 in 2010 to 1,556.0 in 2019, with an average APC of 5.0% (P < 0.001). There were statistically significant trends of increasing inpatient mortality, THC, and LOS over the studied period. In general, female gender, younger age, and Black race were associated with better clinical outcomes. Conclusions: Our study showed an increase in the incidence and admission rates of AWD. Mortality, LOS, and THC increased over the studied period. Younger age, female gender, and Black race were associated with better clinical outcomes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.001 | 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".