Declining Inpatient Mortality Despite Increasing Thirty-Day Readmissions of Alcoholic Hepatitis in the United States From 2010 to 2018
Bibliographic record
Abstract
BACKGROUND: Alcoholic hepatitis (AH) readmissions are commonly secondary to relapse to alcohol use after discharge from the hospital. METHODS: This retrospective interrupted trend study analyzed the National Readmissions Database (NRD) from 2010 to 2018 to identify 30-day readmissions of AH using the International Classification of Diseases (ICD)-9 and ICD-10 codes (571.1 and K70.1). Individuals < 18 years, elective and traumatic readmissions were excluded. The biodemographic characteristics and hospitalization trends were highlighted over an 8-year time frame. A multivariate regression analysis was used to calculate the risk-adjusted odds of trends for all-cause 30-day readmissions, AH-specific readmissions, inpatient mortality, mean length of stay (LOS), and mean total hospital charge (THC) after adjusting for age, gender, grouped Charlson Comorbidity Index (CCI), type of insurance, mean household income, and hospital characteristics. P-values ≤ 0.05 were considered statistically significant. RESULTS: We noted an increasing trend for total 30-day readmissions of AH from 1,839 in 2010 to 3,784 in 2018 (P-trend < 0.001). Males made up a majority of the population; however, gender distribution was not statistically significant. Additionally, 30-day AH readmissions had an increasing comorbidity burden with time. The 30-day all-cause readmission rate increased from 18.8% in 2010 to 24.4% in 2018 and AH-specific readmission rate from 2.9% in 2010 to 3.9% in 2018 without a statistically significant trend. However, we noted a declining risk-adjusted trend of inpatient mortality for 30-day readmissions of AH from 8.7% in 2010 to 7.4% in 2018 (P-trend = 0.022). Furthermore, the total LOS attributable to 30-day readmissions of AH increased by 132.5% from 11,275 days in 2010 to 26,220 days in 2018 and the attributable THC increased by 160.9% to over $67 million in 2018. CONCLUSIONS: For 30-day AH readmissions, inpatient mortality declined to 7.4% in 2018, while the total number of hospitalizations, LOS and THC increased from 2010 to 2018.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".