Independent Predictors and Causes of Thirty-Day Gastrointestinal Readmissions Following COVID-19-Related Hospitalizations: Analysis of the National Readmission Database
Bibliographic record
Abstract
Background: The coronavirus disease 2019 (COVID-19) pandemic led to significant mortality and morbidity in the United States. The burden of COVID-19 was not limited to the respiratory tract alone but had significant extrapulmonary manifestations. We decided to examine the causes, predictors, and outcomes of gastrointestinal (GI)-related causes of 30-day readmission following index COVID-19 hospitalization. Methods: We used the National Readmission Database (NRD) from 2020 to identify hospitalizations among adults with principal diagnosis of COVID-19. We identified GI-related hospitalizations within 30 days of index admission after excluding elective and traumatic admissions. We identified the top causes of GI-related readmission, and the outcomes of these hospitalizations. We used a multivariate Cox regression analysis to identify the independent predictors of readmission. Results: Among 1,024,492 index hospitalizations with a primary diagnosis of COVID-19 in the 2020 NRD database, 644,903 were included in the 30-day readmission study. Of these 3,276 (0.5%) were readmitted in 30 days due to primary GI causes. The top five causes of readmissions we identified in this study were GI bleeding, intestinal obstruction, acute diverticulitis, acute pancreatitis, and acute cholecystitis. Multivariate Cox regression analysis done adjusting for confounders showed that renal failure, alcohol abuse, and peptic ulcer disease were associated with increased odds of 30-day readmission from GI-related causes. Conclusions: GI manifestations of COVID-19 are not uncommon and remain an important cause of readmission. Targeted interventions addressing the modifiable predictors of readmission identified will be beneficial in reducing the burden on already limited healthcare resources.
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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 | 0.007 | 0.135 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".