Abstract 137: Gastrointestinal Bleeding in Acute Ischemic Stroke: A Nationwide Inpatient Sample Study
Bibliographic record
Abstract
Introduction: Gastrointestinal bleeding (GIB) has been reported as a complication in acute cerebrovascular ailments. Here, we examined the incidence, risk factors, and outcomes of GIB in acute ischemic stroke (AIS) using the largest all-payer inpatient database in the U.S. Methods: The Nationwide Inpatient Sample (2002-2011) was queried to identify all adult patients with a primary diagnosis of AIS both with and without a secondary diagnosis of GIB. We utilized multivariate analyses, adjusting for patient and hospital factors, to identify risk factors for GIB in AIS patients and to determine the effect of GIB on in-hospital complications and outcomes. Results: Of 3,988,667 patients admitted with AIS, there were 49,348 cases of GIB (1,237 per 100,000 patients) with 25.7% receiving blood transfusions. Multivariate analysis revealed several independent predictors of GIB in AIS: age 55-64 (OR: 1.39, 95% CI: 1.33-1.45), age 65-74 (OR: 1.52, 95% CI: 1.44-1.59), age 75+ (OR: 1.79, 95% CI: 1.71-1.88), alcohol use (OR: 1.23, 95% CI: 1.17-1.30), coagulopathy (OR: 1.69, 95% CI: 1.62-1.77), cancer (OR: 1.70, 95% CI: 1.60-1.80), fluid/electrolyte disturbance (OR: 1.91, 95% CI: 1.87-1.96), liver disease (OR: 2.44, 95% CI: 2.29-2.60), and history of peptic ulcer disease (OR: 2.47, 95% CI: 2.13-2.86) (all p<0.0001). Administration of thrombolytic therapy decreased the likelihood of GIB by 10% (p<0.0001). In further multivariate models, GIB was determined to be an independent predictor of septicemia (OR: 1.47, 95% CI: 1.39-1.55), gastrostomy (OR: 1.75, 95% CI: 1.70-1.81), pulmonary embolism (OR: 1.75, 95% CI: 1.55-2.00), intubation (OR: 2.04, 95% CI: 1.95-2.13), and blood transfusion (OR: 11.31, 95% CI: 11.00-11.63; all p<0.0001). The occurrence of GIB increased hospital length of stay by an average of 5.8 days and total costs by $14,120 per patient (all p<0.0001). GIB was independently associated with a 46% increased likelihood of severe disability and 82% increased likelihood of in-hospital death (all p<0.0001). Conclusion: Age and several preexisting comorbidities are strong risk factors for GIB in AIS. GIB occurrence precipitates further in-hospital complications and adverse clinical outcomes in AIS, significantly increasing disability and mortality.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".