600 Impact of Anti Coagulation and Anti Platelet Treatment on 30-Days Readmission Rate of Patients With Gastrointestinal Bleeding in Tertiary Care Setting of Northern Pennsylvania
Bibliographic record
Abstract
INTRODUCTION: Gastrointestinal bleeding remains the major complication of blood thinners such as anti platelet agents and anticoagulants leading to frequent hospitalization. It requires involvement of multi disciplinary approach for its management. Despite the hard efforts, patient readmission remains a major concern. Our study aims to highlight the impact of these agents on 30 day readmission. METHODS: Patients admitted with Gastrointestinal bleeding were extracted using ICD 10 codes K 92.0, K 92.1, and K 92.2. Essential baseline characteristics including age, race, Primary Diagnosis, Readmission Diagnosis, Patients not on blood thinners, Patients on anti platelet, Patients on anti coagulants, Patients on combined medications, Atrial fibrillation, Deep Vein Thrombosis, Pulmonary embolism, Metallic Heart Valve, History of Stroke, 30-day mortality, causes of re admissions were identified by reviewing the electronic medical record of individual patients. Patients were stratified into 4 groups as Patients not on blood thinners (group A), taking Anti platelet (group B), Anticoagulant (group C) and combined anti platelet with anticoagulant (group D). Statistical analysis was performed using SPSS version 25. RESULTS: Total of 1296 patients was admitted in the last decade from 2009-2018 with the diagnosis of Gastrointestinal bleeding. Mean age of patients was 70.31± 15.38 (Mean ± SD), the majority were Males (50.2%) and Caucasians (97.1%). Patients had following associated comorbidities such as Deep venous thrombosis (9.0%), Pulmonary Embolism (5.8%), Atrial Fibrillation (33.4%), History of Stroke/TIA (16.4%) and Metallic Heart Valve(5.8%).Majority of Patients were not on any agent (41.7%) followed by Anti platelet (29.6%), Combined (16.5%) and Anti coagulation (12.2%). The 30-days Readmission rate was higher among patients on Combined agents followed by Anticoagulant group, No agent group and Anti platelet agent (29.0% vs. 19% vs. 17.7 vs. 16.4%, respectively, OR: 1.89 CI 1.20-2.73 P -value = 0.01, whereas 30-days mortality was not significantly different among patients in different agents P -value= 0.70. CONCLUSION: This study reveals a very significant finding that odds of 30-day readmission approximately double for patients who are on combined anti platelet and anticoagulant therapy when compared with other groups, whereas 30-day mortality remains unchanged.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".