S973 Impact of Obesity on Outcomes of Patients With Inflammatory Bowel Disease: A National Inpatient Sample Analysis
Bibliographic record
Abstract
Introduction: Ulcerative colitis (UC) and Crohn’s disease (CD) are chronic idiopathic inflammatory bowel diseases (IBD). The underlying disease mechanism had been attributed to genetic susceptibility and environmental factors including dietary factors. Obesity defined as a BMI >30 is a growing co-morbidity that are on the rise and affect over 40% of the adult population in the US. Previous studies suggested obesity leads to worse outcomes in IBD patients through decreased medication absorption and increased post op complications. Using a large inpatient database, we sought to compare the inpatient outcome and complications in IBD patients with and without obesity. Methods: A retrospective cohort analysis of all adult inpatient hospitalizations for inflammatory bowel disease (IBD) using the National Inpatient Sample (NIS) from 2015 to 2019 was conducted. Our study population included patients admitted with IBD exacerbation. We divided our population into obese and non-obese groups. Baseline demographic characteristics, past medical history and inpatient outcome and complications were compared using Wilcoxon rank-sum test for complex survey samples and chi-squared test with Rao & Scott's second-order correction. Results: We identified 398,200 patients with IBD. Of which, 36,375 were obese (9.1%) with female predominance at 65%. Patient Characteristics were otherwise similar in race and age. The obese patient group had higher comorbidities (Table). Obese patients with IBD had significantly higher total hospital charges ($48,607 vs $46,661 P< 0.001) and longer length of stay (5.2 vs 5 days P< 0.001) compared to the non-obese group. Obese patients with IBD were also significantly more likely to experience adverse outcomes during their hospitalization including cardiac arrest and mechanical ventilation (Figure). Conclusion: Obesity is becoming increasingly prevalent in the IBD patient population and correlates with more co-morbidities. Hospitalizations of IBD patients with obesity were significantly longer and more costly. Furthermore, they were significantly more likely to experience serious adverse outcomes during their hospitalization.Figure 1.: Risk analysis of adverse inpatient outcomes of obese IBD patients. OR; Odds ratio, 95% CI; 95% confidence interval. Table 1. - Patient Characteristics Patients with Inflammatory Bowel Disease Characteristic Non-Obese,N = 361,8251 Obese,N = 36,3751 p-value2 AGE 45 (18) 48 (16) < 0.001 Gender (Female) 190,035 (53) 23,540 (65) < 0.001 Race < 0.001 White 256,955 (73) 25,705 (73) Black 48,645 (14) 5,590 (16) COPD 44,330 (12) 7,395 (20) < 0.001 HLD 47,120 (13) 8,450 (23) < 0.001 CAD 515 (0.1) 95 (0.3) 0.013 T2DM 31,500 (8.7) 8,400 (23) < 0.001 HTN 98,865 (27) 17,495 (48) < 0.001 CHF 10,115 (2.8) 2,120 (5.8) < 0.001 CKD 16,915 (4.7) 2,740 (7.5) < 0.001 Liver Failure 14,450 (4.0) 2,925 (8.0) < 0.001 Hypothyroidism 24,915 (6.9) 4,215 (12) < 0.001 Smoking 133,470 (37) 14,740 (41) < 0.001 Alcohol abuse 7,000 (1.9) 645 (1.8) 0.35 Drug abuse 21,670 (6.0) 1,890 (5.2) 0.007 COPD; chronic obstructive pulmonary disease, HLD; hyperlipidemia, CAD; coronary artery disease, T2DM; type 2 diabetes mellitus, HTN; hypertension, CHF; congestive heart failure, CKD; chronic kidney disease.1Mean (SD); n (%)2Wilcoxon rank-sum test for complex survey samples; chi-squared test with Rao & Scott's second-order correction
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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 | 0.000 | 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".