| Title: | Investigating the impact of steroid dependence on gastrointestinal surgical outcomes from UK Biobank |
| Journal: | Scientific Reports |
| Published: | 25 Nov 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/39587092/ |
| DOI: | https://doi.org/10.1038/s41598-024-75215-5 |
Publication 14263
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Abstract
Although corticosteroids are an important treatment for inflammatory bowel disease (IBD) patients, many subjects develop dependence, leading to serious long-term side effects. We applied causal inference analyses to investigate the length of steroid use on reoperations in IBD patients. We identified subjects in the UK Biobank general practice dataset with at least one major GI surgery and followed them for at least 5 years to evaluate subsequent operations. We defined steroid dependence as at least 12 weeks of use (vs. acute steroid use) prior to baseline surgery. Of the 363 subjects included in our analyses, 163 (45%) were prescribed steroids on or before baseline surgery, and of these (N = 125 of 163, 77%) were dependent. Additional analyses for time-dependent data on prescriptions found a link between prescription length and reoperation. Among UC subjects with acute use, the odds of reoperation were significantly lower (OR: 0.32, 95% CI: 0.0-0.73). Steroid dependence resulted in a delay of reoperation (median 1.2 vs. 2.3 years, P = 0.01). Our findings indicate that long-term steroid use tends to increase the need for reoperation, whereas short-term use may reduce it.</p>
15 Keywords
- Adrenal Cortex Hormones
- Adult
- Aged
- Biological Specimen Banks
- Digestive System Surgical Procedures
- Female
- Humans
- Inflammatory Bowel Diseases
- Male
- Middle Aged
- Reoperation
- Steroids
- Treatment Outcome
- UK Biobank
- United Kingdom
12 Authors
- Uri Kartoun
- Akira Koseki
- Akihiro Kosugi
- Kingsley Njoku
- Tesfaye Yadete
- Eileen Koski
- Joao Bettencourt-Silva
- Natasha Mulligan
- Jianying Hu
- Julia Liu
- Thaddeus Stappenbeck
- Vibha Anand
1 Application
| Application ID | Title |
|---|---|
| 95318 | Multi-modal AI representation learning to accelerate discovery of composite biomarkers |
Enabling scientific discoveries that improve human health