Joint involvement in Mexican patients with ulcerative colitis: a hospital-based retrospective study.
Jesus K Yamamoto-FurushoAndrea Sarmiento-AguilarPublished in: Clinical rheumatology (2017)
The most frequent extra-intestinal manifestation in ulcerative colitis (UC) around the world is joint involvement. There are no previous data in Latin America that is about this aspect of disease; hence, the aim of this study was to determine the frequency and factors associated to joint involvement in Mexican patients with UC. A total of 295 patients with histological diagnosis of UC were studied, divided into two groups: (1) 154 cases with at least one joint affection (arthralgia, peripheral, or axial arthropathy (sacroilitis (SI) or ankylosing spondylitis (AS))) and (2) 141 controls that had never presented any joint involvement during the clinical course of UC. Demographic, clinical, and laboratory variables were collected from the clinical records, at the time of presentation of the joint involvement for the cases and with the last information available for controls. A total of 52.2% of the patients had joint involvement, which was also the most frequent extra-intestinal manifestation (EIM). The frequency of peripheral arthralgia was 46.8% and of axial arthropathy was 5.4% (2.7% AS, 2.4% SI, and 0.3% both). The female gender (P = 0.01, OR = 3.061 95% CI: 1.311-7.15), elevated erythrocyte sedimentation rate (ESR) (P = 0.07, OR = 8.04 95% CI: 1.759-36.764), and moderate disease activity by Truelove and Witts criteria (P = 0.024, OR = 4.37 95% CI: 1.211-15.78) were factors associated at the time of presentation of the joint affection. Joint involvement is the most frequent EIM in Mexican patients with UC. The female gender, elevated ESR, and disease activity are factors associated with its presentation.
Keyphrases
- disease activity
- ankylosing spondylitis
- rheumatoid arthritis
- systemic lupus erythematosus
- ulcerative colitis
- rheumatoid arthritis patients
- juvenile idiopathic arthritis
- healthcare
- ejection fraction
- newly diagnosed
- machine learning
- artificial intelligence
- prognostic factors
- big data
- patient reported outcomes
- health information
- deep learning
- patient reported