期刊
CLINICAL GASTROENTEROLOGY AND HEPATOLOGY
卷 14, 期 3, 页码 348-+出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.cgh.2015.06.001
关键词
Disease Severity; Disease Course; Inflammatory Bowel Disease
资金
- AbbVie, Inc
- AbbVie
Although most treatment algorithms in inflammatory bowel disease (IBD) begin with classifying patients according to disease severity, no formal validated or consensus definitions of mild, moderate, or severe IBD currently exist. There are 3 main domains relevant to the evaluation of disease severity in IBD: impact of the disease on the patient, disease burden, and disease course. These measures are not mutually exclusive and the correlations and interactions between them are not necessarily proportionate. A comprehensive literature search was performed regarding current definitions of disease severity in both Crohn's disease and ulcerative colitis, and the ability to categorize disease severity in a particular patient. Although numerous assessment tools for symptoms, quality of life, patient-reported outcomes, fatigue, endoscopy, cross-sectional imaging, and histology (in ulcerative colitis) were identified, few have validated thresholds for categorizing disease activity or severity. Moving forward, we propose a preliminary set of criteria that could be used to classify IBD disease severity. These are grouped by the 3 domains of disease severity: impact of the disease on the patient (clinical symptoms, quality of life, fatigue, and disability); measurable inflammatory burden (C-reactive protein, mucosal lesions, upper gastrointestinal involvement, and disease extent), and disease course (including structural damage, history/extension of intestinal resection, perianal disease, number of flares, and extraintestinal manifestations). We further suggest that a disease severity classification should be developed and validated by an international group to develop a pragmatic means of identifying patients with severe disease. This is increasingly important to guide current therapeutic strategies for IBD and to develop treatment algorithms for clinical practice.
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