Mobile monitoring system to detect the disease activity pattern and predict clinical outcomes in patients with newly diagnosed Crohn’s disease

Abstract

Abstract We aimed to determine whether Crohn’s disease (CD) activity patterns depicted in a web-based symptom diary could help predict clinical outcomes in patients with newly diagnosed CD. Patients diagnosed with CD within 3 months were prospectively enrolled from four tertiary centres. They recorded their symptoms on a website using a smartphone at least once a week. The index outcomes were disease-related admissions and surgery during follow-up. The disease activity from enrolment to outcome or last follow-up was reviewed for pattern analysis. Cox regression was used to identify the predictors of disease outcomes. Among 135 patients with new CD, 102 were enrolled in the study. During a median follow-up period of 42 months, 25 (24.5%) and 6 (5.9%) patients required admission and surgery, respectively. Poor activity pattern was an independent predictor of disease-related hospitalisation (adjusted hazard ratio [aHR], 3.96; 95% confidence interval [CI], 1.5–10.45; p=0.005). A poor activity pattern (aHR, 19.48; 95% CI, 1.86–203.95; p=0.013) and female (aHR, 11.28; 95% CI, 1.49–85.01; p=0.018) were found to be independent predictors of bowel resection. CD disease activity patterns monitored through the mobile monitoring system could predict clinical outcomes, such as disease-related hospitalisation and surgery, in patients with newly diagnosed CD.