Development and Validation of a Segmental Intestinal Ultrasound Score for Monitoring Disease Activity and Therapeutic Response in Ulcerative Colitis

Abstract Background and aims Intestinal ultrasound (IUS) is increasingly used as a non-invasive, point-of-care tool for monitoring ulcerative colitis (UC). While the Milan Ultrasound Criteria (MUC) have been validated against endoscopic activity, they only capture the most affected segment and may hence miss changes in disease extent. A segmental IUS score, analogous to the Modified Mayo Endoscopic Score (MMES), could enhance the monitoring and prognostication of UC. We aim to develop and validate a modified, segmental version of the MUC score by correlating it with clinical, biological, and endoscopic disease activity; and assess whether a segmental MUS score can predict long-term clinical outcomes. Methods After defining the most optimal modified MUC score (sum or mean of segmental MUC scores) based on retrospective data in patients with a UC flare, we will conduct a prospective, single-center study at the UZ Leuven IBD clinic to validate this score. 100 consecutive patients presenting with an active UC flare requiring therapeutic escalation will be enrolled. All patients will undergo baseline clinical evaluation with patient-reported outcomes, fecal calprotectin testing, colonoscopy with MMES scoring, and IUS. Post-induction (week 8–14 depending on therapy) and year 1 assessments will repeat the same evaluations. The correlation between modified MUC and MMES scores will be assessed by Spearman’s correlation. ROC curve analyses will be performed to define cut-oKs for clinical and endoscopic response and remission. Logistic regression analyses will identify baseline predictors of long-term clinical outcomes. Anticipated impact We hypothesize that the segmental modified MUC score will strongly correlate with endoscopic disease activity, taking also disease extent into consideration. By standardizing IUS monitoring, this project could reduce dependence on endoscopy, thereby minimizing patient burden, healthcare costs, and resource utilization. We hope that we could also use this score to predict therapeutic response, which would allow us to improve individualized management strategies in UC.