Introduction: Fecal calprotectin (FC) is established as a diagnostic marker to differentiate between inflammatory bowel diseases and non-inflammatory conditions. Furthermore, it may be effective in monitoring response to treatment, and to predict relapse during maintenance therapy.
Design: This was a prospective longitudinal study carried out in Italy, France and Spain. The primary objective was to correlate the LIAISON® Calprotectin assay measurements to quiescent ulcerative colitis (UC) or relapse as assessed by clinical data. Patients were assessed every 3 months for 12 months, and at 18 months.
Results: The last FC measured prior to relapse was the variable that predicted relapse in a statistically significant manner. With a 62.3 μg/g cut-off the area under the curve was 0.619, and the sensitivity was 62.9% (95% Confidence Interval [CI] 44.9%-78.5%) and specificity 63.0% (95% CI 53.1%-72.1%). Using machine learning methods, the last FC measurement was shown to have the largest impact in predicting relapse. An algorithm was developed that included other variables available following a clinician's visit, which resulted in an area under the curve of 0.754 for predicting relapse.
Conclusion: In the present study FC measured by the LIAISON® Calprotectin assay on the visit before relapse is predictive of relapse in patients with quiescent UC. In a proof of concept, the accuracy of prediction can further be improved including other variables in an algorithm developed by machine learning.
Trial registration: The trial is registered at clinicaltrials.gov with reference number NCT05168917.
Keywords: algorithm; calprotectin; flare; inflammatory bowel disease; machine learning; relapse; ulcerative colitis.
© 2022 The Authors. United European Gastroenterology Journal published by Wiley Periodicals LLC on behalf of United European Gastroenterology.