LIAISON® Calprotectin for the prediction of relapse in quiescent ulcerative colitis: The EuReCa study

Abstract

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.

Conflict of interest statement

Fabrizio Bonelli and Mariella Calleri are employees of DiaSorin the manufacturer of the LIAISON® Calprotectin test. Claudia Zierold is a consultant to DiaSorin. Employees and consultant of DiaSorin participated in the study design, data collection, data interpretation, and in the preparation of the manuscript for publication. Gionata Fiorino received consultancy fees from Ferring, MSD, AbbVie, Takeda, Janssen, Amgen, Sandoz, Samsung Bioepis, Celltrion. Silvio Danese has served as a speaker, consultant, and advisory board member for Schering‐Plough, Abbott (AbbVie) Laboratories, Merck and Co, UCB Pharma, Ferring, Cellerix, Millenium Takeda, Nycomed, Pharmacosmos, Actelion, Alfa Wasserman, Genentech, Grunenthal, Pfizer, Astra Zeneca, Novo Nordisk, Cosmo Pharmaceuticals, Vifor, and Johnson and Johnson. Laurent Peyrin‐Biroulet has served as a speaker, consultant, and advisory board member for Merck, Abbvie, Janssen, Genentech, Mitsubishi, Ferring, Norgine, Tillots, Vifor, Hospira/Pfizer, Celltrion, Takeda, Biogaran, Boerhinger‐Ingelheim, Lilly, HAC‐Pharma, Index Pharmaceuticals, Amgen, Sandoz, Forward Pharma GmbH, Celgene, Biogen, Lycera, Samsung Bioepis, and Theravance. Miquel Sans has served as a speaker, consultant, and advisory board member for UCB Pharma, Ferring, Falk, Millenium Takeda, Pfizer, Astra Zeneca, Janssen, Chiesi, Tillots, Kern, Amgen, Gebro and Cellgene. Alberto Malesci has received consultancies from DiaSorin. Roberta Pollastro and Fabio Moretti are employees of Quantyca and have no conflicts of interests to disclose.

© 2022 The Authors. United European Gastroenterology Journal published by Wiley Periodicals LLC on behalf of United European Gastroenterology.

Figures

FIGURE 1

Flow diagram of enrolled patients

FIGURE 2

Receiver operating characteristic analysis with relapse as the outcome for FC measurements (a) at the relapse visit with associated criterion at >454 μg/g (79.4% sensitivity and 94.4% specificity), and (b) at the visit prior to relapse (last FC) with associated criterion at >62.3 μg/g (62.9% sensitivity and 63.0% specificity). FC, fecal calprotectin

FIGURE 3

Kaplan‐Meier survival functions were fitted using the last fecal calprotectin measurements with a cut‐point of 62.3 μg/g and relapse as an outcome. The curves diverge significantly after approximately 100 days (p = 0.0093)

FIGURE 4

Impact of the features on the predicted patient outcome in a machine learning model. The quantification of the contribution that each feature brings to the prediction made by the model is express as SHAP values (SHapley Additive exPlanations)