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LIAISON(®) Calprotectin for the prediction of relapse in quiescent ulcerative colitis: The EuReCa study
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...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9557957/ https://www.ncbi.nlm.nih.gov/pubmed/35789124 http://dx.doi.org/10.1002/ueg2.12268 |
Sumario: | 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. |
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