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Serum Calprotectin: A Novel Diagnostic and Prognostic Marker in Inflammatory Bowel Diseases.

OBJECTIVES: There is an unmet need for novel blood-based biomarkers that offer timely and accurate diagnostic and prognostic testing in inflammatory bowel diseases (IBD). We aimed to investigate the diagnostic and prognostic utility of serum calprotectin (SC) in IBD.

METHODS: A total of 171 patients (n=96 IBD, n=75 non-IBD) were prospectively recruited. A multi-biomarker model was derived using multivariable logistic regression analysis. Cox proportional hazards model was derived to assess the contribution of each variable to disease outcomes.

RESULTS: SC correlated strongly with current biomarkers, including fecal calprotectin (FC) (n=50, ρ=0.50, P=1.6 × 10-4 ). SC was the strongest individual predictor of IBD diagnosis (odds ratio (OR): 9.37 (95% confidence interval (CI): 2.82-34.68), P=4.00 × 10-4 ) compared with other markers (C-reactive protein (CRP): OR 8.52 (95% CI: 2.75-28.63), P=2.80 × 10-4 ); albumin: OR 6.12 (95% CI: 1.82-22.16), P=0.004). In a subset of 50 patients with paired SC and FC, the area under receiver operating characteristic discriminating IBD from controls was better for FC than for SC (0.99, (95% CI 0.87-1.00) and 0.87 (95% CI:0.78-0.97), respectively; P=0.01). At follow-up (median 342 days; interquartile range: 88-563), SC predicted treatment escalation and/or surgery in IBD (hazard ratio (HR) 2.7, 95% CI: 1.1-4.9), in particular Crohn's disease (CD) (HR 4.2, 95% CI 1.2-15.3). A model incorporating SC and either CRP or albumin has a positive likelihood ratio of 24.14 for IBD. At 1 year, our prognostic model can predict treatment escalation in IBD in 65% of cases (95% CI: 43-79%) and 80% (95% CI: 31-94%) in CD if ≥2 blood marker criteria are met.

CONCLUSIONS: A diagnostic and prognostic model that combines SC and other blood-based biomarkers accurately predicts the inflammatory burden in IBD and has the potential to predict disease and its outcomes. Our data warrant further detailed exploration and validation in large multicenter cohorts.

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