A Matrix-based Model Predicts Primary Response to Infliximab in Crohn’s Disease

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Abstract

Background:

Prediction of primary non-response [PNR] to anti-tumour necrosis factors [TNFs] in inflammatory bowel disease [IBD] is direly needed to select the optimal therapeutic class for a given patient. We developed a matrix-based prediction tool to predict response to infliximab [IFX] in Crohn’s disease [CD] patients.

Methods:

This retrospective single-centre study included 201 anti-TNF naïve CD patients who started with IFX induction therapy. PNR occurred in 16 [8%] patients. Clinical, biological [including serum TNF and the IBD serology 6 panel and genetic [the 163 validated IBD risk loci] markers were collected before start. Based on the best fitted regression model, probabilities of primary response to IFX were calculated and arranged in a prediction matrix tool.

Results:

Multiple logistic regression withheld three final independent predictors [p < 0.05] for PNR: age at first IFX, {odds ratio (OR) (95% confidence interval [CI] of 1.1 (1.0–1.1)}, body mass index [BMI] (0.86 [0.7–1.0]), and previous surgery (4.4 [1.2–16.5]). The accuracy of this prediction model did not improve when the genetic markers were added (area under the curve [AUC] from 0.80 [0.67–0.93] to 0.78 [0.65–0.91]). The predicted probabilities for PNR to IFX increased from 1% to 53% depending on the combination of final predictors.

Conclusions:

Readily available clinical factors [age at first IFX, BMI, and previous surgery] outperform serological and IBD risk loci in prediction of primary response to infliximab in this real-life cohort of CD patients. This matrix tool could be useful for guiding physicians and may avoid unnecessary or inappropriate exposure to IFX in IBD patients unlikely to benefit.

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