Defining management strategies for acute severe ulcerative colitis using predictive models: a simulation-modeling study

Abstract

Background/Aims Robust management algorithms are required to reduce the residual risk of colectomy in acute severe ulcerative colitis (ASUC) refractory to standard infliximab salvage therapy. The aim of this study was to evaluate the performance and benefits of alternative ASUC management strategies using simulated prediction models of varying accuracy. Methods This was a simulation-based modeling study using a hypothetical cohort of 5,000 steroid-refractory ASUC patients receiving standard infliximab induction. Simulated predictive models were used to risk-stratify patients and escalate treatment in patients at high risk of failing standard infliximab induction. The main outcome of interest was colectomy by 3 months. Results The 3-month colectomy rate in the base scenario where all 5,000 patients received standard infliximab induction was 23%. The best-performing management strategy assigned high-risk patients to sequential Janus kinase inhibitor inhibition and medium-risk patients to accelerated infliximab induction. Using a 90% area under the curve (AUC) prediction model and optimistic treatment efficacy assumptions, this strategy reduced the 3-month colectomy rate to 8% (65% residual risk reduction). Using an 80% AUC prediction model with only modest treatment efficacy assumptions, the 3-month colectomy rate was reduced to 15% (35% residual risk reduction). Overall management strategy efficacy was highly dependent on predictive model accuracy and underlying treatment efficacy assumptions. Conclusions This is the first study to simulate predictive model-based management strategies in steroid-refractory ASUC and evaluate their effect on short-term colectomy rates. Future studies on predictive model development should incorporate simulation studies to better understand their expected benefit.

Keywords

• infliximab
• tumor necrosis factor-alpha
• colectomy
• inflammatory bowel diseases