Personalizing treatment selection in Crohn's disease: a meta-analysis of individual participant data from fifteen randomized controlled trials

BACKGROUND Crohn's disease is characterized by diverse manifestations that likely reflect differences in disease biology and treatment responsiveness across patients. Prior meta-analyses have found anti-TNF drugs to be more efficacious than others based on their cohort-averaged effects. We performed a subgroup analysis to determine if it is possible to achieve even more efficacious outcomes by individualizing treatment selection. METHODS We obtained participant-level data from 15 trials of FDA-approved treatments (N=5703). We used sequential regression and simulation to model week six disease activity as a function of drug class, demographics, and disease-related features. We used hypothesis testing to define subgroups based on rank-ordered preferences for drug classes. We queried data from University of California Health (UCH) to estimate the impacts these models could have on clinical practice. We computed the sample size needed to prospectively test a key prediction of our model. Lastly, we prototyped a treatment recommendation tool that uses patient features as inputs. FINDINGS 55% of all participants (N=3142) did not show superior efficacy with any one drug class (anti-TNF, anti-IL-12/23, anti-integrin). We classified the remainder into 6 treatment subgroups, two of which showed greatest efficacy with anti-TNFs (36%, N=2064). We also identified a subgroup of women over 50 with superior responses to anti-IL-12/23s. Although they represented only 2% of the trial-based cohort, 25% of Crohn's patients at UCH are women over 50 (N=5,647). During the timeframe when all drug classes were FDA-approved, 75% of biologic-exposed women over 50 did not receive an anti-IL12/23 first-line. We calculate that a trial with 250 patients per arm will have 97% power to show anti-IL-12/23s as being superior to anti-TNFs in these patients. INTERPRETATION Personalizing treatment selection with the aid of decision support tools can improve outcomes in Crohn's disease. FUNDING: UCSF, USF