Reduction of Recruitment Costs in Preclinical AD Trials: Validation of Automatic Pre-Screening Algorithm for Brain Amyloidosis

Researchers tested a two-step process for recruiting asymptomatic, amyloid-positive individuals into clinical trials. During a pre-screening phase, a subset of individuals were selected who were more likely to be amyloid positive based on an automatic analysis of data acquired during routine clinical practice. A confirmatory positron emission tomography (PET) scan was conducted with those selected individuals. This method led to an increased number of recruitments and to a reduced number of PET scans, resulting in a decrease in overall recruitment costs. The method was validated on three different cohorts, using five different classification algorithms for the pre-screening phase. The best results were obtained using cognitive, genetic, and sociodemographic features. The proposed approach shows how machine learning can be used effectively in practice to optimize recruitment costs in clinical trials.

Ansart M, Epelbaum S, Gagliardi G, et al. Reduction of recruitment costs in preclinical AD trials: Validation of automatic pre-screening algorithm for brain amyloidosis.Statistical Methods in Medical Research 2019; 30:962280218823036.