A screening algorithm to identify clinically significant changes in neuropsychological functions in the Diabetes Control and Complications Trial. DCCT Research Group.

Abstract

Neuropsychological (NP) evaluations provide an accepted means of monitoring safety in multi-center long-term medical trials. However, using neuropsychologists to review test protocols and rate level of clinical impairment can be a costly and logistically complex undertaking. To facilitate that process, the DCCT Research Group developed a computerized screening strategy that utilized statistical models to identify individuals with possible cognitive deterioration. Two hundred and eight subjects with insulin-dependent diabetes mellitus were assessed twice, 2 years apart, with an extensive battery of NP tests, and the results were rated by expert clinicians. Multiple logistic regression was used to develop a statistical model to predict clinically significant NP worsening (as determined by clinical raters) on the basis of changes in scores (year 2--baseline) derived from the actual tests. A subsequent performance evaluation with an additional 1087 subjects demonstrated that the computerized algorithm was highly successful in identifying individuals with significantly worsened NP performance. Despite a high false positive rate, the algorithm can achieve an 80-90% reduction in the number of cases requiring evaluation by expert neuropsychologists.