Mild cognitive impairment cases affect the predictive power of Alzheimer's disease diagnostic models using routine clinical variables.

Abstract

Diagnostic models using primary care routine clinical variables have been limited in their ability to identify Alzheimer's disease (AD) patients. In this study, we sought to better understand the effect of mild cognitive impairment (MCI) on the predictive performance of AD diagnostic models. We sourced data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. CatBoost was used to assess the utility of routine clinical variables that are accessible to primary care physicians, such as hematological and blood tests and medical history, in multiclass classification between healthy controls, MCI, and AD. Our results indicated that MCI indeed affected the predictive performance of AD diagnostic models. Of the three subgroups of MCI that we found, this finding was driven by a subgroup of MCI patients who likely have prodromal AD. This work highlights the importance of continuing to focus on better classification of the different types of MCI to improve diagnostic models of AD, rather than focusing on binary classifications between AD and control cases. Future research should focus on distinguishing MCI from prodromal AD as the utmost priority for improving translational AD diagnostic models for primary care physicians.