Predicting Progression from Normal to MCI and from MCI to AD Using Clinical Variables in the National Alzheimer’s Coordinating Center Uniform Data Set Version 3: Application of Machine Learning Models and a Probability Calculator

Y. Pang, W. Kukull, M. Sano, R. L. Albin, C. Shen, J. Zhou, Hiroko H. Dodge

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Clinical trials are increasingly focused on pre-manifest and early Alzheimer’s disease (AD). Accurately predicting clinical progressions from normal to MCI or from MCI to dementia/AD versus non-progression is challenging. Accurate identification of symptomatic progressors is important to avoid unnecessary treatment and improve trial efficiency. Due to large inter-individual variability, biomarker positivity and comorbidity information are often insufficient to identify those destined to have symptomatic progressions. Using only clinical variables, we aimed to predict clinical progressions, estimating probabilities of progressions with a small set of variables selected by machine learning approaches. This work updates our previous work that was applied to the National Alzheimer’s Coordinating Center (NACC) Uniform Data Set Version 2 (V2), by using the most recent version (V3) with additional analyses. We generated a user-friendly conversion probability calculator which can be used for effectively pre-screening trial participants.

Original languageEnglish (US)
Pages (from-to)301-313
Number of pages13
JournalThe journal of prevention of Alzheimer's disease
Volume10
Issue number2
DOIs
StatePublished - Feb 2023

Keywords

  • AD
  • Alzheimer’s disease
  • MCI
  • MCI conversion
  • National Alzheimer’s Coordinating Center (NACC)
  • PET amyloid
  • dementia
  • machine learning

ASJC Scopus subject areas

  • Clinical Neurology
  • Psychiatry and Mental health

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