Predicting clinical progression trajectories of early Alzheimer's disease patients

Viswanath Devanarayan, Yuanqing Ye, Arnaud Charil, Erica Andreozzi, Pallavi Sachdev, Daniel A. Llano, Lu Tian, Liang Zhu, Harald Hampel, Lynn Kramer, Shobha Dhadda, Michael Irizarry

Research output: Contribution to journalArticlepeer-review

Abstract

BACKGROUND: Models for forecasting individual clinical progression trajectories in early Alzheimer's disease (AD) are needed for optimizing clinical studies and patient monitoring. METHODS: Prediction models were constructed using a clinical trial training cohort (TC; n = 934) via a gradient boosting algorithm and then evaluated in two validation cohorts (VC 1, n = 235; VC 2, n = 421). Model inputs included baseline clinical features (cognitive function assessments, APOE ε4 status, and demographics) and brain magnetic resonance imaging (MRI) measures. RESULTS: The model using clinical features achieved R2 of 0.21 and 0.31 for predicting 2-year cognitive decline in VC 1 and VC 2, respectively. Adding MRI features improved the R2 to 0.29 in VC 1, which employed the same preprocessing pipeline as the TC. Utilizing these model-based predictions for clinical trial enrichment reduced the required sample size by 20% to 49%. DISCUSSION: Our validated prediction models enable baseline prediction of clinical progression trajectories in early AD, benefiting clinical trial enrichment and various applications.

Original languageEnglish (US)
Pages (from-to)1725-1738
Number of pages14
JournalAlzheimer's and Dementia
Volume20
Issue number3
DOIs
StatePublished - Mar 2024

Keywords

  • clinical trial enrichment
  • disease progression
  • machine learning
  • mild cognitive impairment
  • prognosis

ASJC Scopus subject areas

  • Clinical Neurology
  • Geriatrics and Gerontology
  • Psychiatry and Mental health
  • Cellular and Molecular Neuroscience
  • Health Policy
  • Developmental Neuroscience
  • Epidemiology

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