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A reliable method of detecting Alzheimer disease (AD) in its prodromal state is needed for patient stratification in clinical trials or for personalizing existing or potential upcoming therapies. Current cerebrospinal fluid (CSF)– or imaging-based single biomarkers for AD offer reliable identification of patients with underlying AD but insufficient prediction of the rate of AD progression.

To optimize prediction of progression from mild cognitive impairment (MCI) to AD dementia by combining information from diverse patient variables.

This cohort study from the Alzheimer Disease Neuroimaging Initiative (ADNI) enrolled 928 patients with MCI at baseline and 249 selected variables available in the ADNI data set. Variables included clinical and demographic data, cognitive scores, magnetic resonance imaging–based brain volumetric data, the apolipoprotein E (APOE) and …