Clinical decision trees for predicting conversion from cognitive impairment no dementia (CIND) to dementia in a longitudinal population-based study.

The lack of gold standard diagnostic criteria for cognitive impairment in the absence of dementia has resulted in variable nomenclature, case definitions, outcomes, risk factors, and prognostic utilities. Our objective was to elucidate the clinical correlates of conversion to dementia in a longitudinal population-based sample. Using data from the Canadian Study of Health and Aging, a machine learning algorithm was used to identify symptoms that best differentiated converting from nonconverting cognitively impaired not demented participants. Poor retrieval was the sole predictor of conversion to dementia over 5 years. This finding suggests that patients with impaired retrieval are at greater risk for progression to dementia at follow-up. Employing significant predictors as markers for ongoing monitoring and assessment, rather than as clinical markers of conversion, is recommended given the less than optimal specificity of the predictive algorithms.