Smaller hippocampal and entorhinal cortex volumes each contribute to the prediction of conversion to Alzheimer disease. Age and cognitive variables also contribute to prediction, and the added value of hippocampal and entorhinal cortex volumes is small. Nonetheless, combining these MRI volumes with age and cognitive measures leads to high levels of predictive accuracy that may have potential clinical application.

Mild cognitively impaired patients with memory plus other cognitive domain deficits, rather than those with pure amnestic MCI, constituted the high-risk group. Deficits in verbal memory and psychomotor speed/executive function abilities strongly predicted conversion to AD.

These findings indicate that in patients with MCI, the patient's lack of awareness of functional deficits identified by informants strongly predicts a future diagnosis of AD. If replicated, these findings suggest that clinicians evaluating MCI patients should obtain both self-reports and informant reports of functional deficits to help in prediction of long-term outcome.

Background The utility of combining early markers to predict conversion from mild cognitive impairment (MCI) to Alzheimer’s Disease (AD) remains uncertain. Methods 148 outpatients with MCI, broadly defined, were followed at 6-month intervals. Hypothesized baseline predictors for follow-up conversion to AD (entire sample: 39/148 converters) were cognitive test performance, informant report of functional impairment, apolipoprotein E genotype, olfactory identification deficit, MRI hippocampal and entorhinal cortex volumes. Results In the 3-year follow-up patient sample (33/126 converters), five of eight hypothesized predictors were selected by backward and stepwise logistic regression: FAQ (informant report of functioning), UPSIT (olfactory identification), SRT immediate recall (verbal memory), MRI hippocampal volume, MRI entorhinal cortex volume. For 10% false positives (90% specificity), this five-predictor combination showed 85.2% sensitivity, combining age and MMSE showed 39.4% sensitivity, and combining age, MMSE, and the three clinical predictors (SRT immediate recall, FAQ, and UPSIT) showed 81.3% sensitivity. Area under ROC curve was greater for the five-predictor combination (0.948) than age plus MMSE (0.821; p =.0009), and remained high in sub-samples with MMSE ≥ 27/30 and amnestic MCI. For the entire patient sample, based on dichotomizing estimated risk at 0.5, positive likelihood ratio was 16.8 (95% CI 6.4, 44.3) and negative likelihood ratio was 0.2 (95% CI 0.1, 0.4). Conclusions The five-predictor combination strongly predicted conversion to AD and was markedly superior to combining age and MMSE. Combining only clinically administered measures also led to strong predictive accuracy. If independently replicated, the findings have potential utility for early detection of AD.

University of Pennsylvania Smell Identification Test data from control subjects (n = 63), patients with mild cognitive impairment (n = 147), and patients with Alzheimer's disease (n = 100) were analyzed to derive an optimal subset of items related to risk for Alzheimer's disease (ie, healthy through mild cognitive impairment to early and moderate disease stages). The derived 10-item scale performed comparably with the University of Pennsylvania Smell Identification Test in classifying subjects, and it strongly predicted conversion to Alzheimer's disease on follow-up evaluation in patients with mild cognitive impairment. Independent replication is needed to validate these findings.