Prediction Model of Conversion to Dementia Risk in Subjects with Amnestic Mild Cognitive Impairment: A Longitudinal, Multi-Center Clinic-Based Study

Abstract

Background: Patients with amnestic mild cognitive impairment (aMCI) have an increased risk of dementia. However, conversion rate varies. Therefore, predicting the dementia conversion in these patients is important. Objective: We aimed to develop a nomogram to predict dementia conversion in aMCI subjects using neuropsychological profiles. Methods: A total of 338 aMCI patients from two hospital-based cohorts were used in analysis. All patients were classified into 1) verbal, visual, or both, 2) early or late, and 3) single or multiple-domain aMCI according to the modality, severity of memory dysfunction, and multiplicity of involved cognitive domains, respectively. Patients were followed up, and conversion to dementia within 3 years was defined as the primary outcome. Our patients were divided into a training data set and a validation data set. The associations of potential covariates with outcome were tested, and nomogram was constructed by logistic regression model. We also developed another model with APOE data, which included 242 patients. Results: In logistic regression models, both modalities compared with visual only (OR 4.44, 95% CI 1.83-10.75, p = 0.001), late compared to early (OR 2.59, 95% CI 1.17-5.72, p = 0.019), and multiple compared to single domain (OR 3.51, 95% CI 1.62-7.60, p = 0.002) aMCI were significantly associated with dementia conversion within 3 years. A nomogram incorporating these clinical variables was constructed on the training data set and validated on the validation data set. Both nomograms with and without APOE data showed good prediction performance (c-statistics ≥ 0.75). Conclusions: This study showed that several neuropsychological profiles of aMCI are significantly associated with imminent dementia conversion, and a nomogram incorporating these clinical subtypes is simple and useful to help to predict disease progression. © 2017 - IOS Press and the authors. All rights reserved.