Decision Tree Clinical Algorithm for Screening of Mild Cognitive Impairment in the Elderly in Primary Health Care: Development, Test of Accuracy, and Time-Effectiveness Analysis

This study intends to develop a decision tree clinical algorithm based on a combination of simple neurological physical examination and brief cognitive assessment for distinguishing elderly with MCI from normal elderly in primary health care. This is a diagnostic study, comparative analysis in elderly with normal cognition and those presenting with MCI. We enrolled 212 elderly people aged 60.04 –79.92 years old. Multivariate statistical analysis showed that the existence of subjective memory complaints, history of lack of physical exercise, abnormal verbal semantic fluency, and poor one-leg balance were found to be predictors of MCI diagnosis (p≤ 0.001;p = 0.036;p≤ 0.001;p = 0.013). The decision trees clinical algorithm, which is a combination of these variables, has a fairly good accuracy in distinguishing elderly with MCI from normal elderly (accuracy = 89.62%; sensitivity  = 71.05%; specificity = 100%; positive predictive value = 100%; negative predictive value = 86.08%; negative likelihood ratio = 0.29; and time effectiveness ratio = 3.03). These results suggest that the decision tree clinical algorithm can be used for screening of MCI in the elderly in primary hea lth care.Neuroepidemiology 2020;54:243 –250
Source: Neuroepidemiology - Category: Epidemiology Source Type: research