%0 Journal Article
%@ 0251-5350 (Print); e-ISSN: 1423-0208 (Online)
%A Pandhita S, Gea
%A Sutrisna, Bambang
%A Wibowo, Samekto
%A Adisasmita, Asri C
%A Rahardjo, Tri Budi Wahyuni
%A Amir, Nurmiati
%A Rustika, Rustika
%A Kosen, Soewarta
%A Syarif, Syahrizal
%A Wreksoatmodjo, Budi Riyanto
%A Department of Neurology, Faculty of Medicine, Universitas Muhammadiyah Prof. Dr. HAMKA, Jakarta, Indonesia,
%A Department of Epidemiology, Faculty of Public Health, Universitas Indonesia, Jakarta , Indonesia,
%A Department of Neurology, Faculty of Medicine, Universitas Gadjah Mada, Yogyakarta, Indonesia,
%A Faculty of Health Science, Universitas Respati Indonesia, Jakarta, Indonesia,
%A Department of Psychiatry, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia,
%A National Institute of Health Research and Development, Ministry of Health, Republic of Indonesia, Jakarta, Indonesia,
%A Department of Neurology, Faculty of Medicine, Universitas Katolik Atma Jaya, Jakarta, Indonesia,
%D 2020
%F repository:11705
%I Karger
%J Neuroepidemiology
%N 3
%P 243-250
%R https://doi.org/10.1159/000503830
%T 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
%U http://repository.uhamka.ac.id/id/eprint/11705/
%V 54
%X Mild cognitive impairment (MCI) is predicted to be a common cognitive impairment in primary health care. Early detection and appropriate management of MCI can slow the rate of deterioration in cognitive deficits. The current methods for early detection of MCI have not been satisfactory for some doctors in primary health care. Therefore, an easy, fast, accurate, and reliable method for screening MCI in primary health care is needed. 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 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 health care.