| Literature DB >> 28845168 |
Jee Wook Kim1, Min Soo Byun2, Bo Kyung Sohn3, Dahyun Yi2, Eun Hyun Seo4, Young Min Choe5, Shin Gyeom Kim6, Hyo Jung Choi2, Jun Ho Lee2, Ik Seung Chee7, Jong Inn Woo2, Dong Young Lee2.
Abstract
OBJECTIVE: This study aimed to examine the usefulness of each subscale score of the Clinical Dementia Rating (CDR) for predicting Alzheimer's disease (AD) dementia progression in amnestic mild cognitive impairment (MCI) elderly subjects.Entities:
Keywords: Alzheimer's disease; Clinical Dementia Rating,; Mild cognitive impairment; Orientation; Prediction
Year: 2017 PMID: 28845168 PMCID: PMC5561399 DOI: 10.4306/pi.2017.14.4.420
Source DB: PubMed Journal: Psychiatry Investig ISSN: 1738-3684 Impact factor: 2.505
Baseline characteristics of MCI group that progressed to AD dementia (MCIp) and the group that did not progress to AD dementia (MCInp) at two-year follow-up (N=59)
Data are presented as mean±SD or number (%). *by Student t-test, †by χ2 test. MCI: mild cognitive impairment, AD: Alzheimer's disease, CDR: Clinical Dementia Rating, BDS-ADL: Blessed Dementia scale-Activities of Daily Living, HRSD: Hamilton Rating Scale for Depression, NPI: neuropsychiatric inventory, mHIS: modified Hachinski ischemic score, MMSE: Mini-Mental State Examination, CERAD: the Consortium to Establish a Registry for Alzheimer's Disease, NP: neuropsychological assessment battery
Logistic regression analyses designed to select appropriate models for AD dementia prediction in MCI
AD: Alzheimer's disease, MCI: mild cognitive impairment, CDR: Clinical Dementia Rating, SOB: sum of box, MMSE: Mini-Mental State Examination, -2LL: -2 log likelihood
Final logistic regression model* for AD dementia prediction in MCI
*χ2 of the model=18.2, df=1, p<0.001, Nagelkerke R2=0.4. AD: Alzheimer's disease, MCI: mild cognitive impairment, CDR: Clinical Dementia Rating