Clinical value of sleep characteristics in distinguishing mild cognitive impairment from Alzheimer′s disease
10.3760/cma.j.cn431274-20220407-00287
- VernacularTitle:睡眠特征在区分轻度认知障碍和阿尔茨海默病中的临床价值
- Author:
Ruobing QI
1
;
Jia WEI
;
Fei XIE
Author Information
1. 杭州市第七人民医院老年精神科，杭州 310013
- Keywords:
Polysomnography;
Cognition disorders;
Alzheimer disease
- From:
Journal of Chinese Physician
2022;24(9):1345-1348,1353
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To observe the sleep characteristics of mild cognitive impairment (MCI) patients and Alzheimer disease (AD) patients, and to evaluate their values in distinguishing MCI and AD.Methods:50 patients with MCI and 50 patients with AD diagnosed in Hangzhou Seventh People′s Hospital from June 2020 to March 2022 were recruited in this study. All-night polysomnography (PSG) was performed for each patient. The total sleep time (TST) , proportion of sleep structure (N1%, N2%, N3%, REM%) , sleep efficiency (SE), sleep latency (SL), rapid eye movement sleep latency (REML), wake time after sleep onset (WASO), sleep spindle density (ρ spindle) and sleep k-complex density (ρ KC) were compared between the two groups. The indexes of sleep characteristics with statistical significance between the two groups were included to perform logistic stepwise regression. The single-factorial and multi-factorial prediction models were established. Receiver operating characteristic (ROC) curve was used to compare the value of single-factorial model and multi-factorial model in distinguishing MCI and AD patients. Results:There were statistically significant difference in TST, N3, SE, ρ spindle between MCI and AD groups ( t=3.315, 2.798, 3.682, 6.488, all P<0.05). Logistic stepwise regression analysis showed that N3, SE, ρ spindle was included in the modeling portfolio, and the joint prediction model was Logit (pre)=-19.972-0.269N3-0.141SE-3.303 ρ spindle. The sensitivity of N3, SE, ρ spindle and multi-factorial model for distinguishing MCI and AD was 64%, 32%, 96%, 90%, and the specificity was 58%, 98%, 50%, 76%, respectively. The area under the ROC curve (AUC) of the multi-factorial model and multi-factorial model were 0.639, 0.684, 0.810 and 0.901, respectively. The AUC of multi-factorial model was significantly better than that of single-factorial models ( P<0.05). Conclusions:In AD suspected population, the multi-factorial prediction model based on N3, SE and ρ spindle has a certain predictive value in distinguishing MCI and AD. Comprehensive judgment combined with the prediction model is helpful to improve the scientificity and accuracy of diagnosis.
