Value of different clock drawing tests in identifying acute post-stroke cognitive impairment no dementia

doi:10.3969/j.issn.1004-583X.2023.02.008

Abstract

Abstract:

Objective To explore the value of three clock drawing tests (CDT) scoring systems and error types in identifying acute post-stroke cognitive impairment no dementia (PSCIND). Methods From October 2019 to April 2021, 114 patients with acute PSCIND treated in the Department of Neurology, Hebei General Hospital were as the PSCIND group, meantime, 95 healthy examination people in the Department of Physical Examination of our hospital were as the control group. The cognitive functions of these patients were assessed, including CDT scoring systems (Cahn, Babins, and three-point) and Mini Mental State Examination (MMSE),and an analysis of CDT error type was performed between the two groups. The receiver operating characteristic (ROC) curve was conducted to identify the diagnostic value of CDT, MMSE, and the two combination for PSCIND. Results Babins, Cohen of CDT scoring systems and MMSE score were independent risk factors for PSCIND. The sensitivity and the specificity of Babins, Cohen and MMSE in the diagnosis of PSCIND was (87.5% vs 58.7% vs 76.9%) and (55.8% vs 81.1% vs 61.1%), respectively. The sensitivity and specificity of Babins and Cohen combined with MMSE were 57.5% and 70.2%, and 85.3% and 75.8% respectively. The analysis of error types found that the two groups of common error types were mainly concept loss (type Ⅳ error), space plan error (type Ⅴ error) and drawing difficulty (type Ⅱ error). Compared with the control group, PSCIND group accounted for more type Ⅱ errors, stimulus-limiting response (type Ⅲ errors), type Ⅳ errors and repetition (type Ⅵ errors) (all P<0.05). Conclusion The CDT score applied alone fails to identify PSCIND well, while Cohen method of CDT score is eligible to identify PSCIND well with high sensitivity and specificity after being combined with MMSE score. CDT error types are eligible to well distinguish PSCIND and control persons and be used as a preliminary screening tool for PSCIND.

Key words: cognition disorder, stroke, clock drawing test, sensitivity and specificity

CLC Number:

R741

Jia Yangjuan, Han Ning, Guo Hui, Li Cancan, Li Jianguo. Value of different clock drawing tests in identifying acute post-stroke cognitive impairment no dementia[J]. Clinical Focus, 2023, 38(2): 143-148.

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URL: https://huicui.hebmu.edu.cn/EN/10.3969/j.issn.1004-583X.2023.02.008

https://huicui.hebmu.edu.cn/EN/Y2023/V38/I2/143

Figures/Tables 7

Tab.1 Comparison of clinical data between the two groups

Tab.2 Analysis of relevant risk factors of PSCIND

因素	回归系数	标准误	Wald χ²值	$P$ 值	$O R$ 值	95% $C I$
因素	回归系数	标准误	Wald χ²值	$P$ 值	$O R$ 值	下限	上限
Babins法	-0.261	0.055	22.406	0.000	0.770	0.691	0.858
Cohen法	-0.315	0.067	22.231	0.000	0.730	0.640	0.832
3分法	-0.420	0.215	3.817	0.051	0.657	0.431	1.001
MMSE评分	-0.405	0.067	36.142	0.000	0.667	0.584	0.761

Tab.2 Analysis of relevant risk factors of PSCIND

因素	回归系数	标准误	Wald χ²值	$P$ 值	$O R$ 值	95% $C I$
因素	回归系数	标准误	Wald χ²值	$P$ 值	$O R$ 值	下限	上限
Babins法	-0.261	0.055	22.406	0.000	0.770	0.691	0.858
Cohen法	-0.315	0.067	22.231	0.000	0.730	0.640	0.832
3分法	-0.420	0.215	3.817	0.051	0.657	0.431	1.001
MMSE评分	-0.405	0.067	36.142	0.000	0.667	0.584	0.761

Fig.1 ROC curve of PSCIND predicted by MMSE and different CDT scoring methods

Tab.3 The predictive value of different CDT scoring methods and MMSE on PSCIND

因素	AUC	标准误	$P$ 值	95% $C I$		敏感度	特异度	约登指数	界值
因素	AUC	标准误	$P$ 值	下限	上限	敏感度	特异度	约登指数	界值
CDT(Babins法)	0.759	0.035	0.000	0.691	0.826	87.5	55.8	0.433	16.5
CDT(Cohen法)	0.726	0.036	0.000	0.655	0.796	58.7	81.1	0.398	15.5
MMSE评分	0.752	0.034	0.000	0.686	0.818	76.9	61.1	0.385	26.5

Tab.3 The predictive value of different CDT scoring methods and MMSE on PSCIND

因素	AUC	标准误	$P$ 值	95% $C I$		敏感度	特异度	约登指数	界值
因素	AUC	标准误	$P$ 值	下限	上限	敏感度	特异度	约登指数	界值
CDT(Babins法)	0.759	0.035	0.000	0.691	0.826	87.5	55.8	0.433	16.5
CDT(Cohen法)	0.726	0.036	0.000	0.655	0.796	58.7	81.1	0.398	15.5
MMSE评分	0.752	0.034	0.000	0.686	0.818	76.9	61.1	0.385	26.5

Fig.2 ROC curve of PSCIND predicted by different CDT scoring methods combined with MMSE

Tab.4 Prediction value of different CDT scoring methods combined with MMSE on PSCIND

因素	AUC	标准误	$P$ 值	95% $C I$		敏感度	特异度	约登指数	界值
因素	AUC	标准误	$P$ 值	下限	上限	敏感度	特异度	约登指数	界值
MMSE+CDT(Babins法)	0.798	0.031	0.000	0.738	0.859	57.5	85.3	0.428	38.5
MMSE+CDT(Cohen法)	0.805	0.030	0.000	0.745	0.864	70.2	75.8	0.460	41.5

Tab.4 Prediction value of different CDT scoring methods combined with MMSE on PSCIND

因素	AUC	标准误	$P$ 值	95% $C I$		敏感度	特异度	约登指数	界值
因素	AUC	标准误	$P$ 值	下限	上限	敏感度	特异度	约登指数	界值
MMSE+CDT(Babins法)	0.798	0.031	0.000	0.738	0.859	57.5	85.3	0.428	38.5
MMSE+CDT(Cohen法)	0.805	0.030	0.000	0.745	0.864	70.2	75.8	0.460	41.5

Tab.5 Comparison of CDT error types in the two groups[cases (%)]

组别	例数	Ⅰ类错误	Ⅱ类错误	Ⅲ类错误	Ⅳ类错误	Ⅴ类错误	Ⅵ类错误
对照组	95	10(10.5)	9(9.5)	8(8.4)	14(14.7)	12(12.6)	5(5.3)
PSCIND组	104	21(20.2)	36(34.6)	21(20.2)	31(29.8)	22(21.2)	16(15.4)
χ²值		3.527	17.934	5.526	6.444	2.545	148.24
$P$ 值		0.060	0.000	0.019	0.011	0.111	<0.01

Tab.5 Comparison of CDT error types in the two groups[cases (%)]

组别	例数	Ⅰ类错误	Ⅱ类错误	Ⅲ类错误	Ⅳ类错误	Ⅴ类错误	Ⅵ类错误
对照组	95	10(10.5)	9(9.5)	8(8.4)	14(14.7)	12(12.6)	5(5.3)
PSCIND组	104	21(20.2)	36(34.6)	21(20.2)	31(29.8)	22(21.2)	16(15.4)
χ²值		3.527	17.934	5.526	6.444	2.545	148.24
$P$ 值		0.060	0.000	0.019	0.011	0.111	<0.01

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