Accuracy of the Brief Cognitive Screening Battery for diagnosing Alzheimer's disease defined by cerebrospinal fluid biomarkers and AT(N) classification: a case-control study

Peles, Patrícia Regina Henrique; Salvador, Larissa de Souza; Souza, Leonardo Cruz de; Caramelli, Paulo

doi:10.1590/0004-282X-ANP-2021-0012

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Article • Arq. Neuro-Psiquiatr. 80 (1) • Jan 2022 • https://doi.org/10.1590/0004-282X-ANP-2021-0012 copy

Accuracy of the Brief Cognitive Screening Battery for diagnosing Alzheimer's disease defined by cerebrospinal fluid biomarkers and AT(N) classification: a case-control study

Acurácia da Bateria Breve de Rastreio Cognitivo no diagnóstico da doença de Alzheimer definida por biomarcadores no líquido cefalorraquidiano e classificação pelo sistema AT(N): estudo caso-controle

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

Background:

Validation of cognitive instruments for detection of Alzheimer's disease (AD) based on correlation with diagnostic biomarkers allows more reliable identification of the disease.

Objectives:

To investigate the accuracy of the Brief Cognitive Screening Battery (BCSB) in the differential diagnosis between AD, non-AD cognitive impairment (both defined by cerebrospinal fluid [CSF] biomarkers) and healthy cognition, and to correlate CSF biomarker results with cognitive performance.

Methods:

Overall, 117 individuals were evaluated: 45 patients with mild cognitive impairment (MCI) or mild dementia within the AD continuum defined by the AT(N) classification [A+T+/-(N)+/]; 27 non-AD patients with MCI or mild dementia [A-T+/-(N)+/-]; and 45 cognitively healthy individuals without CSF biomarker results. All participants underwent evaluation using the BCSB.

Results:

The total BCSB and delayed recall (DR) scores of the BCSB memory test showed high diagnostic accuracy, as indicated by areas under the ROC curve (AUC): 0.89 and 0.87, respectively, for discrimination between AD and non-AD versus cognitively healthy controls. Similarly, total BCSB and DR displayed high accuracy (AUC-ROC curves of 0.89 and 0.91, respectively) for differentiation between AD and controls. BCSB tests displayed low accuracy for differentiation between AD and non-AD. The CSF levels of biomarkers correlated significantly, though weakly, with DR.

Conclusions:

Total BCSB and DR scores presented good accuracy for differentiation between patients with a biological AD diagnosis and cognitively healthy individuals, but low accuracy for differentiating AD from non-AD patients.

Keywords:
Alzheimer Disease; Cognitive Dysfunction; Diagnosis; Biomarkers; Cognition

Subtests	AD (n = 45)	Non-AD (n = 27)	Controls (n = 45)	K	P	Post-hoc (Dunn test)	Effect size
Age	65.3 (6.5)	64.5 (6.4)	68.9 (5.6)	5.35	< 0.005	0 = 1 < 2	d = 0.31
Schooling	13.1 (5.1)	11.9 (4.6)	10.0 (5.1)	4.48	< 0.01	0 = 1 > 2	d = 0.28
Sex					p < 0.007	x² = 5.28
Men	19	17	16
Woman	26	10	29
MMSE	24.1 (2.8)	24.2 (2.1)	28.0 (1.2)	14.31	< 0.001	0 = 1 < 2	η² = 0.10
Naming	9.8 (0.5)	9.7 (0.8)	9.9 (0.2)	0.59	0.74	-	-
Inc. Mem	4.5 (1.9)	5.1 (1.8)	5.8 (1.3)	9.34	< 0.001	0 < 2; 1 = 2	η² = 0.06
Im. Mem	6.2 (1.64)	6.7 (1.6)	8.1 (1.19)	31.77	< 0.001	0 = 1 < 2	η² = 0.25
Learning	7.0 (1.7)	7.4 (2.2)	8.9 (1.0)	26.26	< 0,001	0 = 1 < 2	η² = 0.21
DR	4.5 (2.2)	6.1 (2.4)	8.3 (1.2)	48.46	< 0.001	0 < 1 < 2	η² = 0.40
Recognition	8.7 (2.0)	8.0 (2.2)	9.8 (0.4)	16.26	< 0.001	0 = 1 < 2	η² = 0.12
VF	13.3 (5.1)	11.4 (4.6)	17.7 (4.7)	25.61	< 0.001	1 < 0 < 2	η² = 0.21
CDT	6.9 (2.3)	6.6 (2.3)	8.3 (1.7)	6.48	< 0.03	-	-
BCSB total	59.4 (12.4)	59.7 (10.7)	77.1 (7.4)	31.98	< 0.001	0 = 1 < 2	η² = 0.26

	Variable	Naming	Inc Mem	Im Mem	Learning	DR	Recognition	VF	CDT	BCSB total
Clinical groups vs. Controls	AUC	0.53	0.68	0.79	0.78	0.87	0.69	0.78	0.69	0.89
	95% CI	0.43 to 0.62	0.58 to 0.76	0.71 to 0.86	0.69 to 0.851	0.79 to 0.92	0.59 to 0.77	0.69 to 0.85	0.59 to 0.78	0.83 to 0.95
	Sensitivity	5.56	65.28	51.39	76.39	80.56	48.61	68.18	63.33	88.89
	Specificity	100	64.44	93.33	71.11	77.78	86.67	80.00	75.56	81.94
	Cutoff	≤ 8	≤ 5	≤ 6	< 8	< 7	≤ 9	≤ 13	≤ 8	≤ 68
AD vs. Controls	AUC	0.53	0.71	0.82	0.82	0.91	0.70	0.75	0.67	0.89
	95% CI	0.43 to 0.62	0.60 to 0.81	0.73 to 0.89	0.72 to 0.89	0.84 to 0.96	0.60 to 0.80	0.64 to 0.83	0.56 to 0.77	0.81 to 0.94
	Sensitivity	5.57	71.11	57.78	82.22	68.89	51.11	67.44	62.16	80
	Specificity	100.00	64.44	93.33	71.11	100	86.67	73.33	75.56	91.11
	Cutoff	≤ 8	≤ 5	≤ 6	≤ 8	≤ 5	≤ 9	≤ 14	≤ 8	≤ 67
AD vs. non-AD	AUC	0.51	0.58	0.58	0.60	0.69	0.55	0.63	0.54	0.50
	95% CI	0.39 to 0.63	0.46 to 0.70	0.46 to 0.70	0.48 to 0.71	0.57 to 0.79	0.43 to 0.67	0.50 to 0.75	0.41 to 0.67	0.38 to 0.62
	Sensitivity	100	71.11	57.78	53.33	53.33	26.67	72.09	64.86	80.00
	Specificity	3.7	44.44	59.26	62.96	81.48	85.19	52.17	43.48	7.41
	Cutoff	> 5	≤ 5	≤ 6	≤ 7	≤ 4	≤ 8	> 10	> 5	> 48

	Variable	Naming	Inc Mem	Im Mem	Learning	DR	Recognition	VF	CDT	BCSB total
Clinical groups vs. Controls	Odds ratio	1.00	0.80	1.23	0.99	2.40	1.04	1.17	1.10	1.22
	95% CI	0.82 to 1.21	0.46 to 1.36	0.69 to 2.24	0.58 to 1.68	1.40 to 4.69	0.83 to 1.38	0.94 to 1.46	0.83 to 1.46	1.04 to 1.52
	p value	0.72	< 0.001	< 0.001	0.08	< 0.001	0.18	0.09	0.3	< 0.05
AD vs. Controls	Odds ratio	0.9	0.8	1.22	1.23	2.95	0.94	1.06	1.14	1.26
	95% CI	0.63 to 1.23	0.39 to 1.56	0.61 to 2.47	0.59 to 2.14	1.55 to 6.88	0.66 to 1.33	0.75 to 1.37	0.80 to 1.63	1.02 to 1.80
	p value	0.77	< 0.001	< 0.001	< 0.01	< 0.001	0.74	0.58	0.44	0.02
AD vs. Non-AD	Odds ratio	0.78	0.71	0.69	1.88	1.38	0.85	0.68	1.01	1.3
	95% CI	0.56 to 1.01	0.31 to 1.50	0.34 to 1.29	1.15 to 3.50	0.90 to 2.38	0.66 to 1.07	0.38 to 1.03	0.67 to 1.57	0.89 to 2.09
	p value	0.2	0.99	0.51	< 0.01	0.09	0.09	0.06	0.92	0.19

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[1] Correspondence:
Paulo Caramelli; Email: caramelli@ufmg.br.