Can the Automated Neuropsychological Assessment Metrics (ANAM) Predict Cognitive Impairment Compared to a Comprehensive Neuropsychological Battery in Patients with Lupus?

Zahi Touma¹, Dorcas Beaton², Carmela Tartaglia³, Lesley Ruttan⁴, Sabrina Lombardi⁴, Nicole Anderson⁵, Jiandong Su⁶, Kenneth Colosimo⁷, Michelle Vitti⁷, Dennisse Bonilla⁸, Joan E. Wither⁹, Marvin J. Fritzler¹⁰ and Robin Green¹¹, ¹Rheumatology, University of Toronto, Division of Rheumatology, Institute of Health Policy, Management and Evaluation, Toronto, ON, Canada, ²Mobility Program Clinical Research Unit, St Michael's Hospital, Toronto, ON, Canada, ³University of Toronto, Krembil Neurosciences Centre, Toronto, ON, Canada, ⁴Toronto Rehabilitation Institute, Toronto, ON, Canada, ⁵Division of Rheumatology, Toronto Western Hospital and University of Toronto, Toronto, ON, Canada, ⁶Rheumatology, Centre for Prognosis Studies in the Rheumatic Diseases, Toronto Western Hospital and University of Toronto, Toronto, ON, Canada, ⁷University of Toronto, Toronto Western Hospital, Toronto, ON, Canada, ⁸Krembil Research Institute, University Health Network, Toronto, ON, Canada, ⁹Rheumatology, University of Toronto, Toronto Western Hospital, Toronto, ON, Canada, ¹⁰Medicine, University of Calgary, Calgary, AB, Canada, ¹¹Brain and Therapeutics, Toronto Rehabilitation Institute, Toronto, ON, Canada

Meeting: 2017 ACR/ARHP Annual Meeting

Date of first publication: September 18, 2017

Keywords: Cognitive dysfunction and systemic lupus erythematosus (SLE)

Session Information

Date: Tuesday, November 7, 2017

Title: Systemic Lupus Erythematosus – Clinical Aspects and Treatment IV: Neuropsychiatric Disease and Health Economics

Session Type: ACR Concurrent Abstract Session

Session Time: 2:30PM-4:00PM

Background/Purpose: Currently, the diagnosis of cognitive impairment (CI) is often delayed requiring use of a comprehensive battery (CB) which imposes a time- and cost-burden. It would therefore be beneficial to have a more expedient CI screening tool that could be applied in the ambulatory clinic setting. The Automated Neuropsychological Assessment Metrics (ANAM) is a computerized tool that can be used to screen for CI. We determined the ability of ANAM (v4) GNS Battery to predict CI in patients with systemic lupus erythematosus (SLE).

Methods: Consecutive consenting SLE patients (n=98), aged 18-65 years, who attended a single center between July 2016 and April 2017 were recruited. Participants were administered the ANAM and CB on the same day. ANAM throughput scores were used to provide an estimate of ‘cognitive efficiency’. Patient scores on the ANAM and CB were compared to a normative sample of age- and gender-matched healthy controls to obtain z-scores.

The CB evaluates the following major cognitive domains: manual motor speed and dexterity, simple attention and processing speed, visual-spatial construction, verbal fluency, learning and memory (visuospatial and memory), and executive functioning (untimed and timed). ANAM evaluates the following major cognitive domains: attention and processing speed, memory, visual-spatial processing, executive functioning, abstract language function and fine motor processing. Tests comprising the CB and ANAM are listed in Table 1.

CI was operationalized on the CB and ANAM as a z-score of ≤ -1.5 on ≥2 domains or a z-score ≤-2.0 on ≥1 domains, or either (Table 2).

The performance of ANAM was compared against the CB using different CI definitions. Descriptive analysis was used to determine prevalence, sensitivity (Sn), specificity (Sp), Positive Predictive Value (PPV) and Negative Predictive Value (NPV).

Results: Of the 98 patients (90.8% female), the mean age at SLE diagnosis was 28.5 ± 10.2 and disease duration at enrolment was 15.5 ± 10.0 years. Prevalence of CI using CB ranged between 40.0-44.8 % (z≤-1.5 in ≥2 domains and z≤-2.0 in ≥1 domains, respectively) and 55.2% for either. Prevalence of CI using the ANAM ranged between 30.8-39.3% % (z≤-1.5 in ≥2 domains and z≤-2.0 in ≥1 domains, respectively) and 43.0% for either.

ANAM sensitivity/specificity was 52/73% and PPV/NPV was 70/55% [based on z≤-1.5 in ≥2 domains or z≤-2.0 in ≥1 domains for ANAM and CB (corresponding for A+B and E+F in Table 2)].

Conclusion: ANAM is a promising tool for the assessment of CI in SLE. Future studies are required to determine if the sensitivity of the ANAM can be improved against the current CB.

Table 1. Comprehensive battery and ANAM domains and subtests
Comprehensive Battery		ANAM (v4) GNS Battery
Domains	Comprehensive Battery Subtest	Domains	ANAM Subtest
Manual motor speed and dexterity	Finger Tapping Test Dominant Hand Non-Dominant Hand	Attention and processing speed	Running memory Procedural reaction time Choice reaction time Simple reaction time 1
Simple attention and processing speed	Trails A Stroop colour naming Stroop word reading	Memory	Code substitution delay Match to sample
Visual-spatial construction	RCFT Copy	Visual-spatial processing	Spatial processing
Verbal fluency	COWAT ANIMALS	Executive functioning	Math processing Go no go hits Tower test
Learning and memory Visuospatial Verbal	RCFT Delay Recall RCFT Delay Recognition HVLT-R Delayed Recall HVLT-R Recognition HVLT-R total recall	Abstract language function	Logical relations
Executive Functioning Untimed Executive timed	Stroop (interference score) WAIS Letter-Number Consonant Trigrams (used lower value from 18 second or 36 second) WAIS-III Digit Symbol Trails B	Learning (implicit learning)	Code substitution learning
		Fine motor processing	Tapping right hand Tapping left hand

Table 2. Performance of ANAM against the comprehensive battery
	Definitions of CI		Comprehensive Battery
			z≤-1.5 in ≥2 domains	z≤-2.0 in ≥1 domains
			E Sn/Sp PPV/NNV	F Sn/Sp PPV/NNV	E + F Sn/Sp PPV/NNV
ANAM	z≤-1.5 in ≥2 domains	A	45/81% 62/68%	37/76% 55/61%	37/80% 69/51%
	z≤-2.0 in ≥1 domains	B	55/76% 61/71%	49/72% 58/65%	50/80% 75/57%
		A+B	55/69% 55/69%	51/67% 55/64%	52/73% 70/55%
Sn sensitivity; Sp specificity; PPV Positive Predictive Value/ NPV Negative Predictive Value

Disclosure: Z. Touma, None; D. Beaton, None; C. Tartaglia, None; L. Ruttan, None; S. Lombardi, None; N. Anderson, None; J. Su, None; K. Colosimo, None; M. Vitti, None; D. Bonilla, None; J. E. Wither, None; M. J. Fritzler, Inova Diagnostics, Inc., 5; R. Green, None.

To cite this abstract in AMA style:

Touma Z, Beaton D, Tartaglia C, Ruttan L, Lombardi S, Anderson N, Su J, Colosimo K, Vitti M, Bonilla D, Wither JE, Fritzler MJ, Green R. Can the Automated Neuropsychological Assessment Metrics (ANAM) Predict Cognitive Impairment Compared to a Comprehensive Neuropsychological Battery in Patients with Lupus? [abstract]. Arthritis Rheumatol. 2017; 69 (suppl 10). https://acrabstracts.org/abstract/can-the-automated-neuropsychological-assessment-metrics-anam-predict-cognitive-impairment-compared-to-a-comprehensive-neuropsychological-battery-in-patients-with-lupus/. Accessed .

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