Clinical Utility of Select Neuropsychological Assessment Battery Tests in Predicting Functional Abilities in Dementia

Clinical Utility of Select Neuropsychological Assessment Battery Tests in Predicting Functional... Abstract Objective Neuropsychological test performance can provide insight into functional abilities in patients with dementia, particularly in the absence of an informant. The relationship between neuropsychological measures and instrumental activities of daily living (IADLs) is unclear due to hetereogeneity in cognitive domains assessed and neuropsychological tests administered. Practical and ecologically valid performance-based measures of IADLs are also limited. The Neuropsychological Assessment Battery (NAB) is uniquely positioned to provide a dual-purpose assessment of cognitive and IADL function, as it includes Daily Living tests that simulate real-world functional tasks. We examined the utility of select NAB tests in predicting informant-reported IADLs in mild cognitive impairment and dementia. Methods The sample of 327 participants included 128 normal controls, 97 individuals with mild cognitive impairment, and 102 individuals with Alzheimer’s disease dementia from the Boston University Alzheimer’s Disease Center research registry. Informants completed the Lawton Brody Instrumental Activities of Daily Living Scale, and study participants were administered selected NAB tests that were complementary to the existing protocol. Results ROC curves showed strongest prediction of IADL (AUC > 0.90) for memory measures (List Learning delayed recall and Daily Living Memory delayed recall) and Daily Living Driving Scenes. At a predetermined level of specificity (95%), List Learning delayed recall (71%) and Daily Living Memory delayed recall (88%) were the most sensitive. The Daily Living Memory and Driving Scenes tests strongly predicted IADL status, and the other Daily Living tests contributed unique variance. Conclusions NAB memory measures and Daily Living Tests may have clinical utility in detecting informant-rated functional impairment in dementia. Alzheimer’s disease, Mild cognitive impairment, Instrumental ADLs, Ecological validity, Geriatrics Background Assessment of functional abilities is a cornerstone in the clinical evaluation of Alzheimer’s disease (AD) in order to ascertain diagnostic status and disease severity. Alzheimer’s disease represents a continuum of clinical status, ranging from normal cognition to mild cognitive impairment (MCI) to AD dementia, with the diagnosis of AD dementia being made following onset of functional impairment (Sperling et al., 2011). Functional abilities are classified into two categories: basic activities of daily living (BADL) and instrumental activities of daily living (IADL). Impairments in BADLs, or self-care tasks, are typically present in moderate-to-severe dementia. Relative to BADLs, IADLs (e.g., managing finances, driving, household chores) require greater cognitive capacity, and are thus more sensitive to dementia-related cognitive decline, with impairments beginning in the early stages (e.g., mild dementia) (Njegovan, Man-Son-Hing, Mitchell, & Molnar, 2001). Of note, although MCI is diagnostically defined to include “minimal” disturbance in IADLs (Winblad et al., 2004), there is debate on this matter (Lindbergh, Dishman, & Miller, 2016) and deficits in complex IADLs may begin even before a clinical diagnosis of MCI (i.e., preclinical dementia) (Marshall, Dekhtyar et al., 2015; Marshall, Zoller et al., 2015). Assessment of IADLs may be a time- and cost-efficient method for the early detection of dementia (Marshall, Dekhtyar et al., 2015; Marshall, Zoller et al., 2015; Zoller et al., 2014) that could help facilitate timely treatment planning and initiation of disease modifying interventions. Traditionally, a description of the patient’s daily functioning by a reliable informant is optimal to evaluate IADLs and ascertain dementia status (Farias, Mungas, & Jagust, 2005). A number of informant-based methods for evaluating IADLs exist, such as the Lawton Brody Instrumental Activities of Daily Living Scale (LBIADL; Gold, 2012; Lawton & Brody, 1970; Strauss, Sherman, & Spreen, 2006). Clinical evaluations of dementia, however, are often conducted in the absence of an informant, and clinicians must rely on the patient’s self-report to determine functional status, which can lack reliability (Farias et al., 2005) due to memory impairment and/or anosognosia (Vogel et al., 2004). Scores from neuropsychological tests administered as part of dementia evaluations can provide insight into an individual’s IADL capabilities in the absence of an informant. A number of neuropsychological measures have been identified as strong determinants of IADL impairment in dementia, with robust effects for tests of memory and executive function (de Paula et al., 2015; Jefferson, Paul, Ozonoff, & Cohen, 2006; Mlinac & Feng, 2016; Razani et al., 2011; Tomaszewski Farias et al., 2009). However, the literature on the relationship between neuropsychological test performance and IADLs is not entirely consistent largely due to heterogeneity in the cognitive domains assessed and the neuropsychological tests administered, and differences in disease status across samples (de Paula et al., 2015; Gold, 2012; Mlinac & Feng, 2016). The relationship between cognitive function and IADLs in AD also remains poorly understood due to minimal research conducted in samples that span the dementia continuum, and poor operationalization of IADLs (e.g., patient self-report, non-validated IADL measures) (Gold, 2012). Performance-based IADL measures (i.e., direct observation of the patient performing a simulated IADL task) are optimal and can potentially detect functional decline in the early stages of AD when deficits are subtle (Burton, Strauss, Bunce, Hunter, & Hultsch, 2009). Recent diagnostic guidelines encourage the use of performance-based tasks that are sensitive to cognitively complex ADLs in order to detect preclinical AD changes in functioning (Marson, 2015). Although several performance-based measures have been developed that can accurately distinguish clinically normal elderly from MCI, they are typically time intensive (most requiring >30 min) (Binegar, Hynan, Lacritz, Weiner, & Cullum, 2009; Chisholm, Toto, Raina, Holm, & Rogers, 2014; Cullum et al., 2001; Diehl et al., 2005; Farina et al., 2010; Foster, 2014; Goldberg et al., 2010; Heaton et al., 2004; Loewenstein et al., 1989; McDougall, Becker, Vaughan, Acee, & Delville, 2009; Patterson, Goldman, McKibbin, Hughs, & Jeste, 2001; Sadek, Stricker, Adair, & Haaland, 2011; Triebel et al., 2009; Weening-Dijksterhuis, Kamsma, & Van Heuvelen, 2010), with the exception of the Harvard Automated Phone Task that takes only 10 min (Marshall, Dekhtyar et al., 2015; Marshall, Zoller et al., 2015). The scope of the Harvard Automated Phone Task, and other existing performance-based IADL measures, is limited to the isolated assessment of complex functional capabilities, and thereby requires additional time and resources to administer a complete neuropsychological test protocol to evaluate cognitive function. There is need for a practical and ecologically valid dual-purpose evaluation that assesses both neuropsychological function and IADL status across the dementia clinical spectrum. The Neuropsychological Assessment Battery (NAB) is a psychometrically sound cognitive test battery that may fulfill such purpose. The NAB includes 33 tests, grouped within five modules encompassing the major domains of neuropsychological functioning (Attention, Language, Spatial, Memory, and Executive Function). All tests were co-normed on the same sample of individuals (n = 1448), with demographic corrections available for age, sex, and education. Because the normative samples included a large proportion of individuals ages 60–97 (n = 841), the NAB is well suited for use in dementia evaluations. Each of the five NAB modules includes a Daily Living test designed to simulate real-world tasks of everyday living associated with the cognitive domain of the module. Each NAB test includes an equivalent/alternate form for repeat testing (Stern & White, 2003). Several NAB tests have been shown to have excellent diagnostic classification accuracy in AD (Gavett et al., 2009, 2010, 2012), and NAB tests, including the Daily Living tests, have been shown to be associated with IADLs in samples with mixed disorders or other non-AD conditions (Cahn-Weiner, Wittenberg, & McDonald, 2009; MacDougall & Mansbach, 2013; Sadek et al., 2011; Zgaljardic, Yancy, Temple, Watford, & Miller, 2011). Two tests from the NAB (Mazes (Niewoehner et al., 2012) and Driving Scenes (Brown et al., 2005; Stern et al., 2016)) have been found to predict on-road driving in older subjects across the AD spectrum. The present study is the first to investigate the ability of select NAB tests to predict informant-reported IADL function in a sample of normal controls (NC) and subjects with MCI and AD dementia from the Boston University Alzheimer’s Disease Center (BU ADC) participant registry. It was hypothesized that the NAB memory and Daily Living tests used in this study would be strong predictors of IADL status. Methods Participants The present sample included 327 subjects (128 NC, 97 with MCI, and 102 with AD dementia) from the BU ADC Clinical Core registry. The BU ADC is one of 27 centers funded by the National Institute on Aging (NIA) that contributes data to the National Alzheimer’s Coordinating Center (NACC). The BU ADC registry, including participant recruitment and inclusion/exclusion criteria, has been described elsewhere (Ashendorf, Jefferson, Green, & Stern, 2009; Galetta et al., 2016, 2012; Jefferson et al., 2007). Briefly, participants are included if they are community-dwelling, English-speaking, and have an available informant who can provide collateral information about daily functioning. Exclusion criteria include a history of major psychiatric illness (e.g., schizophrenia, bipolar disorder), neurological illness other than AD (e.g., stroke or epilepsy), or significant traumatic brain injury with loss of consciousness. The sample included new participants diagnosed with normal cognition, MCI, or AD dementia based on their initial evaluation. The BU Medical Campus Institutional Review Board approved all BU ADC data collection procedures, and all participants (or their Legally Authorized Representatives) provided written informed consent for study participation. Diagnostic Status Diagnoses were made at BU ADC multidisciplinary consensus conferences by a team of neurologists, neuropsychologists, geriatricians, and geriatric psychiatrists. Each diagnosis was made following presentation and discussion of all evaluation data, including neuropsychological testing, neuroimaging, and psychosocial and medical history. Subjects were deemed to be NC if their objective neuropsychological test scores were all within the normal range, they had a Clinical Dementia Rating (CDR; Morris, 1993) Global Score of 0.0, and they were determined by the consensus team to be cognitively normal. MCI diagnoses followed criteria outlined by Petersen (2004). Of the MCI subjects, 37 were amnestic single domain, 27 amnestic multiple domains, 26 non-amnestic single domain, and seven non-amnestic multiple domains. The National Institute of Neurological and Communicative Disorders and Stroke and Alzheimer’s Disease and Related Disorders Association (NINCDS-ADRDA) criteria (McKhann et al., 1984) were used to diagnose subjects with “Possible AD” or “Probable AD”, who were combined into a single group with AD dementia. Participants who were instead diagnosed with a different dementia etiology were excluded from this dataset. Measures Neuropsychological Assessment Battery All subjects completed a set of NAB tests as part of a more comprehensive, single-session neuropsychological test protocol, including those tests included in the NACC Unified Data Set (Beekly et al., 2007). The NAB tests in the present study had originally been selected for inclusion in the larger BU ADC registry protocol to allow for representation of each domain. These measures included the Design Construction and Visual Discrimination tests from the Screening Module, List Learning from the Memory Module, and the Daily Living tests from the Attention, Executive, Language, and Memory modules: Driving Scenes, Judgment, Bill Payment, and Daily Living Memory, respectively. For Design Construction, the participant is asked to reproduce two-dimensional figures using plastic geometric pieces. Visual Discrimination asks the individual to identify which of four possible options is identical to a target design. List Learning features a list of 12 words that are learned over three consecutive exposure trials. Memory for the list is assessed following an interference trial, and again after a long delay. Total learning (across three trials), short-delay free recall, and long-delay free recall scores were evaluated. On Driving Scenes, a series of similar but slightly different scenes as viewed from behind a steering wheel are sequentially displayed, and the participant identifies the changes from the preceding picture. Judgment poses several questions assessing social and health-related reasoning. Bill Payment asks the participant to answer questions and perform procedures associated with paying a utility bill, including writing a check, entering it into a ledger, and addressing an envelope. Daily Living Memory assesses immediate and delayed recall for fictionalized personal information (name, address, and phone number) and medication instructions; immediate and delayed free recall scores were included in this study. Instrumental activities of daily living The Lawton & Brody Activities of Daily Living Scale (LBADL; Jefferson et al., 2008; Lawton & Brody, 1970) evaluated IADL performance. The LBADL is a 14-item rating scale that involves informant ratings of the subject’s level of dependence across six basic (e.g., feeding, dressing) and eight instrumental (e.g., financial management, driving, shopping) activities of daily living. The scale is sensitive to disease status and quality of life in individuals with AD (Barberger‐Gateau et al., 1992; Lawton, 1994; Wlodarczyk, Brodaty, & Hawthorne, 2004). Each skill is rated on a 3-point scale (2 = fully independent, 0 = fully dependent). Only the total score for IADL items (LB; range = 0–16) was examined in this study. To assess whether the select NAB tests differentially predicted informant-reported daily living skills, binary informant ratings (complaint/no complaint) of each participant’s judgment and problem-solving skills and bill payment abilities were also included in these analyses. These subjective ratings were assessed separately from the IADL questionnaire during each visit. Mini-Mental State Examination All participants were administered the Mini-Mental State Examination (MMSE) (Folstein, Folstein, & McHugh, 1975) to characterize the global cognitive status of the sample. Statistical Analyses Analyses were conducted using IBM SPSS Statistics version 24. Determination of intact versus impaired daily functioning on the LB was made by evaluating cumulative frequencies of performance at each level within control and dementia subgroups and selecting the cutoff value with the highest differential diagnostic accuracy. All three diagnostic groups were then combined into a single group. Receiver Operating Characteristic (ROC) curves examined the classification accuracy of each of the neuropsychological measures in predicting overall LB scores, as well as individual informant-reported skill assessments (i.e., bill payment and judgment). Sensitivity and specificity values for select NAB cutoff scores were calculated. To avoid overpathologizing scores due to normal variability in cognitive performance, cutoff scores that yielded low false positive rates (at least 95% specificity) were identified for each of the neuropsychological measures. Linear regression analyses examined the relative contributions of neuropsychological abilities (measured by raw neuropsychological test scores) to informant-reported functional ratings. Age, gender, and education were entered as covariates in the regression analyses. Results Sample Characteristics Means and standard deviations for all measures are reported in Table 1. The overall raw scores are provided, as well as the data for subsamples classified by functional abilities (groups with high LB scores and low LB scores). To create a clinically meaningful cutoff on the LB scale, we compared those with complaints who were clinically determined to have intact ADLs (the MCI sample) with those who had impaired ADLs (the Dementia sample). While LB scores were available for the consensus diagnostic process, potentially creating a tautological concern with this classification method, ADL capacity was also determined using multiple other sources of data, prominently including the CDR interview, making this a less salient concern for the present analysis. The resulting ROC curve had AUC = 0.941, demonstrating an effective classification accuracy. At the potential cutoff score with the greatest hit rate, only 4% of individuals with MCI (four cases) had LB scores <15, whereas 89% of those diagnosed with dementia were scored below that level (91 cases). Those with LB scores below 15 were therefore considered to have impaired ADLs. Individuals with greater functional impairment according to LB score were older, t(325) = −4.05, p < .001, less educated, t(325) = 2.71, p = .007, more likely to be men, χ2 = 7.52, p = .006, and more likely to be White, χ2 = 7.78, p = .02. As expected, this lower-functioning group also performed more poorly on the MMSE, t(102.24) = 14.22, p < .001. Table 1. Descriptive statistics Total sample LB intact LB impaired N 327 232 95 Age: M(SD) years 73.5 (8.2) 72.4 (7.5) 76.4 (9.2)*** Education: M(SD) years 15.7 (2.8) 15.9 (2.7) 15.0 (2.9)** Gender: % women 59.1 63.8 47.4** Race (%)  White 81.4 78.4 89.5*  African American 17.4 20.7 8.4  Asian 1.2 0.9 2.1 LB: M(SD) range 13.9 (3.7) 15.9 (0.4) 9.1 (3.8)*** 0–16 15–16 0–14 MMSE: M(SD) 27.1 (3.9) 29.0 (1.4) 22.6 (4.3)*** DES: M(SD) 6.6 (4.1) 7.2 (4.1) 4.4 (3.7) *** VIS: M(SD) 6.6 (4.1) 7.2 (4.1) 4.4 (3.7) *** Driving Scenes: M(SD) 39.2 (12.7) 45.0 (7.9) 25.0 (11.0) *** LLA-irc: M(SD) 18.3 (7.3) 21.5 (5.4) 10.1 (4.6) *** LLA-sd: M(SD) 5.3 (3.8) 7.0 (3.0) 0.9 (1.5) *** LLA-ld: M(SD) 5.1 (3.9) 6.8 (3.2) 0.6 (1.4) *** DLM-irc: M(SD) 37.2 (9.8) 41.1 (6.3) 25.5 (9.2) *** DLM-drc: M(SD) 10.5 (5.8) 13.0 (3.6) 2.5 (3.8) *** Bill Payment: M(SD) 16.4 (4.0) 17.8 (1.8) 12.5 (5.8) *** Judgment: M(SD) 15.1 (3.0) 15.8 (2.3) 12.8 (3.8) *** Total sample LB intact LB impaired N 327 232 95 Age: M(SD) years 73.5 (8.2) 72.4 (7.5) 76.4 (9.2)*** Education: M(SD) years 15.7 (2.8) 15.9 (2.7) 15.0 (2.9)** Gender: % women 59.1 63.8 47.4** Race (%)  White 81.4 78.4 89.5*  African American 17.4 20.7 8.4  Asian 1.2 0.9 2.1 LB: M(SD) range 13.9 (3.7) 15.9 (0.4) 9.1 (3.8)*** 0–16 15–16 0–14 MMSE: M(SD) 27.1 (3.9) 29.0 (1.4) 22.6 (4.3)*** DES: M(SD) 6.6 (4.1) 7.2 (4.1) 4.4 (3.7) *** VIS: M(SD) 6.6 (4.1) 7.2 (4.1) 4.4 (3.7) *** Driving Scenes: M(SD) 39.2 (12.7) 45.0 (7.9) 25.0 (11.0) *** LLA-irc: M(SD) 18.3 (7.3) 21.5 (5.4) 10.1 (4.6) *** LLA-sd: M(SD) 5.3 (3.8) 7.0 (3.0) 0.9 (1.5) *** LLA-ld: M(SD) 5.1 (3.9) 6.8 (3.2) 0.6 (1.4) *** DLM-irc: M(SD) 37.2 (9.8) 41.1 (6.3) 25.5 (9.2) *** DLM-drc: M(SD) 10.5 (5.8) 13.0 (3.6) 2.5 (3.8) *** Bill Payment: M(SD) 16.4 (4.0) 17.8 (1.8) 12.5 (5.8) *** Judgment: M(SD) 15.1 (3.0) 15.8 (2.3) 12.8 (3.8) *** Note: For Intact/Impaired contrasts: *p < .05; **p < .01; ***p < .001; M = mean; SD = standard deviation; LB = Instrumental Activities of Daily Living and Physical Self-Maintenance Scale; MMSE = Mini Mental State Examination; DES = Design Construction; VIS = Visual Discrimination; LLA = List Learning (list A); irc = Immediate Recall; SD = Short-delay recall; LD = long-delay recall; DLM = Daily Living Memory; drc = Delayed Recall. Table 1. Descriptive statistics Total sample LB intact LB impaired N 327 232 95 Age: M(SD) years 73.5 (8.2) 72.4 (7.5) 76.4 (9.2)*** Education: M(SD) years 15.7 (2.8) 15.9 (2.7) 15.0 (2.9)** Gender: % women 59.1 63.8 47.4** Race (%)  White 81.4 78.4 89.5*  African American 17.4 20.7 8.4  Asian 1.2 0.9 2.1 LB: M(SD) range 13.9 (3.7) 15.9 (0.4) 9.1 (3.8)*** 0–16 15–16 0–14 MMSE: M(SD) 27.1 (3.9) 29.0 (1.4) 22.6 (4.3)*** DES: M(SD) 6.6 (4.1) 7.2 (4.1) 4.4 (3.7) *** VIS: M(SD) 6.6 (4.1) 7.2 (4.1) 4.4 (3.7) *** Driving Scenes: M(SD) 39.2 (12.7) 45.0 (7.9) 25.0 (11.0) *** LLA-irc: M(SD) 18.3 (7.3) 21.5 (5.4) 10.1 (4.6) *** LLA-sd: M(SD) 5.3 (3.8) 7.0 (3.0) 0.9 (1.5) *** LLA-ld: M(SD) 5.1 (3.9) 6.8 (3.2) 0.6 (1.4) *** DLM-irc: M(SD) 37.2 (9.8) 41.1 (6.3) 25.5 (9.2) *** DLM-drc: M(SD) 10.5 (5.8) 13.0 (3.6) 2.5 (3.8) *** Bill Payment: M(SD) 16.4 (4.0) 17.8 (1.8) 12.5 (5.8) *** Judgment: M(SD) 15.1 (3.0) 15.8 (2.3) 12.8 (3.8) *** Total sample LB intact LB impaired N 327 232 95 Age: M(SD) years 73.5 (8.2) 72.4 (7.5) 76.4 (9.2)*** Education: M(SD) years 15.7 (2.8) 15.9 (2.7) 15.0 (2.9)** Gender: % women 59.1 63.8 47.4** Race (%)  White 81.4 78.4 89.5*  African American 17.4 20.7 8.4  Asian 1.2 0.9 2.1 LB: M(SD) range 13.9 (3.7) 15.9 (0.4) 9.1 (3.8)*** 0–16 15–16 0–14 MMSE: M(SD) 27.1 (3.9) 29.0 (1.4) 22.6 (4.3)*** DES: M(SD) 6.6 (4.1) 7.2 (4.1) 4.4 (3.7) *** VIS: M(SD) 6.6 (4.1) 7.2 (4.1) 4.4 (3.7) *** Driving Scenes: M(SD) 39.2 (12.7) 45.0 (7.9) 25.0 (11.0) *** LLA-irc: M(SD) 18.3 (7.3) 21.5 (5.4) 10.1 (4.6) *** LLA-sd: M(SD) 5.3 (3.8) 7.0 (3.0) 0.9 (1.5) *** LLA-ld: M(SD) 5.1 (3.9) 6.8 (3.2) 0.6 (1.4) *** DLM-irc: M(SD) 37.2 (9.8) 41.1 (6.3) 25.5 (9.2) *** DLM-drc: M(SD) 10.5 (5.8) 13.0 (3.6) 2.5 (3.8) *** Bill Payment: M(SD) 16.4 (4.0) 17.8 (1.8) 12.5 (5.8) *** Judgment: M(SD) 15.1 (3.0) 15.8 (2.3) 12.8 (3.8) *** Note: For Intact/Impaired contrasts: *p < .05; **p < .01; ***p < .001; M = mean; SD = standard deviation; LB = Instrumental Activities of Daily Living and Physical Self-Maintenance Scale; MMSE = Mini Mental State Examination; DES = Design Construction; VIS = Visual Discrimination; LLA = List Learning (list A); irc = Immediate Recall; SD = Short-delay recall; LD = long-delay recall; DLM = Daily Living Memory; drc = Delayed Recall. Classification accuracy statistics ROC analyses (Table 2) showed good classification accuracy for all NAB measures, but were strongest for List Learning delayed recall, Daily Living Memory delayed recall, and Driving Scenes across all informant-reported variables, with areas under the curve (AUC) exceeding 0.90. Daily living proxy measures were not found to be differentially predictive of their target skills. That is, scores on NAB measures of bill payment skill, memory for day-to-day information, and judgment did not respectively correspond to informant-reported scores for bill management and judgment. However, two of the four Daily Living measures (i.e., Driving Scenes and Daily Living Memory) were among the strongest predictors of each of the global and individual measures of functional impairment. Table 2. Classification accuracy (as measured by ROC area under the curve, AUC) of NAB subtests in the prediction of informant-rated daily living skills LB Bills Judgment DES 0.698 0.676 0.694 VIS 0.699 0.684 0.717 Driving Scenes 0.928 0.892 0.920 LLA-irc 0.946 0.896 0.932 LLA-sd 0.951 0.906 0.928 LLA-ld 0.947 0.903 0.929 DLM-irc 0.919 0.878 0.913 DLM-drc 0.951 0.905 0.941 Bill Payment 0.835 (0.779, 0.874) 0.806 0.835 Judgment 0.736 0.713 0.747 LB Bills Judgment DES 0.698 0.676 0.694 VIS 0.699 0.684 0.717 Driving Scenes 0.928 0.892 0.920 LLA-irc 0.946 0.896 0.932 LLA-sd 0.951 0.906 0.928 LLA-ld 0.947 0.903 0.929 DLM-irc 0.919 0.878 0.913 DLM-drc 0.951 0.905 0.941 Bill Payment 0.835 (0.779, 0.874) 0.806 0.835 Judgment 0.736 0.713 0.747 Note: Numbers in parentheses are 95% confidence intervals. LB = Instrumental Activities of Daily Living and Physical Self-Maintenance Scale; DES = Design Construction; VIS = Visual Discrimination; LLA = List Learning (list A); irc = Immediate Recall; sd = Short-delay recall; ld = long-delay recall; DLM = Daily Living Memory; drc = Delayed Recall. Table 2. Classification accuracy (as measured by ROC area under the curve, AUC) of NAB subtests in the prediction of informant-rated daily living skills LB Bills Judgment DES 0.698 0.676 0.694 VIS 0.699 0.684 0.717 Driving Scenes 0.928 0.892 0.920 LLA-irc 0.946 0.896 0.932 LLA-sd 0.951 0.906 0.928 LLA-ld 0.947 0.903 0.929 DLM-irc 0.919 0.878 0.913 DLM-drc 0.951 0.905 0.941 Bill Payment 0.835 (0.779, 0.874) 0.806 0.835 Judgment 0.736 0.713 0.747 LB Bills Judgment DES 0.698 0.676 0.694 VIS 0.699 0.684 0.717 Driving Scenes 0.928 0.892 0.920 LLA-irc 0.946 0.896 0.932 LLA-sd 0.951 0.906 0.928 LLA-ld 0.947 0.903 0.929 DLM-irc 0.919 0.878 0.913 DLM-drc 0.951 0.905 0.941 Bill Payment 0.835 (0.779, 0.874) 0.806 0.835 Judgment 0.736 0.713 0.747 Note: Numbers in parentheses are 95% confidence intervals. LB = Instrumental Activities of Daily Living and Physical Self-Maintenance Scale; DES = Design Construction; VIS = Visual Discrimination; LLA = List Learning (list A); irc = Immediate Recall; sd = Short-delay recall; ld = long-delay recall; DLM = Daily Living Memory; drc = Delayed Recall. A similar pattern was found when examining classification accuracy of individual cutoff scores on the NAB tests (Table 3). At a predetermined level of specificity (95%), the highest sensitivity to impairment on the LB was found for the Daily Living Memory delayed recall trial (sensitivity = 88%). Targeted NAB Daily Living tests (i.e., Bill Payment and Judgment) did not preferentially predict their corresponding informant-reported ADL skills. Given the weak classification accuracy of the two NAB visuospatial measures based on ROC AUC (see Table 2), sensitivity values were not calculated. Table 3. Sensitivity and specificity of neuropsychological tests in the prediction of informant-rated daily living skills. LB Bill Payment Judgment Cutoff SN SP Cutoff SN SP Cutoff SN SP Driving Scenes <30 55 96 <30 59 96 <30 63 96 LLA-irc <14 66 95 <11 47 96 <12 60 96 LLA-sd <2 78 95 <1 54 95 <1 61 95 LLA-ld <1 77 95 <1 67 93 <1 76 94 DLM-irc <30 63 95 <28 49 95 <30 66 96 DLM-drc <7 88 95 <6 72 95 <7 85 96 Bill Payment <14 45 96 <14 41 96 <14 45 96 Judgment <11 25 97 <11 25 98 <11 26 97 LB Bill Payment Judgment Cutoff SN SP Cutoff SN SP Cutoff SN SP Driving Scenes <30 55 96 <30 59 96 <30 63 96 LLA-irc <14 66 95 <11 47 96 <12 60 96 LLA-sd <2 78 95 <1 54 95 <1 61 95 LLA-ld <1 77 95 <1 67 93 <1 76 94 DLM-irc <30 63 95 <28 49 95 <30 66 96 DLM-drc <7 88 95 <6 72 95 <7 85 96 Bill Payment <14 45 96 <14 41 96 <14 45 96 Judgment <11 25 97 <11 25 98 <11 26 97 Note: LB = Instrumental Activities of Daily Living and Physical Self-Maintenance Scale; LLA = List Learning (list A); irc = Immediate Recall; sd = Short-delay recall; ld = long-delay recall; DLM = Daily Living Memory; drc = Delayed Recall. Table 3. Sensitivity and specificity of neuropsychological tests in the prediction of informant-rated daily living skills. LB Bill Payment Judgment Cutoff SN SP Cutoff SN SP Cutoff SN SP Driving Scenes <30 55 96 <30 59 96 <30 63 96 LLA-irc <14 66 95 <11 47 96 <12 60 96 LLA-sd <2 78 95 <1 54 95 <1 61 95 LLA-ld <1 77 95 <1 67 93 <1 76 94 DLM-irc <30 63 95 <28 49 95 <30 66 96 DLM-drc <7 88 95 <6 72 95 <7 85 96 Bill Payment <14 45 96 <14 41 96 <14 45 96 Judgment <11 25 97 <11 25 98 <11 26 97 LB Bill Payment Judgment Cutoff SN SP Cutoff SN SP Cutoff SN SP Driving Scenes <30 55 96 <30 59 96 <30 63 96 LLA-irc <14 66 95 <11 47 96 <12 60 96 LLA-sd <2 78 95 <1 54 95 <1 61 95 LLA-ld <1 77 95 <1 67 93 <1 76 94 DLM-irc <30 63 95 <28 49 95 <30 66 96 DLM-drc <7 88 95 <6 72 95 <7 85 96 Bill Payment <14 45 96 <14 41 96 <14 45 96 Judgment <11 25 97 <11 25 98 <11 26 97 Note: LB = Instrumental Activities of Daily Living and Physical Self-Maintenance Scale; LLA = List Learning (list A); irc = Immediate Recall; sd = Short-delay recall; ld = long-delay recall; DLM = Daily Living Memory; drc = Delayed Recall. Neuropsychological variables as predictors of IADL ratings Given that all memory variables (from List Learning and Daily Living Memory tests) were highly intercorrelated (intercorrelations within measures ranged from r = 0.81 to r = 0.94; intercorrelations between tests ranged from r = 0.70 to r = 0.81, all p < .001), only the Daily Living Memory – Delayed Recall, due to its strong sensitivity in the above analyses, was used in regression analyses to avoid multicollinearity. Similarly, as high correlations between Driving Scenes and two other variables (Daily Living Memory – Delayed Recall, r = 0.79, and Bill Payment, r = 0.63) raised multicollinearity concerns, these combinations of variables were not entered into the same regression models. Demographic variables and all non-redundant NAB variables were entered into regression equations to predict LB scores; due to the above-noted linear relationships among variables, two separate models were created. Overall regression analyses that included neuropsychological test performance as a predictor of functional ratings were significant for both models. Poorer scores on all of the Daily Living neuropsychological tests were associated with greater functional difficulty. See Tables 4 and 5. Gender contributed unique variance to one model, but not the other. Table 4. Summary of linear regression analyses predicting Lawton Brody Instrumental Activities of Daily Living Scale: model excludes Driving Scenes R2 = 0.52, F(8,184) = 24.71, p < .001 B SE β t Age 0.00 0.02 0.01 0.16 Education −0.10 0.06 −0.10 −1.89 Gender 0.16 0.30 0.03 0.52 DES −0.02 0.04 −0.03 −0.56 VIS −0.22 0.15 −0.09 −1.52 Bill Payment 0.13 0.07 0.14 2.02* DLM-drc 0.31 0.04 0.59 8.00** Judgment 0.14 0.07 0.14 2.13* R2 = 0.52, F(8,184) = 24.71, p < .001 B SE β t Age 0.00 0.02 0.01 0.16 Education −0.10 0.06 −0.10 −1.89 Gender 0.16 0.30 0.03 0.52 DES −0.02 0.04 −0.03 −0.56 VIS −0.22 0.15 −0.09 −1.52 Bill Payment 0.13 0.07 0.14 2.02* DLM-drc 0.31 0.04 0.59 8.00** Judgment 0.14 0.07 0.14 2.13* Note: *p < .05, **p < .001; DES = Design Construction; VIS = Visual Discrimination; DLM-drc = NAB Daily Living Memory – delayed recall. Table 4. Summary of linear regression analyses predicting Lawton Brody Instrumental Activities of Daily Living Scale: model excludes Driving Scenes R2 = 0.52, F(8,184) = 24.71, p < .001 B SE β t Age 0.00 0.02 0.01 0.16 Education −0.10 0.06 −0.10 −1.89 Gender 0.16 0.30 0.03 0.52 DES −0.02 0.04 −0.03 −0.56 VIS −0.22 0.15 −0.09 −1.52 Bill Payment 0.13 0.07 0.14 2.02* DLM-drc 0.31 0.04 0.59 8.00** Judgment 0.14 0.07 0.14 2.13* R2 = 0.52, F(8,184) = 24.71, p < .001 B SE β t Age 0.00 0.02 0.01 0.16 Education −0.10 0.06 −0.10 −1.89 Gender 0.16 0.30 0.03 0.52 DES −0.02 0.04 −0.03 −0.56 VIS −0.22 0.15 −0.09 −1.52 Bill Payment 0.13 0.07 0.14 2.02* DLM-drc 0.31 0.04 0.59 8.00** Judgment 0.14 0.07 0.14 2.13* Note: *p < .05, **p < .001; DES = Design Construction; VIS = Visual Discrimination; DLM-drc = NAB Daily Living Memory – delayed recall. Table 5. Summary of linear regression analyses predicting Lawton Brody Instrumental Activities of Daily Living Scale: model excludes DLM-drc and Bill Payment R2 = 0.50, F(7,222) = 31.09, p < .001 B SE β t Age −0.003 0.02 −0.01 −0.14 Education −0.08 0.06 −0.07 −1.45 Gender 0.78 0.33 0.12 2.38* DES 0.01 0.04 0.02 0.29 VIS 0.02 0.15 0.01 0.14 Driving Scenes 0.13 0.02 0.49 7.57** Judgment 0.30 0.06 0.28 4.80** R2 = 0.50, F(7,222) = 31.09, p < .001 B SE β t Age −0.003 0.02 −0.01 −0.14 Education −0.08 0.06 −0.07 −1.45 Gender 0.78 0.33 0.12 2.38* DES 0.01 0.04 0.02 0.29 VIS 0.02 0.15 0.01 0.14 Driving Scenes 0.13 0.02 0.49 7.57** Judgment 0.30 0.06 0.28 4.80** Note: **p < .001, *p < .05; DES = Design Construction; VIS = Visual Discrimination; DLM-drc = NAB Daily Living Memory – delayed recall. Table 5. Summary of linear regression analyses predicting Lawton Brody Instrumental Activities of Daily Living Scale: model excludes DLM-drc and Bill Payment R2 = 0.50, F(7,222) = 31.09, p < .001 B SE β t Age −0.003 0.02 −0.01 −0.14 Education −0.08 0.06 −0.07 −1.45 Gender 0.78 0.33 0.12 2.38* DES 0.01 0.04 0.02 0.29 VIS 0.02 0.15 0.01 0.14 Driving Scenes 0.13 0.02 0.49 7.57** Judgment 0.30 0.06 0.28 4.80** R2 = 0.50, F(7,222) = 31.09, p < .001 B SE β t Age −0.003 0.02 −0.01 −0.14 Education −0.08 0.06 −0.07 −1.45 Gender 0.78 0.33 0.12 2.38* DES 0.01 0.04 0.02 0.29 VIS 0.02 0.15 0.01 0.14 Driving Scenes 0.13 0.02 0.49 7.57** Judgment 0.30 0.06 0.28 4.80** Note: **p < .001, *p < .05; DES = Design Construction; VIS = Visual Discrimination; DLM-drc = NAB Daily Living Memory – delayed recall. Discussion The presence of functional impairment is necessary for the clinical diagnosis of AD dementia and staging of disease severity, and is a significant source of patient distress and family burden. Early detection of diminished functional capacity is critical for timely implementation of pharmacological and other intervention, as well as ADL compensatory strategies to help preserve functional independence for as long as possible. The NAB is a practical test battery that can simultaneously assses cognitive function and IADL status in older adults. The current study found that certain NAB tests accurately detected informant-rated functional impairment in a sample of older adults from the BU ADC Clinical Core registry. The NAB List Learning test and select Daily Living tests emerged as the most accurate predictors of functional impairment, with the Daily Living Memory and Driving Scenes tests, in particular, accounting for the greatest proportion of unique variance in the prediction of LB scores. The memory measures had the highest sensitivity in the context of a low predetermined false positive rate (5%). As shown in Table 3, a Driving Scenes raw score <30 identified between 55% and 63% of lower-functioning individuals. A Daily Living Memory delayed recall raw score <7 was the most sensitive cognitive test cutoff, as it correctly classified 88% of individuals with functional impairment. Neuropsychological tests of episodic memory (e.g., list learning tasks) have been previously identified as potential predictors of functional status in dementia, and the current findings support the role of this domain in IADL performance (de Paula et al., 2015; Razani et al., 2011). NAB List Learning and Daily Living Memory tests are measures of episodic memory, with the Daily Living Memory test developed to mimic real-world to-be-remembered information (i.e., names, phone numbers, medication dosing instructions). Impairment in episodic memory is typically the initial presenting symptom in AD that reflects the pathological course of the disease, which begins in medial temporal lobe structures (e.g., hippocampus) that modulate episodic memory (Braak & Braak, 1995; Hodges & Patterson, 1995; Hodges, 2000). Of the six major memory systems, episodic memory is the most clinically relevant (Gold, 2012). Intact episodic memory is necessary for remembering to turn the stove off, take medications, pay the bills, and avoid getting lost while driving. Deficits in these functional domains are initially subtle and often attributed by the patient to be normal aging; however, they can be indicators of the beginning stages of AD, and more serious incidences of episodic memory failure (e.g., getting lost while driving, leaving the stove on, mixing medications) tend to precipitate the initial presentation for clinical evaluation. These NAB List Learning and Daily Living tests may thus help with detection of episodic memory-related functional impairment and allow for early implementation of tailored cognitive compensatory strategies, such as the use of pillbox organizers, and/or assistance with finances and driving. The Daily Living Driving Scenes test also independently predicted IADL function in this sample of older adults. As a standalone measure, this test displayed better classification accuracy relative to all other non-memory measures. The NAB Driving Scenes test is a component of the Attention Module of the NAB and is a measure of visual attention and working memory. It is a performance-based measure that taps into mental abilities important for driving operations that could help identify patients potentially at risk for unsafe driving. The Driving Scenes test has been shown to be a valid predictor of on-road driving performance in healthy older adults and individuals with mild dementia (Brown et al., 2005). Nevertheless, the neural networks that underpin attention and working memory are disrupted in AD, and impairments in these domains can present early in the disease course (Baddeley, Baddeley, Bucks, & Wilcock, 2001; Belleville, Chertkow, & Gauthier, 2007; Perry, Watson, & Hodges, 2000). Attention and working memory play a role in other non-driving-related ADLs, with a substantial literature emphasizing the importance of intact higher-ordered cognitive processes, such as working memory and other executive functions, for optimal performance of complex and cognitively challenging IADLs, like assembling tax records or managing bank accounts (Marshall, Amariglio, Sperling, & Rentz, 2012). These complex ADLs may begin to deteriorate during the preclinical stages of AD (Marshall, Dekhtyar et al., 2015; Marshall, Zoller et al., 2015). Performance on the NAB Driving Scenes test may therefore be an indicator of overall functional abilities, potentially sensitive to complex ADLs. The most effectively predictive measures investigated in this study (the NAB recall subtests) were 77%–88% sensitive to impaired informant-rated IADLs, with only a 5% false positive rate. This compares favorably with the results of similar investigations. For example, using the Direct Assessment of Functional Status to predict AD diagnosis, Razani and colleagues (2011) found that individual subtests had highest sensitivity values range of 81–82% with specificity ranging from 76% to 83%. Memory and executive functioning domains each accounted for about 50% of the unique variance in IADL performance in a study by Farias and colleagues (2005). NAB Judgment was previously investigated as a predictor of IADLs in individuals with MCI (Jefferson et al., 2008) and was found to be among the cognitive measures that were most highly correlated with IADL score, with a similarly-strong finding for list recall scores. In the present study, list memory was quite a bit more predictive of IADL status than Judgment, which displayed only 25% sensitivity. The NAB memory subtests appear to compare favorably with other measures used in previous studies. Our findings emphasize the importance of inclusion of performance-based tools in the assessment of functional status in dementia. Recent guidelines proposed by Marson (2015) suggest that as an alternative to self- or informant-reported questionnaires, clinicians should use interval-scaled, direct performance measures that can tap into cognitively challenging functional tasks that are affected early in the disease process. Consequently, there has been emergence of research examining various performance-based measures of ADLs, such as the Direct Assessment of Functional Status, The University of California, San Diego Performance-Based Skills Assessment, and the Financial Capacity Instrument (Binegar et al., 2009; Chisholm et al., 2014; Cullum et al., 2001; Diehl et al., 2005; Farina et al., 2010; Foster, 2014; Goldberg et al., 2010; Heaton et al., 2004; Loewenstein et al., 1989; McDougall et al., 2009; Patterson et al., 2001; Sadek et al., 2011; Triebel et al., 2009; Weening-Dijksterhuis et al., 2010). These tools can accurately distinguish clinically normal elderly from MCI, but are time intensive. Most recently, the 10-min, Harvard Automated Phone Task was developed and asks participants to use a voice response system to refill a prescription, select a new primary care physician, and make bank transfers (Marshall, Dekhtyar et al., 2015; Marshall, Zoller et al., 2015). This task distinguished across clinically normal elderly, MCI, and young normal participants. Future work should examine whether the NAB Daily Living tests can detect preclinical changes in functioning in AD. Contrary to expectations, scores on two of the domain-specific NAB Daily Living measures (Bill Payment and Judgment) did not correspond with informants’ binary report (i.e., complaint-no complaint) on these functional skills. NAB Bill Payment and Judgment were less sensitive to the individual informant-rated functional skills (bill payment and judgment, respectively) compared to the NAB memory and Driving Scenes variables, and they were less sensitive to those skills than they were to the more global LB. One possible reason for this finding is that informant report may not be specific to those individual skills. That is, informants often note deficits across multiple domains, thus individuals with bill payment difficulties might also exhibit problems in other functional areas, obscuring the more narrow-based functional deficit in question. It is also possible that the individual NAB measures themselves might be too multifaceted to specifically assess the target functional skill. For example, the skills underlying Bill Payment might draw upon the multiple executive demands of family financial management, such as planning and abstract reasoning, and might predict performance of other skills that call upon those demands (e.g., driving, meal preparation). This would limit the specificity of each test to the corresponding functional skill. Although the NAB daily living tasks lacked specificity in predicting informant-reported concerns in the areas they aim to assess, these measures still had utility in predicting ADL impairment in general. Several NAB memory and Daily Living measures demonstrated comparable overall effectiveness at predicting ADL status. Future research might explore comparative value of these various measures in other contexts, such as for the prediction of naturalistic everyday-action task performance or for the prediction of other instrumental ADLs (such as driving). This study is not without limitations. The sampling of NAB tests included in this protocol represents a small percentage of the NAB’s components because they were considered to be complementary to the existing BU ADC registry protocol (and mandatory NACC measures) in order to allow for adequate assessment of each cognitive domain, within time limitations and subject burden associated with the evaluation. The tests assess domains most commonly involved in the performance of IADLs; however, future research could investigate the utility of the NAB as a whole in predicting functional status in dementia. For example, the Mazes test of the NAB has been shown to predict on-road driving performance (Niewoehner et al., 2012) and, like the NAB Driving Scenes test, it too may extend to detection of IADL impairment more broadly. The current study was also cross-sectional and limits causal inferences or understanding on the relationship between change in NAB scores and functional status. Longitudinal work will help to clarify whether the NAB predicts functional and dementia progression over time. Additionally, the ADL ratings for each participant depend upon informant ratings, which can be questionably reliable due to a few factors, including the rater’s familiarity with the participant, personal characteristics, and cognitive status (De Medeiros et al., 2010). The structure of the LB also does not control for premorbid functioning, and some individuals who were rated as lacking the ability to perform an ADL might not have ever needed to engage in the task, thus artificially lowering their scores. The LB scale itself, as with most self- or informant-rated scales, has not been extensively evaluated in the literature, and its psychometric properties are not well established (Sikkes et al., 2009), which could limit the applicability of the current findings. Finally, there were 33 participants in this study who had non-amnestic MCI, and these individuals have a lower likelihood of underlying AD neuropathology than those with an amnestic presentation. However, there is still a substantial proportion of individuals with non-amnestic MCI who do progress to AD (68% in Busse, Hensel, Gühne, Angermeyer, & Riedel-Heller, 2006), and there is significant variability in the clinical presentation of AD dementia. Indeed, all participants in this sample were judged by a multidisciplinary panel of clinicians to have AD as the underlying etiology, and this consensus decision was based not only on neuropsychological testing performance but on numerous data points (e.g., clinical course, informant report of symptoms, and previous test patterns). For these reasons, non-amnestic participants were not excluded from this study. In sum, findings from this study suggest that select tests from the NAB, including List Learning and performance-based Daily Living tests, may have key clinical utility in detecting functional impairment in dementia. The NAB is well-normed and has equivalent forms, making it suitable for future research to examine whether NAB tests, especially the performance-based Daily Living tests, can detect changes in more complex ADLs that could be present in the very early stages of dementia (e.g., preclinical AD). Funding This work was supported by grants from the NIH (P30 AG13846, F32NS096803, 1UL1TR001430). The funding sources provided data and salary support for some of the authors. However, these funding sources did not play any role in the study design, analysis and interpretation of data, in the writing of the report, or in the decision to submit the article for publication. Conflict of interest None declared. References Ashendorf , L. , Jefferson , A. L. , Green , R. C. , & Stern , R. A. ( 2009 ). Test–retest stability on the WRAT-3 reading subtest in geriatric cognitive evaluations . Journal of Clinical and Experimental Neuropsychology , 31 , 605 – 610 . Google Scholar CrossRef Search ADS PubMed Baddeley , A. D. , Baddeley , H. , Bucks , R. , & Wilcock , G. ( 2001 ). Attentional control in Alzheimer’s disease . Brain , 124 , 1492 – 1508 . Google Scholar CrossRef Search ADS PubMed Barberger‐Gateau , P. , Commenges , D. , Gagnon , M. , Letenneur , L. , Sauvel , C. , & Dartigues , J. F. 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J. , Amariglio , R. E. , Blacker , D. , et al. . ( 2014 ). SIST-M-IR activities of daily living items that best discriminate clinically normal elderly from those with mild cognitive impairment . Current Alzheimer Research , 11 , 785 . Google Scholar CrossRef Search ADS PubMed Published by Oxford University Press 2017. This work is written by (a) US Government employee(s) and is in the public domain in the US. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Archives of Clinical Neuropsychology Oxford University Press

Clinical Utility of Select Neuropsychological Assessment Battery Tests in Predicting Functional Abilities in Dementia

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Oxford University Press
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Published by Oxford University Press 2017. This work is written by (a) US Government employee(s) and is in the public domain in the US.
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0887-6177
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1873-5843
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10.1093/arclin/acx100
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Abstract

Abstract Objective Neuropsychological test performance can provide insight into functional abilities in patients with dementia, particularly in the absence of an informant. The relationship between neuropsychological measures and instrumental activities of daily living (IADLs) is unclear due to hetereogeneity in cognitive domains assessed and neuropsychological tests administered. Practical and ecologically valid performance-based measures of IADLs are also limited. The Neuropsychological Assessment Battery (NAB) is uniquely positioned to provide a dual-purpose assessment of cognitive and IADL function, as it includes Daily Living tests that simulate real-world functional tasks. We examined the utility of select NAB tests in predicting informant-reported IADLs in mild cognitive impairment and dementia. Methods The sample of 327 participants included 128 normal controls, 97 individuals with mild cognitive impairment, and 102 individuals with Alzheimer’s disease dementia from the Boston University Alzheimer’s Disease Center research registry. Informants completed the Lawton Brody Instrumental Activities of Daily Living Scale, and study participants were administered selected NAB tests that were complementary to the existing protocol. Results ROC curves showed strongest prediction of IADL (AUC > 0.90) for memory measures (List Learning delayed recall and Daily Living Memory delayed recall) and Daily Living Driving Scenes. At a predetermined level of specificity (95%), List Learning delayed recall (71%) and Daily Living Memory delayed recall (88%) were the most sensitive. The Daily Living Memory and Driving Scenes tests strongly predicted IADL status, and the other Daily Living tests contributed unique variance. Conclusions NAB memory measures and Daily Living Tests may have clinical utility in detecting informant-rated functional impairment in dementia. Alzheimer’s disease, Mild cognitive impairment, Instrumental ADLs, Ecological validity, Geriatrics Background Assessment of functional abilities is a cornerstone in the clinical evaluation of Alzheimer’s disease (AD) in order to ascertain diagnostic status and disease severity. Alzheimer’s disease represents a continuum of clinical status, ranging from normal cognition to mild cognitive impairment (MCI) to AD dementia, with the diagnosis of AD dementia being made following onset of functional impairment (Sperling et al., 2011). Functional abilities are classified into two categories: basic activities of daily living (BADL) and instrumental activities of daily living (IADL). Impairments in BADLs, or self-care tasks, are typically present in moderate-to-severe dementia. Relative to BADLs, IADLs (e.g., managing finances, driving, household chores) require greater cognitive capacity, and are thus more sensitive to dementia-related cognitive decline, with impairments beginning in the early stages (e.g., mild dementia) (Njegovan, Man-Son-Hing, Mitchell, & Molnar, 2001). Of note, although MCI is diagnostically defined to include “minimal” disturbance in IADLs (Winblad et al., 2004), there is debate on this matter (Lindbergh, Dishman, & Miller, 2016) and deficits in complex IADLs may begin even before a clinical diagnosis of MCI (i.e., preclinical dementia) (Marshall, Dekhtyar et al., 2015; Marshall, Zoller et al., 2015). Assessment of IADLs may be a time- and cost-efficient method for the early detection of dementia (Marshall, Dekhtyar et al., 2015; Marshall, Zoller et al., 2015; Zoller et al., 2014) that could help facilitate timely treatment planning and initiation of disease modifying interventions. Traditionally, a description of the patient’s daily functioning by a reliable informant is optimal to evaluate IADLs and ascertain dementia status (Farias, Mungas, & Jagust, 2005). A number of informant-based methods for evaluating IADLs exist, such as the Lawton Brody Instrumental Activities of Daily Living Scale (LBIADL; Gold, 2012; Lawton & Brody, 1970; Strauss, Sherman, & Spreen, 2006). Clinical evaluations of dementia, however, are often conducted in the absence of an informant, and clinicians must rely on the patient’s self-report to determine functional status, which can lack reliability (Farias et al., 2005) due to memory impairment and/or anosognosia (Vogel et al., 2004). Scores from neuropsychological tests administered as part of dementia evaluations can provide insight into an individual’s IADL capabilities in the absence of an informant. A number of neuropsychological measures have been identified as strong determinants of IADL impairment in dementia, with robust effects for tests of memory and executive function (de Paula et al., 2015; Jefferson, Paul, Ozonoff, & Cohen, 2006; Mlinac & Feng, 2016; Razani et al., 2011; Tomaszewski Farias et al., 2009). However, the literature on the relationship between neuropsychological test performance and IADLs is not entirely consistent largely due to heterogeneity in the cognitive domains assessed and the neuropsychological tests administered, and differences in disease status across samples (de Paula et al., 2015; Gold, 2012; Mlinac & Feng, 2016). The relationship between cognitive function and IADLs in AD also remains poorly understood due to minimal research conducted in samples that span the dementia continuum, and poor operationalization of IADLs (e.g., patient self-report, non-validated IADL measures) (Gold, 2012). Performance-based IADL measures (i.e., direct observation of the patient performing a simulated IADL task) are optimal and can potentially detect functional decline in the early stages of AD when deficits are subtle (Burton, Strauss, Bunce, Hunter, & Hultsch, 2009). Recent diagnostic guidelines encourage the use of performance-based tasks that are sensitive to cognitively complex ADLs in order to detect preclinical AD changes in functioning (Marson, 2015). Although several performance-based measures have been developed that can accurately distinguish clinically normal elderly from MCI, they are typically time intensive (most requiring >30 min) (Binegar, Hynan, Lacritz, Weiner, & Cullum, 2009; Chisholm, Toto, Raina, Holm, & Rogers, 2014; Cullum et al., 2001; Diehl et al., 2005; Farina et al., 2010; Foster, 2014; Goldberg et al., 2010; Heaton et al., 2004; Loewenstein et al., 1989; McDougall, Becker, Vaughan, Acee, & Delville, 2009; Patterson, Goldman, McKibbin, Hughs, & Jeste, 2001; Sadek, Stricker, Adair, & Haaland, 2011; Triebel et al., 2009; Weening-Dijksterhuis, Kamsma, & Van Heuvelen, 2010), with the exception of the Harvard Automated Phone Task that takes only 10 min (Marshall, Dekhtyar et al., 2015; Marshall, Zoller et al., 2015). The scope of the Harvard Automated Phone Task, and other existing performance-based IADL measures, is limited to the isolated assessment of complex functional capabilities, and thereby requires additional time and resources to administer a complete neuropsychological test protocol to evaluate cognitive function. There is need for a practical and ecologically valid dual-purpose evaluation that assesses both neuropsychological function and IADL status across the dementia clinical spectrum. The Neuropsychological Assessment Battery (NAB) is a psychometrically sound cognitive test battery that may fulfill such purpose. The NAB includes 33 tests, grouped within five modules encompassing the major domains of neuropsychological functioning (Attention, Language, Spatial, Memory, and Executive Function). All tests were co-normed on the same sample of individuals (n = 1448), with demographic corrections available for age, sex, and education. Because the normative samples included a large proportion of individuals ages 60–97 (n = 841), the NAB is well suited for use in dementia evaluations. Each of the five NAB modules includes a Daily Living test designed to simulate real-world tasks of everyday living associated with the cognitive domain of the module. Each NAB test includes an equivalent/alternate form for repeat testing (Stern & White, 2003). Several NAB tests have been shown to have excellent diagnostic classification accuracy in AD (Gavett et al., 2009, 2010, 2012), and NAB tests, including the Daily Living tests, have been shown to be associated with IADLs in samples with mixed disorders or other non-AD conditions (Cahn-Weiner, Wittenberg, & McDonald, 2009; MacDougall & Mansbach, 2013; Sadek et al., 2011; Zgaljardic, Yancy, Temple, Watford, & Miller, 2011). Two tests from the NAB (Mazes (Niewoehner et al., 2012) and Driving Scenes (Brown et al., 2005; Stern et al., 2016)) have been found to predict on-road driving in older subjects across the AD spectrum. The present study is the first to investigate the ability of select NAB tests to predict informant-reported IADL function in a sample of normal controls (NC) and subjects with MCI and AD dementia from the Boston University Alzheimer’s Disease Center (BU ADC) participant registry. It was hypothesized that the NAB memory and Daily Living tests used in this study would be strong predictors of IADL status. Methods Participants The present sample included 327 subjects (128 NC, 97 with MCI, and 102 with AD dementia) from the BU ADC Clinical Core registry. The BU ADC is one of 27 centers funded by the National Institute on Aging (NIA) that contributes data to the National Alzheimer’s Coordinating Center (NACC). The BU ADC registry, including participant recruitment and inclusion/exclusion criteria, has been described elsewhere (Ashendorf, Jefferson, Green, & Stern, 2009; Galetta et al., 2016, 2012; Jefferson et al., 2007). Briefly, participants are included if they are community-dwelling, English-speaking, and have an available informant who can provide collateral information about daily functioning. Exclusion criteria include a history of major psychiatric illness (e.g., schizophrenia, bipolar disorder), neurological illness other than AD (e.g., stroke or epilepsy), or significant traumatic brain injury with loss of consciousness. The sample included new participants diagnosed with normal cognition, MCI, or AD dementia based on their initial evaluation. The BU Medical Campus Institutional Review Board approved all BU ADC data collection procedures, and all participants (or their Legally Authorized Representatives) provided written informed consent for study participation. Diagnostic Status Diagnoses were made at BU ADC multidisciplinary consensus conferences by a team of neurologists, neuropsychologists, geriatricians, and geriatric psychiatrists. Each diagnosis was made following presentation and discussion of all evaluation data, including neuropsychological testing, neuroimaging, and psychosocial and medical history. Subjects were deemed to be NC if their objective neuropsychological test scores were all within the normal range, they had a Clinical Dementia Rating (CDR; Morris, 1993) Global Score of 0.0, and they were determined by the consensus team to be cognitively normal. MCI diagnoses followed criteria outlined by Petersen (2004). Of the MCI subjects, 37 were amnestic single domain, 27 amnestic multiple domains, 26 non-amnestic single domain, and seven non-amnestic multiple domains. The National Institute of Neurological and Communicative Disorders and Stroke and Alzheimer’s Disease and Related Disorders Association (NINCDS-ADRDA) criteria (McKhann et al., 1984) were used to diagnose subjects with “Possible AD” or “Probable AD”, who were combined into a single group with AD dementia. Participants who were instead diagnosed with a different dementia etiology were excluded from this dataset. Measures Neuropsychological Assessment Battery All subjects completed a set of NAB tests as part of a more comprehensive, single-session neuropsychological test protocol, including those tests included in the NACC Unified Data Set (Beekly et al., 2007). The NAB tests in the present study had originally been selected for inclusion in the larger BU ADC registry protocol to allow for representation of each domain. These measures included the Design Construction and Visual Discrimination tests from the Screening Module, List Learning from the Memory Module, and the Daily Living tests from the Attention, Executive, Language, and Memory modules: Driving Scenes, Judgment, Bill Payment, and Daily Living Memory, respectively. For Design Construction, the participant is asked to reproduce two-dimensional figures using plastic geometric pieces. Visual Discrimination asks the individual to identify which of four possible options is identical to a target design. List Learning features a list of 12 words that are learned over three consecutive exposure trials. Memory for the list is assessed following an interference trial, and again after a long delay. Total learning (across three trials), short-delay free recall, and long-delay free recall scores were evaluated. On Driving Scenes, a series of similar but slightly different scenes as viewed from behind a steering wheel are sequentially displayed, and the participant identifies the changes from the preceding picture. Judgment poses several questions assessing social and health-related reasoning. Bill Payment asks the participant to answer questions and perform procedures associated with paying a utility bill, including writing a check, entering it into a ledger, and addressing an envelope. Daily Living Memory assesses immediate and delayed recall for fictionalized personal information (name, address, and phone number) and medication instructions; immediate and delayed free recall scores were included in this study. Instrumental activities of daily living The Lawton & Brody Activities of Daily Living Scale (LBADL; Jefferson et al., 2008; Lawton & Brody, 1970) evaluated IADL performance. The LBADL is a 14-item rating scale that involves informant ratings of the subject’s level of dependence across six basic (e.g., feeding, dressing) and eight instrumental (e.g., financial management, driving, shopping) activities of daily living. The scale is sensitive to disease status and quality of life in individuals with AD (Barberger‐Gateau et al., 1992; Lawton, 1994; Wlodarczyk, Brodaty, & Hawthorne, 2004). Each skill is rated on a 3-point scale (2 = fully independent, 0 = fully dependent). Only the total score for IADL items (LB; range = 0–16) was examined in this study. To assess whether the select NAB tests differentially predicted informant-reported daily living skills, binary informant ratings (complaint/no complaint) of each participant’s judgment and problem-solving skills and bill payment abilities were also included in these analyses. These subjective ratings were assessed separately from the IADL questionnaire during each visit. Mini-Mental State Examination All participants were administered the Mini-Mental State Examination (MMSE) (Folstein, Folstein, & McHugh, 1975) to characterize the global cognitive status of the sample. Statistical Analyses Analyses were conducted using IBM SPSS Statistics version 24. Determination of intact versus impaired daily functioning on the LB was made by evaluating cumulative frequencies of performance at each level within control and dementia subgroups and selecting the cutoff value with the highest differential diagnostic accuracy. All three diagnostic groups were then combined into a single group. Receiver Operating Characteristic (ROC) curves examined the classification accuracy of each of the neuropsychological measures in predicting overall LB scores, as well as individual informant-reported skill assessments (i.e., bill payment and judgment). Sensitivity and specificity values for select NAB cutoff scores were calculated. To avoid overpathologizing scores due to normal variability in cognitive performance, cutoff scores that yielded low false positive rates (at least 95% specificity) were identified for each of the neuropsychological measures. Linear regression analyses examined the relative contributions of neuropsychological abilities (measured by raw neuropsychological test scores) to informant-reported functional ratings. Age, gender, and education were entered as covariates in the regression analyses. Results Sample Characteristics Means and standard deviations for all measures are reported in Table 1. The overall raw scores are provided, as well as the data for subsamples classified by functional abilities (groups with high LB scores and low LB scores). To create a clinically meaningful cutoff on the LB scale, we compared those with complaints who were clinically determined to have intact ADLs (the MCI sample) with those who had impaired ADLs (the Dementia sample). While LB scores were available for the consensus diagnostic process, potentially creating a tautological concern with this classification method, ADL capacity was also determined using multiple other sources of data, prominently including the CDR interview, making this a less salient concern for the present analysis. The resulting ROC curve had AUC = 0.941, demonstrating an effective classification accuracy. At the potential cutoff score with the greatest hit rate, only 4% of individuals with MCI (four cases) had LB scores <15, whereas 89% of those diagnosed with dementia were scored below that level (91 cases). Those with LB scores below 15 were therefore considered to have impaired ADLs. Individuals with greater functional impairment according to LB score were older, t(325) = −4.05, p < .001, less educated, t(325) = 2.71, p = .007, more likely to be men, χ2 = 7.52, p = .006, and more likely to be White, χ2 = 7.78, p = .02. As expected, this lower-functioning group also performed more poorly on the MMSE, t(102.24) = 14.22, p < .001. Table 1. Descriptive statistics Total sample LB intact LB impaired N 327 232 95 Age: M(SD) years 73.5 (8.2) 72.4 (7.5) 76.4 (9.2)*** Education: M(SD) years 15.7 (2.8) 15.9 (2.7) 15.0 (2.9)** Gender: % women 59.1 63.8 47.4** Race (%)  White 81.4 78.4 89.5*  African American 17.4 20.7 8.4  Asian 1.2 0.9 2.1 LB: M(SD) range 13.9 (3.7) 15.9 (0.4) 9.1 (3.8)*** 0–16 15–16 0–14 MMSE: M(SD) 27.1 (3.9) 29.0 (1.4) 22.6 (4.3)*** DES: M(SD) 6.6 (4.1) 7.2 (4.1) 4.4 (3.7) *** VIS: M(SD) 6.6 (4.1) 7.2 (4.1) 4.4 (3.7) *** Driving Scenes: M(SD) 39.2 (12.7) 45.0 (7.9) 25.0 (11.0) *** LLA-irc: M(SD) 18.3 (7.3) 21.5 (5.4) 10.1 (4.6) *** LLA-sd: M(SD) 5.3 (3.8) 7.0 (3.0) 0.9 (1.5) *** LLA-ld: M(SD) 5.1 (3.9) 6.8 (3.2) 0.6 (1.4) *** DLM-irc: M(SD) 37.2 (9.8) 41.1 (6.3) 25.5 (9.2) *** DLM-drc: M(SD) 10.5 (5.8) 13.0 (3.6) 2.5 (3.8) *** Bill Payment: M(SD) 16.4 (4.0) 17.8 (1.8) 12.5 (5.8) *** Judgment: M(SD) 15.1 (3.0) 15.8 (2.3) 12.8 (3.8) *** Total sample LB intact LB impaired N 327 232 95 Age: M(SD) years 73.5 (8.2) 72.4 (7.5) 76.4 (9.2)*** Education: M(SD) years 15.7 (2.8) 15.9 (2.7) 15.0 (2.9)** Gender: % women 59.1 63.8 47.4** Race (%)  White 81.4 78.4 89.5*  African American 17.4 20.7 8.4  Asian 1.2 0.9 2.1 LB: M(SD) range 13.9 (3.7) 15.9 (0.4) 9.1 (3.8)*** 0–16 15–16 0–14 MMSE: M(SD) 27.1 (3.9) 29.0 (1.4) 22.6 (4.3)*** DES: M(SD) 6.6 (4.1) 7.2 (4.1) 4.4 (3.7) *** VIS: M(SD) 6.6 (4.1) 7.2 (4.1) 4.4 (3.7) *** Driving Scenes: M(SD) 39.2 (12.7) 45.0 (7.9) 25.0 (11.0) *** LLA-irc: M(SD) 18.3 (7.3) 21.5 (5.4) 10.1 (4.6) *** LLA-sd: M(SD) 5.3 (3.8) 7.0 (3.0) 0.9 (1.5) *** LLA-ld: M(SD) 5.1 (3.9) 6.8 (3.2) 0.6 (1.4) *** DLM-irc: M(SD) 37.2 (9.8) 41.1 (6.3) 25.5 (9.2) *** DLM-drc: M(SD) 10.5 (5.8) 13.0 (3.6) 2.5 (3.8) *** Bill Payment: M(SD) 16.4 (4.0) 17.8 (1.8) 12.5 (5.8) *** Judgment: M(SD) 15.1 (3.0) 15.8 (2.3) 12.8 (3.8) *** Note: For Intact/Impaired contrasts: *p < .05; **p < .01; ***p < .001; M = mean; SD = standard deviation; LB = Instrumental Activities of Daily Living and Physical Self-Maintenance Scale; MMSE = Mini Mental State Examination; DES = Design Construction; VIS = Visual Discrimination; LLA = List Learning (list A); irc = Immediate Recall; SD = Short-delay recall; LD = long-delay recall; DLM = Daily Living Memory; drc = Delayed Recall. Table 1. Descriptive statistics Total sample LB intact LB impaired N 327 232 95 Age: M(SD) years 73.5 (8.2) 72.4 (7.5) 76.4 (9.2)*** Education: M(SD) years 15.7 (2.8) 15.9 (2.7) 15.0 (2.9)** Gender: % women 59.1 63.8 47.4** Race (%)  White 81.4 78.4 89.5*  African American 17.4 20.7 8.4  Asian 1.2 0.9 2.1 LB: M(SD) range 13.9 (3.7) 15.9 (0.4) 9.1 (3.8)*** 0–16 15–16 0–14 MMSE: M(SD) 27.1 (3.9) 29.0 (1.4) 22.6 (4.3)*** DES: M(SD) 6.6 (4.1) 7.2 (4.1) 4.4 (3.7) *** VIS: M(SD) 6.6 (4.1) 7.2 (4.1) 4.4 (3.7) *** Driving Scenes: M(SD) 39.2 (12.7) 45.0 (7.9) 25.0 (11.0) *** LLA-irc: M(SD) 18.3 (7.3) 21.5 (5.4) 10.1 (4.6) *** LLA-sd: M(SD) 5.3 (3.8) 7.0 (3.0) 0.9 (1.5) *** LLA-ld: M(SD) 5.1 (3.9) 6.8 (3.2) 0.6 (1.4) *** DLM-irc: M(SD) 37.2 (9.8) 41.1 (6.3) 25.5 (9.2) *** DLM-drc: M(SD) 10.5 (5.8) 13.0 (3.6) 2.5 (3.8) *** Bill Payment: M(SD) 16.4 (4.0) 17.8 (1.8) 12.5 (5.8) *** Judgment: M(SD) 15.1 (3.0) 15.8 (2.3) 12.8 (3.8) *** Total sample LB intact LB impaired N 327 232 95 Age: M(SD) years 73.5 (8.2) 72.4 (7.5) 76.4 (9.2)*** Education: M(SD) years 15.7 (2.8) 15.9 (2.7) 15.0 (2.9)** Gender: % women 59.1 63.8 47.4** Race (%)  White 81.4 78.4 89.5*  African American 17.4 20.7 8.4  Asian 1.2 0.9 2.1 LB: M(SD) range 13.9 (3.7) 15.9 (0.4) 9.1 (3.8)*** 0–16 15–16 0–14 MMSE: M(SD) 27.1 (3.9) 29.0 (1.4) 22.6 (4.3)*** DES: M(SD) 6.6 (4.1) 7.2 (4.1) 4.4 (3.7) *** VIS: M(SD) 6.6 (4.1) 7.2 (4.1) 4.4 (3.7) *** Driving Scenes: M(SD) 39.2 (12.7) 45.0 (7.9) 25.0 (11.0) *** LLA-irc: M(SD) 18.3 (7.3) 21.5 (5.4) 10.1 (4.6) *** LLA-sd: M(SD) 5.3 (3.8) 7.0 (3.0) 0.9 (1.5) *** LLA-ld: M(SD) 5.1 (3.9) 6.8 (3.2) 0.6 (1.4) *** DLM-irc: M(SD) 37.2 (9.8) 41.1 (6.3) 25.5 (9.2) *** DLM-drc: M(SD) 10.5 (5.8) 13.0 (3.6) 2.5 (3.8) *** Bill Payment: M(SD) 16.4 (4.0) 17.8 (1.8) 12.5 (5.8) *** Judgment: M(SD) 15.1 (3.0) 15.8 (2.3) 12.8 (3.8) *** Note: For Intact/Impaired contrasts: *p < .05; **p < .01; ***p < .001; M = mean; SD = standard deviation; LB = Instrumental Activities of Daily Living and Physical Self-Maintenance Scale; MMSE = Mini Mental State Examination; DES = Design Construction; VIS = Visual Discrimination; LLA = List Learning (list A); irc = Immediate Recall; SD = Short-delay recall; LD = long-delay recall; DLM = Daily Living Memory; drc = Delayed Recall. Classification accuracy statistics ROC analyses (Table 2) showed good classification accuracy for all NAB measures, but were strongest for List Learning delayed recall, Daily Living Memory delayed recall, and Driving Scenes across all informant-reported variables, with areas under the curve (AUC) exceeding 0.90. Daily living proxy measures were not found to be differentially predictive of their target skills. That is, scores on NAB measures of bill payment skill, memory for day-to-day information, and judgment did not respectively correspond to informant-reported scores for bill management and judgment. However, two of the four Daily Living measures (i.e., Driving Scenes and Daily Living Memory) were among the strongest predictors of each of the global and individual measures of functional impairment. Table 2. Classification accuracy (as measured by ROC area under the curve, AUC) of NAB subtests in the prediction of informant-rated daily living skills LB Bills Judgment DES 0.698 0.676 0.694 VIS 0.699 0.684 0.717 Driving Scenes 0.928 0.892 0.920 LLA-irc 0.946 0.896 0.932 LLA-sd 0.951 0.906 0.928 LLA-ld 0.947 0.903 0.929 DLM-irc 0.919 0.878 0.913 DLM-drc 0.951 0.905 0.941 Bill Payment 0.835 (0.779, 0.874) 0.806 0.835 Judgment 0.736 0.713 0.747 LB Bills Judgment DES 0.698 0.676 0.694 VIS 0.699 0.684 0.717 Driving Scenes 0.928 0.892 0.920 LLA-irc 0.946 0.896 0.932 LLA-sd 0.951 0.906 0.928 LLA-ld 0.947 0.903 0.929 DLM-irc 0.919 0.878 0.913 DLM-drc 0.951 0.905 0.941 Bill Payment 0.835 (0.779, 0.874) 0.806 0.835 Judgment 0.736 0.713 0.747 Note: Numbers in parentheses are 95% confidence intervals. LB = Instrumental Activities of Daily Living and Physical Self-Maintenance Scale; DES = Design Construction; VIS = Visual Discrimination; LLA = List Learning (list A); irc = Immediate Recall; sd = Short-delay recall; ld = long-delay recall; DLM = Daily Living Memory; drc = Delayed Recall. Table 2. Classification accuracy (as measured by ROC area under the curve, AUC) of NAB subtests in the prediction of informant-rated daily living skills LB Bills Judgment DES 0.698 0.676 0.694 VIS 0.699 0.684 0.717 Driving Scenes 0.928 0.892 0.920 LLA-irc 0.946 0.896 0.932 LLA-sd 0.951 0.906 0.928 LLA-ld 0.947 0.903 0.929 DLM-irc 0.919 0.878 0.913 DLM-drc 0.951 0.905 0.941 Bill Payment 0.835 (0.779, 0.874) 0.806 0.835 Judgment 0.736 0.713 0.747 LB Bills Judgment DES 0.698 0.676 0.694 VIS 0.699 0.684 0.717 Driving Scenes 0.928 0.892 0.920 LLA-irc 0.946 0.896 0.932 LLA-sd 0.951 0.906 0.928 LLA-ld 0.947 0.903 0.929 DLM-irc 0.919 0.878 0.913 DLM-drc 0.951 0.905 0.941 Bill Payment 0.835 (0.779, 0.874) 0.806 0.835 Judgment 0.736 0.713 0.747 Note: Numbers in parentheses are 95% confidence intervals. LB = Instrumental Activities of Daily Living and Physical Self-Maintenance Scale; DES = Design Construction; VIS = Visual Discrimination; LLA = List Learning (list A); irc = Immediate Recall; sd = Short-delay recall; ld = long-delay recall; DLM = Daily Living Memory; drc = Delayed Recall. A similar pattern was found when examining classification accuracy of individual cutoff scores on the NAB tests (Table 3). At a predetermined level of specificity (95%), the highest sensitivity to impairment on the LB was found for the Daily Living Memory delayed recall trial (sensitivity = 88%). Targeted NAB Daily Living tests (i.e., Bill Payment and Judgment) did not preferentially predict their corresponding informant-reported ADL skills. Given the weak classification accuracy of the two NAB visuospatial measures based on ROC AUC (see Table 2), sensitivity values were not calculated. Table 3. Sensitivity and specificity of neuropsychological tests in the prediction of informant-rated daily living skills. LB Bill Payment Judgment Cutoff SN SP Cutoff SN SP Cutoff SN SP Driving Scenes <30 55 96 <30 59 96 <30 63 96 LLA-irc <14 66 95 <11 47 96 <12 60 96 LLA-sd <2 78 95 <1 54 95 <1 61 95 LLA-ld <1 77 95 <1 67 93 <1 76 94 DLM-irc <30 63 95 <28 49 95 <30 66 96 DLM-drc <7 88 95 <6 72 95 <7 85 96 Bill Payment <14 45 96 <14 41 96 <14 45 96 Judgment <11 25 97 <11 25 98 <11 26 97 LB Bill Payment Judgment Cutoff SN SP Cutoff SN SP Cutoff SN SP Driving Scenes <30 55 96 <30 59 96 <30 63 96 LLA-irc <14 66 95 <11 47 96 <12 60 96 LLA-sd <2 78 95 <1 54 95 <1 61 95 LLA-ld <1 77 95 <1 67 93 <1 76 94 DLM-irc <30 63 95 <28 49 95 <30 66 96 DLM-drc <7 88 95 <6 72 95 <7 85 96 Bill Payment <14 45 96 <14 41 96 <14 45 96 Judgment <11 25 97 <11 25 98 <11 26 97 Note: LB = Instrumental Activities of Daily Living and Physical Self-Maintenance Scale; LLA = List Learning (list A); irc = Immediate Recall; sd = Short-delay recall; ld = long-delay recall; DLM = Daily Living Memory; drc = Delayed Recall. Table 3. Sensitivity and specificity of neuropsychological tests in the prediction of informant-rated daily living skills. LB Bill Payment Judgment Cutoff SN SP Cutoff SN SP Cutoff SN SP Driving Scenes <30 55 96 <30 59 96 <30 63 96 LLA-irc <14 66 95 <11 47 96 <12 60 96 LLA-sd <2 78 95 <1 54 95 <1 61 95 LLA-ld <1 77 95 <1 67 93 <1 76 94 DLM-irc <30 63 95 <28 49 95 <30 66 96 DLM-drc <7 88 95 <6 72 95 <7 85 96 Bill Payment <14 45 96 <14 41 96 <14 45 96 Judgment <11 25 97 <11 25 98 <11 26 97 LB Bill Payment Judgment Cutoff SN SP Cutoff SN SP Cutoff SN SP Driving Scenes <30 55 96 <30 59 96 <30 63 96 LLA-irc <14 66 95 <11 47 96 <12 60 96 LLA-sd <2 78 95 <1 54 95 <1 61 95 LLA-ld <1 77 95 <1 67 93 <1 76 94 DLM-irc <30 63 95 <28 49 95 <30 66 96 DLM-drc <7 88 95 <6 72 95 <7 85 96 Bill Payment <14 45 96 <14 41 96 <14 45 96 Judgment <11 25 97 <11 25 98 <11 26 97 Note: LB = Instrumental Activities of Daily Living and Physical Self-Maintenance Scale; LLA = List Learning (list A); irc = Immediate Recall; sd = Short-delay recall; ld = long-delay recall; DLM = Daily Living Memory; drc = Delayed Recall. Neuropsychological variables as predictors of IADL ratings Given that all memory variables (from List Learning and Daily Living Memory tests) were highly intercorrelated (intercorrelations within measures ranged from r = 0.81 to r = 0.94; intercorrelations between tests ranged from r = 0.70 to r = 0.81, all p < .001), only the Daily Living Memory – Delayed Recall, due to its strong sensitivity in the above analyses, was used in regression analyses to avoid multicollinearity. Similarly, as high correlations between Driving Scenes and two other variables (Daily Living Memory – Delayed Recall, r = 0.79, and Bill Payment, r = 0.63) raised multicollinearity concerns, these combinations of variables were not entered into the same regression models. Demographic variables and all non-redundant NAB variables were entered into regression equations to predict LB scores; due to the above-noted linear relationships among variables, two separate models were created. Overall regression analyses that included neuropsychological test performance as a predictor of functional ratings were significant for both models. Poorer scores on all of the Daily Living neuropsychological tests were associated with greater functional difficulty. See Tables 4 and 5. Gender contributed unique variance to one model, but not the other. Table 4. Summary of linear regression analyses predicting Lawton Brody Instrumental Activities of Daily Living Scale: model excludes Driving Scenes R2 = 0.52, F(8,184) = 24.71, p < .001 B SE β t Age 0.00 0.02 0.01 0.16 Education −0.10 0.06 −0.10 −1.89 Gender 0.16 0.30 0.03 0.52 DES −0.02 0.04 −0.03 −0.56 VIS −0.22 0.15 −0.09 −1.52 Bill Payment 0.13 0.07 0.14 2.02* DLM-drc 0.31 0.04 0.59 8.00** Judgment 0.14 0.07 0.14 2.13* R2 = 0.52, F(8,184) = 24.71, p < .001 B SE β t Age 0.00 0.02 0.01 0.16 Education −0.10 0.06 −0.10 −1.89 Gender 0.16 0.30 0.03 0.52 DES −0.02 0.04 −0.03 −0.56 VIS −0.22 0.15 −0.09 −1.52 Bill Payment 0.13 0.07 0.14 2.02* DLM-drc 0.31 0.04 0.59 8.00** Judgment 0.14 0.07 0.14 2.13* Note: *p < .05, **p < .001; DES = Design Construction; VIS = Visual Discrimination; DLM-drc = NAB Daily Living Memory – delayed recall. Table 4. Summary of linear regression analyses predicting Lawton Brody Instrumental Activities of Daily Living Scale: model excludes Driving Scenes R2 = 0.52, F(8,184) = 24.71, p < .001 B SE β t Age 0.00 0.02 0.01 0.16 Education −0.10 0.06 −0.10 −1.89 Gender 0.16 0.30 0.03 0.52 DES −0.02 0.04 −0.03 −0.56 VIS −0.22 0.15 −0.09 −1.52 Bill Payment 0.13 0.07 0.14 2.02* DLM-drc 0.31 0.04 0.59 8.00** Judgment 0.14 0.07 0.14 2.13* R2 = 0.52, F(8,184) = 24.71, p < .001 B SE β t Age 0.00 0.02 0.01 0.16 Education −0.10 0.06 −0.10 −1.89 Gender 0.16 0.30 0.03 0.52 DES −0.02 0.04 −0.03 −0.56 VIS −0.22 0.15 −0.09 −1.52 Bill Payment 0.13 0.07 0.14 2.02* DLM-drc 0.31 0.04 0.59 8.00** Judgment 0.14 0.07 0.14 2.13* Note: *p < .05, **p < .001; DES = Design Construction; VIS = Visual Discrimination; DLM-drc = NAB Daily Living Memory – delayed recall. Table 5. Summary of linear regression analyses predicting Lawton Brody Instrumental Activities of Daily Living Scale: model excludes DLM-drc and Bill Payment R2 = 0.50, F(7,222) = 31.09, p < .001 B SE β t Age −0.003 0.02 −0.01 −0.14 Education −0.08 0.06 −0.07 −1.45 Gender 0.78 0.33 0.12 2.38* DES 0.01 0.04 0.02 0.29 VIS 0.02 0.15 0.01 0.14 Driving Scenes 0.13 0.02 0.49 7.57** Judgment 0.30 0.06 0.28 4.80** R2 = 0.50, F(7,222) = 31.09, p < .001 B SE β t Age −0.003 0.02 −0.01 −0.14 Education −0.08 0.06 −0.07 −1.45 Gender 0.78 0.33 0.12 2.38* DES 0.01 0.04 0.02 0.29 VIS 0.02 0.15 0.01 0.14 Driving Scenes 0.13 0.02 0.49 7.57** Judgment 0.30 0.06 0.28 4.80** Note: **p < .001, *p < .05; DES = Design Construction; VIS = Visual Discrimination; DLM-drc = NAB Daily Living Memory – delayed recall. Table 5. Summary of linear regression analyses predicting Lawton Brody Instrumental Activities of Daily Living Scale: model excludes DLM-drc and Bill Payment R2 = 0.50, F(7,222) = 31.09, p < .001 B SE β t Age −0.003 0.02 −0.01 −0.14 Education −0.08 0.06 −0.07 −1.45 Gender 0.78 0.33 0.12 2.38* DES 0.01 0.04 0.02 0.29 VIS 0.02 0.15 0.01 0.14 Driving Scenes 0.13 0.02 0.49 7.57** Judgment 0.30 0.06 0.28 4.80** R2 = 0.50, F(7,222) = 31.09, p < .001 B SE β t Age −0.003 0.02 −0.01 −0.14 Education −0.08 0.06 −0.07 −1.45 Gender 0.78 0.33 0.12 2.38* DES 0.01 0.04 0.02 0.29 VIS 0.02 0.15 0.01 0.14 Driving Scenes 0.13 0.02 0.49 7.57** Judgment 0.30 0.06 0.28 4.80** Note: **p < .001, *p < .05; DES = Design Construction; VIS = Visual Discrimination; DLM-drc = NAB Daily Living Memory – delayed recall. Discussion The presence of functional impairment is necessary for the clinical diagnosis of AD dementia and staging of disease severity, and is a significant source of patient distress and family burden. Early detection of diminished functional capacity is critical for timely implementation of pharmacological and other intervention, as well as ADL compensatory strategies to help preserve functional independence for as long as possible. The NAB is a practical test battery that can simultaneously assses cognitive function and IADL status in older adults. The current study found that certain NAB tests accurately detected informant-rated functional impairment in a sample of older adults from the BU ADC Clinical Core registry. The NAB List Learning test and select Daily Living tests emerged as the most accurate predictors of functional impairment, with the Daily Living Memory and Driving Scenes tests, in particular, accounting for the greatest proportion of unique variance in the prediction of LB scores. The memory measures had the highest sensitivity in the context of a low predetermined false positive rate (5%). As shown in Table 3, a Driving Scenes raw score <30 identified between 55% and 63% of lower-functioning individuals. A Daily Living Memory delayed recall raw score <7 was the most sensitive cognitive test cutoff, as it correctly classified 88% of individuals with functional impairment. Neuropsychological tests of episodic memory (e.g., list learning tasks) have been previously identified as potential predictors of functional status in dementia, and the current findings support the role of this domain in IADL performance (de Paula et al., 2015; Razani et al., 2011). NAB List Learning and Daily Living Memory tests are measures of episodic memory, with the Daily Living Memory test developed to mimic real-world to-be-remembered information (i.e., names, phone numbers, medication dosing instructions). Impairment in episodic memory is typically the initial presenting symptom in AD that reflects the pathological course of the disease, which begins in medial temporal lobe structures (e.g., hippocampus) that modulate episodic memory (Braak & Braak, 1995; Hodges & Patterson, 1995; Hodges, 2000). Of the six major memory systems, episodic memory is the most clinically relevant (Gold, 2012). Intact episodic memory is necessary for remembering to turn the stove off, take medications, pay the bills, and avoid getting lost while driving. Deficits in these functional domains are initially subtle and often attributed by the patient to be normal aging; however, they can be indicators of the beginning stages of AD, and more serious incidences of episodic memory failure (e.g., getting lost while driving, leaving the stove on, mixing medications) tend to precipitate the initial presentation for clinical evaluation. These NAB List Learning and Daily Living tests may thus help with detection of episodic memory-related functional impairment and allow for early implementation of tailored cognitive compensatory strategies, such as the use of pillbox organizers, and/or assistance with finances and driving. The Daily Living Driving Scenes test also independently predicted IADL function in this sample of older adults. As a standalone measure, this test displayed better classification accuracy relative to all other non-memory measures. The NAB Driving Scenes test is a component of the Attention Module of the NAB and is a measure of visual attention and working memory. It is a performance-based measure that taps into mental abilities important for driving operations that could help identify patients potentially at risk for unsafe driving. The Driving Scenes test has been shown to be a valid predictor of on-road driving performance in healthy older adults and individuals with mild dementia (Brown et al., 2005). Nevertheless, the neural networks that underpin attention and working memory are disrupted in AD, and impairments in these domains can present early in the disease course (Baddeley, Baddeley, Bucks, & Wilcock, 2001; Belleville, Chertkow, & Gauthier, 2007; Perry, Watson, & Hodges, 2000). Attention and working memory play a role in other non-driving-related ADLs, with a substantial literature emphasizing the importance of intact higher-ordered cognitive processes, such as working memory and other executive functions, for optimal performance of complex and cognitively challenging IADLs, like assembling tax records or managing bank accounts (Marshall, Amariglio, Sperling, & Rentz, 2012). These complex ADLs may begin to deteriorate during the preclinical stages of AD (Marshall, Dekhtyar et al., 2015; Marshall, Zoller et al., 2015). Performance on the NAB Driving Scenes test may therefore be an indicator of overall functional abilities, potentially sensitive to complex ADLs. The most effectively predictive measures investigated in this study (the NAB recall subtests) were 77%–88% sensitive to impaired informant-rated IADLs, with only a 5% false positive rate. This compares favorably with the results of similar investigations. For example, using the Direct Assessment of Functional Status to predict AD diagnosis, Razani and colleagues (2011) found that individual subtests had highest sensitivity values range of 81–82% with specificity ranging from 76% to 83%. Memory and executive functioning domains each accounted for about 50% of the unique variance in IADL performance in a study by Farias and colleagues (2005). NAB Judgment was previously investigated as a predictor of IADLs in individuals with MCI (Jefferson et al., 2008) and was found to be among the cognitive measures that were most highly correlated with IADL score, with a similarly-strong finding for list recall scores. In the present study, list memory was quite a bit more predictive of IADL status than Judgment, which displayed only 25% sensitivity. The NAB memory subtests appear to compare favorably with other measures used in previous studies. Our findings emphasize the importance of inclusion of performance-based tools in the assessment of functional status in dementia. Recent guidelines proposed by Marson (2015) suggest that as an alternative to self- or informant-reported questionnaires, clinicians should use interval-scaled, direct performance measures that can tap into cognitively challenging functional tasks that are affected early in the disease process. Consequently, there has been emergence of research examining various performance-based measures of ADLs, such as the Direct Assessment of Functional Status, The University of California, San Diego Performance-Based Skills Assessment, and the Financial Capacity Instrument (Binegar et al., 2009; Chisholm et al., 2014; Cullum et al., 2001; Diehl et al., 2005; Farina et al., 2010; Foster, 2014; Goldberg et al., 2010; Heaton et al., 2004; Loewenstein et al., 1989; McDougall et al., 2009; Patterson et al., 2001; Sadek et al., 2011; Triebel et al., 2009; Weening-Dijksterhuis et al., 2010). These tools can accurately distinguish clinically normal elderly from MCI, but are time intensive. Most recently, the 10-min, Harvard Automated Phone Task was developed and asks participants to use a voice response system to refill a prescription, select a new primary care physician, and make bank transfers (Marshall, Dekhtyar et al., 2015; Marshall, Zoller et al., 2015). This task distinguished across clinically normal elderly, MCI, and young normal participants. Future work should examine whether the NAB Daily Living tests can detect preclinical changes in functioning in AD. Contrary to expectations, scores on two of the domain-specific NAB Daily Living measures (Bill Payment and Judgment) did not correspond with informants’ binary report (i.e., complaint-no complaint) on these functional skills. NAB Bill Payment and Judgment were less sensitive to the individual informant-rated functional skills (bill payment and judgment, respectively) compared to the NAB memory and Driving Scenes variables, and they were less sensitive to those skills than they were to the more global LB. One possible reason for this finding is that informant report may not be specific to those individual skills. That is, informants often note deficits across multiple domains, thus individuals with bill payment difficulties might also exhibit problems in other functional areas, obscuring the more narrow-based functional deficit in question. It is also possible that the individual NAB measures themselves might be too multifaceted to specifically assess the target functional skill. For example, the skills underlying Bill Payment might draw upon the multiple executive demands of family financial management, such as planning and abstract reasoning, and might predict performance of other skills that call upon those demands (e.g., driving, meal preparation). This would limit the specificity of each test to the corresponding functional skill. Although the NAB daily living tasks lacked specificity in predicting informant-reported concerns in the areas they aim to assess, these measures still had utility in predicting ADL impairment in general. Several NAB memory and Daily Living measures demonstrated comparable overall effectiveness at predicting ADL status. Future research might explore comparative value of these various measures in other contexts, such as for the prediction of naturalistic everyday-action task performance or for the prediction of other instrumental ADLs (such as driving). This study is not without limitations. The sampling of NAB tests included in this protocol represents a small percentage of the NAB’s components because they were considered to be complementary to the existing BU ADC registry protocol (and mandatory NACC measures) in order to allow for adequate assessment of each cognitive domain, within time limitations and subject burden associated with the evaluation. The tests assess domains most commonly involved in the performance of IADLs; however, future research could investigate the utility of the NAB as a whole in predicting functional status in dementia. For example, the Mazes test of the NAB has been shown to predict on-road driving performance (Niewoehner et al., 2012) and, like the NAB Driving Scenes test, it too may extend to detection of IADL impairment more broadly. The current study was also cross-sectional and limits causal inferences or understanding on the relationship between change in NAB scores and functional status. Longitudinal work will help to clarify whether the NAB predicts functional and dementia progression over time. Additionally, the ADL ratings for each participant depend upon informant ratings, which can be questionably reliable due to a few factors, including the rater’s familiarity with the participant, personal characteristics, and cognitive status (De Medeiros et al., 2010). The structure of the LB also does not control for premorbid functioning, and some individuals who were rated as lacking the ability to perform an ADL might not have ever needed to engage in the task, thus artificially lowering their scores. The LB scale itself, as with most self- or informant-rated scales, has not been extensively evaluated in the literature, and its psychometric properties are not well established (Sikkes et al., 2009), which could limit the applicability of the current findings. Finally, there were 33 participants in this study who had non-amnestic MCI, and these individuals have a lower likelihood of underlying AD neuropathology than those with an amnestic presentation. However, there is still a substantial proportion of individuals with non-amnestic MCI who do progress to AD (68% in Busse, Hensel, Gühne, Angermeyer, & Riedel-Heller, 2006), and there is significant variability in the clinical presentation of AD dementia. Indeed, all participants in this sample were judged by a multidisciplinary panel of clinicians to have AD as the underlying etiology, and this consensus decision was based not only on neuropsychological testing performance but on numerous data points (e.g., clinical course, informant report of symptoms, and previous test patterns). For these reasons, non-amnestic participants were not excluded from this study. In sum, findings from this study suggest that select tests from the NAB, including List Learning and performance-based Daily Living tests, may have key clinical utility in detecting functional impairment in dementia. The NAB is well-normed and has equivalent forms, making it suitable for future research to examine whether NAB tests, especially the performance-based Daily Living tests, can detect changes in more complex ADLs that could be present in the very early stages of dementia (e.g., preclinical AD). Funding This work was supported by grants from the NIH (P30 AG13846, F32NS096803, 1UL1TR001430). The funding sources provided data and salary support for some of the authors. However, these funding sources did not play any role in the study design, analysis and interpretation of data, in the writing of the report, or in the decision to submit the article for publication. Conflict of interest None declared. References Ashendorf , L. , Jefferson , A. L. , Green , R. C. , & Stern , R. A. ( 2009 ). Test–retest stability on the WRAT-3 reading subtest in geriatric cognitive evaluations . Journal of Clinical and Experimental Neuropsychology , 31 , 605 – 610 . Google Scholar CrossRef Search ADS PubMed Baddeley , A. D. , Baddeley , H. , Bucks , R. , & Wilcock , G. ( 2001 ). Attentional control in Alzheimer’s disease . Brain , 124 , 1492 – 1508 . Google Scholar CrossRef Search ADS PubMed Barberger‐Gateau , P. , Commenges , D. , Gagnon , M. , Letenneur , L. , Sauvel , C. , & Dartigues , J. F. 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Published: Nov 8, 2017

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