Journal of Cardiovascular Nursing

Vol. 31, No. 1, pp 31Y41 x Copyright B 2016 Wolters Kluwer Health, Inc. All rights reserved.

Reduced Gray Matter Volume Is Associated With Poorer Instrumental Activities of Daily Living Performance in Heart Failure Michael L. Alosco, MA; Adam M. Brickman, PhD; Mary Beth Spitznagel, PhD; Atul Narkhede, MS; Erica Y. Griffith, BS; Ronald Cohen, PhD; Lawrence H. Sweet, PhD; Richard Josephson, MS, MD; Joel Hughes, PhD; John Gunstad, PhD Background: Heart failure patients require assistance with instrumental activities of daily living in part because of the high rates of cognitive impairment in this population. Structural brain insult (eg, reduced gray matter volume) is theorized to underlie cognitive dysfunction in heart failure, although no study has examined the association among gray matter, cognition, and instrumental activities of daily living in heart failure. Objectives: The aim of this study was to investigate the associations among gray matter volume, cognitive function, and functional ability in heart failure. Methods: A total of 81 heart failure patients completed a cognitive test battery and the Lawton-Brody self-report questionnaire to assess instrumental activities of daily living. Participants underwent magnetic resonance imaging to quantify total gray matter and subcortical gray matter volume. Results: Impairments in instrumental activities of daily living were common in this sample of HF patients. Regression analyses controlling for demographic and medical confounders showed that smaller total gray matter volume predicted decreased scores on the instrumental activities of daily living composite, with specific associations noted for medication management and independence in driving. Interaction analyses showed that reduced total gray matter volume interacted with worse attention/executive function and memory to negatively impact instrumental activities of daily living. Conclusions: Smaller gray matter volume is associated with greater impairment in instrumental activities of daily living in persons with heart failure, possibly via cognitive dysfunction. Prospective studies are needed to clarify the utility of clinical correlates of gray matter volume (eg, cognitive dysfunction) in identifying heart failure patients at risk for functional decline and determine whether interventions that target improved brain and cognitive function can preserve functional independence in this high-risk population. KEY WORDS:

H

brain volume, cognitive function, heart failure, instrumental activities of daily living

eart failure (HF) is associated with an array of poor outcomes, including premature death and

recurrent hospital readmissions.1 A rich literature also indicates high rates of functional impairment in HF

Michael L. Alosco, MA

Lawrence H. Sweet, PhD

Graduate student, Department of Psychological Sciences, Kent State University, Ohio.

Gary R. Sperduto Professor of Clinical Psychology, Department of Psychology, University of Georgia, Athens.

Adam M. Brickman, PhD

Richard Josephson, MS, MD

College of Physicians and Surgeons, Taub Institute for Research on Alzheimer’s Disease and Associate Professor of Neuropsychology, the Aging Brain, Department of Neurology, Columbia University, New York.

Medical Director of CICU and CVP Rehabilitation, University Hospitals Case Medical Center Cleveland; Harrington Heart & Vascular Institute; and Professor of Medicine, School of Medicine, Case Western Reserve University, Cleveland, Ohio.

Mary Beth Spitznagel, PhD AssistantProfessor, Department of Psychological Sciences, Kent State University; and Department of Psychiatry, Summa Health System, Akron City Hospital, Ohio.

Atul Narkhede, MS

Joel Hughes, PhD Assistant Professor, Department of Psychological Sciences, Kent State University; and Department of Psychiatry, Summa Health System, Akron City Hospital, Akron, Ohio.

College of Physicians and Surgeons, Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Department of Neurology, Columbia University, New York.

John Gunstad, PhD

Erica Y. Griffith, BS

Support for this work included National Institutes of Health (NIH) grants DK075119 and HL089311. A.M.B. is supported by NIH grant R01 AG034189. The authors have no conflicts of interest to disclose.

College of Physicians and Surgeons, Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Department of Neurology, Columbia University, New York.

Ronald Cohen, PhD Professor and Director, Cognitive Aging and Memory Program Departments of Neurology Psychiatry and the Institute on Aging, Center for Cognitive Aging and Memory, University of Florida, Gainesville.

Assistant Professor, Department of Psychological Sciences, Kent State University, Ohio.

Correspondence John Gunstad, PhD, Department of Psychology, Kent State University, OH 44242 ([email protected]). DOI: 10.1097/JCN.0000000000000218

31 Copyright © 2015 Wolters Kluwer Health, Inc. All rights reserved.

32 Journal of Cardiovascular Nursing x January/February 2016 populations. For example, up to 80% of patients with HF are dependent on others for performance of key instrumental activities of daily living (ADLs) such as housekeeping tasks, shopping, driving, and medication management.2,3 Impairments in instrumental ADLs (ie, medication management) are well known to contribute to heightened risk for rehospitalizations and increased mortality risk, among many other poor outcomes in persons with HF.4 Much attention has been paid to risk factors of reduced instrumental ADL function in HF and includes demographic and psychological variables (eg, older age, being female, depression), as well as HF-related factors (eg, cardiac output, dyspnea, decreased physical fitness, and reduced muscle strength).3,5,6 Performance of many instrumental ADLs requires intact cognitive processes, and unfortunately, cognitive impairment can be found in nearly 80% of patients with HF and also increases risk for functional dependence in this population.7 For example, executive dysfunction is an independent predictor of instrumental ADL impairment in HF,8 and persons with HF also exhibit declines in instrumental ADLs over time suggested to in part be a consequence of deterioration in overall global cognitive status.9 These findings are consistent with the observed elevated risk for neurological conditions in HF (eg, Alzheimer’s disease, vascular dementia)10 that are classically characterized by accelerated declines in cognition and self-care abilities. Although not yet examined, adverse brain changes are theorized to underlie cognitive dysfunction in HF and may ultimately be the primary culprit of instrumental ADL impairment in this population. In particular, HF patients exhibit smaller gray matter volume relative to healthy controls,11 and reduced gray matter volume has been linked with deficits in cognitive domains important for instrumental ADL performance (eg, executive function) in this population.12,13 Smaller brain volume is also an identified predictor of impairments in instrumental ADLs among neurological samples (eg, Alzheimer’s disease).14 These findings raise the possibility that insult to the cerebral structure, particularly gray matter volumetric abnormalities, may contribute to decreased self-care abilities in HF, perhaps via cognitive dysfunction. Supporting this notion is past work that shows significant interrelationships among smaller gray matter volume, executive dysfunction, and reduced instrumental ADL function in non-HF elderly populations.15,16 No study to date has examined the relationship among gray matter volume, cognitive function, and instrumental ADL performance in HF. The purpose of this study was to examine the impact of total and subcortical gray matter volume on instrumental ADLs in older adults with HF. We targeted gray matter volume because of its sensitivity to cardiac dysfunction11,17 and past work that shows a close association between cortical and subcortical gray matter volume and instrumental ADL function,

including in older adults with vascular dementia.15,18 We then investigated the association between the brain indices and cognitive test performance to explore the possible interactive effects of gray matter volume and cognitive function on instrumental ADL performance. Specifically, we conducted moderation analyses to determine whether instrumental ADLs were less affected by gray matter volume reductions in participants who exhibited higher cognitive function. Moderation rather than mediation analyses were conducted for several theoretical reasons: (1) Subtle brain alterations do not necessarily correspond to the manifestation of cognitive dysfunction; that is, there can be a lack of correlation between magnetic resonance imaging (MRI) and cognitive indices in relatively young samples without severe levels of neurological disease; (2) cognitive function may be preserved in the presence of brain alterations owing to cognitive reserve factors (eg, higher education); and (3) cognitive dysfunction is likely not the only mechanism by which brain abnormalities impact instrumental ADLs, as would be suggested by the implementation of mediation analyses.

Methods Participants A total of 226 patients with HF were recruited for participation in a larger National Institutes of HealthY funded study examining neurocognitive function in older adults with HF. Participants were stable, medically monitored, HF patients who were recruited from outpatient cardiology practices at Summa Health System in Akron, Ohio. As part of the larger study’s protocol, all participants completed a single baseline assessment that consisted of cognitive testing, completion of self-report medical and psychosocial measures, and performance of a brief physical fitness assessment. A subset of participants (n = 92) also underwent neuroimaging within 2 weeks of the baseline time point. Only 92 individuals underwent neuroimaging because the remaining participants refused neuroimaging, were excluded because of MRI contraindications (eg, pacemaker), or did not complete the MRI within 2 weeks of the baseline assessment. Although there were no differences between those who completed neuroimaging and those who did not in terms of age, sex, or education (P 9 .05), those excluded did exhibit statistically significantly lower Mini-Mental State Examination scores (P G .05), but this difference was small, with a mean difference of 0.69. After exclusion for non-MRI participants, the sample was further reduced to 81 after exclusion of participants with missing cognitive, left ventricular ejection fraction, self-report, and/or physical fitness data. Thus, the final sample of 81 includes all participants with complete data on all study measures used for the purposes of this study. Those excluded for missing data (n = 11) did not differ from the final sample in terms of

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Brain Volume and ADL 33

age, sex, education, or Mini-Mental State Examination scores (P 9 .10 for all). There were also no differences on the demographic variables between the final sample of 81 and those participants who were excluded for both not completing neuroimaging and missing data (P 9 .10 for all); however, participants excluded for these reasons exhibited a slightly lower Mini-Mental State Examination score (P = .02; mean difference, 0.73). See the Figure for a detailed participant flow diagram. As part of the larger study’s procedures, strict inclusion/ exclusion criteria were chosen for study entry. These criteria were implemented to maximize generalizability to other HF samples and minimize potential confounders. The participants were between 50 and 85 years of age, were native English speakers, and had an established diagnosis of New York Heart Association class II, III, or IV at the time of enrollment. Exclusion criteria included

history of significant neurological disorder (eg, dementia, stroke), head injury with more than 10-minutes loss of consciousness, severe psychiatric disorder (eg, schizophrenia, bipolar disorder), history of substance abuse/ dependence, and stage 5 chronic kidney disease. Measures Activities of Daily Living The self-report Lawton-Brody Activities of Daily Living Scale operationalized instrumental ADLs.19 Instrumental ADLs include complex activities such as transportation and management of finances, telephone use, meal preparation, housekeeping, laundry, shopping, and medication maintenance. Instrumental ADL scores range from 0 to 16. Any response that indicated receiving assistance was deemed impaired on that activity, and a higher total

FIGURE 1. Participant flow diagram.

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34 Journal of Cardiovascular Nursing x January/February 2016 score signifies better functionality. The Lawton-Brody scale exhibits an interrater reliability of r = .85 and has strong concurrent validity with other measures of functional status.20 Neuroimaging Whole-brain, standard 3-dimensional T1-weighted images were acquired on a Siemens Symphony 1.5-T MRI scanner for morphologic analysis. Twenty-six slices were acquired in the sagittal plane with a 230  100 mm field of view. The acquisition parameters were as follows: echo time, 17 ms; repetition time, 360 ms; acquisition matrix, 256  100; and slice thickness, 5 mm. Morphometric analysis of brain structure was completed with FreeSurfer Version 5.1 (http://surfer.nmr.mgh .harvard.edu). FreeSurfer software has been shown to demonstrate high reliability relative to other automatic brain segmentation software (eg, SPM8, FSL) and is insensitive to noise factors.21 Detailed methods for regional and total volume derivation have been described in detail previously.22Y24 FreeSurfer was used to perform image preprocessing (eg, intensity normalization, skull stripping) then to provide both cortical and subcortical volume measures using the surface stream and the subcortical segmentation stream, respectively. FreeSurfer performs such parcellations by registering images to a probabilistic brain atlas, built from a manually labeled training set, and then, using this probabilistic atlas, assigning a neuroanatomical label to each voxel in an MRI volume. Total gray matter volume (ie, subcortical gray matter, left and right hemisphere cortex, and cerebellum) and subcortical gray matter volume (ie, thalamus, caudate, hippocampus, amygdala, accumbens, ventral DC, and substantia nigra) were automatically derived with the subcortical processing stream (ie, ‘‘aseg.stats’’). Several quality control postprocessing procedures were also performed to ensure reliability and validity of brain tissue quantification. Specifically, an initial reconstruction run was first conducted, which was followed by a manual and visual check/correction of the Talairach transform that was computed for each subject. The Talairach volume was fitted to match the target volume of the participant brain along the anterior/posterior commissures, temporal lobes, and midline. Subsequent to this check, each participant underwent another reconstruction run. The quality of the skull strip was checked manually, along with segmentations and the boundaries of the pial and white matter surfaces. This procedure was performed through the addition or deletion of voxels and using control points to renegotiate the surface topology. A final reconstruction was then completed and rechecked to ensure that all edits were factored in. Cognitive Function A series of neuropsychological measures were administered to assess cognitive function in multiple domains,

including global cognitive status, attention/executive function, memory, and language. All measures are widely used in medical populations and demonstrate excellent psychometric properties. The domains and their respective measures include the following. Global Cognitive Status. The Mini-Mental State Examination25 was used to characterize the overall global cognitive status of the sample. This measure taps into a range of mental abilities, including attention, orientation, memory, and language and visuospatial abilities. Attention/Executive Function. The Frontal Assessment Battery and Digit Symbol Coding26,27 were used to examine attention and executive function. The Frontal Assessment Battery28 assesses several different aspects of executive function, including abstract reasoning, lexical fluency, higher-order motor programming, inhibitory control, sensitivity to interference, and environmental autonomy. In the Digit Symbol Coding task, participants use a key to quickly match symbols with corresponding numbers over a 2-minute period. Memory. The California Verbal Learning TestYSecond Edition short and long delay free recall29 indices were used to test memory function. During this task, participants learn and then recall a 16-item word list immediately after the learning trials and then again after a delay period. Language. The Boston Naming Test30 and Animal Fluency Test31 were used to assess language abilities. The Boston Naming Test measures confrontation naming, and the Animal Fluency Test has participants name as many animals as possible for a 60-second period, tapping into verbal fluency. Depressive Symptoms The Beck Depression Inventory-II was used to assess depressive symptoms in this sample. The Beck Depression Inventory-II is a commonly used checklist of depressive symptoms that demonstrates good psychometric properties in persons with medical conditions.32,33 Scores on the Beck Depression Inventory-II range from 0 to 63, with higher scores indicative of greater symptoms. In particular, scores between 0 and 13 are reflective of minimal depression; 14 to 19, mild depression; 20 to 28, moderate depression; and 29 to 63, severe depression. The Beck Depression Inventory-II score was included as a continuous covariate in all analyses to account for the effects of depressive symptoms on cognitive and instrumental ADL function in HF. Heart Failure Severity The 2-minute step test is a measure of physical fitness34 and served as an estimate of HF severity. The 2-minute step test requires participants to step in place lifting his/her knees to a marked target set on the wall set at the midpoint between the kneecap and crest of the iliac for a 2-minute period. Greater step count reflects better physical fitness. Average step count for healthy men between

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Brain Volume and ADL 35

the ages of 60 and 85 years ranges from 71 to 115 and between 60 and 107 steps for women.34 Below-average 2-minute step test according to the average age of men in this sample (mean, 68.10) is fewer than 87, whereas that for women with the average age of the current sample (mean, 67.89) is fewer than 73. The 2-minute step test is a valid measure of fitness, as previous work shows that the 2-minute step test is closely correlated with metabolic equivalents derived from stress testing.35 Demographic and Medical History Demographic and medical characteristics were obtained through participant self-report and were corroborated by medical record review. Specifically, participants were administered a medical history questionnaire and a trained research assistant then also performed a medical record review to confirm participants’ responses and supplement their medical history. Through these methods, a diagnostic history of medical variables such as hypertension, type 2 diabetes mellitus, and sleep apnea was ascertained and included as covariates to account for their variance in instrumental ADL and neurocognitive outcomes in HF. A medical record review was also used to quantify left ventricular ejection fraction for descriptive purposes. Procedures The Kent State University and Summa Health System Institutional Review Board approved the study procedures, and all participants provided written informed consent before study enrollment. During a single time assessment, all participants completed demographic and psychosocial self-report measures, including the LawtonBrody Activities of Daily Living Scale and the Beck Depression Inventory-II. Participants were also administered a comprehensive cognitive test battery and performed the 2-minute step test. All procedures were performed by a trained research assistant under the supervision of a licensed clinical neuropsychologist. At a separate study session, but within 2 weeks of cognitive testing, participants underwent MRI that was conducted by a trained hospital-based technician. Statistical Analyses To facilitate clinical interpretation and avoid discrepancy among scales, raw scores of neuropsychological measures assessing attention/executive function, memory, and language were transformed to T scores (a distribution with a mean of 50 and a standard deviation of 10) using existing normative data correcting for age, and gender in the case of memory. The normative data used to transform raw scores to T scores are commonly used in clinical settings (ie, Halstead-Reitan normative data and specific test developer norms) to characterize cognitive function and identify cognitive impairment. Higher T scores are reflective of better performance, and a

T score between 45 and 55 is representative of average performances. An Mini-Mental State Examination score36 lower than 27 and/or a T score lower than 35 (ie, 1.5 SD below the normative mean) were used to define cognitive impairment. Composite scores for attention/executive function, memory, and language were computed, which consisted of the mean of T scores of measures that comprise their respective domain. Hierarchical regression analyses were performed to examine the impact of gray matter volume on instrumental ADL function. The dependent variable included the instrumental ADL total composite. Block 1 of each model included intracranial volume, age, sex (1 = men, 0 = women), education, 2-minute step test, Beck Depression Inventory-II and diagnostic history of hypertension, type 2 diabetes mellitus, and sleep apnea (1 = positive diagnostic history, 0 = negative diagnostic history). Indeed, although HF patients exhibit a multitude of medical comorbidities, hypertension, type 2 diabetes mellitus, and sleep apnea are among the most prevalent medical comorbidities in HF that are well known to affect brain outcomes in this population and are also associated with increased risk for severe neurological conditions such as Alzheimer’s disease. Thus, only these medical conditions were included in analyses to preserve statistical power. Volumetric brain indices (ie, total and subcortical gray matter volume) were individually entered in block 2, yielding a total of 2 separate regression analyses. A series of interaction analyses were then conducted within a hierarchical regression framework to examine the interaction between gray matter volume and cognitive function on ADL function. To limit the number of analyses, only those brain indices that exhibited a significant effect with instrumental ADL performance were examined. The instrumental ADL composite was the dependent variable for all analyses. All continuous predictor variables were transformed to within-sample z scores. Block 1 included the above-described demographic, medical, and clinical variables. Block 2 included the MRI variable and cognitive domain of interest. Interaction terms (ie, cross product) between the MRI index (ie, independent variable) and each cognitive domain (ie, moderator) were then computed and individually entered in block 3 in separate models. A simple slopes test was then performed to examine the nature of the interaction. Lastly, poor fitness and greater depressive symptoms are sensitive to instrumental ADL abilities, and we sought to determine whether these factors interact with MRI indices to exacerbate reductions in functional independence. Specifically, exploratory analyses were performed that examined physical fitness (ie, 2-minute step test) and depression (ie, Beck Depression Inventory-II) as the moderators of the relationship between gray matter volume and instrumental ADL performance. For physical fitness, block 1 included the above-described covariates except for the 2-minute step test, as this moderating variable

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36 Journal of Cardiovascular Nursing x January/February 2016 was entered in block 2 along with the MRI index. The cross-product between the MRI index and the 2-minute step test was entered in block 3. This same procedure was performed for the Beck Depression Inventory-II.

TABLE 2 Reported Instrumental Activities of Daily Living Performance (N = 81)

Instrumental ADL

Results Sample Characteristics See Table 1 for the medical and demographic characteristics of the sample. The mean (SD) age of the sample was 68.01 (8.65) years (range, 50Y83 years), 39.5% of the participants were women, 70.4% were married (9.9% were divorced, 11.1% were widowed, and 8.6% were single/never married), and the sample had a mean (SD) of 13.80 (2.73) years of education. Medical chart review revealed a mean (SD) left ventricular ejection fraction of 43.01 (13.5), with 33.3% exhibiting an left ventricular ejection fraction lower than 40. Medical comorbidities were also prevalent in this sample, including hypertension (70.4%), type 2 diabetes mellitus (29.6%), and sleep apnea (24.7%). Depressive symptoms were also common (mean [SD] Beck Depression Inventory-II, 6.48 [6.86]) and 9.9% had mild levels of depression, 1.2% had moderate depression, and 2.5% had severe depression. Physical fitness was poor (mean [SD] 2-minute step test, 64.75 [22.47]). Specifically, 75.5% of male participants exhibited below-average levels of physical fitness (ie, G87 steps in 2 minutes) and 75.0% of women had below-average 2-minute step test performance (ie, G73 steps). Instrumental Activities of Daily Living Performance Refer to Table 2 for instrumental ADL performance in the sample. Impairments were most common in instrumental ADLs such as shopping (17.3%), food preparation (19.8%), housekeeping duties (28.4%), and laundry (32.1%). Reduced independence in critical instrumental ADLs such as driving and medication and financial manTABLE 1 Demographic, Medical, and Clinical Characteristics Demographic Characteristics Age, y Sex, female Education, y Marital status, married Medical and clinical characteristics Left ventricular ejection fraction Diabetes Hypertension Sleep apnea Myocardial infarction Elevated total cholesterol Coronary artery disease Beck Depression Inventory-II 2-Minute step test

Mean (SD), Median or % (n) 68.01 39.5 13.80 70.4

(8.65), 67.00 (32) (2.73), 13.00 (57)

43.01 29.6 70.4 24.7 51.9 67.9 76.5 6.48 64.75

(13.5), 42.00 (24) (57) (20) (42) (55) (62) (6.86), 5.00 (22.47), 64.00

Telephone use Shopping Food preparation Housekeeping Laundry Transportation Medication management Finances

Mean (SD)

Median

14.36 (2.41)

16.00

% Impaired

N

0.0 17.3 19.8 28.4 32.1 4.9 2.5 3.7

0 14 16 23 26 4 2 3

Abbreviation: ADL, activities of daily living.

agement was also evident. Bivariate correlations and independent-samples t tests showed that age, education, Beck Depression Inventory-II, 2-minute step test, or diagnostic history of conditions like hypertension, type 2 diabetes mellitus, and sleep apnea were not associated with instrumental ADLs (P 9 .05 for all). However, relative to women, men exhibited worse instrumental (t79.00 = j3.19, P = .002) ADL performance. Moreover, married participants exhibited lower scores on the total instrumental ADL composite than their nonmarried counterparts did (t63.56 = j2.47, P = .02), with specific effects noted on food preparation (t69.29 = j3.75, P G .001) and laundry (t70.23 = j3.63, P = .001). Brain Volume and Instrumental Activities of Daily Living Performance Table 3 shows the hierarchical regression analyses conducted to examine the impact of total and subcortical gray matter volume on instrumental ADL performance in this sample of patients with HF. For all regression analyses performed, evaluation of Cook distance (ie, G 1) and normal P-P plots of regression residuals revealed no significant deviations from regression model assumptions. Skewness and kurtosis values were also less than 2 and 7, respectively, for the primary predictor and dependent variables, thus suggesting no violations in univariate normality. After adjustment for age, sex, education, 2-minute step test, Beck Depression Inventory-II, intracranial volume, and diagnostic history of hypertension, type 2 diabetes mellitus, and sleep apnea, total gray matter volume exhibited a significant association with the instrumental ADL composite (" = .31, P = .02). Smaller gray matter volume was associated with worse instrumental ADL function. The overall model that considers all of the predictor variables simultaneously was also significant (F10,70 =2.83,P = .01). Subcortical gray matter volume was not associated with instrumental ADLs (P 9 .10 for all). Follow-up partial correlation analyses controlling for the above-listed variables were conducted to clarify the

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Brain Volume and ADL 37 TABLE 3

The Impact of Gray Matter Volume on Instrumental Activities of Daily Living in Heart Failure (N = 81) IADL

Block 1, model 1 Age Sex Education ICV 2MST BDI-II Hypertension T2DM Sleep apnea F R2 Block 2, model 2 Total gray matter Model F $F R2 Block 2, model 3 Subcortical gray matter Model F $F R2

"

SE b

P

.00 j.14 j.11 j.32 .22 .10 j.12 j.10 j.25

.03 .73 .10 .00 .01 .04 .60 .57 .65

.98 .34 .34 .05 .09 .40 .32 .38 .03 .02 Y

.00

.02 .01 .02 Y

.00

.59 .03 .59 Y

2.41 0.14 .31 2.83 5.29 0.19 .07 2.18 0.29 0.13

" = standardized "; SE b = standard error of the unstandardized ". Abbreviations: 2MST, 2-minute step test; BDI-II, Beck Depression Inventory-II; IADL, instrumental activities of daily living; ICV, intracranial volume; T2DM, type 2 diabetes mellitus.

association between total gray matter volume and the individual instrumental ADL items. Total gray matter volume was associated with management of medications (r70 = 0.28, P = .02) and independence in transportation (r70 = 0.24, P = .04); there were also trends for shopping (r70 = 0.22, P = .059), housekeeping duties (r70 = 0.21, P = .07), and laundry (r70 = 0.23, P = .055). Smaller gray matter volume was linked with poorer performance for each instrumental ADL. Brain Volume, Cognitive Function, and Instrumental Activities of Daily Living Performance Cognitive dysfunction was common in this sample, with a mean (SD) Mini-Mental State Examination of 27.86 (1.91), and 24.7% of participants exhibited a score lower than 27. Impairments, as defined by a T score lower than 35, were also common in the cognitive domains, particularly on a measure of attention/executive function (ie, Frontal Assessment Battery) and on a task assessing language abilities (ie, Boston Naming Test) (see Table 4). Regression analyses revealed that the interaction term between total gray matter volume and attention/executive function emerged as a significant predictor of the total instrumental ADL composite, even after controlling for demographic, medical, and clinical variables ($F1, 68 = 5.13, P = .03,

$R2 = 0.05; " = j.25); the overall model was also significant (F12, 68 = 2.97, P G .01). This same pattern emerged for the interaction between total gray matter volume and memory ($F1, 68 = 4.25, P = .04, $R2 = 0.04; " = j.23) that also exhibited a significant overall model (F12, 68 = 2.82, P G .01). A simple slopes test revealed that reduced total gray matter volume and lower attention/executive function and worse memory interacted to exacerbate impairments in instrumental ADLs. High cognitive function (ie, z score 1 SD above the mean) in these domains attenuated the adverse effects of smaller gray matter volume on instrumental ADLs. There was no interaction effect between total gray matter volume and language (P 9 .05). Exploratory Analyses: Physical Fitness and Depression Exploratory hierarchical regression analyses were performed to determine whether physical fitness and/or depression interacted with brain volume to impair instrumental ADLs. Interaction terms between total gray matter volume and the 2-minute step test and the Beck Depression Inventory-II did not demonstrate an association with the instrumental ADL composite, suggesting that these variables do not moderate the association between total gray matter volume and instrumental ADLs.

Discussion Consistent with the extant literature, this sample of HF patients required frequent assistance with instrumental ADLs. Recent work identifies cognitive dysfunction as an important risk factor for reduced functional independence in this population. The current study extends these findings by showing that smaller gray matter volume is also associated with decreased independence in instrumental ADLs among persons with HF, possibly because of the negative impact of brain alterations on cognitive function in this population. Many aspects of these findings warrant further discussion. TABLE 4

Cognitive Test Performance (N = 81)

Global cognitive status MMSE Attention/executive function Digit Symbol Coding Frontal Assessment Battery Memory CVLT short delay free recall CVLT long delay free recall Language Animal Fluency Test Boston Naming Test

Mean (SD), Median

Impaired, % (n)

27.86 (1.91), 28.00

24.7 (20)

49.05 (9.00), 50.00 7.4 (6) 45.56 (19.53), 50.90 22.2 (21) 48.33 (10.64), 50.00 48.40 (10.95), 50.00

6.2 (12) 8.6 (10)

54.70 (12.25), 51.90 3.7 (3) 50.06 (14.01), 55.00 16.0 (13)

% Impairment for MMSE = G27; all other measures impairment is reflective of T score G35. Abbreviations: CVLT, California Verbal Learning Test; MMSE, Mini-Mental State Examination.

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38 Journal of Cardiovascular Nursing x January/February 2016 We found that smaller total gray matter volume was associated with reduced independence in instrumental ADLs. These findings are consistent with past work that shows decreased gray matter volume contributes to instrumental ADL impairments among elderly persons along the dementia continuum.15 Cardiac dysfunction is sensitive to smaller gray matter volume,11,17 and persons with HF have been shown to exhibit accelerated brain atrophy over time relative to healthy controls.37 These findings raise the possibility that persons with HF may be at risk for decline in self-care as disease severity progresses, particularly in light of the elevated risk for neurodegenerative conditions in this population.10 There is some support for this notion in past work that shows worsening instrumental ADL function over a 1-year period in HF,9 as well as previous research that identifies brain atrophy as a significant predictor of accelerated functional decline in patients with mild cognitive impairment.38 Interestingly, volumetric MRI has been suggested to be a useful clinical tool in the identification of mild cognitive impairment patients at greatest risk for functional deterioration and disease progression.38 Unfortunately, MRI is expensive and is often an uncomfortable experience for patients. Thus, clinical correlates of gray matter volume that can be assessed in the confines of physicians’ examination room such as cognitive testing may offer more clinical utility. Cognitive testing is practical, inexpensive, and brief and might also provide better insight into the functioning of the brain; that is, neuroimaging does not necessarily provide insight into the exact severity or nature of impaired cognitive abilities, particularly in patients in whom pathology is subtle and has yet to impact daily function. Prospective studies are needed to determine the most clinically useful assessments for detecting patients with HF that are at greatest risk for impairments in functional abilities. Total gray matter volume demonstrated a significant association (ie, P G .05) with medication management and independence in transportation; however, trends were observed between gray matter volume and other instrumental ADLs (eg, shopping, laundry, housekeeping duties). Nonetheless, the effect sizes across the instrumental ADLs were comparable (ie, r = 0.21 to r = 0.28), suggesting that the differences in the significance level may be attributable partly to statistical-related limitations (eg, differences in variability across instrumental ADL items, modest sample size). Beyond possible statistical explanations, it is also not entirely surprising that slightly larger effects were observed for medication management and independence in transportation. These tasks are more cognitively challenging and may be more sensitive to factors that affect mental abilities (eg, gray matter volume reductions) relative to other instrumental ADLs such as shopping, housekeeping duties, and laundry. In contrast, there is possibly a higher threshold for gray matter abnormalities before impairments are ob-

served on over learned or less cognitively challenging tasks (eg, laundry). Clearly, future work that uses larger samples and more objective instrumental ADL assessments are needed to clarify the association between gray matter and instrumental ADL performance in HF. We found no effects for subcortical gray matter on instrumental ADLs. The exact reason for this pattern of findings is not entirely clear and is a bit surprising. Subcortical decreases in gray matter volume are particularly prevalent in HF relative to healthy controls, including of medial temporal lobe and surrounding structures. In fact, targeted papers have focused on the sensitivity of HF to only subcortical brain regions (eg, mammillary bodies). It is likely that the lack of subcortical effects observed in this study stems from the MRI parameters implemented. The MRI parameters utilized in this study (ie, slice thickness of 5 mm) are more useful for the detection of large brain abnormalities in a clinical setting and are not necessarily the best option for research. Thus, such MRI parameters may have lacked sensitivity to some subtle subcortical tissue pathology and may be less likely to detect brain abnormalities. Future work that uses more sensitive slice thickness parameters is much needed to provide a better understanding of the relationship between gray matter volume and instrumental ADL performance in HF. The composition of the MRI indices may also help explain the lack of effect for subcortical gray matter on instrumental ADLs. It is possible that total gray matter volume is more sensitive because it accounts for greater amount of brain tissue (ie, subcortical gray matter, left and right hemisphere cortex, and the cerebellum) and is thus more likely to detect volume abnormalities relative to the subcortical gray matter index on its own (ie, sum of thalamus, caudate, hippocampus, amygdala, accumbens, ventral DC, and substantia nigra). Our findings provide initial support for cognitive impairment as a possible mechanism for the association between reduced brain volume and instrumental ADL impairment. This finding has significant clinical implications given that cognitive testing has become routine in the management of HF and may help to provide key insight into patients’ ability to perform important self-care tasks independently. Reduced cardiac pumping efficiency is associated with reduced cerebral blood flow to the brain. Co-occurring medical conditions in this population (eg, diabetes, hypertension) exacerbate cerebral hypoperfusion via negative effects on the arterial structure. These alterations in cerebral hemodynamics can lead to glucose and oxygen deprivation to produce biochemical and metabolic events that trigger tissue volume reductions. Such pathological changes underpin into the clinical manifestation of impaired mental abilities. In particular, smaller gray matter volume has been linked to reduced cognitive function in HF,13 and cognitive dysfunction is a sensitive predictor of poor clinical outcomes in this population (eg, increased mortality risk), including decreased

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Brain Volume and ADL 39

ability to perform instrumental ADLs.3,39 Indeed, patients with HF often exhibit impairments in memory and executive function,40 and impairments in these domains may preclude optimal performance of instrumental ADLs (eg, driving, medication management) that require intact higher-ordered cognitive processes such as planning, organizing, and remembering and monitoring of behavior. Dysfunction in these mental abilities may also help explain the association between medical comorbidities (ie, sleep apnea) and instrumental ADLs observed in this study. Sleep apnea is well known to affect the integrity of the brain and is associated with reduced cognitive function in HF.41 Nevertheless, we controlled for the effects of conditions such as sleep apnea, suggesting that brain abnormalities may account for some unique variance in instrumental ADLs, perhaps through unexamined mechanisms (ie, genetics) that have previously been shown to interact with brain atrophy to negatively impact instrumental ADLs.38 Longitudinal work with larger samples of HF patients is needed to implement model-based statistical techniques (ie, structural equation modeling) to elucidate the role of cognitive dysfunction in the relationship between smaller brain volume and instrumental ADL impairment. In the present study, impairment rates were as high as greater than 30% for some instrumental ADLs (ie, laundry). Although this rate of impairment is lower than that in past work that found nearly 80% of HF patients,2 the discrepancy appears to be a manifestation of differences in participant characteristics. That is, relative to the current sample with a mean age of approximately 68 years, Norberg and colleagues2 had a smaller sample of considerably older participants (ie, mean age of 81 years). Regardless, the high rate of instrumental ADL impairments in HF is concerning, as greater dependence in instrumental ADLs in this population is associated with elevated risk for mortality and hospitalizations.42,43 However, interventions that improve neurocognitive function in HF may lead to preservation of functional independence in this population. Exercise is a key treatment recommendation in the management of HF that simultaneously improves cognitive function and instrumental ADL performance in patients with Alzheimer’s disease.44 There is reason to believe that this pattern is also evident in HF populations. For example, HF patients who complete cardiac rehabilitation have been shown to exhibit gains among cognitive domains critical for instrumental ADL performance (eg, executive function).45 These findings may be attributed largely to the impact of exercise on the brain given the link between exercise and increased gray matter volume in patients with other forms of cardiovascular disease (eg, coronary artery disease).46 Common medication therapies in HF (eg, angiotensin-converting enzyme inhibitors) have also been shown to reduce hospital readmissions and reduce the rate of functional decline in older adults with HF,47 and

such effects may be related to the benefits of such medications on cognitive function.48 Again, such effects may indeed stem from brain benefits, including increased cerebral blood flow.49 Randomized control trials that implement neuroimaging modalities (eg, MRI) are needed to determine whether medical and exercise interventions can benefit the brain to improve instrumental ADLs and/or attenuate functional decline in patients with HF. The current study is not without limitations. First, we examined the cross-sectional association between MRI indices, cognitive function, and instrumental ADL performance, and prospective studies are needed to clarify and confirm our findings. Prospective studies will also help to determine whether brain atrophy and cognitive decline over time underlie recent findings showing functional decline in patients with HF. In addition, we did not use a control group, and the power of the current study was reduced in our attempt to statistically control for potential confounds. Larger samples are needed to better account for potential medical confounders and determine whether the negative impact of gray matter volume on instrumental ADLs in HF extend beyond what is found in normative aging. The current study assessed instrumental ADL function using a well-validated selfreport measure. However, possible subjective, memory, and gender biases associated with self-reported functional independence can introduce confounds, and future work that uses objective assessment of functional abilities is needed to examine the aims of the current study. As an example, the current study demonstrated that instrumental ADL impairments, particularly for food preparation and laundry, were more common among married participants. It is possible that gender roles may contribute to these findings, as men (or women) may have reported impairments in these domains despite a lifelong history of never performing these duties. Unfortunately, we did not have access to data on social support, and, similar to the confound of gender roles, HF patients who completed the ADL instrument may have reported perceived independence in tasks such as food preparation despite having their meals provided for them or receiving assistance by family members. The rates of cognitive impairments in this sample were relatively modest relative to those in past work. Past work shows that the prevalence rates of cognitive impairment approach 80% in hospitalized, unstable HF patients.7 Thus, the stable levels of HF severity in this sample (ie, left ventricular ejection fraction, 43) may in part explain the overall reduced rates of cognitive impairment, particularly in memory. Prospective studies with more disease diverse samples are needed to clarify the effects of brain volume and cognitive function, including onset of possible dementia, on instrumental ADLs along the HF severity continuum. Several additional limitations deserve further discussion. Patients with HF may have limited awareness into

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40 Journal of Cardiovascular Nursing x January/February 2016

What’s New and Important h Clinical correlates of smaller gray matter volume may help inform clinicians of HF patients who require assistance with self-care. h Routine cognitive testing in outpatient cardiology settings may help to monitor neurological changes and subsequent need to intervene with assistance for ADL.

their functional deficits given the prevalence of cognitive impairment in this population. This is concerning, as HF patients may continue to perform instrumental ADLs despite being at risk for adverse consequences. The current study also examined possible mechanisms between brain volume and instrumental ADL performance using interaction-based analyses. This statistical approach and the creation of cognitive composites were performed to reduce the number of analyses performed, although the risk of type I error in the current study is still relatively high. We did not use Bonferroni correction, as some may argue that this conservative method may also inflate type 2 error. Nonetheless, the best solution to reduce type I error is to increase sample size. Indeed, the relatively modest sample size precluded more formal model-based analyses (ie, structural equation modeling) in which guidelines for these approaches suggest a minimal sample size of 100. The null effects observed for physical fitness and depression as moderators between total brain volume and instrumental ADLs may have been a manifestation of an insensitive analytic approach, reduced sample size, or a combination of both. Alternatively, these factors may be more sensitive to basic ADLs. Future work with larger HF samples should examine potential clinical mediators and/or moderators of the relationship between brain volume and ADLs (both instrumental and basic) to help clinicians identify HF patients at greatest risk for impairments in self-care. Lastly, regional brain abnormalities (eg, frontal lobe atrophy) and other neuropathology (eg, white matter hyperintensities) are also found in HF patients and future work is needed to examine the predictive validity of such pathology on cognition and instrumental ADLs, particularly as it relates to gray matter volume.

Conclusions In brief summary, the current study suggests that smaller gray matter volume is associated with ADL impairment in patients with HF, possibly via cognitive dysfunction. Cognitive screening and clinical correlates of gray matter volume may be useful clinical tools for the identification of HF patients at greatest risk for cognitive impairment and subsequent inability to perform self-care tasks. Prospective studies are needed to test this possibility and determine whether interventions (eg, structured exercise programs, medication therapy) that improve brain and

cognitive function can preserve functional independence in this population.

REFERENCES 1. Go AS, Mozaffarian D, Roger VL, et al. Heart disease and stroke statisticsV2014 update: a report from the American Heart Association. Circulation. 2014;129(3):e28Ye292. 2. Norberg EB, Boman K, Lofgren B. Activities of daily living for old persons in primary health care with chronic heart failure. Scand J Caring Sci. 2008;22(2):203Y210. 3. Alosco ML, Spitznagel MB, Cohen R, et al. Cognitive impairment is independently associated with reduced instrumental activities of daily living in persons with heart failure. J Cardiovasc Nurs. 2012;27(1):44Y50. 4. Fitzgerald AA, Powers JD, Ho PM, et al. Impact of medication nonadherence on hospitalizations and mortality in heart failure. J Card Fail. 2011;17(8):664Y669. 5. Friedman B, Lyness JM, Delavan RL, Li C, Barker WH. Major depression and disability in older primary care patients with heart failure. J Geriatr Psychiatry Neurol. 2008;21(2):111Y122. 6. Seo Y, Roberts BL, LaFramboise L, Yates BC, Yurkovich JM. Predictors of modifications in instrumental activities of daily living in persons with heart failure. J Cardiovasc Nurs. 2011; 26(2):89Y98. 7. Hajduk AM, Lemon SC, McManus DD, et al. Cognitive impairment and self-care in heart failure. Clin Epidemiol. 2013; 5:407Y416. 8. Alosco ML, Spitznagel MB, Raz N, et al. Executive dysfunction is independently associated with reduced functional independence in heart failure. J Clin Nurs. 2014;23(5Y6): 829Y836. 9. Alosco ML, Spitznagel MB, Cohen R, et al. Reduced cognitive function predicts functional decline in patients with heart failure over 12 months. Eur J Cardiovasc Nurs. 2013;13(4): 304Y310. 10. Qiu C, Winblad B, Marengoni A, Klarin I, Fastbom J, Fratiglioni L. Heart failure and risk of dementia and Alzheimer’s disease: a population-based cohort study. Arch Intern Med. 2006;166(9):1003Y1008. 11. Almeida OP, Garrido GJ, Beer C, Lautenschlager NT, Arnolda L, Flicker L. Cognitive and brain changes associated with ischaemic heart disease and heart failure. Eur Heart J. 2012;33(14):1769Y1776. 12. Vogels RL, Oosterman JM, van Harten B, et al. Neuroimaging and correlates of cognitive function among patients with heart failure. Dement Geriatr Cogn Disord. 2007;24(6): 418Y423. 13. Alosco ML, Brickman AM, Spitznagel MB, et al. The adverse impact of type 2 diabetes on brain volume in heart failure. J Clin Exp Neuropsychol. 2013;35(3):309Y318. 14. Marshall GA, Lorius N, Locascio JJ, et al. Regional cortical thinning and cerebrospinal biomarkers predict worsening daily functioning across the Alzheimer’s disease spectrum. J Alzheimers Dis. 2014;41(3):719Y728. 15. Cahn-Weiner DA, Farias ST, Julian L, et al. Cognitive and neuroimaging predictors of instrumental activities of daily living. J Int Neuropsychol Soc. 2007;13(5):747Y757. 16. Sakai H, Takahara M, Honjo NF, Doi S, Sadato N, Uchiyama Y. Regional frontal gray matter volume associated with executive function capacity as a risk factor for vehicle crashes in normal aging adults. PLoS One. 2012;7(9):e45920. 17. Bhattacharya P, Bao F, Shah M, Ramesh G, Madhavan R, Khan O. Left ventricular dysfunction is associated with cerebral grey matter injury: an in-vivo brain MRI segmentation. J Neurol Sci. 2012;321:111Y113.

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Brain Volume and ADL 41 18. Boyle PA, Paul RH, Moser DJ, Cohen RA. Executive impairments predict functional declines in vascular dementia. Clin Neuropsychol. 2004;18(1):75Y82. 19. Lawton MP, Brody EM. Assessment of older people: selfmaintaining and instrumental activities of daily living. Gerontologist. 1969;9(3):179Y186. 20. Graf C. The Lawton Instrumental Activities of Daily Living Scale. Am J Nurs. 2008;108(4):52Y62. 21. Eggert LD, Sommer J, Jansen A, Kircher T, Konrad C. Accuracy and reliability of automated gray matter segmentation pathways on real and simulated structural magnetic resonance images of the human brain. PLoS One. 2012;7(9):e45081. 22. Fischl B, Salat DH, Busa E, et al. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron. 2002;33(3):341Y355. 23. Fischl B, Sereno MI, Dale AM. Cortical surface-based analysis. II: inflation, flattening, and a surface-based coordinate system. NeuroImage. 1999;9(2):195Y207. 24. Fischl B, van der Kouwe A, Destrieux C, et al. Automatically parcellating the human cerebral cortex. Cerebral Cortex. 2004; 14(1):11Y22. 25. Folstein MF, Folstein SE, McHugh PR. ‘‘Mini-Mental State’’: a practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. 1975;12(3):189Y198. 26. Wechsler D. Wechsler Adult Intelligence ScaleVThird Edition (WAIS-III). San Antonio, TX: Psychological Corporation; 1997. 27. Wechsler D. Wechsler Memory ScaleVThird Edition (WMS-III). San Antonio, TX: Psychological Corporation; 1997. 28. Dubois B, Slachevsky A, Litvan I, Pillon B. The FAB: a Frontal Assessment Battery at bedside. Neurology. 2000;55(11): 1621Y1626. 29. Delis D, Kramer J, Kaplan E, Ober B. California Verbal Learning TestVSecond Edition: Adult Version. Manual. San Antonio, TX: Psychological Corporation; 2000. 30. Hawkins KA, Sledge WH, Orleans JE, Quinlan DM, Rakfeldt J, Huffman RE. Normative implications of the relationship between reading vocabulary and Boston Naming Test performance. Arch Clin Neuropsychol. 1993;8(6):525Y537. 31. Morris J, Heyman A, Mohs R, et al. The Consortium to Establish a Registry for Alzheimer’s Disease (CERAD), part I: clinical and neuropsychological assessment of Alzheimer’s disease. Neurology. 1989;39(9):1159Y1165. 32. Arnau R, Meagher M, Norris M, Bramson R. Psychometric evaluation of the Beck Depression Inventory-II with primary care medical patients. Health Psychol. 2001;20(2):112Y119. 33. Beck AT, Steer RA, Brown GK. Beck Depression Inventory. 2nd ed. San Antonio, TX: The Psychological Corporation; 1996. 34. Jones CJ, Rikli RE. Measuring functional fitness of older adults. J Active Aging. 2002:24Y30. 35. Garcia S, Alosco ML, Spitznagel MB, et al. Cardiovascular fitness associated with cognitive performance in heart failure

36.

37.

38.

39.

40. 41.

42.

43.

44.

45.

46.

47.

48.

49.

patients enrolled in cardiac rehabilitation. BMC Cardiovasc Disord. 2013;13:29. O’Bryant SE, Humphreys JD, Smith GE, et al. Detecting dementia with the mini-mental state examination in highly educated individuals. Arch Neurol. 2008;65(7):963Y967. Almeida OP, Garrido GJ, Etherton-Beer C, et al. Brain and mood changes over 2 years in healthy controls and adults with heart failure and ischaemic heart disease. Eur J Heart Fail. 2013;15(8):850Y858. Okonkwo OC, Alosco ML, Jerskey BA, et al. Cerebral atrophy, apolipoprotein E varepsilon4, and rate of decline in everyday function among patients with amnestic mild cognitive impairment. Alzheimers Dement. 2010;6(5):404Y411. Zuccala G, Pedone C, Cesari M, et al. The effects of cognitive impairment on mortality among hospitalized patients with heart failure. Am J Med. 2003;115(2):97Y103. Pressler SJ, Subramanian U, Kareken D, et al. Cognitive deficits in chronic heart failure. Nurs Res. 2010;59(2):127Y139. Knecht KM, Alosco ML, Spitznagel MB, et al. Sleep apnea and cognitive function in heart failure. Cardiovasc Psychiatry Neurol. 2012;2012:402079. Delgado Parada E, Suarez Garcia FMS, Lopez Gaona F, Gutierrez Vara SG, Solano Jaurrieta JJS. Mortality and functional evolution at one year after hospital admission due to heart failure (HF) in elderly patients. Arch Gerontol Geriatr. 2012;54(1):261Y265. Riegel B, Knafl GJ. Electronically monitored medication adherence predicts hospitalization in heart failure patients. Patient Prefer Adherence. 2013;8:1Y13. Vreugdenhil A, Cannell J, Davies A, Razay G. A communitybased exercise programme to improve functional ability in people with Alzheimer’s disease: a randomized control. Scan J Caring Sci. 2012;26(1):12Y19. Alosco ML, Spitznagel MB, Cohen R, et al. Cardiac rehabilitation is associated with lasting improvements in cognitive function in older adults with heart failure. Acta Cardiol. 2014;69(4):407Y414. Anazodo UC, Shoemaker JK, Suskin N, St Lawrence KS. An investigation of changes in regional gray matter volume in cardiovascular disease patients, pre and post cardiovascular rehabilitation. NeuroImage Clin. 2013;3:388Y395. Gambassi G, Lapane KL, Sgadari A, et al. Effects of angiotensinconverting enzyme inhibitors and digoxin on health outcomes of very old patients with heart failure. SAGE Study Group. Systematic Assessment of Geriatric drug use via Epidemiology. Arch Intern Med. 2000;160(1):53Y60. Zuccala G, Onder G, Marzetti E, et al. Use of angiotensinconverting enzyme inhibitors and variations in cognitive performance among patients with heart failure. Eur Heart J. 2005;26(3):226Y233. Kamishirado H, Inoue T, Fujito T, et al. Effect of enalapril maleate on cerebral blood flow in patients with chronic heart failure. Angiology. 2005;48(8):707Y713.

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