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Cognition Predicts Quality of Life Among Patients with End Stage Liver Disease Daniel Paulson PhD, Mona Shah MA, Lisa Renee Matero-Miller PhD, Anne Eshelman PhD, Marwan Abouljoud MD
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Cognition Predicts Quality of Life among Patients with End Stage Liver Disease
Daniel Paulson, PhD1, 2; Mona Shah, MA1; Lisa Renee Matero-Miller, PhD2; Anne Eshelman, PhD2; Marwan Abouljoud, MD3 1. University of Central Florida Department of Psychology 4111Pictor Lane, Orlando, FL 32816 2. Henry Ford Health System Consultation/Liaison Psychiatry 2799 W. Grand Blvd., Detroit, MI, 48202 3. Henry Ford Health System Transplant Institute 2799 W. Grand Blvd., Detroit, MI, 48202 Authors’ E-mail addresses: Daniel Paulson, PhD: [email protected]
Mona Shah, MA: [email protected]
Lisa Renee Matero-Miller, PhD: [email protected]
Anne Eshelman, PhD: [email protected]
Marwan Abouljoud, MD: [email protected]
Mona Shah, MS 4111 Pictor Lane, Orlando, FL 32816 Phone: 407-823-4135 Fax: 407-823-4356 E-mail: [email protected]
Study Location: Henry Ford Health System, Transplant Institute, Liver Transplant Detroit, MI Funding Support: Gift of Life Michigan donation Conflicts of Interest: None
2 ABSTRACT Background and Aims: Impaired cognitive functioning and poor quality of life (QoL) are both common among patients with end-stage liver disease (ESLD); however, it is unclear how these are related. This study examines how specific cognitive domains predict QoL among liver transplant candidates by replicating Stewart and colleagues’ (2010) 3-factor model of cognitive functioning, and determining how variability in these cognitive domains predicts mental health and physical QoL. Methods: The sample included 246 patients with ESLD who were candidates for liver transplant at a large, mid-western health care center. Measures, including the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), Trail Making Test (TMT), Shipley Institute of Living Scale (SILS), Short-Form Health Survey-36 (SF-36) Version 2, and Hospital Anxiety and Depression Scale (HADS), comprised latent variables representing global intellectual functioning, psychomotor speed, and learning and memory functioning. Results: Confirmatory factor analysis results indicate that the 3-factor solution model comprised of global intellectual functioning, psychomotor speed, and learning and memory functioning fit the data well. Addition of physical and mental health QoL latent factors resulted in a structural model also with good fit. Results related physical QoL to global intellectual functioning, and mental health QoL to global intellectual functioning and psychomotor functioning. Conclusions: Findings elucidate a relationship between cognition and QoL and support the use of routine neuropsychological screening with ESLD patients, specifically examining the cognitive domains of global intellectual, psychomotor, and learning and memory functioning. Subsequently, screening results may inform implementation of targeted interventions to improve QoL.
3 Keywords: hepatic encephalopathy; cognitive functioning; neuropsychological screening; integrated care.
Introduction Impaired cognitive functioning and poor quality of life (QoL) are both common among patients with end-stage liver disease (ESLD) (1). Individuals with ESLD often experience varying degrees of cognitive impairment ranging from transient confusion to coma (2). Proposed causes of cognitive impairment in patients with ESLD include accumulation of ammonia, chronic alcohol use, and hepatitis C (2, 3). The majority of cognitive impairment research with ESLD patients examines the construct hepatic encephalopathy (HE), defined as progressive cognitive impairment leading to functional impairment among patients with liver disease (4). Estimated prevalence for HE ranges from 30% to 70% and for subclinical HE, a milder form of cognitive impairment, varies from 30-84%, reflecting the variability in diagnostic strategies and clinical characterizations (5-7). Myriad cognitive deficits have been associated with ESLD, including deficits in attention, immediate memory, visuospatial functioning, executive functioning and psychomotor speed (8-10). Findings also suggest that all types of liver disease demonstrate similar cognitive deficits, with the exception of cholestatic liver disease, which shows less cognitive impairment when compared to other types (2). Whether the severity of cognitive impairment tends to covary with severity of liver disease as measured by the Model for End-stage Liver Disease (MELD) score remains unclear with mixed results in the extant literature (9, 11). Stewart and colleagues (12) compared neuropsychological functioning of patients with cirrhosis and Grade 1 HE to patients with inflammatory bowel disease (IBD). Results rendered a
4 model including three factors of cognitive functioning – global cognitive functioning, psychomotor speed, and learning and memory – that were associated specifically with patients with cirrhosis. Given that the patients in Stewart’s sample had low levels of cognitive impairment, it is important to determine whether this model generalizes to other patients with ESLD with varying levels of impairment. By identifying how these individual cognitive domains combine to affect quality of life in the broader ESLD population, we aim to validate a theoretical framework on cognitive changes associated with ESLD. Eventually, a comprehensive model such as that proposed here may be used in lieu of inconsistently defined cognitive constructs in ESLD research. To our knowledge, Stewart’s model has not been replicated among patients representing the broader range of neurocognitive pathology seen among candidates for liver transplantation. QoL is often conceptualized as having two broad dimensions; physical functioning and mental health (13). In addition to reflecting patients’ subjective experiences, QoL is increasingly viewed as a critical variable in healthcare service delivery. Poor QoL is related to lower medication compliance (14) greater recidivism, morbidity and mortality among patients with substance abuse histories (15), and alcoholism (16). Poor QoL has been associated with disorders contributing to ESLD such as hepatitis C (15, 17) and is characteristic of ESLD patients as a group (18). Therefore, because poor QoL is common in this population and contributes to worse medical outcomes, it is important to identify factors that predict poor QoL among ESLD patients. Current research relating cognitive functioning and QoL in patients with liver disease has produced mixed findings. While substantial research supports cognitive impairment as a predictor of poorer quality of life in patients with liver disease (19, 20), other research has produced contradictory findings (21, 22). Limitations to the aforementioned
5 research include not examining how individual domains of cognitive functioning relate to quality of life and dichotomizing neuropsychological functioning when related it to quality of life. Further research is required to elucidate the relationship between neuropsychological functioning and QoL among patients with ESLD (23), and the overall aim of this study is to build on past work by examining how impairment in specific cognitive domains relates to physical and mental health QoL utilizing continuous measures. The first objective of the present research is to validate Stewart and colleagues’ (2010) 3factor model of cognitive functioning in a sample representing the broader population of ESLD patients awaiting liver transplant, so as to establish an empirically defensible framework for studying cognition among patients with ESLD. The second objective is to determine how variability in these individual cognitive domains relates to physical and mental health QoL among patients with ESLD. Method Participants As part of an ongoing Liver Transplant Quality of Life study, the present sample included 246 patients (see Table 1) listed for liver transplant at a large, Mid-western health care center. Participants were evaluated by a multidisciplinary team and listed on United Network for Organ Sharing (UNOS) for liver transplantation between 11/05/2004 and 03/01/2010. Causes of ESLD included hepatitis C alone (28.4%), chronic alcoholism (16.6%), comorbid hepatitis C and alcoholism (16.2%), primary biliary cirrhosis (8.1%), cryptogenic cirrhosis (8.1%), nonalcoholic cirrhosis (8.1%), primary sclerosing cholangitis (4.8%), autoimmune (2.2%), hemochromatosis (1.5%), hepatitis B (1.1%), alpha-1 antitrypsin deficiency (1.1%), polycystic liver disease (.7%), and other diseases (3.1%). Participant’s liver function and severity of liver
6 disease was measured using the Model for End-stage Liver Disease (MELD) score. Given that all participants were listed as candidates for liver transplantation on UNOS, it is believed that they were free from any significant health comorbidities that would interfere with transplant success (severe cardiopulmonary disease, morbid obesity, and active infection, among others) and had stopped any alcohol and illicit substance use (24). No exclusionary criteria were applied, and this sample is believed to be representative of the population of ESLD patients listed for transplantation. Use of an externally valid sample allows us to capture all levels of cognitive functioning that may be present in a sample of ESLD patients awaiting transplant. Measures The Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) is a 30-minute battery that includes indices of immediate memory, delayed memory, attention, language and visuospatial functioning. The RBANS is common in both research and clinical practice with test-retest reliability ranging from .80-.94 (25). While specific liver disease diagnosis is not associated with significant differences on any RBANS index, significant deficits were observed on RBANS Attention, Visuospatial/ Constructional, and Immediate Memory among ESLD patients awaiting liver transplant (26). The RBANS subscales are conceptualized and developed as largely orthogonal; a strategy common to neuropsychological test design, such that moderate deficits of attention do not compromise the validity of other cognitive domains measurements (27). The Trail Making Test (28) is a neuropsychological measure with a simple task requiring respondents to draw lines from one number to the next in numerical order (TMT-A), and a more complex task requiring respondents to connect numbers and letters in a consecutive, alternating
7 fashion (TMT-B). These tasks are often interpreted as measuring psychomotor functioning, and visual scanning and executive functioning. Scores reflect time to completion. The Shipley Institute of Living Scale (29) (SILS) is a brief measure of intellectual functioning and includes indices measuring vocabulary and abstraction.. The SILS has a testretest reliability coefficient of .80(30) and is known to be highly correlated the WAIS (31). The Short-Form Health Survey-36 (SF-36) Version 2 is a well-established measure used to assess subjective quality of life. Four scales (Physical Functioning, Role Physical, Bodily Pain, and General Health) comprise the Physical Health Summary Measure, and four scales (Vitality, Social Functioning, Role-Emotional and Mental Health) comprise the Mental Health Summary Measure (32). Reliability estimates for the physical and mental health summary scales exceed .90 (33). The Hospital Anxiety and Depression Scale (HADS) is a valid 14-item self-report measure of emotional functioning with indices assessing anxiety (HADS-A) and depression (HADS-D) (34, 35). Respondents are asked to indicate to what degree they have experienced symptoms over the past 7 days. The mean Cronbach’s alpha ranges from .82-.83 (35), suggesting adequate internal consistency. Procedure This study was approved by Henry Ford Health System’s institutional review board. Patients at the research site listed for liver transplant on UNOS were petitioned for study enrollment by telephone. A battery including the above measures was individually administered to consenting patients in a standardized order by trained Master’s-level clinicians under the supervision of a licensed and board-certified clinical psychologist. Testing was completed in an outpatient hospital-based clinical setting during one session.
8 Results Means and standard deviations for all measures used are listed in Table 2. Degree of impairment in this sample is grossly indicated by the high proportion of participants with RBANS Index scores falling below the first standard deviation below the mean (score of 85; 16th percentile). In this sample, 46.2% scores on the Immediate Memory Index, 48.7% of scores on the Visuospatial Index, 26.3% of scores on the Verbal Index, 48.7% of scores on the Attention index, and 31% of scores on the Delayed Memory Index, and 55.2% of scores on the RBANS Total Index were below the first standard deviation below the mean. In the present sample, the mean TMT-A score was in the moderately impaired range of functioning. A confirmatory factor analysis (CFA) procedure was used to examine how well data on cognitive measures used in this study related to the three-factor structure proposed by Stewart and colleagues (12). Results were fit indices suggesting that the proposed model fit the data well (RMSEA=.077, Non-Normed Fit Index=.952, Comparative Fit Index=.973, Χ2(df=17)=29.74 p<.001) based on commonly-used criteria (36). All factor loadings were statistically significant (p<.05), suggesting that in this sample of patients with ESLD scores on the SILS Vocabulary and Abstraction indices and RBANS Visuospatial and Attention indices all meaningfully contributed to the measurement of general cognition, and that the RBANS Immediate Memory and Delayed Memory Indices both contributed to the measurement of memory. Results also supported use of the TMT-A as an indicator of a unique construct, psychomotor speed. Modification indices indicated that overall model fit would be modestly improved by including the RBANS Attention Index as an indicator on the Psychomotor Speed latent variable. No theoretical justification exists for this model revision, so the proposed factor structure was retained.
9 Next we examined the contribution of cognitive functioning to mental health and physical quality of life, respectively, while controlling for the effects of education and condition severity (MELD score). Overall, this model did not fit adequately (RMSEA=.089, CFI=.904, NNF=.867, χ2 =1289.37, p<.05) based upon standard criteria (36). This finding indicates that the observed data do not adequately support the hypothesized relationships. Examination of modification indices, standardized residuals, and pathway coefficients suggested exclusion of education from the structural model. The second structural model tested, illustrated in Figure 1 demonstrated adequate fit (RMSEA=0.065, CFI=.957, NNFI=.938, χ2(df = 45)=92.20) suggesting that the data supports this hypothesized model. Results of the second structural model reflect significant contribution of global cognitive functioning to both physical and mental health quality of life. The maximum modification index value of 25.55 suggested that the model fit could be improved by allowing the error terms of the SF-36 Physical Summary Measure and HADS-D scales to inter-correlate. While this may speculatively suggest a connection between psychomotor slowing and depressive symptomatology, no strong theoretical justification supports this model modification. Overall, this indicated that, in combination, variability in global cognitive functioning, psychomotor speed, and learning and memory and MELD score accounted for 6% of variability in physical QoL and 8% of variability in mental health QoL. Global cognitive functioning was made up of vocabulary, visuospatial functioning, attention, and abstraction. Higher scores on the global cognitive functioning latent variable, suggesting better cognitive functioning in this domain, predicted higher scores on both physical QoL and (standardized pathway coefficient=.30, p<.05), and mental health QoL (standardized pathway coefficient=.34, p< .05). Higher scores on the psychomotor speed latent variable significantly predicted higher scores on
10 the mental health QoL latent variable (standardized pathway coefficient=.28, p<.05). The learning and memory latent factor did not significantly predict either physical or mental health QoL. The factor structure proposed by Stewart and colleagues (12) included Trail Making TestB (TMT-B) as an indicator of the psychomotor latent variable. The TMT-B subtest was not included in reported analyses above because scores on that measure reflect both psychomotor speed and executive functioning. In the interests of empirical rigor, the TMT-B subtest was included as an indicator of psychomotor speed in post-hoc analyses. Inclusion of the TMT-B did not significantly change model fit nor interpretation of results, though it did generate substantial and unfavorable standardized residual values, possibly reflecting incongruity of TMT-B as a pure measure of psychomotor speed. Discussion The first primary finding is that cognitive functioning among ESLD patients listed for liver transplantation can be described with a three-factor model of neuropsychological functioning comprised of global intellectual, psychomotor, and learning and memory functioning. The second primary finding is that global intellectual functioning affects physical QoL, and both global intellectual functioning and psychomotor functioning affect mental health QoL. By contrast to past studies employing regression-based analyses, these statistically significant pathway coefficients relating cognitive domains to QoL variables suggest causal relationships. Thus, these findings are strengthened by the use of both an empirically-based cognitive model and well-selected statistical procedures. The factor structure replicated in this study was originally derived from a sample of patients experiencing only negligible cognitive symptoms (12). By contrast, the sample of
11 ESLD patients awaiting transplant used in this study demonstrated a much broader range of cognitive functioning. The factor structure appears robust to the escalation of cognitive impairment resulting from progressed liver disease. These results suggest that the factor structure proposed by Stewart and colleagues (12) may identify aspects of cognition that co-vary among individuals with ESLD, and thus be informative to the development of future research and treatment. Results support past research relating cognition to QoL (19, 20), and build on those findings by identifying how specific cognitive domains relate to influence mental and physical health quality of life. Global intellectual functioning was found to be a primary influence on QoL having effects on both physical and mental health domains. Global intellectual functioning is comprised of a diverse range of cognitive abilities, suggesting that liver disease exerts a diffuse neurocognitive effect. Results suggest that deficits in intellectual functioning, attention and visuospatial ability are associated with poor overall QoL, underscoring the relevance of these domains to subjective patient well-being. Mental health QoL was predicted by both global cognitive functioning and processing speed, suggesting that among patients with ESLD, mental health is influenced by a wide range of cognitive domains, including gross intellectual functioning, visuospatial and language functioning, and fine motor control. Together, impairment in these cognitive domains disrupts competency for variety of undertakings including vocational and social activities. The observed relationships suggest that cognitive impairment presents a complex threat to patient well-being and quality of life. These findings support the extant literature on attention deficits and psychomotor impairment related to hepatic encephalopathy (37, 38). The presence of memory impairment in hepatic encephalopathy remains unclear (39), however, participants in this sample demonstrated
12 memory functioning that was considerably below age-based norms. Findings suggest the relationship between cognition and QoL is varied across disorders. For example, patients with Korsakoff’s syndrome or psychoses may have compromised reality testing unlike patients with ESLD. Further, the cognitive impairment (i.e., profound amnesia) experienced by patients with Korsakoff’s syndrome appears to have less affect on QoL than patients with dementia (40). Predictors of quality of life in patients with epilepsy include cognitive (psychomotor speed, verbal memory, and language) and non-cognitive (mood and social functioning) factors (41, 42). In contrast, research on quality of life in Parkinson’s disease finds parallel results with ESLD as cognitive impairment is prominent predictor of quality of life (43). Current interventions used to manage cognitive symptoms of ESLD often include pharmacological treatments, including lactulose and lactitol. Given that these cause significant gastrointestinal complains among patients, clinical experience suggests some patients use these interventions inconsistently in an effort to mitigate unenjoyable effects (dysentery) while preserving their own cognitive functioning (44). This raises the possibility that effects of lactulose may partially explain the relationship between cognition and QoL. While Rifaximin treatment may minimize the gastrointestinal problems, this treatment is costly and requires patients to adhere to a twice-daily dose (45). Thus, the addition of routine neuropsychological assessment and behavioral (non-pharmacological) treatments may provide further assistance in improving quality of life. These findings elucidate a relationship between cognitive domains and QoL and suggest intervention targets. First, findings support the use of routine neuropsychological screening with ESLD patients that specifically examines the cognitive domains of global intellectual, psychomotor, and learning and memory functioning. Currently, the Mini Mental State Exam
13 (MMSE) remains to be the most frequently used cognitive screening instrument despite its limitations (46). Further, current screening tools for minimal HE are also subjected to be influenced by patients’ anxiety and fatigue, and thus not an adequate measures of possible cognitive impairment (47). Our findings support the use of validated, comprehensive test batteries, such as the RBANS or Psychometric Hepatic Encephalopathy Score (PHES; 48), that can help identify specific cognitive deficits in the aforementioned domains. Subsequently upon identifying areas of cognitive impairment, interventions can be formulated accordingly to improve related QoL. Most medical providers currently utilize case managers to assist in care coordination and links to appropriate referrals for patients with ESLD. Case management focuses on organization and practical problem-solving by hospital staff on the behalf of patients. In addition, we suggest evidenced-based therapies such as problem-solving therapy, as administered by behavioral health specialists. Problem solving therapy is a behavioral intervention that focuses on identifying specific stressors and creating concrete plans that can be enacted by the patient (rather than by clinic staff) to cope with these stressors (49). This intervention is goal-oriented and pragmatic, is empowering to patients, and does not overly rely heavily on a patient’s memory and executive function. Problem solving therapy has been empirically supported as a behavioral treatment for depression among older adults with cognitive impairment (50), and thus may also prove beneficial with ESLD patients experiencing cognitive impairment. Interestingly, the expected relationship between psychomotor speed and physical QoL was not found. Physical QoL is affected by factors including perceived health, pain and capacity to accomplish physically demanding tasks. It may be that poor physical QoL is more directly related to physical symptoms of ESLD such as fatigue, wasting, and discomfort, than by declines
14 in psychomotor speed. These findings emphasize the importance of closely monitoring cognitive functioning among ESLD patients and advising empirically supported interventions such as lactulose (19). It is also interesting that memory deficits did not significantly predict poor QoL. It may be that patients with ESLD have poor insight with respect to how memory affects their daily functioning (51), and future research should investigate this question. One weakness of this study is the use of TMT-A for measurement of psychomotor functioning. Inclusion of the TMT-B measure as a processing speed latent variable indicator did not meaningfully improve the model in any way. In addition, other factors such as visual scanning deficits or motivation can affect performance on this measure, suggesting cautious interpretation of the finding that psychomotor speed predicts mental health QoL. Future research might replicate this finding using additional measures of psychomotor control and speed to improve measurement of this domain. Another relevant question raised by these findings is whether this effect is specific to ESLD, or whether it generalizes across disorders. Future research also might relate the rapid changes in mood and QoL after transplantation to postsurgical improvements in cognitive functioning (18), allowing for a better understanding of both subjective treatment outcome and patient self-care. Finally, future research also should identify modifiers of the relationships between the identified cognitive factors and quality of life.
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Table 1 Demographic characteristics
Table 2 Mean and standard deviations for all measures. Scores for all measures are presented as standard scores (mean = 100, standard deviation =15) or raw scores. Figure 1. Structural model depicting the relationship between cognitive domains and QoL domains. *p < .05; SH-VOC=Shipley Institute of Living Vocabulary Measure; SH-ABS=Shipley Institute of Living Abstraction; RB-AT=RBANS Attention Index; RB-VS=RBANS Visuospatial Index; TMT-A=Trail Making Test-A; RB-IM=RBANS Immediate Memory Index; RB-DM=RBANS Delayed Memory; SF-PHY=SF-36 Physical Quality of Life Index; HADS-A=Hospital Anxiety and Depression Scale-Anxiety; HADS-D=Hospital Anxiety and Depression Scale-Depression; SF-EMO=SF-36 Emotional Quality of Life Index; MELD=Model of End-Stage Liver Disease Score
Table 1 Demographic characteristics Variable Age in years (range) % Female Ethnicity White Black Hispanic Other Years of Education (range) MELD Score 6-9 10-19 ≥20
54.6 (18-74) 41% 186 47 8 5 13.26 (4-22) 53 163 30
Table 2 Mean and standard deviations for all measures. Scores for all measures are presented as standard scores (mean = 100, standard deviation =15) or raw scores. Measure Shipley Institute of Living Scale Verbal Abstraction RBANS Immediate Memory Visuospatial/Constructional Attention Language Delayed Memory SF-36 Physical Component SF-36 Mental Health Component
TMT-A HADS-A HADS-D
Standard Score (M ± SD) 98.8 ± 15.6 101.8 ± 13.35 88 88.2 87.3 91.8 91.4 77.65 89.65
± ± ± ± ± ± ±
16.6 19.7 15.8 10 14.9 14.7 17.55
Raw Score 45.7 ± 22.7 6.7 ± 3.9 5.5 ± 3.8