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Newkirk LA, Dao VL, Jordan JT, Alving LI, Davies HD, Hewett L, Beaudreau SA, Schneider LD, Gould CE, Chick CF, Hirst RB, Rose SMSF, Anker LA, Tinklenberg JR, O'Hara R. Factors Associated with Supportive Care Service Use Among California Alzheimer's Disease Patients and Their Caregivers. J Alzheimers Dis 2021; 73:77-86. [PMID: 31743997 DOI: 10.3233/jad-190438] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
BACKGROUND Existing literature on factors associated with supportive care service (SCS) use is limited. A better understanding of these factors could help tailor SCS to the needs of frequent users, as well as facilitate targeted outreach to populations that underutilize available services. OBJECTIVE To investigate the prevalence of SCS use and to identify factors associated with, and barriers to, service use. METHODS California Alzheimer's Disease Center patients with AD (n = 220) participated in the study from 2006-2009. Patients and their caregivers completed assessments to determine SCS use. Cognitive, functional, and behavioral status of the patients were also assessed. A two-part hurdle analysis identified 1) factors associated with any service use and 2) service use frequency among users. RESULTS Forty percent of participants reported using at least one SCS. Patients with more impaired cognition and activities of daily living and more of the following: total number of medications, comorbid medical conditions, and years of education were more likely to use any SCS (p < 0.05). Factors associated with more frequent SCS use included younger age, more years of education, older age of AD onset, female gender, and having a spouse or relative for a caregiver (p < 0.05). Caregivers frequently indicated insufficient time as a reason for not receiving enough services. CONCLUSION Factors associated with any SCS use mostly differed from those associated with SCS frequency, suggesting different characteristics between those who initiate versus those who continue SCS use. Our findings highlight the importance of targeted education on services and identifying barriers to long-term SCS use.
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Affiliation(s)
- Lori A Newkirk
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford University, Stanford, CA, USA
| | - Virginia L Dao
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford University, Stanford, CA, USA.,Sierra Pacific Mental Illness Research Education and Clinical Centers, Palo Alto, CA, USA
| | - Joshua T Jordan
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford University, Stanford, CA, USA.,Department of Psychiatry, University of California, San Francisco, CA, USA
| | - Loren I Alving
- California Alzheimer's Disease Center, University of California San Francisco at Fresno, Fresno, CA, USA
| | - Helen D Davies
- Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA
| | - Linda Hewett
- California Alzheimer's Disease Center, University of California San Francisco at Fresno, Fresno, CA, USA
| | - Sherry A Beaudreau
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford University, Stanford, CA, USA.,Sierra Pacific Mental Illness Research Education and Clinical Centers, Palo Alto, CA, USA.,Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA.,School of Psychology, University of Queensland, Brisbane, Australia
| | - Logan D Schneider
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford University, Stanford, CA, USA.,Sierra Pacific Mental Illness Research Education and Clinical Centers, Palo Alto, CA, USA.,Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA
| | - Christine E Gould
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford University, Stanford, CA, USA.,Geriatric Research Education and Clinical Center, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA
| | - Christina F Chick
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford University, Stanford, CA, USA
| | - Rayna B Hirst
- Pacific Graduate School of Psychology, Palo Alto University, Palo Alto, CA, USA
| | | | - Lauren A Anker
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford University, Stanford, CA, USA.,Sierra Pacific Mental Illness Research Education and Clinical Centers, Palo Alto, CA, USA.,Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA
| | - Jared R Tinklenberg
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford University, Stanford, CA, USA.,Sierra Pacific Mental Illness Research Education and Clinical Centers, Palo Alto, CA, USA.,Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA
| | - Ruth O'Hara
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford University, Stanford, CA, USA.,Sierra Pacific Mental Illness Research Education and Clinical Centers, Palo Alto, CA, USA.,Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA
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Main KL, Soman S, Pestilli F, Furst A, Noda A, Hernandez B, Kong J, Cheng J, Fairchild JK, Taylor J, Yesavage J, Wesson Ashford J, Kraemer H, Adamson MM. DTI measures identify mild and moderate TBI cases among patients with complex health problems: A receiver operating characteristic analysis of U.S. veterans. Neuroimage Clin 2017; 16:1-16. [PMID: 28725550 PMCID: PMC5503837 DOI: 10.1016/j.nicl.2017.06.031] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2016] [Revised: 06/10/2017] [Accepted: 06/23/2017] [Indexed: 01/10/2023]
Abstract
Standard MRI methods are often inadequate for identifying mild traumatic brain injury (TBI). Advances in diffusion tensor imaging now provide potential biomarkers of TBI among white matter fascicles (tracts). However, it is still unclear which tracts are most pertinent to TBI diagnosis. This study ranked fiber tracts on their ability to discriminate patients with and without TBI. We acquired diffusion tensor imaging data from military veterans admitted to a polytrauma clinic (Overall n = 109; Age: M = 47.2, SD = 11.3; Male: 88%; TBI: 67%). TBI diagnosis was based on self-report and neurological examination. Fiber tractography analysis produced 20 fiber tracts per patient. Each tract yielded four clinically relevant measures (fractional anisotropy, mean diffusivity, radial diffusivity, and axial diffusivity). We applied receiver operating characteristic (ROC) analyses to identify the most diagnostic tract for each measure. The analyses produced an optimal cutpoint for each tract. We then used kappa coefficients to rate the agreement of each cutpoint with the neurologist's diagnosis. The tract with the highest kappa was most diagnostic. As a check on the ROC results, we performed a stepwise logistic regression on each measure using all 20 tracts as predictors. We also bootstrapped the ROC analyses to compute the 95% confidence intervals for sensitivity, specificity, and the highest kappa coefficients. The ROC analyses identified two fiber tracts as most diagnostic of TBI: the left cingulum (LCG) and the left inferior fronto-occipital fasciculus (LIF). Like ROC, logistic regression identified LCG as most predictive for the FA measure but identified the right anterior thalamic tract (RAT) for the MD, RD, and AD measures. These findings are potentially relevant to the development of TBI biomarkers. Our methods also demonstrate how ROC analysis may be used to identify clinically relevant variables in the TBI population.
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Key Words
- AD, axial diffusivity
- Axon degeneration
- CC, corpus callosum
- Concussion
- DAI, diffuse axonal injury
- DTI, diffusion tensor imaging
- FA, fractional anisotropy
- GN, genu
- Imaging
- LAT, left anterior thalamic tract
- LCG, left cingulum
- LCH, left cingulum – hippocampus
- LCS, left cortico-spinal tract
- LIF, left inferior fronto-occipital fasciculus
- LIL, left inferior longitudinal fasciculus
- LSL, left superior longitudinal fasciculus
- LST, left superior longitudinal fasciculus – temporal
- LUN, left uncinate
- MD, mean diffusivity
- Neurodegeneration
- PTSD, post-traumatic stress disorder
- RAT, right anterior thalamic tract
- RCG, right cingulum
- RCH, right cingulum – Hippocampus
- RCS, right cortico-spinal tract
- RD, radial diffusivity
- RIF, right inferior fronto-occipital fasciculus
- RIL, right inferior longitudinal fasciculus
- ROC, receiver operating characteristic
- RSL, right superior longitudinal fasciculus
- RST, right superior longitudinal fasciculus – temporal
- RUN, right uncinate
- SP, splenium
- TBI, traumatic brain injury
- Traumatic brain injury
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Affiliation(s)
- Keith L. Main
- War Related Illness and Injury Study Center, Veterans Affairs, Palo Alto Health Care System (VAPAHCS), Palo Alto, CA, United States
- Defense and Veterans Brain Injury Center (DVBIC), Silver Spring, MD, United States
- General Dynamics Health Solutions (GDHS), Fairfax, VA, United States
| | - Salil Soman
- War Related Illness and Injury Study Center, Veterans Affairs, Palo Alto Health Care System (VAPAHCS), Palo Alto, CA, United States
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, United States
- Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Franco Pestilli
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, United States
| | - Ansgar Furst
- War Related Illness and Injury Study Center, Veterans Affairs, Palo Alto Health Care System (VAPAHCS), Palo Alto, CA, United States
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, United States
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States
| | - Art Noda
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, United States
| | - Beatriz Hernandez
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, United States
| | - Jennifer Kong
- War Related Illness and Injury Study Center, Veterans Affairs, Palo Alto Health Care System (VAPAHCS), Palo Alto, CA, United States
| | - Jauhtai Cheng
- War Related Illness and Injury Study Center, Veterans Affairs, Palo Alto Health Care System (VAPAHCS), Palo Alto, CA, United States
| | - Jennifer K. Fairchild
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, United States
| | - Joy Taylor
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, United States
| | - Jerome Yesavage
- War Related Illness and Injury Study Center, Veterans Affairs, Palo Alto Health Care System (VAPAHCS), Palo Alto, CA, United States
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, United States
| | - J. Wesson Ashford
- War Related Illness and Injury Study Center, Veterans Affairs, Palo Alto Health Care System (VAPAHCS), Palo Alto, CA, United States
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, United States
| | - Helena Kraemer
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, United States
| | - Maheen M. Adamson
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, United States
- Department of Neurosurgery, Stanford School of Medicine, Stanford, CA, United States
- Defense and Veterans Brain Injury Center (DVBIC), Veterans Affairs, Palo Alto Health Care System (VAPAHCS), Palo Alto, CA, United States
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Ashford JW. Treatment of Alzheimer's Disease: The Legacy of the Cholinergic Hypothesis, Neuroplasticity, and Future Directions. J Alzheimers Dis 2016; 47:149-56. [PMID: 26402763 DOI: 10.3233/jad-150381] [Citation(s) in RCA: 43] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
In this issue, an article by Waring et al. provides a meta-analysis of the effects of apo-lipo-protein E (APOE) genotype on the beneficial effect of acetyl-cholinesterase inhibitors (AChEIs) in patients with Alzheimer's disease (AD). There was no significant effect found. As of 2015, AChEI medications are the mainstay of AD treatment, and APOE genotype is the most significant factor associated with AD causation. This lack of a significant effect of APOE is analyzed with respect to the "Cholinergic Hypothesis" of AD, dating from 1976, through the recognition that cholinergic neurons are not the sole target of AD, but rather that AD attacks all levels of neuroplasticity in the brain, an idea originated by Ashford and Jarvik in 1985 and which still provides the clearest explanation for AD dementia. The "Amyloid Hypothesis" is dissected back to the alpha/beta pathway switching mechanism affecting the nexin-amyloid pre-protein (NAPP switch). The NAPP switch may be the critical neuroplasticity component of all learning involving synapse remodeling and subserve all learning mechanisms. The gamma-secretase cleavage is discussed, and its normal complementary products, beta-amyloid and the NAPP intracellular domain (NAICD), appear to be involved in natural synapse removal, but the link to AD dementia may involve the NAICD rather than beta-amyloid. Understanding neuroplasticity and the critical pathways to AD dementia are needed to determine therapies and preventive strategies for AD. In particular, the effect of APOE on AD predisposition needs to be established and a means found to adjust its effect to prevent AD.
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