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4 Traumatic Brain Injury Does Not Alter the Course of Neurocognitive Functioning Later in Life
- Jeff Schaffert, Hsueh-Sheng Chiang, Hudaisa Fatima, Christian LoBue, John Hart, Munro Cullum
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- Journal:
- Journal of the International Neuropsychological Society / Volume 29 / Issue s1 / November 2023
- Published online by Cambridge University Press:
- 21 December 2023, pp. 105-106
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Objective:
History of traumatic brain injury (TBI) is associated with increased risk of dementia, but few studies have evaluated whether TBI history alters the course of neurocognitive decline, and existing literature on this topic is limited to short follow-up and smaller samples. The primary aim of this study was to evaluate whether a history of TBI (TBI+) influences neurocognitive decline later-in-life among older adults with or without cognitive impairment [i.e., normally aging, Mild Cognitive Impairment (MCI), or dementia].
Participants and Methods:Participants included individuals from the National Alzheimer’s Coordinating Center (NACC) who were at least 50 years old and with 3 to 6 visits (M number of visits = 4.43). Participants with any self-reported history of TBI (n = 1,467) were matched 1:1 to individuals with no reported history of TBI (TBI-) from a sample of approximately 45,000 participants using case-control matching based on age (+/- 2 years), sex, education, race, ethnicity, cognitive diagnosis [cognitively normal (CN), MCI, or all-cause dementia], etiology of cognitive impairment, functional decline (Clinical Dementia Rating Scale, CDR), number of Apolipoprotein E4 (APOE ε4) alleles, and number of annual visits (3 to 6). Mixed linear models were used to assess longitudinal neuropsychological test composites (using NACC normative data) of executive functioning/attention/speed (EFAS), language, and memory in TBI+ and TBI- participants. Interactions between TBI and demographics, APOE ε4 status, and cognitive diagnosis were also examined.
Results:Following matching procedures, TBI+ (n=1467) and TBI- (n=1467) groups were nearly identical in age (TBI+ M = 71.59, SD = 8.49; TBI- M = 71.63, SD = 8.44), education (TBI+ M = 16.12, SD = 2.59; TBI- M = 16.10, SD = 2.52), sex (both 55% male), race (both 90% White), ethnicity (both 98% non-Hispanic), APOE ε4 alleles (both 0 = 62%, 1 = 33%, 2 = 5%), baseline cognitive diagnoses (both CN = 60%, MCI = 18%, dementia = 12%), and global CDR (TBI+ M = 0.30, SD = 0.38, TBI- M = 0.30, SD = 0.38). At baseline, groups had similar Z-scores of in EFAS (TBI+ Mefas = -0.02, SD = 1.21; TBI- Mefas = -0.04, SD = 1.27), language (TBI+ MLanguage = -0.48, SD = 0.98; TBI- MLanguage = -0.55, SD = 1.05), and memory (TBI+ MMemory = -0.45, SD = 1.28; TBI- MMemory = -0.45, SD =1.28). The course of change in neuropsychological functioning worsened longitudinally, but did not differ between TBI groups (p’s > .110). There were no significant interactions between TBI history and age, sex, education, race/ethnicity, number of APOE ε4 status, or cognitive diagnosis (all p’s > .027).
Conclusions:In this matched case-control design, our findings suggest that a history of TBI, regardless of demographic factors, APOE ε4 status, and cognitive diagnosis, does not significantly alter the course of neurocognitive functioning later-in-life in older adults with and without cognitive impairment. Future clinicopathological longitudinal studies with well characterized TBI histories and the associated clinical course are needed to help clarify the mechanism by which TBI may increase dementia risk for some individuals, without affecting course of decline.
73 Sex and Race/Ethnicity in Reporting of Lingering Concussion Symptoms by Adolescents
- Stephen C Bunt, Nyaz Didehbani, Cheryl H Silver, Linda S Hynan, Hannah E Wadsworth, Hudaisa Fatima, Cason Hicks, Mathew Stokes, Shane M Miller, Kathleen Bell, C M Cullum
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- Journal:
- Journal of the International Neuropsychological Society / Volume 29 / Issue s1 / November 2023
- Published online by Cambridge University Press:
- 21 December 2023, pp. 176-177
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Objective:
Consideration of individual differences in recovery after concussion has become a focus of concussion research. Sex and racial/ethnic identity as they may affect reporting of concussion symptoms have been studied at single time points but not over time. Our objective was to investigate the factors of self-defined sex and race/ethnicity in reporting of lingering concussion symptoms in a large sample of adolescents.
Participants and Methods:Concussed, symptomatic adolescents (n=849; Female=464, Male=385) aged 13-18 years were evaluated within 30 days of injury at a North Texas Concussion Registry (ConTex) clinic. Participants were grouped by self-defined race/ethnicity into three groups: Non-Hispanic Caucasian (n=570), Hispanic Caucasian (n=157), and African American (n=122). Measures collected at the initial visit included medical history, injury related information, and the Sport Concussion Assessment Tool-5 Symptom Evaluation (SCAT-5SE). At a three-month follow-up, participants completed the SCAT-5SE. Pearson’s Chi-Square analyses examined differences in categorical measures of demographics, medical history, and injury characteristics. Prior to analysis, statistical assumptions were examined, and log base 10 transformations were performed to address issues of unequal group variances and nonnormal distributions. A three-way repeated measures ANOVA (Sex x Race/Ethnicity x Time) was conducted to examine total severity scores on the SCAT-5SE. Bonferroni post-hoc tests were performed to determine specific group differences. SPSS V28 was used for analysis with p<0.05 for significance. Data reported below has been back transformed.
Results:A significant interaction of Time by Race/Ethnicity was found for SCAT-5SE scores reported at initial visit and three-month follow-up (F(2, 843)=7.362, p<0.001). To understand this interaction, at initial visit, Race/Ethnicity groups reported similar levels of severity for concussion symptoms. At three month follow-up, African Americans reported the highest level of severity of lingering symptoms (M= 3.925, 95% CIs [2.938-5.158]) followed by Hispanic Caucasians(M= 2.978, 95% CIs [2.2663.845]) and Non-Hispanic Caucasians who were the lowest(M= 1.915, 95% CIs [1.6262.237]). There were significant main effects for Time, Sex, and Race/Ethnicity. Average symptom levels were higher at initial visit compared to three-month follow-up (F(1, 843)=1531.526, p<0.001). Females had higher average symptom levels compared to males (F(1, 843)=35.58, p<0.001). For Race/Ethnicity (F(2, 843)=9.236, p<0.001), Non-Hispanic Caucasians were significantly different than African Americans (p<0.001) and Hispanic Caucasians (p=0.021) in reported levels of concussion symptom severity.
Conclusions:Data from a large sample of concussed adolescents supported a higher level of reported symptoms by females, but there were no significant differences in symptom reporting between sexes across racial/ethnic groups. Overall, at three-months, the African American and Hispanic Caucasians participants reported a higher level of lingering symptoms than Non-Hispanic Caucasians. In order to improve care, the difference between specific racial/ethnic groups during recovery merits exploration into the factors that may influence symptom reporting.
96 The Proportion of Patients with Cerebrospinal Fluid Biomarkers Consistent with Alzheimer’s Disease in a Cohort with Suspected Normal Pressure Hydrocephalus
- Hudaisa Fatima, Trung P Nguyen, Anne Carlew, Munro Cullum, Jonathan White, Robert Ruchinskas
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- Journal:
- Journal of the International Neuropsychological Society / Volume 29 / Issue s1 / November 2023
- Published online by Cambridge University Press:
- 21 December 2023, pp. 396-397
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Objective:
Normal pressure hydrocephalus (NPH) is characterized by pathologically enlarged ventricles without elevated cerebrospinal fluid (CSF) pressure along with a triad of clinical symptoms including gait disturbances, urinary incontinence, and cognitive impairment. NPH is evaluated with lumbar drain trials (LDTs) where CSF is removed over several days to determine if patients would benefit from ventricular shunting. Candidate selection and success for these surgeries remains challenging because other diseases such as Alzheimer’s disease (AD) share common features with NPH in cognitive impairment and enlarged ventricles. Prior research has found that 20%-40% of presumed NPH cases have AD pathology as determined by brain biopsy or autopsy. CSF biomarkers of AD can be altered in NPH and are not always conclusive, complicating the interpretation of results when formulating diagnoses and prognoses. Studies to refine the analyses of AD CSF biomarkers in NPH are needed. We aimed to examine the frequency of CSF biomarker results among patients presenting for NPH evaluations with LDTs.
Participants and Methods:62 patients presented for LDTs upon physician recommendations. CSF specimens were sent to Mayo Clinic Laboratories for Alzheimer Disease Evaluation (ADEVL) that utilizes Elecsys (Lenexa, KS) CSF electrochemiluminescence immunoassays (Roche Diagnostics, Basel, Switzerland) to measure levels of amyloid-beta 42 (Aβ42), total tau (t-tau), and phosphorylatedtau (p-tau), and p-tau:Aβ42 ratio. Results were classified based on interpretation through the Amyloid/Tau/Neurodegeneration (ATN) framework1: 1) AD - biomarker profile consistent with AD pathologic change, 2) non-AD profile - biomarker levels normal or inconsistent with AD pathologic change, or 3) indeterminate - biomarkers were incongruous with only one or two abnormal levels of Aβ42, t-tau, p-tau, or ptau: Aβ42. Indeterminate cases may represent altered protein levels due to CSF dynamics or AD-related pathologic change. In reviewing recent research on CSF dynamics and AD biomarkers in NPH2 a p-tau threshold of 15 pg/mL was derived and implemented such that cases with Aß42 <=1026 pg/mL and p-tau <15 pg/mL were designated as suspected non-AD, and those with Aß42 <=1026 pg/mL and p-tau >15 pg/mL were designated suspected AD.
Results:Of the 62 LDT cases, 12 (19.35%) were classified as AD, 31 (50%) were indeterminate and 22 (35.48%) were non-AD. Of the 31 indeterminate cases, 21 (33.87% of the overall sample) were suspected non-AD and 7 (11.29% of the full sample) were categorized as suspected AD.
Conclusions:Our findings show that 20%-30% of patients presenting for LDT showed evidence for AD-type pathologic change, consistent with prior reports of AD pathology in cases of possible NPH. Half of all LDT cases had indeterminate AD CSF biomarker results, the interpretations of which were confounded by the potential alterations of CSF biomarkers levels due to NPH independent of AD. Our findings emphasize the need to establish better approaches to interpreting CSF AD biomarkers in evaluating NPH. Future research should examine the discriminative utility of CSF AD biomarkers and the selected p-tau threshold in indeterminate cases for predicting response to LDT and shunting.
31 Machine Learning Algorithm to Predict Duration to Full Time Care after Alzheimer's Disease Diagnosis
- Jessica H Helphrey, Jayme M Palka, Jake Rossmango, Hudaisa Fatima, Michael Conley, Anthony Longoria, Jennifer Sawyer, Jeffrey Schaffert, Anne Carlew, Munro Cullum, Laura Lacritz, John Hart, Hsueh-Sheng Chiang, Trung Nguyen, Alka Khera, Christian LoBue
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- Journal:
- Journal of the International Neuropsychological Society / Volume 29 / Issue s1 / November 2023
- Published online by Cambridge University Press:
- 21 December 2023, p. 241
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Objective:
Patients and their families often ask clinicians to estimate when full-time care (FTC) will be needed after Alzheimer's Disease (AD) is diagnosed. Although a few algorithms predictive algorithms for duration to FTC have been created, these have not been widely adopted for clinical use due to questions regarding precision from limited sample sizes and lack of an easy, user friendly prediction model. Our objective was to develop a clinically relevant, data-driven predictive model using machine learning to estimate time to FTC in AD based on information gathered from a) clinical interview alone, and b) clinical interview plus neuropsychological data.
Participants and Methods:The National Alzheimer's Coordinating Center dataset was used to examine 3,809 participants (M age at AD diagnosis = 76.05, SD = 9.76; 47.10% male; 87.20% Caucasian) with AD dementia who were aged >50 years, had no history of stroke, and not dependent on others for basic activities of daily living at time of diagnosis based on qualitative self or informant report. To develop a predictive model for time until FTC, supervised machine learning algorithms (e.g., gradient descent, gradient boosting) were implemented. In Model 1, 29 variables captured at the time of AD diagnosis and often gathered in a clinical interview, including sociodemographic factors, psychiatric conditions, medical history, and MMSE, were included. In Model 2, additional neuropsychological variables assessing episodic memory, language, attention, executive function, and processing speed were added. To train and test the algorithm(s), data were split into a 70:30 ratio. Prediction optimization was examined via cross validation using 1000 bootstrapped samples. Model evaluation included assessment of confusion matrices and calculation of accuracy and precision.
Results:The average time to requiring FTC after AD diagnosis was 3.32 years (Range = 0.53-14.57 years). For the clinical interview only model (Model 1), younger age of onset, use of cholinesterase inhibitor medication, incontinence, and apathy were among the clinical variables that significantly predicted duration to FTC, with the largest effects shown for living alone, a positive family history of dementia, and lower MMSE score. In Model 2, the clinical predictors remained significant, and lower Boston Naming Test and Digit-Symbol Coding scores showed the largest effects in predicting duration to FTC among the neuropsychological measures. Final prediction models were further tested using five randomly selected cases. The average estimated time to FTC using the clinical interview model was within an average of 5.2 months of the recorded event and within an average of 5.8 months for the model with neuropsychological data.
Conclusions:Predicting when individuals diagnosed with AD will need FTC is important as the transition often carries significant financial costs related to caregiving. Duration to FTC was predicted by clinical and neuropsychological variables that are easily obtained during standard dementia evaluations. Implementation of the model for prediction of FTC in cases showed encouraging prognostic accuracy. The two models show promise as a first step towards creation of a user friendly prediction calculator that could help clinicians better counsel patients on when FTC after AD diagnosis may occur, though the development of separate models for use in more diverse populations will be essential.
52 Bayesian Logistic Regression Bias Adjustment for Data Observed without a Gold Standard: A Simulation Study of Clinical Alzheimer’s Disease
- William F Goette, Hudaisa Fatima, Jeff Schaffert, Anne R Carlew, Heidi Rossetti, Laura H Lacritz, C. Munro Cullum
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- Journal:
- Journal of the International Neuropsychological Society / Volume 29 / Issue s1 / November 2023
- Published online by Cambridge University Press:
- 21 December 2023, pp. 259-260
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Objective:
Definitive diagnosis of Alzheimer’s disease (AD) is often unavailable, so clinical diagnoses with some degree of inaccuracy are often used in research instead. When researchers test methods that may improve clinical accuracy, the error in initial diagnosis can penalize predictions that are more accurate to true diagnoses but differ from clinical diagnoses. To address this challenge, the current study investigated the use of a simple bias adjustment for use in logistic regression that accounts for known inaccuracy in initial diagnoses.
Participants and Methods:A Bayesian logistic regression model was developed to predict unobserved/true diagnostic status given the sensitivity and specificity of an imperfect reference. This model considers cases as a mixture of true (with rate = sensitivity) and false positives (rate = 1 - specificity) while controls are mixtures of true (rate = specificity) and false negatives (rate = 1 - sensitivity). This bias adjustment was tested using Monte Carlo simulations over four conditions that varied the accuracy of clinical diagnoses. Conditions utilized 1000 iterations each generating a random dataset of n = 1000 based on a true logistic model with an intercept and three arbitrary predictors. Coefficients for parameters were randomly selected in each iteration and used to produce a set of two diagnoses: true diagnoses and observed diagnoses with imperfect accuracy. Sensitivity and specificity of the simulated clinical diagnosis varied with each of the four conditions (C): C1 = (0.77, 0.60), C2 = (0.87, 0.44), C3 = (0.71, 0.71), and C4 = (0.83, 0.55), which are derived from published values for clinical AD diagnoses against autopsy-confirmed pathology. Unadjusted and bias-adjusted logistic regressions were then fit to the simulated data to determine the models’ accuracy in estimating regression parameters and prediction of true diagnosis.
Results:Under all conditions, the bias-adjusted logistic regression model outperformed its unadjusted counterpart. Root mean square error (the variability of estimated coefficients around their true parameter values) ranged from 0.23 to 0.79 for the unadjusted model versus 0.24 to 0.29 for the bias-adjusted model. The empirical coverage rate (the proportion of 95% credible intervals that include their true parameter) ranged from 0.00 to 0.47 for the unadjusted model versus 0.95 to 0.96 for the bias-adjusted model. Finally, the bias-adjusted model produced the best overall diagnostic accuracy with correct classification of true diagnostic values about 78% of the time versus 62-72% without adjustment.
Conclusions:Results of this simulation study, which used published AD sensitivity and specificity statistics, provide evidence that bias-adjustments to logistic regression models are needed when research involves diagnoses from an imperfect standard. Results showed that unadjusted methods rarely identified true effects with credible intervals for coefficients including the true value anywhere from never to less than half of the time. Additional simulations are needed to examine the bias-adjusted model’s performance under additional conditions. Future research is needed to extend the bias adjustment to multinomial logistic regressions and to scenarios where the rate of misdiagnosis is unknown. Such methods may be valuable for improving detection of other neurological disorders with greater diagnostic error as well.
23 The Utility of Global versus Domain-specific Neuropsychological Test Score Dispersion as Markers of Cognitive Decline
- Hudaisa Fatima, Jeff Schaffert, Anne Carlew, Will Goette, Jessica Helphrey, Laura Lacritz, Heidi Rossetti, C. Munro Cullum
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- Journal:
- Journal of the International Neuropsychological Society / Volume 29 / Issue s1 / November 2023
- Published online by Cambridge University Press:
- 21 December 2023, pp. 233-234
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Objective:
Higher baseline dispersion (intra-individual variability) across neuropsychological test scores at a single time-point has been associated with more rapid cognitive decline, onset of Mild Cognitive Impairment (MCI) and Alzheimer’s disease (AD), faster rates of hippocampal and entorhinal atrophy, and increased AD neuropathology. Comparison between predictions made from test score dispersion within a cognitive domain versus global, cross-domain dispersion is understudied. Global dispersion may be influenced by ability-and test-specific characteristics. This study examined the performance of global versus domain-specific dispersion metrics to identify which is most predictive of cognitive decline over time.
Participants and Methods:Data for baseline and five follow-up visits of 308 participants with normal cognition (Mage=73.90, SD=8.12) were selected from the National Alzheimer’s Coordinating Center (NACC) Dataset. Participants were required to have no focal neurological deficits, or history of depression, stroke, or heart attack. Diagnoses and progression to MCI and/or dementia were determined at each visit through consensus conferences. Raw neuropsychological scores were standardized using NACC norms. Global baseline dispersion was defined as the intraindividual standard deviation (ISD) across the 10 scores in the NACC battery. Domain-specific dispersions were calculated by constructing composites and ISD was computed across tests sampling their respective domains (executive functioning/attention/processing speed [EFAS], language, and memory; see Table 1 for details on these tests). Higher values on each of these metrics reflect greater dispersion. Multinomial logistic regression model fit statistics and parameter estimates were compared across four different models (global, EFAS, Language, and Memory dispersion) covarying for age, years of education, sex, race, ethnicity, and ApoE4 status. Models were compared using the Likelihood Ratio Test (LRT) and the Akaike Information Criteria (AIC) of Models statistics.
Results:Of the 308 participants, 70 (22.7%) progressed to MCI, and 82 (26.6%) progressed to dementia. Tables 1 and 2 show the results of the logistic regressions for the four models. All models fit the data well, with statistically significant predictions of conversion. Model 1 (global dispersion) showed a better fit than domain-specific models of dispersion per LRT and AIC values. Consistent with the results from mean differences between groups, parameter estimates showed that only global dispersion and EFAS dispersion significantly predicted conversion to dementia (when included with other covariates in models), with higher dispersion reflecting a greater risk of conversion.
Conclusions:In this sample, baseline global and EFAS dispersion measures significantly predicted conversion to dementia. Although global dispersion was a stronger predictor of dementia progression, findings suggest that executive functioning performance may be driving this relationship. A single index of global variability, from the calculation of standard deviation across test scores, may be supplementary for clinicians when distinguishing individuals at risk for dementia progression. None of the models were predictive of conversion to MCI. Further research is required to examine cognitive variability differences among patients who progress to MCI and patient-specific factors that may relate to test score dispersion and its utility in predicting the progression of symptoms.