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 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.

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).

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.

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.

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.

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.

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.