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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.
68 Preliminary Evidence of a Therapeutic Effect of Electrical Neuromodulation on Cognitive Deficits in Patients with Mild Cognitive Impairment
- Christian LoBue, Didehbani Nyaz, Hsueh-Sheng Chiang, John Hart, Jessica Helphrey, Michael Conley, Eric Smernoff, Laura Lacritz, Caitlin Reese, C M Cullum, Cindy Marshall, Trung Nguyen, Brendan J Kelley, Alka Khera, Alex Frolov, Natalie Martinez, Rebecca Logan
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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. 475
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- Article
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Objective:
Episodic memory functioning is distributed across two brain circuits, one of which courses through the dorsal anterior cingulate cortex (dACC). Thus, delivering non-invasive neuromodulation technology to the dACC may improve episodic memory functioning in patients with memory problems such as in amnestic mild cognitive impairment (aMCI). This preliminary study is a randomized, double-blinded, sham-controlled clinical trial to examine if high definition transcranial direct current stimulation (HD-tDCS) can be a viable treatment in aMCI.
Participants and Methods:Eleven aMCI participants, of whom 9 had multidomain deficits, were randomized to receive 1 mA HD-tDCS (N=7) or sham (N=4) stimulation. HD-tDCS was applied over ten 20-minute sessions targeting the dACC. Neuropsychological measures of episodic memory, verbal fluency, and executive function were completed at baseline and after the last HD-tDCS session. Changes in composite scores for memory and language/executive function tests were compared between groups (one-tailed t-tests with a = 0.10 for significance). Clinically significant change, defined as > 1 SD improvement on at least one test in the memory and non-memory domains, was compared between active and sham stimulation based on the frequency of participants in each.
Results:No statistical or clinically significant change (N-1 X2; p = 0.62) was seen in episodic memory for the active HD-tDCS (MDiff = 4.4; SD = 17.1) or sham groups (MDiff = -0.5; SD = 9.7). However, the language and executive function composite showed statistically significant improvement (p = 0.04; MDiff = -15.3; SD = 18.4) for the active HD-tDCS group only (Sham MDiff = -5.8; SD = 10.7). Multiple participants (N=4) in the active group had clinically significant enhancement in language and executive functioning tests, while nobody in the sham group did (p = 0.04).
Conclusions:HD-tDCS targeting the dACC had no direct benefit for episodic memory deficits in aMCI based on preliminary findings for this ongoing clinical trial. However, significant improvement in language and executive function skills occurred in response to HD-tDCS, suggesting HD-tDCS in this configuration has promising potential as an intervention for language and executive function deficits in MCI.