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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.
87 Not Normal but not MCI: Course of Memory over time
- Michael Conley, Jeff Schaffert, Anthony Longoria, Jessica Helphrey, C Munro Cullum, Laura Lacritz
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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. 389-390
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Objective:
A diagnosis of mild cognitive impairment (MCI) requires memory complaint and objective memory impairment. However, some individuals report subjective memory complaints (SMC) despite having intact memory performance, while others demonstrate subtle impairment on memory testing but have no memory complaints; neither case would meet criteria for MCI. This study aimed to compare memory performances over time in individuals who do not meet traditional MCI criteria to those with normal cognition and those who converted to MCI.
Participants and Methods:Diagnoses for a longitudinal sample from the Texas Alzheimer’s Research and Care Consortium were reviewed by a consensus panel of neuropsychologists and neurologists and reclassified at time of last visit. Diagnostic categories included SMC (i.e., memory complaint but no impairment on testing), objective cognitive impairment but no complaint (Impaired but not MCI), normal control (NC), MCI, and dementia. In this study, 827 participants were divided into 4 groups: 1) NC over 5 visits (n=511, 71% female; 42% Latinx/Hispanic), 2) baseline NC to amnestic MCI (n=62; 63% female; 57% Latinx/Hispanic), 3) SMC at last visit (n=133; 58% female; 70% Latinx/Hispanic), and 4) impaired but not MCI at last visit (n=121; 71% female; 60% Latinx/Hispanic). A memory composite (z-score) was created from the CERAD list-learning task (immediate, delayed, and recognition-discrimination) and Wechsler Memory Scale (Immediate and Delayed Logical Memory and Visual Reproduction) to evaluate memory performance over time. A linear mixed-model adjusting for age, education, sex, ethnicity, and number of APOE e4 alleles evaluated memory performance across 5 visits for the groups. To assess if depression followed a similar course, a linear mixed-model evaluated Geriatric Depression Scale (GDS) scores over time.
Results:At baseline, groups differed by age (F=22.82; p<.001), education (F=8.60; p<.001), MMSE scores (F=9.38; p<.001), GDS-30 scores (F=3.56; p=.015), and memory composites (F=24.29; p<.001). A significant group X time interaction was observed (F=4.83, p<.001). Memory performance improved in both the SMC and the NC groups, remained stable in the impaired but not MCI group, and declined (as expected) in those who converted to amnestic MCI. Depression scores also showed a significant group X time interaction (F=2.43; p=.004), in which the NC to MCI group endorsed slightly more depression symptoms over time, while other groups declined or remained stable.
Conclusions:Memory trajectories in this diverse sample differed across groups. Individuals with SMC but without objective memory impairment and normal controls showed some improvement in memory over time, presumably due to practice effects. Those with subtle memory impairments but no complaint (i.e., did not meet MCI criteria) remained stable and those who converted to amnestic MCI had worse memory across time. The stability of memory performances in the impaired not MCI group suggests these subtle memory inefficiencies may be longstanding or unperceived. However, because our sample achieved retrospective diagnoses of SMC and impaired not MCI, it will be important for future studies to prospectively follow these groups to determine which risk factors may predict progression to MCI and what impact ethnicity may have on these trajectories.