Dataset Information

Evaluation of a multimodal diagnostic algorithm for prediction of cognitive impairment in elderly patients with dizziness.

ABSTRACT:

Background

The current diagnostic workup for chronic dizziness in elderly patients often neglects neuropsychological assessment, thus missing a relevant proportion of patients, who perceive dizziness as a subjective chief complaint of a concomitant cognitive impairment. This study aimed to establish risk prediction models for cognitive impairment in chronic dizzy patients based on data sources routinely collected in a dizziness center.

Methods

One hundred patients (age: 74.7 ± 7.1 years, 41.0% women) with chronic dizziness were prospectively characterized by (1) neuro-otological testing, (2) quantitative gait assessment, (3) graduation of focal brain atrophy and white matter lesion load, and (4) cognitive screening (MoCA). A linear regression model was trained to predict patients' total MoCA score based on 16 clinical features derived from demographics, vestibular testing, gait analysis, and imaging scales. Additionally, we trained a binary logistic regression model on the same data sources to identify those patients with a cognitive impairment (i.e., MoCA < 25).

Results

The linear regression model explained almost half of the variance of patients' total MoCA score (R² = 0.49; mean absolute error: 1.7). The most important risk-predictors of cognitive impairment were age (β = - 0.75), pathological Romberg's sign (β = - 1.05), normal caloric test results (β = - 0.8), slower timed-up-and-go test (β = - 0.67), frontal (β = - 0.6) and temporal (β = - 0.54) brain atrophy. The binary classification yielded an area under the curve of 0.84 (95% CI 0.70-0.98) in distinguishing between cognitively normal and impaired patients.

Conclusions

The need for cognitive testing in patients with chronic dizziness can be efficiently approximated by available data sources from routine diagnostic workup in a dizziness center.

SUBMITTER: Felfela K

PROVIDER: S-EPMC11233323 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature

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Publications

Evaluation of a multimodal diagnostic algorithm for prediction of cognitive impairment in elderly patients with dizziness.

Felfela K K Jooshani N N Möhwald K K Huppert D D Becker-Bense S S Schöberl F F Schniepp R R Filippopulos F F Dieterich M M Wuehr M M Zwergal A A

Journal of neurology 20240503 7

<h4>Background</h4>The current diagnostic workup for chronic dizziness in elderly patients often neglects neuropsychological assessment, thus missing a relevant proportion of patients, who perceive dizziness as a subjective chief complaint of a concomitant cognitive impairment. This study aimed to establish risk prediction models for cognitive impairment in chronic dizzy patients based on data sources routinely collected in a dizziness center.<h4>Methods</h4>One hundred patients (age: 74.7 ± 7.1 ...[more]

PMID: 38702563

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Project description:Accurately identifying the patients that have mild cognitive impairment (MCI) who will go on to develop Alzheimer's disease (AD) will become essential as new treatments will require identification of AD patients at earlier stages in the disease process. Most previous work in this area has centred around the same automated techniques used to diagnose AD patients from healthy controls, by coupling high dimensional brain image data or other relevant biomarker data to modern machine learning techniques. Such studies can now distinguish between AD patients and controls as accurately as an experienced clinician. Models trained on patients with AD and control subjects can also distinguish between MCI patients that will convert to AD within a given timeframe (MCI-c) and those that remain stable (MCI-s), although differences between these groups are smaller and thus, the corresponding accuracy is lower. The most common type of classifier used in these studies is the support vector machine, which gives categorical class decisions. In this paper, we introduce Gaussian process (GP) classification to the problem. This fully Bayesian method produces naturally probabilistic predictions, which we show correlate well with the actual chances of converting to AD within 3 years in a population of 96 MCI-s and 47 MCI-c subjects. Furthermore, we show that GPs can integrate multimodal data (in this study volumetric MRI, FDG-PET, cerebrospinal fluid, and APOE genotype with the classification process through the use of a mixed kernel). The GP approach aids combination of different data sources by learning parameters automatically from training data via type-II maximum likelihood, which we compare to a more conventional method based on cross validation and an SVM classifier. When the resulting probabilities from the GP are dichotomised to produce a binary classification, the results for predicting MCI conversion based on the combination of all three types of data show a balanced accuracy of 74%. This is a substantially higher accuracy than could be obtained using any individual modality or using a multikernel SVM, and is competitive with the highest accuracy yet achieved for predicting conversion within three years on the widely used ADNI dataset.

Project description:BackgroundMild cognitive impairment (MCI) poses significant challenges in early diagnosis and timely intervention. Underdiagnosis, coupled with the economic and social burden of dementia, necessitates more precise detection methods. Machine learning (ML) algorithms show promise in managing complex data for MCI and dementia prediction.ObjectiveThis study assessed the predictive accuracy of ML models in identifying the onset of MCI and dementia using the Korean Longitudinal Study of Aging (KLoSA) dataset.MethodsThis study used data from the KLoSA, a comprehensive biennial survey that tracks the demographic, health, and socioeconomic aspects of middle-aged and older Korean adults from 2018 to 2020. Among the 6171 initial households, 4975 eligible older adult participants aged 60 years or older were selected after excluding individuals based on age and missing data. The identification of MCI and dementia relied on self-reported diagnoses, with sociodemographic and health-related variables serving as key covariates. The dataset was categorized into training and test sets to predict MCI and dementia by using multiple models, including logistic regression, light gradient-boosting machine, XGBoost (extreme gradient boosting), CatBoost, random forest, gradient boosting, AdaBoost, support vector classifier, and k-nearest neighbors, and the training and test sets were used to evaluate predictive performance. The performance was assessed using the area under the receiver operating characteristic curve (AUC). Class imbalances were addressed via weights. Shapley additive explanation values were used to determine the contribution of each feature to the prediction rate.ResultsAmong the 4975 participants, the best model for predicting MCI onset was random forest, with a median AUC of 0.6729 (IQR 0.3883-0.8152), followed by k-nearest neighbors with a median AUC of 0.5576 (IQR 0.4555-0.6761) and support vector classifier with a median AUC of 0.5067 (IQR 0.3755-0.6389). For dementia onset prediction, the best model was XGBoost, achieving a median AUC of 0.8185 (IQR 0.8085-0.8285), closely followed by light gradient-boosting machine with a median AUC of 0.8069 (IQR 0.7969-0.8169) and AdaBoost with a median AUC of 0.8007 (IQR 0.7907-0.8107). The Shapley values highlighted pain in everyday life, being widowed, living alone, exercising, and living with a partner as the strongest predictors of MCI. For dementia, the most predictive features were other contributing factors, education at the high school level, education at the middle school level, exercising, and monthly social engagement.ConclusionsML algorithms, especially XGBoost, exhibited the potential for predicting MCI onset using KLoSA data. However, no model has demonstrated robust accuracy in predicting MCI and dementia. Sociodemographic and health-related factors are crucial for initiating cognitive conditions, emphasizing the need for multifaceted predictive models for early identification and intervention. These findings underscore the potential and limitations of ML in predicting cognitive impairment in community-dwelling older adults.

Dataset Information

Evaluation of a multimodal diagnostic algorithm for prediction of cognitive impairment in elderly patients with dizziness.

Background

Methods

Results

Conclusions

Publications

Evaluation of a multimodal diagnostic algorithm for prediction of cognitive impairment in elderly patients with dizziness.

Similar Datasets

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