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

Abstract

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 $$\pm$$ ± 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.