Heart-Rate-Based Machine-Learning Algorithms for Screening Orthostatic Hypotension

Abstract

Background and Purpose Many elderly patients are unable to actively stand up by themselves and have contraindications to performing the head-up tilt test (HUTT). We aimed to develop screening algorithms for diagnosing orthostatic hypotension (OH) before performing the HUTT. Methods This study recruited 663 patients with orthostatic intolerance (78 with and 585 without OH, as confirmed by the HUTT) and compared their clinical characteristics. Univariate and multivariate analyses were performed to investigate potential predictors of an OH diagnosis. Machine-learning algorithms were applied to determine whether the accuracy of OH prediction could be used for screening OH without performing the HUTT. Results Differences between expiration and inspiration (E-I differences), expiration:inspiration ratios (E:I ratios), and Valsalva ratios were smaller in patients with OH than in those without OH. The univariate analysis showed that increased age and baseline systolic blood pressure (BP) as well as decreased E-I difference, E:I ratio, and Valsalva ratio were correlated with OH. In the multivariate analysis, increased baseline systolic BP and decreased Valsalva ratio were found to be independent predictors of OH. Using those variables as input features, the classification accuracies of the support vector machine, k-nearest neighbors, and random forest methods were 84.4%, 84.4%, and 90.6%, respectively. Conclusions We have identified clinical parameters that are strongly associated with OH. Machine-learning analysis using those parameters was highly accurate in differentiating OH from non-OH patients. These parameters could be useful screening factors for OH in patients who are unable to perform the HUTT.