Machine learning for analysis of active stand responses in older adults with vasovagal syncope

ObjectivesTo assess 1) differences in the hemodynamic response to the active stand test in older adults with a clinical diagnosis of vasovagal syncope compared to age-matched controls 2) if the active stand test combined with machine learning approaches can be used to identify the presence of vasovagal syncope in older adults. ApproachAdults aged 50 and over (Vasovagal Syncope N=46 Age=66.9{+/-}10.3; Control N=86 Age=65.3{+/-}9.5) completed an active stand test. Multiple features were extracted to characterize the hemodynamic responses to the active stand test and were compared between groups. Classification was performed using machine learning algorithms including linear discriminant analysis, quadratic discriminant analysis, support vector machine and an ensemble majority vote classifier. Main ResultsSubjects with vasovagal syncope demonstrated a higher resting (supine) heart rate (69.8{+/-}13.1 bpm vs 63.3{+/-}12.1 bpm; P=0.007), a smaller initial systolic blood pressure drop (-20.2{+/-}20.1% vs -27.3{+/-}17.5%; P=0.005), larger drops in stroke volume (-14.7{+/-}24.0% vs -2.7{+/-}23.3%; P=0.010) and cardiac output (-6.4{+/-}18.5% vs 5.8{+/-}22.3%;P<0.001) and a larger increase in total peripheral resistance (8.1{+/-}30.4% vs -6.03{+/-}22.8%; P=0.002) compared to controls. A majority vote classifier identified the presence of vasovagal syncope with 82.6% sensitivity, 76.8% specificity, and average accuracy of 78.9%. SignificanceOlder adults with vasovagal syncope display a unique hemodynamic and autonomic response to active standing characterized by relative autonomic hypersensitivity and larger drops in cardiac output compared to age-matched controls. With suitable machine learning algorithms, the active stand test holds the potential to be used to screen older adults for reflex syncopes and hypotensive susceptibility potentially reducing test time, cost, and patient discomfort. More broadly this paper presents a machine learning framework to support use of the active stand test for classification of clinical outcomes of interest.