Abstract 14096: Heart Rate Variability in Post-COVID-19 Recovered Subjects Using Machine Learning

• Published in: Circulation (New York, N.Y.) N.Y.), 2021-11, Vol.144 (Suppl_1), p.A14096-A14096
• Main Authors: Kunal, Shekhar, Shetty, Manu K, Shah, Bhushan, Girish, MP, BANSAL, ANKIT, Batra, Vishal, Mukhopadhyay, Saibal, Yusuf, Jamal, Gupta, Anubha, Gupta, Mohit
• Format: Article
• Language: English
• ISSN: 0009-7322; 1524-4539
• DOI: 10.1161/circ.144.suppl_1.14096

IntroductionA significant proportion of patients recovering from COVID-19 infection experience symptoms attributable to autonomic cardiovascular dysregulation. Heart rate variability (HRV) is a non-invasive marker of cardiovascular dysautonomia. Machine learning (ML) models based on HRV can be used to identify post COVID-19 patients with autonomic dysfunction. MethodsWe evaluated HRV and blood pressure (BP) responses to orthostatic stress (3-min active standing) in 92 patients within 30-45 days of recovery from COVID-19 infection and 120 healthy controls. HRV was evaluated based on 12-lead electrocardiogram over a 60 second period during supine paced breathing. Lead II was used to extract ECG features including (a) average RR interval, (b) R wave height, (c) Heart Rate (HR) standard deviation and (d) HRV root mean square [HRV-RMS]. We also assed for (1) orthostatic hypotension (OH; >20/10 mmHg fall in BP) and (2) postural orthostatic tachycardia syndrome (POTS; HR increase >30 bpm without OH). Using ML, eleven candidate features were tested with eight algorithms (logistic regression, RandomForests, CatBoost, XGBoost, Extra-tree classifier, Multiple Perceptron (ANN), Support Vector Machines and AdaBoost Classifier) to distinguish between COVID-19 recovered and healthy controls. ResultsHRV was significantly lower in post COVID-19 recovered subjects as compared to healthy controls (6.25+4.9 ms vs 9.8+8.9 ms; P