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Heart rate variability as a marker of cardiovascular dysautonomia in post-COVID-19 syndrome using artificial
Bhushan Shah1, Shekhar Kunal1, Ankit Bansal1
1Department of Cardiology, Govind Ballabh Pant Institute of Post Graduate Medical Education and Research, Delhi, India.
Insights
Cardiovascular dysautonomia is common after COVID-19 recovery, with lower heart rate variability (HRV) observed. An AI model effectively identified HRV measures to distinguish between recovered patients and healthy individuals.
Area of Science:
- Cardiology
- Infectious Diseases
- Artificial Intelligence
Background:
- Cardiovascular dysautonomia, including postural orthostatic tachycardia syndrome (POTS) and orthostatic hypotension (OH), is a recognized post-COVID-19 complication.
- This study investigates the prevalence of cardiovascular dysautonomia in individuals recently recovered from COVID-19.
Purpose of the Study:
- To determine the prevalence of cardiovascular dysautonomia in post-COVID-19 patients.
- To evaluate an Artificial Intelligence (AI) model for identifying key time-domain heart rate variability (HRV) measures from short-term ECGs in these patients.
Main Methods:
- An observational study included 92 COVID-19 recovered subjects and 120 healthy controls.
- Measurements included heart rate and blood pressure response to standing, and a 60-second 12-lead ECG during paced breathing.
- An AI model with ShAP interpretability was used to analyze HRV features (RMSSD, SDNN).
Main Results:
- Cardiovascular dysautonomia was found in 15.21% of post-COVID-19 subjects (13.04% OH, 2.17% POTS).
- Post-COVID-19 patients exhibited significantly lower HRV (RMSSD) compared to healthy controls (13.9 ± 11.8 ms vs 19.9 ± 19.5 ms, P=0.01).
- A multiple perceptron AI model identified HRV (RMSSD) as the most distinguishing feature between recovered patients and controls.
Conclusions:
- Cardiovascular dysautonomia is prevalent in COVID-19 recovered individuals.
- Significantly reduced heart rate variability is a key finding in post-COVID-19 patients compared to healthy controls.
- AI models can effectively utilize HRV measures to differentiate between COVID-19 recovered patients and healthy individuals.
Introduction:
Cardiovascular dysautonomia comprising postural orthostatic tachycardia syndrome (POTS) and orthostatic hypotension (OH) is one of the presentations in COVID-19 recovered subjects. We aim to determine the prevalence of cardiovascular dysautonomia in post COVID-19 patients and to evaluate an Artificial Intelligence (AI) model to identify time domain heart rate variability (HRV) measures most suitable for short term ECG in these subjects.
Methods:
This observational study enrolled 92 recently COVID-19 recovered subjects who underwent measurement of heart rate and blood pressure response to standing up from supine position and a 12-lead ECG recording for 60 s period during supine paced breathing. Using feature extraction, ECG features including those of HRV (RMSSD and SDNN) were obtained. An AI model was constructed with ShAP AI interpretability to determine time domain HRV features representing post COVID-19 recovered state. In addition, 120 healthy volunteers were enrolled as controls.
Results:
Cardiovascular dysautonomia was present in 15.21% (OH:13.04%; POTS:2.17%). Patients with OH had significantly lower HRV and higher inflammatory markers. HRV (RMSSD) was significantly lower in post COVID-19 patients compared to healthy controls (13.9 ± 11.8 ms vs 19.9 ± 19.5 ms; P = 0.01) with inverse correlation between HRV and inflammatory markers. Multiple perceptron was best performing AI model with HRV(RMSSD) being the top time domain HRV feature distinguishing between COVID-19 recovered patients and healthy controls.
Conclusion:
Present study showed that cardiovascular dysautonomia is common in COVID-19 recovered subjects with a significantly lower HRV compared to healthy controls. The AI model was able to distinguish between COVID-19 recovered patients and healthy controls.
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