Machine Learning Analyses Revealed Distinct Arterial Pulse Variability According to Side Effects of Pfizer-BioNTech COVID-19 Vaccine (BNT162b2)

Abstract

Various adverse events and complications have been attributed to COVID-19 (coronavirus disease 2019) vaccinations, which can affect the cardiovascular system, with conditions such as myocarditis, thrombosis, and ischemia. The aim of this study was to combine noninvasive pulse measurements and frequency domain analysis to determine if the Pfizer-BioNTech COVID19 vaccine (BNT162b2) vaccination and its accompanying cardiovascular side effects will induce changes in arterial pulse transmission and waveform. Radial blood pressure waveform and photoplethysmography signals were measured noninvasively for 1 min in 112 subjects who visited Shuang-Ho Hospital for a BNT162b2 vaccination. Based on side effects, each subject was assigned to Group N (no side effects), Group CV (cardiac or vascular side effects), Group C (cardiac side effects only), or Group V (vascular side effects only). Two classification methods were used: (1) machine-learning (ML) analysis using 40 harmonic pulse indices (amplitude proportions, phase angles, and their variability indices) as features, and (2) a pulse-variability score analysis developed in the present study. Significant effects on the pulse harmonic indices were noted in Group V following vaccination. ML and pulse-variability score analyses provided acceptable AUCs (0.67 and 0.80, respectively) and hence can aid discriminations among subjects with cardiovascular side effects. When excluding ambiguous data points, the AUC of the score analysis further improved to 0.94 (with an adopted proportion of around 64.1%) for vascular side effects. The present findings may help to facilitate a time-saving and easy-to-use method for detecting changes in the vascular properties associated with the cardiovascular side effects following BNT162b2 vaccination.

Using Arterial Pulse and Laser Doppler Analyses to Discriminate between the Cardiovascular Effects of Different Running Levels

“…For BPW and PPG signals, frequency-domain analysis was performed to derive the following 40 harmonic indices for n = 1-10: amplitude proportion (C n ), coefficient of variation in C n (CV n ), phase angle (P n ), and standard deviation of P n (P n _SD) [17][18][19][20][21]. Pulse indices were further used to determine the discrimination ability by utilizing the following self-developed analyses of the relative changes in the pulse effect indices:…”

“…Another category of waveform analysis is frequency-domain analysis. The effects induced on pulse waveforms have also been studied to determine the possible vascular effects on subjects with vascular aging, frozen shoulder, sarcopenia, and Alzheimer's disease, as well as those who received COVID-19 vaccination [17][18][19][20][21][22].…”

The role of machine learning in health policies during the COVID-19 pandemic and in long COVID management

“…Concerning the strategies to prevent the adverse effect provoked by the vaccination programs, ML analyzes revealed distinct arterial pulse variability according to side effects of mRNA vaccine. This can facilitate a time-saving and easy-to-use method for detecting changes in the vascular properties associated with cardiovascular side effects following vaccination ( 102 ).…”