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Machine Learning Analyses Revealed Distinct Arterial Pulse Variability According to Side Effects of Pfizer-BioNTech
Chun-Chao Chen1,2,3,4, Che-Kai Chang5, Chun-Chih Chiu1,2
1Division of Cardiology, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City 23561, Taiwan.
Insights
COVID-19 vaccination side effects can impact cardiovascular health. This study used pulse analysis to detect changes in arterial waveforms, aiding in identifying vascular side effects from the Pfizer-BioNTech vaccine.
Area of Science:
- Cardiovascular science
- Biomedical engineering
- Vaccinology
Background:
- COVID-19 vaccines, including Pfizer-BioNTech (BNT162b2), have been associated with adverse cardiovascular events like myocarditis and thrombosis.
- Monitoring for these side effects is crucial for vaccine safety and public health.
- Noninvasive methods for early detection of vaccine-related cardiovascular changes are needed.
Purpose of the Study:
- To investigate if BNT162b2 vaccination and associated cardiovascular side effects alter arterial pulse transmission and waveform characteristics.
- To evaluate the efficacy of noninvasive pulse measurements combined with frequency domain analysis for detecting these changes.
- To develop and validate analytical methods for discriminating between subjects with and without cardiovascular side effects post-vaccination.
Main Methods:
- Noninvasive radial blood pressure waveforms and photoplethysmography signals were recorded from 112 subjects before and after BNT162b2 vaccination.
- Subjects were categorized into groups based on reported side effects: no side effects (Group N), cardiac or vascular (Group CV), cardiac only (Group C), and vascular only (Group V).
- Two classification approaches were employed: machine learning (ML) analysis of 40 harmonic pulse indices and a novel pulse-variability score analysis.
Main Results:
- Significant alterations in pulse harmonic indices were observed in subjects experiencing vascular side effects (Group V).
- Both ML and pulse-variability score analyses demonstrated utility in discriminating subjects with cardiovascular side effects, with initial AUCs of 0.67 and 0.80, respectively.
- Excluding ambiguous data points significantly improved the AUC for the pulse-variability score analysis to 0.94 for detecting vascular side effects.
Conclusions:
- Noninvasive pulse analysis, particularly the pulse-variability score, shows promise as a sensitive method for detecting vascular property changes linked to BNT162b2 vaccination side effects.
- These findings suggest a potential for a time-saving, user-friendly tool to monitor for vaccine-related cardiovascular complications.
- Further research can refine this method for broader clinical application in vaccine safety surveillance.
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 COVID-19 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.
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