Key result
Carotid waveform machine learning detects abnormal LVEF with ~92% accuracy.
Why the study?
Children with heart failure have high healthcare utilization, creating a need for a simple, non-invasive, and inexpensive method to screen for left ventricular dysfunction.
Does an IF-ML method applied to noninvasive carotid pressure waveforms accurately detect abnormal left ventricular ejection fraction in pediatric patients?
Cross-Sectional (n=50)
Single-blind
No
Does an IF-ML method applied to noninvasive carotid pressure waveforms accurately detect abnormal left ventricular ejection fraction in pediatric patients?
A novel smartphone-based carotid waveform analysis using intrinsic frequencies and machine learning provides a highly accurate, noninvasive method for detecting left ventricular systolic dysfunction in children.
Answers are grounded in this paper and cite their sources.
May support noninvasive pediatric LVEF screening; leaves open prospective validation before clinical adoption.
Objective. Children with heart failure have higher rates of emergency department utilization, health care expenditure, and hospitalization. Therefore, a need exists for a simple, non-invasive, and inexpensive method of screening for left ventricular (LV) dysfunction. We recently demonstrated the practicality and reliability of a wireless smartphone-based handheld device in capturing carotid pressure waveforms and deriving cardiovascular intrinsic frequencies (IFs) in children with normal LV function. Our goal in this study was to demonstrate that an IF-based machine learning method (IF-ML) applied to noninvasive carotid pressure waveforms can distinguish between normal and abnormal LV ejection fraction (LVEF) in pediatric patients. Approach . Fifty patients ages 0 to 21 years underwent LVEF measurement by echocardiogram or cardiac magnetic resonance imaging. On the same day, patients had carotid waveforms recorded using Vivio. The exclusion criterion was known vascular disease that would interfere with obtaining a carotid artery pulse. We adopted a hybrid IF- Machine Learning (IF-ML) method by applying physiologically relevant IF parameters as inputs to Decision Tree classifiers. The threshold for low LVEF was chosen as <50%. Main results. The proposed IF-ML method was able to detect an abnormal LVEF with an accuracy of 92% (sensitivity = 100%, specificity = 89%, area under the curve (AUC) = 0.95). Consistent with previous clinical studies, the IF parameter ω 1 was elevated among patients with reduced LVEF. Significance. A hybrid IF-ML method applied on a carotid waveform recorded by a hand-held smartphone-based device can differentiate between normal and abnormal LV systolic function in children with normal cardiac anatomy.
No takes yet. Share an insight, caveat, or question.
Cheng et al. (2023) conducted a cross-sectional in Left ventricular systolic dysfunction (n=50). Intrinsic frequency-based machine learning (IF-ML) on carotid pressure waveforms vs. Echocardiogram or cardiac MRI was evaluated on Detection of abnormal left ventricular ejection fraction (<50%) (AUC 0.95). The intrinsic frequency-based machine learning method applied to carotid pressure waveforms detected abnormal left ventricular ejection fraction with 92% accuracy and an AUC of 0.95.
Synapse has enriched 10 related papers. Consider them for comparative context: