Dependence of premature ventricular complexes on heart rate-it's not that simple
Adrien Osakwe1, Noah Wightman1, Marc W Deyell2
1Quantitative Life Sciences Program, McGill University, Montreal, QC H3A 1E3, Canada.
Insights
Classifying frequent premature ventricular complexes (PVCs) using linear heart rate correlation is unreliable. The relationship is inconsistent across days and sensitive to measurement methods, necessitating advanced approaches.
Area of Science:
- Cardiology
- Biomedical Engineering
- Data Science
Background:
- Frequent premature ventricular complexes (PVCs) are linked to adverse cardiac conditions like cardiomyopathy.
- A proposed classification method for PVCs involves analyzing the linear correlation between PVC frequency and heart rate over 24 hours.
- This classification aims to guide beta-blocker treatment strategies.
Purpose of the Study:
- To assess the reliability of using linear correlation between PVC frequency and heart rate for patient classification.
- To investigate the impact of measurement methodology, different 24-hour periods, and nonlinear dependencies on this classification.
- To evaluate the robustness of the proposed classification method.
Main Methods:
- Analysis of 82 multi-day Holter recordings from 48 patients with frequent PVCs (1%-44% burden).
- Computation of linear correlation between PVC frequency and heart rate using various 24-hour periods.
- Examination of different time interval lengths for determining PVC frequency.
Main Results:
- Consistent correlation (positive, negative, or neutral) across different days was observed in only 36.6% of patients using 1-hour intervals.
- Correlation consistency improved to 56.1% with shorter time intervals.
- Shorter intervals revealed significant nonlinear and piecewise linear relationships between PVC frequency and heart rate in many patients.
Conclusions:
- The correlation between PVC frequency and heart rate is not strictly linear or stationary, showing significant variability.
- Linear correlation classification is sensitive to the specific 24-hour period and data segmentation methods.
- Advanced classification strategies capturing nonlinear and time-varying dependencies are needed for clinical practice.
Objective:
Frequent premature ventricular complexes (PVCs) can lead to adverse health conditions such as cardiomyopathy. The linear correlation between PVC frequency and heart rate (as positive, negative, or neutral) on a 24-hour Holter recording has been proposed as a way to classify patients and guide treatment with beta-blockers. Our objective was to evaluate the robustness of this classification to measurement methodology, different 24-hour periods, and nonlinear dependencies of PVCs on heart rate.
Materials And Methods:
We analyzed 82 multi-day Holter recordings (1-7 days) collected from 48 patients with frequent PVCs (burden 1%-44%). For each record, linear correlation between PVC frequency and heart rate was computed for different 24-hour periods and using different length intervals to determine PVC frequency.
Results:
Using a 1-hour interval, the correlation between PVC frequency and heart rate was consistently positive, negative, or neutral on different days in only 36.6% of patients. Using shorter time intervals, the correlation was consistent in 56.1% of patients. Shorter time intervals revealed nonlinear and piecewise linear relationships between PVC frequency and heart rate in many patients.
Discussion:
The variability of the correlation between PVC frequency and heart rate across different 24-hour periods and interval durations suggests that the relationship is neither strictly linear nor stationary. A better understanding of the mechanism driving the PVCs, combined with computational and biological models that represent these mechanisms, may provide insight into the observed nonlinear behavior and guide more robust classification strategies.
Conclusion:
Linear correlation as a tool to classify patients with frequent PVCs should be used with caution. It is sensitive to the specific 24-hour period analyzed and the methodology used to segment the data. More sophisticated classification approaches that can capture nonlinear and time-varying dependencies should be developed and considered in clinical practice.
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