How can I calculate the Pearson correlation coefficient between hourly average heart rate and ventricular premature complex count from a 24‑hour Holter monitor?

Calculating Correlation Between Hourly Heart Rate and PVC Frequency on Holter Monitoring

Direct Method for Pearson Correlation Calculation

To calculate the Pearson correlation coefficient between hourly heart rate and PVC frequency, extract the mean heart rate and total PVC count for each of the 24 hours from your Holter report, then apply the standard Pearson correlation formula using these 24 paired data points. 1

Step-by-Step Technical Approach

Data Extraction from Holter Report

• Obtain hourly data: Most modern Holter analysis software provides hourly summaries showing mean heart rate and PVC count for each hour of the 24-hour recording period 1, 2
• Create paired data points: For each hour (1-24), record two values: the mean heart rate (beats/min) and the total number of PVCs during that hour 1, 3
• Minimum data requirement: Ensure each heart rate value represents at least 5 minutes of recorded data during that hour to ensure statistical validity 3

Calculation Formula

• Apply Pearson correlation coefficient formula: Use the standard formula r = Σ[(x - x̄)(y - ȳ)] / √[Σ(x - x̄)² × Σ(y - ȳ)²], where x represents hourly mean heart rate and y represents hourly PVC count 1
• Statistical significance threshold: A correlation is considered statistically significant when P < 0.05 1, 2
• Interpretation of r values: r ≥ 0.4 indicates strong positive correlation (fast-HR-dependent PVCs), r ≤ -0.4 indicates strong negative correlation (slow-HR-dependent PVCs), and -0.4 < r < 0.4 suggests HR-independent PVCs 1

Clinical Significance of Correlation Patterns

Three Distinct PVC Patterns

• Fast-HR-dependent PVCs (F-HR-PVC): Positive correlation (r > 0, P < 0.05) indicates PVCs increase with higher heart rates, seen in approximately 50% of patients with frequent PVCs 1, 2
• Slow-HR-dependent PVCs (S-HR-PVC): Negative correlation (r < 0, P < 0.05) indicates PVCs increase at lower heart rates, seen in approximately 10-14% of patients 1, 2
• Independent-HR-PVCs (I-HR-PVC): No significant correlation (P ≥ 0.05) indicates PVC frequency is unrelated to heart rate, seen in approximately 36-40% of patients 1, 2

Therapeutic Implications

• Beta-blocker response prediction: Only patients with F-HR-PVC pattern (positive correlation, r ≥ 0.4) respond to beta-blocker therapy, with 62% achieving ≥50% PVC burden reduction 1
• Beta-blocker failure or harm: Patients with I-HR-PVC show no response to beta-blockers (0% success rate), while those with S-HR-PVC may experience increased PVC burden with beta-blocker therapy 1
• Ablation procedure planning: The correlation coefficient predicts pharmacologic inducibility during ablation procedures, with F-HR-PVC patients responding to isoproterenol and S-HR-PVC patients responding to phenylephrine or isoproterenol washout 2

Important Methodological Caveats

Variability in Correlation Measurement

• Time interval sensitivity: Using 1-hour intervals for analysis yields consistent correlation patterns across different days in only 36.6% of patients, while shorter intervals (15-30 minutes) improve consistency to 56.1% 4
• Day-to-day reproducibility: The correlation pattern is reproducible on repeat Holter monitoring in 96% of patients when using the same methodology 3, 5
• Nonlinear relationships: Many patients exhibit nonlinear or piecewise linear relationships between PVC frequency and heart rate that may not be captured by simple Pearson correlation 4

Practical Limitations

• Single 24-hour period limitations: The correlation may vary across different 24-hour periods in the same patient, particularly when using longer time intervals 4
• Heart rate range requirements: Ensure adequate heart rate variability during the recording period (typically ranging from 56 ± 10 to 102 ± 15 beats/min) to establish meaningful correlation 3
• Minimum PVC burden: This analysis is most clinically relevant for patients with PVC burden ≥1% (approximately 1,000 PVCs per 24 hours) 1, 2

Alternative Analysis Approaches

• Visual inspection method: Plot hourly PVC count versus hourly mean heart rate on a scatter plot to visually identify linear or nonlinear patterns before calculating correlation 3
• Shorter time intervals: Consider using 15-30 minute intervals instead of hourly intervals to capture more nuanced relationships and improve pattern consistency 4
• Multiple day analysis: When available, analyze correlation patterns across multiple consecutive days to assess pattern stability and account for day-to-day variability 4