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Central venous pressure as a method of optimising atrio-ventricular delay after cardiac surgery
Alexander Tindale1,2, Ioana Cretu3, Naomi Gomez1
1Department of Cardiology, Harefield Hospital, Guys & St Thomas' Foundation Trust, London, United Kingdom.
Insights
Central venous pressure (CVP) signals can optimize atrioventricular delay (AVD) by mirroring arterial blood pressure (ABP) signals. This offers a simpler alternative for AVD optimization in pacemaker patients.
Area of Science:
- Cardiology
- Biomedical Engineering
- Medical Device Technology
Background:
- Atrioventricular delay (AVD) optimization traditionally requires complex hemodynamic monitoring like invasive arterial blood pressure (ABP).
- Central venous pressure (CVP) presents a potentially simpler, non-invasive alternative for acquiring hemodynamic data.
- Current pacing systems lack direct methods for easily acquiring essential hemodynamic signals for AVD optimization.
Purpose of the Study:
- To investigate the feasibility of using standard clinical central venous pressure (CVP) signals for optimizing atrioventricular delay (AVD).
- To evaluate the correlation between CVP and arterial blood pressure (ABP) during AVD optimization protocols.
- To assess the effectiveness of signal processing techniques in improving the reliability of CVP-based AVD optimization.
Main Methods:
- Studied 16 patients with temporary pacemakers post-cardiac surgery.
- Performed AVD optimization by testing various delay settings (40-280ms) against a reference (120ms).
- Compared raw CVP data with ABP, applying respiratory correction methods (cycle limiting, asymmetric least squares, discrete wavelet transform) and a quality control step.
Main Results:
- CVP signals demonstrated a significant inverse correlation with systolic ABP (R values ranging from -0.631 to -0.692, p<0.001).
- The discrete wavelet transform (DWT) with quality control yielded the strongest inverse correlation (R = -0.76, p<0.001).
- Optimized AVD values derived from CVP and ABP showed strong agreement (R = 0.78, p<0.001), with quality control accurately predicting discrepancies.
Conclusions:
- Central venous pressure (CVP) signals can be effectively utilized for optimizing atrioventricular delay (AVD) due to their reliable inverse relationship with arterial blood pressure (ABP).
- This method offers a more accessible alternative to invasive monitoring for AVD optimization in pacemaker patients.
- Careful protocol design is essential to account for biological variability and ensure accurate optimization.
Introduction:
Haemodynamic atrioventricular delay (AVD) optimisation has primarily focussed on signals that are not easy to acquire from a pacing system itself, such as invasive left ventricular catheterisation or arterial blood pressure (ABP). In this study, standard clinical central venous pressure (CVP) signals are tested as a potential alternative.
Methods:
Sixteen patients with a temporary pacemaker after cardiac surgery were studied. AV delay optimisation was performed by alternating between a reference AVD of 120ms and tested settings ranging from 40 to 280ms, with 8 replicates for each setting. Alongside (a) the raw data, three methods of correcting for respiration were tested: (b) limiting analysis to a respiratory cycle, (c) asymmetric least squares (ALS) and (d) discrete wavelet transform (DWT). The utility of a quality control step was tested.
Results:
CVP signals were a mirror image of the systolic ABP signals: The four R values were -0.674, -0.692, -0.631, -0.671 respectively (all p<0.001). With quality control, the mirror image was best for DWT (R = -0.76, p<0.001), with the CVP and ABP optima agreeing well (R = 0.78, p<0.001). The automated quality control signal correctly predicted the gap between the AVD optima calculated from ABP and CVP (R = 0.8, p<0.001).
Conclusions:
Central venous pressure signals could be used to optimise AVD, because they have a reliable inverse relationship with ABP when pacemaker settings undergo protocolised testing. However, protocols need careful design to circumvent spontaneous biological variability.
