Abstract 4141635: Reconstructing Invasive Aortic Pressure Waveforms from Non-Invasive Brachial Measurements Using a Machine Learning Approach

Background: Pulse wave analysis (PWA) of aortic pressure waveforms carries valuable information about cardiovascular health. However, due to the invasiveness of direct measurement, peripheral pressure waveforms are commonly used as surrogates. Recent findings revealed that while algorithmic methods can preserve pressure dynamics in non-invasive measurements, significant waveform morphology changes disrupt the association between peripheral and central parameters (PMID: 38591330, 2024 ). There is a need for non-invasive methods to faithfully reconstruct the central pressure waveform. Aim: Evaluate the fidelity of reconstructed aortic waveforms using a machine learning method applied to non-invasive brachial measurements. Methods: This study analyzed concurrent aortic catheterization and brachial cuff waveform measurements in suprasystolic mode in subjects referred for non-emergent left heart catheterization (N=115; mean age 66, 63% male). Our machine learning method uses a non-linear mode mapping in the frequency domain between non-invasive calibrated brachial pressure waveforms and invasive aortic ones. PWA features captured waveform magnitude and shape: systolic blood pressure (SBP), diastolic blood pressure (DBP), systolic pressure-time integral (SPTI), diastolic pressure time integral (DPTI), peak timing (T peak ), systole duration (T sys ), maximum pressure derivative ((+)dP/dt), and form factor (FF). Percentage error ((true–predicted)/true) was used to assess parameter correspondence in the testing population (N=35; cardiac cycles=994). Results: The non-linear mapping technique successfully reconstructed central pressure waveforms with good agreement between measured and estimated (MEAN=0.5 mmHg; SD=7.3 mmHg) as shown in Fig. 1A. PWA features showed low mean percentage errors for pressure (SBP=-0.7 ±5.9%; DBP=-2.9±9.4%), area (SPTI=0.2±8.7%; DPTI=-2.8±9.3%), timing (T peak =1.0±12.3%; T sys =1.2±7.1%), and shape parameters ((+)dP/dt=11.5±24.3%; FF=1.4±4.7%) shown in Fig. 1B. Conclusion: Our results demonstrate that the machine learning method applied on brachial cuff measurements captures waveform morphological changes along the arterial system allowing for a faithful representation of the central pressure waveform.