Comparing the compensatory reserve metric obtained from invasive arterial measurements and photoplethysmographic volume-clamp during simulated hemorrhage

Abstract

Purpose: The compensatory reserve metric (CRM) is a novel tool to predict cardiovascular decompensation during hemorrhage. The CRM is traditionally computed using waveforms obtained from photoplethysmographic volume-clamp (PPG_VC), yet invasive arterial pressures may be uniquely available. We aimed to examine the level of agreement of CRM values computed from invasive arterial-derived waveforms and values computed from PPG_VC-derived waveforms.

Methods: Sixty-nine participants underwent graded lower body negative pressure to simulate hemorrhage. Waveform measurements from a brachial arterial catheter and PPG_VC finger-cuff were collected. A PPG_VC brachial waveform was reconstructed from the PPG_VC finger waveform. Thereafter, CRM values were computed using a deep one-dimensional convolutional neural network for each of the following source waveforms; (1) invasive arterial, (2) PPG_VC brachial, and (3) PPG_VC finger. Bland-Altman analyses were used to determine the level of agreement between invasive arterial CRM values and PPG_VC CRM values, with results presented as the Mean Bias [95% Limits of Agreement].

Results: The mean bias between invasive arterial- and PPG_VC brachial CRM values at rest, an applied pressure of -45mmHg, and at tolerance was 6% [-17%, 29%], 1% [-28%, 30%], and 0% [-25%, 25%], respectively. Additionally, the mean bias between invasive arterial- and PPG_VC finger CRM values at rest, applied pressure of -45mmHg, and tolerance was 2% [-22%, 26%], 8% [-19%, 35%], and 5% [-15%, 25%], respectively.

Conclusion: There is generally good agreement between CRM values obtained from invasive arterial waveforms and values obtained from PPG_VC waveforms. Invasive arterial waveforms may serve as an alternative for computation of the CRM.

Keywords: Hypovolemia; Machine learning; Patient monitoring; Shock.