Post-extrasystolic characteristics in the arterial blood pressure waveform are associated with right ventricular dysfunction in intensive care patients
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Author(s)
Vistisen, Simon T
Moody, Benjamin
Celi, Leo A
Chen, Christina
Date Issued
November 8, 2018
Publisher
Springer Netherlands
Version
Author's final manuscript
Abstract
Abstract
Right ventricular dysfunction (RVD) is associated with end-organ dysfunction and mortality, but has been an overlooked condition in the ICU. We hypothesized that analysis of the arterial waveform in the presence of ventricular extrasystoles could differentiate patients with RVD from patients with a normally functioning right ventricle, because the 2nd and 3rd post-ectopic beat could reflect right ventricular state (pulmonary transit time) during the preceding ectopy. We retrospectively identified patients with echocardiographic evidence of moderate-to-severe RVD and patients with a normal functioning right ventricle (control) from the MIMIC database. We identified waveform records where ECG and arterial pressure were available in combination, simultaneously with echocardiographic evaluation. Ventricular extrasystoles were visually confirmed and the median systolic blood pressure (SBP) of the 2nd and 3rd post-ectopic beats compared with the median SBP of the ten sinus beats preceding the extrasystole. We identified 34 patients in the control group and 24 patients in the RVD group with ventricular extrasystoles. The mean SBP reduction at the 2nd and 3rd beat was lower in the RVD group compared with the control group [− 1.7 (SD: 1.9) % vs. − 3.6 (SD: 1.9) %, p < 0.001], and this characteristic differentiated RVD subjects from control subjects with an AUC of 0.76 (CI [0.64; 0.89]), with a specificity of 91% and sensitivity of 50%. In this proof-of-concept study, we found that post-extrasystolic ABP characteristics were associated with RVD.
MIT Department
Harvard--MIT Program in Health Sciences and Technology. Laboratory for Computational Physiology
Massachusetts Institute of Technology. Institute for Data, Systems, and Society
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DOI of Published Version
https://doi.org/10.1007/s10877-018-0216-2
