Abstract

Background: Intraoperative hypotension is associated with increased postoperative morbidity and mortality. Methods: We randomly assigned patients undergoing major general surgery to early warning system (EWS) and hemodynamic algorithm (intervention group, n = 20) or standard care (n = 20). The primary outcome was the difference in hypotension (defined as mean arterial pressure < 65 mmHg) and as secondary outcome surrogate markers of organ injury and oxidative stress. Results: The median number of hypotensive episodes was lower in the intervention group (−5.0 (95% CI: −9.0, −0.5); p < 0.001), with lower time spent in hypotension (−12.8 min (95% CI: −38.0, −2.3 min); p = 0.048), correspondent to −4.8% of total surgery time (95% CI: −12.7, 0.01%; p = 0.048).The median time-weighted average of hypotension was 0.12 mmHg (0.35) in the intervention group and 0.37 mmHg (1.11) in the control group, with a median difference of −0.25 mmHg (95% CI: −0.85, −0.01; p = 0.025). Neutrophil Gelatinase-Associated Lipocalin (NGAL) correlated with time-weighted average of hypotension (R = 0.32; p = 0.038) and S100B with number of hypotensive episodes, absolute time of hypotension, relative time of hypotension and time-weighted average of hypotension (p < 0.001 for all). The intervention group showed lower Neuronal Specific Enolase (NSE) and higher reduced glutathione when compared to the control group. Conclusions: The use of an EWS coupled with a hemodynamic algorithm resulted in reduced intraoperative hypotension, reduced NSE and oxidative stress.

Highlights

  • A recent randomized trial showed that the use of a machine learning–derived early warning system (EWS) as compared with standard care resulted in significantly lower intraoperative hypotension [7]

  • We aimed at evaluating the impact of an EWS with an algorithm for hemodynamic management on the intraoperative time spent with hypotension in adult patients undergoing major general surgery; we assessed the impact of the intervention on post-operative levels of biomarkers of organ injury and oxidative stress

  • The intervention group had a lower number of hypotensive events than the control group (3 (IQR 6) vs. 8 (IQR 8) median times over surgery time respectively), with lower time of surgery spent in hypotension

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Summary

Introduction

Intraoperative hypotension is a common condition and may cause organ ischemia leading to injury and increasing the risk of postoperative complications. A recent randomized trial showed that the use of a machine learning–derived early warning system (EWS) as compared with standard care resulted in significantly lower intraoperative hypotension [7] In this context, the key-player leading to organ injury appears to be the mismatch between oxygen supply and demand [8,9,10]. We aimed at evaluating the impact of an EWS with an algorithm for hemodynamic management on the intraoperative time spent with hypotension in adult patients undergoing major general surgery; we assessed the impact of the intervention on post-operative levels of biomarkers of organ injury and oxidative stress. Conclusions: The use of an EWS coupled with a hemodynamic algorithm resulted in reduced intraoperative hypotension, reduced NSE and oxidative stress

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