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Preliminary Communication
Caring for the Critically Ill Patient
February 17, 2020

Effect of a Machine Learning–Derived Early Warning System for Intraoperative Hypotension vs Standard Care on Depth and Duration of Intraoperative Hypotension During Elective Noncardiac Surgery: The HYPE Randomized Clinical Trial

Author Affiliations
  • 1Department of Anesthesiology, Amsterdam UMC, Location AMC, Amsterdam, the Netherlands
  • 2Department of Intensive Care, Amsterdam UMC, Location AMC, Amsterdam, the Netherlands
  • 3Department of Technical Medicine, University of Twente, Enschede, the Netherlands
JAMA. 2020;323(11):1052-1060. doi:10.1001/jama.2020.0592
Key Points

Question  Can a machine learning–derived predictive early warning system for pending intraoperative hypotension in combination with a hemodynamic diagnostic guidance and treatment protocol reduce the time-weighted average of hypotension during noncardiac surgery?

Findings  In this single-center preliminary randomized clinical trial that included 68 patients undergoing elective noncardiac surgery, the time-weighted average of hypotension for those randomized to the early warning system vs those receiving standard care was 0.10 mm Hg vs 0.44 mm Hg, a difference that was statistically significant.

Meaning  While the use of a machine learning–derived early warning system compared with standard care resulted in less intraoperative hypotension, further research with larger study populations in diverse settings is needed to understand the effect on patient outcomes and to fully assess safety and generalizability.

Abstract

Importance  Intraoperative hypotension is associated with increased morbidity and mortality. A machine learning–derived early warning system to predict hypotension shortly before it occurs has been developed and validated.

Objective  To test whether the clinical application of the early warning system in combination with a hemodynamic diagnostic guidance and treatment protocol reduces intraoperative hypotension.

Design, Setting, and Participants  Preliminary unblinded randomized clinical trial performed in a tertiary center in Amsterdam, the Netherlands, among adult patients scheduled for elective noncardiac surgery under general anesthesia and an indication for continuous invasive blood pressure monitoring, who were enrolled between May 2018 and March 2019. Hypotension was defined as a mean arterial pressure (MAP) below 65 mm Hg for at least 1 minute.

Interventions  Patients were randomly assigned to receive either the early warning system (n = 34) or standard care (n = 34), with a goal MAP of at least 65 mm Hg in both groups.

Main Outcomes and Measures  The primary outcome was time-weighted average of hypotension during surgery, with a unit of measure of millimeters of mercury. This was calculated as the depth of hypotension below a MAP of 65 mm Hg (in millimeters of mercury) × time spent below a MAP of 65 mm Hg (in minutes) divided by total duration of operation (in minutes).

Results  Among 68 randomized patients, 60 (88%) completed the trial (median age, 64 [interquartile range {IQR}, 57-70] years; 26 [43%] women). The median length of surgery was 256 minutes (IQR, 213-430 minutes). The median time-weighted average of hypotension was 0.10 mm Hg (IQR, 0.01-0.43 mm Hg) in the intervention group vs 0.44 mm Hg (IQR, 0.23-0.72 mm Hg) in the control group, for a median difference of 0.38 mm Hg (95% CI, 0.14-0.43 mm Hg; P = .001). The median time of hypotension per patient was 8.0 minutes (IQR, 1.33-26.00 minutes) in the intervention group vs 32.7 minutes (IQR, 11.5-59.7 minutes) in the control group, for a median difference of 16.7 minutes (95% CI, 7.7-31.0 minutes; P < .001). In the intervention group, 0 serious adverse events resulting in death occurred vs 2 (7%) in the control group.

Conclusions and Relevance  In this single-center preliminary study of patients undergoing elective noncardiac surgery, the use of a machine learning–derived early warning system compared with standard care resulted in less intraoperative hypotension. Further research with larger study populations in diverse settings is needed to understand the effect on additional patient outcomes and to fully assess safety and generalizability.

Trial Registration  ClinicalTrials.gov Identifier: NCT03376347

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