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.

Autor:	Wijnberge M; Department of Anesthesiology, Amsterdam UMC, Location AMC, Amsterdam, the Netherlands.; Department of Intensive Care, Amsterdam UMC, Location AMC, Amsterdam, the Netherlands., Geerts BF; Department of Anesthesiology, Amsterdam UMC, Location AMC, Amsterdam, the Netherlands., Hol L; Department of Anesthesiology, Amsterdam UMC, Location AMC, Amsterdam, the Netherlands., Lemmers N; Department of Anesthesiology, Amsterdam UMC, Location AMC, Amsterdam, the Netherlands., Mulder MP; Department of Anesthesiology, Amsterdam UMC, Location AMC, Amsterdam, the Netherlands.; Department of Technical Medicine, University of Twente, Enschede, the Netherlands., Berge P; Department of Anesthesiology, Amsterdam UMC, Location AMC, Amsterdam, the Netherlands., Schenk J; Department of Anesthesiology, Amsterdam UMC, Location AMC, Amsterdam, the Netherlands., Terwindt LE; Department of Anesthesiology, Amsterdam UMC, Location AMC, Amsterdam, the Netherlands., Hollmann MW; Department of Anesthesiology, Amsterdam UMC, Location AMC, Amsterdam, the Netherlands., Vlaar AP; Department of Intensive Care, Amsterdam UMC, Location AMC, Amsterdam, the Netherlands., Veelo DP; Department of Anesthesiology, Amsterdam UMC, Location AMC, Amsterdam, the Netherlands.
Jazyk:	angličtina
Zdroj:	JAMA [JAMA] 2020 Mar 17; Vol. 323 (11), pp. 1052-1060.
DOI:	10.1001/jama.2020.0592
Abstrakt:	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.
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu