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

Abstract

IMPORTANCEIntraoperative 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 MEASURESThe 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,. 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 RELEVANCEIn 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.

Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI Extension

“…Explanation: Describe the intended use of the AI intervention in the trial report title and/or abstract. This should describe the purpose of the AI intervention and the disease context 2644. Some AI interventions may have multiple intended uses or the intended use may evolve over time.…”

“…It has been recognised that most recent AI studies are inadequately reported and existing reporting guidelines do not fully cover potential sources of bias specific to AI systems 25. The welcome emergence of randomised controlled trials (RCTs) seeking to evaluate newer interventions based on, or including, an AI component (hereafter “AI interventions”)23262728293031 has similarly been met with concerns about the design and reporting 25323334. This has highlighted the need to provide reporting guidance that is “fit-for-purpose” in this domain.…”

Automated Blood Pressure Control

“…Goal-directed hemodynamic therapy based on HPI reduced the incidence, severity, and duration of intraoperative hypotension compared with the control groups. In a small single-center preliminary randomized trial including 68 adult patients scheduled for elective noncardiac surgery under general anesthesia, Wijnberge et al 88 showed that the use of the HPI in combination with a hemodynamic management protocol reduced the time-weighted average below a MAP of 65 mm Hg (i.e., the area under a MAP of 65 mm Hg divided by the duration of surgery) compared with standard care. In the future, the optimal hypotension threshold per patient might be determined using predictive analytics (machine learning), further personalizing hemodynamic treatment.…”

A Prediction Model using Machine-Learning Algorithm for Assessing&nbsp;Optimal Intrathecal Hyperbaric&nbsp;Bupivacaine Dose during Cesarean Section

“…8 We further established an elemental regression equation between parturient vertebral column length, abdominal girth, and 0.5% hyperbaric intrathecal bupivacaine volume for T5 block level [9]. 9 However, the sample size in these studies was relatively small and the accuracy of the regression equation needed further verification [8,9] In recent years, there has been an advance in machine-learning algorithms in a number of fields including anesthesiology, which allowed large amounts of data for development of robust predictive analytics [10][11][12][13]. These were used to predict, inter alia, postinduction hypotension [13].…”

“…These were used to predict, inter alia, postinduction hypotension [13]. intraoperative hypotension [11], and length of hospital stay [14]. In our hospital, more than 1000 parturient women undergo cesarean section under spinal anesthesia annually.…”