The use of a machine-learning algorithm that predicts hypotension during surgery in combination with personalized treatment guidance: study protocol for a randomized clinical trial

M Wijnberge, J Schenk, L E Terwindt, M P Mulder, M W Hollmann, A P Vlaar, D P Veelo, B F Geerts, M Wijnberge, J Schenk, L E Terwindt, M P Mulder, M W Hollmann, A P Vlaar, D P Veelo, B F Geerts

Abstract

Background: Intraoperative hypotension is associated with increased morbidity and mortality. Current treatment is mostly reactive. The Hypotension Prediction Index (HPI) algorithm is able to predict hypotension minutes before the blood pressure actually decreases. Internal and external validation of this algorithm has shown good sensitivity and specificity. We hypothesize that the use of this algorithm in combination with a personalized treatment protocol will reduce the time weighted average (TWA) in hypotension during surgery spent in hypotension intraoperatively.

Methods/design: We aim to include 100 adult patients undergoing non-cardiac surgery with an anticipated duration of more than 2 h, necessitating the use of an arterial line, and an intraoperatively targeted mean arterial pressure (MAP) of > 65 mmHg. This study is divided into two parts; in phase A baseline TWA data from 40 patients will be collected prospectively. A device (HemoSphere) with HPI software will be connected but fully covered. Phase B is designed as a single-center, randomized controlled trial were 60 patients will be randomized with computer-generated blocks of four, six or eight, with an allocation ratio of 1:1. In the intervention arm the HemoSphere with HPI will be used to guide treatment; in the control arm the HemoSphere with HPI software will be connected but fully covered. The primary outcome is the TWA in hypotension during surgery.

Discussion: The aim of this trial is to explore whether the use of a machine-learning algorithm intraoperatively can result in less hypotension. To test this, the treating anesthesiologist will need to change treatment behavior from reactive to proactive.

Trial registration: This trial has been registered with the NIH, U.S. National Library of Medicine at ClinicalTrials.gov, ID: NCT03376347 . The trial was submitted on 4 November 2017 and accepted for registration on 18 December 2017.

Keywords: Anesthesiology; Artificial intelligence; Blood pressure; Hemodynamics; Perioperative care.

Conflict of interest statement

The Department of Anesthesiology of the Academic Medical Center (AMC) received financial support for this project from Edwards Lifesciences. DPV, APV, MW and BFG receive consultancy fees from Edwards Lifesciences.

Figures

Fig. 1
Fig. 1
Consort Flow diagram
Fig. 2
Fig. 2
HYPE personalized treatment guidance protocol. HPI=hypotension prediction index. MAP= mean arterial pressure. EaDyn= dynamic arterial elastance. SVR= systemic vascular resistance. SVV= stroke volume variation. SV=stroke volume. dP/dT= delta pressure/delta time, measure for left ventricular function
Fig. 3
Fig. 3
HemoSphere with HPI and secondary screen. P↓BP= probability of hypotension, this is a prediction ranging from 0-100%. MAP= mean arterial pressure. CO= cardiac output. SVR= systemic vascular resistance. PR= pulse rate. SV= stroke volume. SVV= stroke volume variation. dP/dt= delta pressure/delta time. Eadyn= dynamic arterial elastance
Fig. 4
Fig. 4
AUT and AAT calculations. a demonstrates the calculation of the area under (AUT) the curve used to calculate the TWA in hypotension. TWA= (depth hypotension below MAP 65 threshold in mmHg x time spent below MAP 65 threshold in minutes, the AUT) / total duration operation in minutes). b and 4c demonstrate the calculation area above the curve (AAT) used to calculate the TWA in hypertension and the TWA of HPI alarm
Fig. 5
Fig. 5
Schedule of enrollment, interventions and assessments. PACU Post Anesthesia Care Unit, ASA American Society of Anesthesiologists, TWA time-weighted average

References

    1. Weiser TG, Haynes AB, Molina G, et al. Estimate of the global volume of surgery in 2012: an assessment supporting improved health outcomes. Lancet. 2015;385(Suppl 2):S11. doi: 10.1016/s0140-6736(15)60806-6.
    1. Bijker JB, van Klei WA, Kappen TH, et al. Incidence of intraoperative hypotension as a function of the chosen definition: literature definitions applied to a retrospective cohort using automated data collection. Anesthesiology. 2007;107(2):213–220. doi: 10.1097/01.anes.0000270724.40897.8e.
    1. Tritapepe L. Hypotension during surgery for high risk patients: cause or consequence of pathology? Minerva Anestesiol. 2013;79(9):978–980.
    1. Harten JKJ. Perioperative optimisation. Scot Med J. 2004;49(1):6–9. doi: 10.1177/003693300404900102.
    1. van Waes JA, van Klei WA, Wijeysundera DN, et al. Association between intraoperative hypotension and myocardial injury after vascular surgery. Anesthesiology. 2015;124(1):35–44. doi: 10.1097/ALN.0000000000000922.
    1. Xu L, Yu C, Jiang J, et al. Major adverse cardiac events in elderly patients with coronary artery disease undergoing noncardiac surgery: a multicenter prospective study in China. Arch Gerontol Geriatr. 2015;61(3):503–509. doi: 10.1016/j.archger.2015.07.006.
    1. Sun LY, Wijeysundera DN, Tait GA, et al. Association of intraoperative hypotension with acute kidney injury after elective noncardiac surgery. Anesthesiology. 2015;123(3):515–523. doi: 10.1097/ALN.0000000000000765.
    1. Hallqvist L, Granath F, Huldt E, et al. Intraoperative hypotension is associated with acute kidney injury in noncardiac surgery: an observational study. Eur J Anaesthesiol. 2018;35(4):273–279. doi: 10.1097/eja.0000000000000735.
    1. Monk TG, Bronsert MR, Henderson WG, et al. Association between intraoperative hypotension and hypertension and 30-day postoperative mortality in noncardiac surgery. Anesthesiology. 2015;123(2):307–319. doi: 10.1097/ALN.0000000000000756.
    1. Bijker JB, van Klei WA, Vergouwe Y, et al. Intraoperative hypotension and 1-year mortality after noncardiac surgery. Anesthesiology. 2009;111(6):1217–1226. doi: 10.1097/ALN.0b013e3181c14930.
    1. Futier E, Lefrant JY, Guinot PG, et al. Effect of individualized vs standard blood pressure management strategies on postoperative organ dysfunction among high-risk patients undergoing major surgery: a randomized clinical trial. JAMA. 2017;318(14):1346–1357. doi: 10.1001/jama.2017.14172.
    1. Wesselink EM, Kappen TH, Torn HM, et al. Intraoperative hypotension and the risk of postoperative adverse outcomes: a systematic review. Br J Anaesth. 2018;121(4):706–721. doi: 10.1016/j.bja.2018.04.036.
    1. Hsieh JK, Dalton JE, Yang D, et al. The association between mild intraoperative hypotension and stroke in general surgery patients. Anesth Analg. 2016;123(4):933–939. doi: 10.1213/ane.0000000000001526.
    1. Salmasi V, Maheshwari K, Yang D, et al. Relationship between intraoperative hypotension, defined by either reduction from baseline or absolute thresholds, and acute kidney and myocardial injury after noncardiac surgery. Anesthesiology. 2017;126(1):47–65. doi: 10.1097/ALN.0000000000001432.
    1. Hatib F, Jian Z, Buddi S, et al. Machine-learning algorithm to predict hypotension based on high-fidelity arterial pressure waveform analysis. Anesthesiology. 2018;129(4):663–674. doi: 10.1097/aln.0000000000002300.
    1. Chan AW, Tetzlaff JM, Gotzsche PC, et al. SPIRIT 2013 explanation and elaboration: guidance for protocols of clinical trials. BMJ. 2013;346:e7586. doi: 10.1136/bmj.e7586.
    1. Castor EDC. Castor Electronic Data Capture. 2019. [online] Available at: . See; .
    1. Maheshwari K, Khanna S, Bajracharya GR, et al. A randomized trial of continuous noninvasive blood pressure monitoring during noncardiac surgery. Anesth Analg. 2018;127(2):424–431. doi: 10.1213/ane.0000000000003482.
    1. R Core Team. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2017. Available from . Accessed 24 July 2017.

Source: PubMed

Patrocinadores y Colaboradores

Condiciones médicas

Intervenciones de drogas

3
Suscribir