학술논문
Hypotension prediction index guided versus conventional goal directed therapy to reduce intraoperative hypotension during thoracic surgery: a randomized trial.
Document Type
Article
Author
Source
Subject
*LENGTH of stay in hospitals
*BLOOD gases analysis
*THORACIC surgery
*INTRAOPERATIVE care
*MACHINE learning
*SURGERY
*PATIENTS
*RISK assessment
*RANDOMIZED controlled trials
*COMPARATIVE studies
*ARTIFICIAL respiration
*TREATMENT effectiveness
*HOSPITAL mortality
*LACTATES
*DESCRIPTIVE statistics
*HYPOTENSION
*INTRAOPERATIVE monitoring
*STATISTICAL sampling
*HEMODYNAMICS
*ALGORITHMS
*DISEASE risk factors
PREVENTION of surgical complications
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Language
ISSN
1471-2253
Abstract
Purpose: Intraoperative hypotension is linked to increased incidence of perioperative adverse events such as myocardial and cerebrovascular infarction and acute kidney injury. Hypotension prediction index (HPI) is a novel machine learning guided algorithm which can predict hypotensive events using high fidelity analysis of pulse-wave contour. Goal of this trial is to determine whether use of HPI can reduce the number and duration of hypotensive events in patients undergoing major thoracic procedures. Methods: Thirty four patients undergoing esophageal or lung resection were randomized into 2 groups -"machine learning algorithm" (AcumenIQ) and "conventional pulse contour analysis" (Flotrac). Analyzed variables were occurrence, severity and duration of hypotensive events (defined as a period of at least one minute of MAP below 65 mmHg), hemodynamic parameters at 9 different timepoints interesting from a hemodynamics viewpoint and laboratory (serum lactate levels, arterial blood gas) and clinical outcomes (duration of mechanical ventilation, ICU and hospital stay, occurrence of adverse events and in-hospital and 28-day mortality). Results: Patients in the AcumenIQ group had significantly lower area below the hypotensive threshold (AUT, 2 vs 16.7 mmHg x minutes) and time-weighted AUT (TWA, 0.01 vs 0.08 mmHg). Also, there were less patients with hypotensive events and cumulative duration of hypotension in the AcumenIQ group. No significant difference between groups was found in terms of laboratory and clinical outcomes. Conclusions: Hemodynamic optimization guided by machine learning algorithm leads to a significant decrease in number and duration of hypotensive events compared to traditional goal directed therapy using pulse-contour analysis hemodynamic monitoring in patients undergoing major thoracic procedures. Further, larger studies are needed to determine true clinical utility of HPI guided hemodynamic monitoring. Trial registration: Date of first registration: 14/11/2022 Registration number: 04729481-3a96-4763-a9d5-23fc45fb722d [ABSTRACT FROM AUTHOR]