Hypotension prediction index guided versus conventional goal directed therapy to reduce intraoperative hypotension during thoracic surgery: a randomized trial.
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 h...
| Publicado en: | BMC Anesthesiology Vol. 23; no. 1; pp. 1 - 11 |
|---|---|
| Autores principales: | , , , , , , |
| Formato: | research tables/charts randomized controlled trial Journal Article |
| Publicado: |
BioMed Central
3/30/2023
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=162801632&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 162801632 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14712253 1CH3 jtl: BMC Anesthesiology issn: 14712253 maglogo: N pubinfo: dt: 3/30/2023 vid: 23 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 162801632 162801632 162801632 10.1186/s12871-023-02069-1 162801632 ppf: 1 ppct: 10 formats: tig: atl: Hypotension prediction index guided versus conventional goal directed therapy to reduce intraoperative hypotension during thoracic surgery: a randomized trial. aug: au: Šribar, Andrej Jurinjak, Irena Sokolović Almahariq, Hani Bandić, Ivan Matošević, Jelena Pejić, Josip Peršec, Jasminka affil: Clinical Department of Anesthesiology, Reanimatology and Intensive Care Medicine, University Hospital Dubrava, Avenija Gojka Šuška 6, 10000, Zagreb, Croatia sug: subj: Thoracic Surgery Adverse Effects Hypotension Prevention and Control Hypotension Risk Factors Intraoperative Complications Prevention and Control Intraoperative Monitoring Methods Machine Learning Risk Assessment Intraoperative Care Human Randomized Controlled Trials Random Assignment Comparative Studies Respiration, Artificial Treatment Outcomes Surgical Patients Lactates Blood Blood Gas Analysis Length of Stay Hospital Mortality Hemodynamics Algorithms Descriptive Statistics ab: 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 pubtype: Academic Journal doctype: research tables/charts randomized controlled trial Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|