Literature DB >> 36233419

Prediction and Prevention of Intraoperative Hypotension with the Hypotension Prediction Index: A Narrative Review.

Tatiana Sidiropoulou1, Marina Tsoumpa1, Panayota Griva1, Vasiliki Galarioti1, Paraskevi Matsota1.   

Abstract

Intraoperative hypotension is common and has been associated with adverse events. Although association does not imply causation, predicting and preventing hypotension may improve postoperative outcomes. This review summarizes current evidence on the development and validation of an artificial intelligence predictive algorithm, the Hypotension Prediction (HPI) (formerly known as the Hypotension Probability Indicator). This machine learning model can arguably predict hypotension up to 15 min before its occurrence. Several validation studies, retrospective cohorts, as well as a few prospective randomized trials, have been published in the last years, reporting promising results. Larger trials are needed to definitively assess the usefulness of this algorithm in optimizing postoperative outcomes.

Entities:  

Keywords:  hypotension prediction index; intraoperative hypotension; machine learning

Year:  2022        PMID: 36233419      PMCID: PMC9571689          DOI: 10.3390/jcm11195551

Source DB:  PubMed          Journal:  J Clin Med        ISSN: 2077-0383            Impact factor:   4.964


  42 in total

1.  Hypotension during surgery for high risk patients: cause or consequence of pathology?

Authors:  L Tritapepe
Journal:  Minerva Anestesiol       Date:  2013-05-29       Impact factor: 3.051

2.  Intraoperative hypotension and the risk of postoperative adverse outcomes: a systematic review.

Authors:  E M Wesselink; T H Kappen; H M Torn; A J C Slooter; W A van Klei
Journal:  Br J Anaesth       Date:  2018-06-20       Impact factor: 9.166

3.  Anesthesiology, automation, and artificial intelligence.

Authors:  John C Alexander; Girish P Joshi
Journal:  Proc (Bayl Univ Med Cent)       Date:  2017-12-05

4.  Relationship between Intraoperative Hypotension, Defined by Either Reduction from Baseline or Absolute Thresholds, and Acute Kidney and Myocardial Injury after Noncardiac Surgery: A Retrospective Cohort Analysis.

Authors:  Vafi Salmasi; Kamal Maheshwari; Dongsheng Yang; Edward J Mascha; Asha Singh; Daniel I Sessler; Andrea Kurz
Journal:  Anesthesiology       Date:  2017-01       Impact factor: 7.892

5.  Development and Validation of a Deep Neural Network Model for Prediction of Postoperative In-hospital Mortality.

Authors:  Christine K Lee; Ira Hofer; Eilon Gabel; Pierre Baldi; Maxime Cannesson
Journal:  Anesthesiology       Date:  2018-10       Impact factor: 7.892

6.  The Promise and Challenges of Predictive Analytics in Perioperative Care.

Authors:  Duminda N Wijeysundera; Daniel I McIsaac; Martin J London
Journal:  Anesthesiology       Date:  2022-09-01       Impact factor: 8.986

7.  Incidence of intraoperative hypotension as a function of the chosen definition: literature definitions applied to a retrospective cohort using automated data collection.

Authors:  Jilles B Bijker; Wilton A van Klei; Teus H Kappen; Leo van Wolfswinkel; Karel G M Moons; Cor J Kalkman
Journal:  Anesthesiology       Date:  2007-08       Impact factor: 7.892

8.  Effect of Individualized vs Standard Blood Pressure Management Strategies on Postoperative Organ Dysfunction Among High-Risk Patients Undergoing Major Surgery: A Randomized Clinical Trial.

Authors:  Emmanuel Futier; Jean-Yves Lefrant; Pierre-Gregoire Guinot; Thomas Godet; Emmanuel Lorne; Philippe Cuvillon; Sebastien Bertran; Marc Leone; Bruno Pastene; Vincent Piriou; Serge Molliex; Jacques Albanese; Jean-Michel Julia; Benoit Tavernier; Etienne Imhoff; Jean-Etienne Bazin; Jean-Michel Constantin; Bruno Pereira; Samir Jaber
Journal:  JAMA       Date:  2017-10-10       Impact factor: 56.272

9.  Prediction of postoperative outcomes using intraoperative hemodynamic monitoring data.

Authors:  Varesh Prasad; Maria Guerrisi; Mario Dauri; Filadelfo Coniglione; Giuseppe Tisone; Elisa De Carolis; Annagrazia Cillis; Antonio Canichella; Nicola Toschi; Thomas Heldt
Journal:  Sci Rep       Date:  2017-11-27       Impact factor: 4.379

10.  Use of Machine Learning to Develop and Evaluate Models Using Preoperative and Intraoperative Data to Identify Risks of Postoperative Complications.

Authors:  Bing Xue; Dingwen Li; Chenyang Lu; Christopher R King; Troy Wildes; Michael S Avidan; Thomas Kannampallil; Joanna Abraham
Journal:  JAMA Netw Open       Date:  2021-03-01
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