Indian Journal of Anaesthesia  
About us | Editorial board | Search | Ahead of print | Current Issue | Past Issues | Instructions
Home | Login  | Users Online: 992  Print this pageEmail this pageSmall font sizeDefault font sizeIncrease font size    

Year : 2019  |  Volume : 63  |  Issue : 11  |  Page : 877-885

Intraoperative hypotension and its prediction

Department of Anesthesiology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands

Correspondence Address:
Prof. Thomas W L Scheeren
Department of Anesthesiology, University Medical Center Groningen, 9700RB Groningen, Groningen, PO Box - 30.001
Login to access the Email id

Source of Support: None, Conflict of Interest: None

DOI: 10.4103/ija.IJA_624_19

Rights and Permissions

Intraoperative hypotension (IOH) very commonly accompanies general anaesthesia in patients undergoing major surgical procedures. The development of IOH is unwanted, since it is associated with adverse outcomes such as acute kidney injury and myocardial injury, stroke and mortality. Although the definition of IOH is variable, harm starts to occur below a mean arterial pressure (MAP) threshold of 65 mmHg. The odds of adverse outcome increase for increasing duration and/or magnitude of IOH below this threshold, and even short periods of IOH seem to be associated with adverse outcomes. Therefore, reducing the hypotensive burden by predicting and preventing IOH through proactive appropriate treatment may potentially improve patient outcome. In this review article, we summarise the current state of the prediction of IOH by the use of so-called machine-learning algorithms. Machine-learning algorithms that use high-fidelity data from the arterial pressure waveform, may be used to reveal 'traits' that are unseen by the human eye and are associated with the later development of IOH. These algorithms can use large datasets for 'training', and can subsequently be used by clinicians for haemodynamic monitoring and guiding therapy. A first clinically available application, the hypotension prediction index (HPI), is aimed to predict an impending hypotensive event, and additionally, to guide appropriate treatment by calculated secondary variables to asses preload (dynamic preload variables), contractility (dP/dtmax), and afterload (dynamic arterial elastance, Eadyn). In this narrative review, we summarise the current state of the prediction of hypotension using such novel, automated algorithms and we will highlight HPI and the secondary variables provided to identify the probable origin of the (impending) hypotensive event.

Print this article     Email this article
 Next article
 Previous article
 Table of Contents

 Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
 Citation Manager
 Access Statistics
 Reader Comments
 Email Alert *
 Add to My List *
 * Requires registration (Free)

 Article Access Statistics
    PDF Downloaded801    
    Comments [Add]    

Recommend this journal