Intraoperative hypotension and its prediction

Abstract

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/dt_max), and afterload (dynamic arterial elastance, Ea_dyn). 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.

Keywords: Blood pressure; hemodynamic monitoring; hypotension prediction index; machine learning; predictive analysis.

Figure 1

Graph showing changes in mean arterial pressure over time before, during and after the onset of intraoperative hypotension (IOH), when conventional haemodynamic monitoring is applied. Usually, reactive therapy is applied (red dot) after hypotension has occurred. Yet, if hypotension were predicted in the respective timeframe (e.g., by using the hypotension prediction index), it may have been prevented (green dotted line) by proactive treatment

Figure 2

Screenshot of the “secondary screen” that is shown in case HPI exceeds 85. Here, a decision tree is provided in order to treat the underlying cause of (impending) hypotension, either by optimising preload (volume administration), by optimising cardiac contractility (inotropic support) or by optimising afterload (administration of vasopressors). These factors are reflected either by stroke volume variation (SVV), by dP/dt_max, or by dynamic arterial elastastance (Eadyn). Additionally, given is cardiac output (CO), systemic vascular resistance (SVR), pulse rate (PR) and stroke volume (SV)