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. 2022 Jul 1;39(7):574-581.
doi: 10.1097/EJA.0000000000001693. Epub 2022 Jun 10.

Prediction of intraoperative hypotension from the linear extrapolation of mean arterial pressure

Matthias Jacquet-Lagrèze  1 , Antoine Larue, Enrique Guilherme, Rémi Schweizer, Philippe Portran, Martin Ruste, Mathieu Gazon, Frédéric Aubrun, Jean-Luc Fellahi
Affiliations

Prediction of intraoperative hypotension from the linear extrapolation of mean arterial pressure

Matthias Jacquet-Lagrèze et al. Eur J Anaesthesiol. .

Abstract

Background: Hypotension prediction index (HPI) software is a proprietary machine learning-based algorithm used to predict intraoperative hypotension (IOH). HPI has shown superiority in predicting IOH when compared to the predictive value of changes in mean arterial pressure (ΔMAP) alone. However, the predictive value of ΔMAP alone, with no reference to the absolute level of MAP, is counterintuitive and poor at predicting IOH. A simple linear extrapolation of mean arterial pressure (LepMAP) is closer to the clinical approach.

Objectives: Our primary objective was to investigate whether LepMAP better predicts IOH than ΔMAP alone.

Design: Retrospective diagnostic accuracy study.

Setting: Two tertiary University Hospitals between May 2019 and December 2019.

Patients: A total of 83 adult patients undergoing high risk non-cardiac surgery.

Data sources: Arterial pressure data were automatically extracted from the anaesthesia data collection software (one value per minute). IOH was defined as MAP < 65 mmHg.

Analysis: Correlations for repeated measurements and the area under the curve (AUC) from receiver operating characteristics (ROC) were determined for the ability of LepMAP and ΔMAP to predict IOH at 1, 2 and 5 min before its occurrence (A-analysis, using the whole dataset). Data were also analysed after exclusion of MAP values between 65 and 75 mmHg (B-analysis).

Results: A total of 24 318 segments of ten minutes duration were analysed. In the A-analysis, ROC AUCs to predict IOH at 1, 2 and 5 min before its occurrence by LepMAP were 0.87 (95% confidence interval, CI, 0.86 to 0.88), 0.81 (95% CI, 0.79 to 0.83) and 0.69 (95% CI, 0.66 to 0.71) and for ΔMAP alone 0.59 (95% CI, 0.57 to 0.62), 0.61 (95% CI, 0.59 to 0.64), 0.57 (95% CI, 0.54 to 0.69), respectively. In the B analysis for LepMAP these were 0.97 (95% CI, 0.9 to 0.98), 0.93 (95% CI, 0.92 to 0.95) and 0.86 (95% CI, 0.84 to 0.88), respectively, and for ΔMAP alone 0.59 (95% CI, 0.53 to 0.58), 0.56 (95% CI, 0.54 to 0.59), 0.54 (95% CI, 0.51 to 0.57), respectively. LepMAP ROC AUCs were significantly higher than ΔMAP ROC AUCs in all cases.

Conclusions: LepMAP provides reliable real-time and continuous prediction of IOH 1 and 2 min before its occurrence. LepMAP offers better discrimination than ΔMAP at 1, 2 and 5 min before its occurrence. Future studies evaluating machine learning algorithms to predict IOH should be compared with LepMAP rather than ΔMAP.

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