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Clinical performance of a machine-learning algorithm to predict intra-operative hypotension with noninvasive arterial pressure waveforms: A cohort study by Marije Wijnberge; Björn J.P. van der Ster; Bart F. Geerts; Friso de Beer; Charlotte Beurskens; Dina Emal; Markus W. Hollmann; Alexander P.J. Vlaar; Denise P. Veelo is a Medicine article available to read on EtoBox.

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## BACKGROUND Intra-operative hypotension is associated with adverse postoperative outcomes. A machine-learning-derived algorithm developed to predict hypotension based on arterial blood pressure (ABP) waveforms significantly reduced intra-operative hypotension. The algorithm calculates the likelihood of hypotension occurring within minutes, expressed as the Hypotension Prediction Index (HPI) which ranges from 0 to 100. Currently, HPI is only available for patients monitored with invasive ABP, which is restricted to high-risk procedures and patients. In this study, the performance of HPI, employing noninvasive continuous ABP measurements, is assessed. ## OBJECTIVES The first aim was to compare the performance of the HPI algorithm, using noninvasive versus invasive ABP measurements, at a mathematically optimal HPI alarm threshold (Youden index). The second aim was to assess the performance of the algorithm using a HPI alarm threshold of 85 that is currently used in clinical trials. Hypotension was defined as a mean arterial pressure (MAP) below 65 mmHg for at least 1 min. The predictive performance of the algorithm at different HPI alarm thresholds (75 and 95) was studied. ## DESIGN

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Author

Marije Wijnberge; Björn J.P. van der Ster; Bart F. Geerts; Friso de Beer; Charlotte Beurskens; Dina Emal; Markus W. Hollmann; Alexander P.J. Vlaar; Denise P. Veelo

Publisher

Ovid Technologies (Wolters Kluwer Health)

Published

2021

Language

EN

Field

Medicine (Health Sciences)