NCT03533205

Prediction of Hemodynamic Instability in Patients Undergoing Surgery

• Status: Completed
• Phase: —
• Study type: Observational
• Enrollment: 507 (actual)
• Sponsor: Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA) · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Accepted

Summary

Intraoperative hypotension occurs often and is associated with adverse patient outcomes such as stroke, myocardial infarction and renal injury. The aim of this study was to test the accuracy of a physiology-based machine-learning algorithm using continuous non-invasive measurement of the blood pressure waveform with the Nexfin® finger cuff during surgery.

Conditions

• Blood Pressure
• Prediction Models
• Machine Learning
• Hemodynamic Instability

Interventions

Type	Name	Description
DIAGNOSTIC_TEST	Hypotension Probability Indicator	The accurary of the Hypotension Probability Indicator (HPI) is tested in the created offline database. This means data was prospectively collected but the HPI algorithm was not tested prospectively but after collection in the offline database.

Timeline

• Start date: 2015-04-01
• Primary completion: 2016-12-01
• Completion: 2018-04-26
• First posted: 2018-05-23
• Last updated: 2018-05-23