Prediction of Hemodynamic Instability in Patients Undergoing Surgery
NCT ID: NCT03533205
Last Updated: 2018-05-23
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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COMPLETED
507 participants
OBSERVATIONAL
2015-04-01
2018-04-26
Brief Summary
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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.
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Detailed Description
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Conditions
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Study Design
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COHORT
PROSPECTIVE
Interventions
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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.
Eligibility Criteria
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Inclusion Criteria
Exclusion Criteria
18 Years
ALL
Yes
Sponsors
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Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)
OTHER
Responsible Party
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D.P.Veelo
MD PhD
References
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Wijnberge M, van der Ster BJP, Geerts BF, de Beer F, Beurskens C, Emal D, Hollmann MW, Vlaar APJ, Veelo DP. Clinical performance of a machine-learning algorithm to predict intra-operative hypotension with noninvasive arterial pressure waveforms: A cohort study. Eur J Anaesthesiol. 2021 Jun 1;38(6):609-615. doi: 10.1097/EJA.0000000000001521.
Other Identifiers
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W15_080
Identifier Type: -
Identifier Source: org_study_id
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