Clinical trial NCT03533205: Prediction of Hemodynamic Instability in Patients Undergoing Surgery
Official title: Prediction of Hemodynamic Instability in Patients Undergoing Surgery
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Blood Pressure
Prediction Models
Machine Learning
Hemodynamic Instability
Lead sponsor: Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)
Sponsor class: OTHER
First posted: 2018-05-23
Enrollment: 507
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: All adult patients undergoing surgery in the AMC were included in the study. Subjects were only excluded when technical problems or strong local vasoconstriction (i.e., cold fingers) prevented the Nexfin® non-invasive blood pressure finger cuff measurement. Subjects were not excluded for any other reason besides technical failure.

Brief 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.


Eligibility criteria: Inclusion Criteria:

* all adult patients undergoing surgery

Exclusion Criteria:

* none

Primary outcomes:
measure: Sensitivity of the HPI algorithm
timeFrame: three minutes prior to the hypotensive event
measure: Specifity of the HPI algorithm
timeFrame: three minutes prior to the hypotensive event
Secondary outcomes:
measure: Predictive positive value of the HPI algorithm
timeFrame: one minute prior to the hypotensive event
measure: Predictive positive value of the HPI algorithm
timeFrame: two minutes prior to the hypotensive event
measure: Predictive positive value of the HPI algorithm
timeFrame: three minutes prior to the hypotensive event
measure: Predictive positive value of the HPI algorithm
timeFrame: four minutes prior to the hypotensive event
measure: Predictive positive value of the HPI algorithm
timeFrame: five minutes prior to the hypotensive event
measure: Predictive positive value of the HPI algorithm
timeFrame: ten minutes prior to the hypotensive event
measure: Predictive positive value of the HPI algorithm
timeFrame: 15 minutes prior to the hypotensive event
measure: Negative predictive value of the HPI algorithm
timeFrame: one minute prior to the hypotensive event
measure: Negative predictive value of the HPI algorithm
timeFrame: two minutes prior to the hypotensive event
measure: Negative predictive value of the HPI algorithm
timeFrame: four minutes prior to the hypotensive event
measure: Negative predictive value of the HPI algorithm
timeFrame: five minutes prior to the hypotensive event
measure: Negative predictive value of the HPI algorithm
timeFrame: ten minutes prior to the hypotensive event
measure: Negative predictive value of the HPI algorithm
timeFrame: 15 minutes prior to the hypotensive event
measure: Time from HPI alarm to hypotensive event during surgery
timeFrame: From the onset of the HPI alarm to the hypotensive event during surgery, this is in minutes. (this can range from 0,1 min to a high number such as 30 or even 40 minutes)
measure: Sensitivity of the HPI algorithm
timeFrame: one minute prior to the hypotensive event
measure: Sensitivity of the HPI algorithm
timeFrame: two minutes prior to the hypotensive event
measure: Sensitivity of the HPI algorithm
timeFrame: five minutes prior to the hypotensive event
measure: Sensitivity of the HPI algorithm
timeFrame: ten minutes prior to the hypotensive event
measure: Sensitivity of the HPI algorithm
timeFrame: 15 minutes prior to the hypotensive event
measure: Specifity of the HPI algorithm
timeFrame: one minute prior to the hypotensive event
measure: Specifity of the HPI algorithm
timeFrame: two minutes prior to the hypotensive event
measure: Specifity of the HPI algorithm
timeFrame: five minutes prior to the hypotensive event
measure: Specifity of the HPI algorithm
timeFrame: ten minutes prior to the hypotensive event
measure: Specifity of the HPI algorithm
timeFrame: 15 minutes prior to the hypotensive event
Other outcomes:
Locations:
Study officials:
References:
citation: 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.