Blood PressureNCT03533205
Prediction of Hemodynamic Instability in Patients Undergoing Surgery
An observational study of Blood Pressure and Prediction Models, run by Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA).
Trial 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… Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Blood Pressure, Prediction Models, Machine Learning
- What is being tested?
- Hypotension Probability Indicator
- How many people
- 507 participants
- Where
- No sites listed yet
- Study dates
- 1 Apr 2015to 26 Apr 2018
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
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.
Who may be able to join
- all adult patients undergoing surgery
Who cannot join
- none
About this study
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.
- Being tested
- Hypotension Probability Indicator
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Hypotension Probability IndicatorDiagnostic test
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.
What is being measured
The primary outcome is the main question the study is designed to answer.
Sensitivity of the HPI algorithm
Sensitivity
Measured: three minutes prior to the hypotensive event
Specifity of the HPI algorithm
Specifity
Measured: three minutes prior to the hypotensive event
Secondary outcomes (24)
Predictive positive value of the HPI algorithm
Predictive positive value
Measured: one minute prior to the hypotensive event
Predictive positive value of the HPI algorithm
Predictive positive value
Measured: two minutes prior to the hypotensive event
Predictive positive value of the HPI algorithm
Predictive positive value
Measured: three minutes prior to the hypotensive event
Predictive positive value of the HPI algorithm
Predictive positive value
Measured: four minutes prior to the hypotensive event
Predictive positive value of the HPI algorithm
Predictive positive value
Measured: five minutes prior to the hypotensive event
Predictive positive value of the HPI algorithm
Predictive positive value
Measured: ten minutes prior to the hypotensive event
Predictive positive value of the HPI algorithm
Predictive positive value
Measured: 15 minutes prior to the hypotensive event
Negative predictive value of the HPI algorithm
Negative predictive value
Measured: one minute prior to the hypotensive event
Negative predictive value of the HPI algorithm
Negative predictive value
Measured: two minutes prior to the hypotensive event
Negative predictive value of the HPI algorithm
Negative predictive value
Measured: four minutes prior to the hypotensive event
Negative predictive value of the HPI algorithm
Negative predictive value
Measured: five minutes prior to the hypotensive event
Negative predictive value of the HPI algorithm
Negative predictive value
Measured: ten minutes prior to the hypotensive event
Negative predictive value of the HPI algorithm
Negative predictive value
Measured: 15 minutes prior to the hypotensive event
Time from HPI alarm to hypotensive event during surgery
Time from HPI alarm to hypotensive event, this can range from 0,1 min to a high number such as 30 or even 40 minutes.
Measured: 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)
Sensitivity of the HPI algorithm
Sensitivity
Measured: one minute prior to the hypotensive event
Sensitivity of the HPI algorithm
Sensitivity
Measured: two minutes prior to the hypotensive event
Sensitivity of the HPI algorithm
Sensitivity
Measured: five minutes prior to the hypotensive event
Sensitivity of the HPI algorithm
Sensitivity
Measured: ten minutes prior to the hypotensive event
Sensitivity of the HPI algorithm
Sensitivity
Measured: 15 minutes prior to the hypotensive event
Specifity of the HPI algorithm
Specifity
Measured: one minute prior to the hypotensive event
Specifity of the HPI algorithm
Specifity
Measured: two minutes prior to the hypotensive event
Specifity of the HPI algorithm
Specifity
Measured: five minutes prior to the hypotensive event
Specifity of the HPI algorithm
Specifity
Measured: ten minutes prior to the hypotensive event
Specifity of the HPI algorithm
Specifity
Measured: 15 minutes prior to the hypotensive event
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted23 May 2018
- Study start1 Apr 2015
- Primary completion1 Dec 2016
- Study completion26 Apr 2018
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Blood PressureStudies of the same condition, open and under way.
- More trials run by Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)The sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 507 participants (actual)
Sponsor and investigators
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, D.P.Veelo, Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT03533205
- Sponsor's protocol ID
- W15_080
- Status last verified
- May 2018
- First submitted
- 26 Apr 2018
- Last updated
- 23 May 2018
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Publications
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.
PubMed 33927105
Study at a glance
From the registry record, last updated by the sponsor on 23 May 2018. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Prediction of Hemodynamic Instability in Patients Undergoing Surgery
- ClinicalTrials.gov ID
- NCT03533205
- Acronym / sponsor ID
- W15_080
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 507 participants (actual)
- Lead sponsor
- Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)University, hospital or other
- Study start
- 1 Apr 2015 (actual)
- Primary completion
- 1 Dec 2016 (actual)
- Study completion
- 26 Apr 2018 (actual)
- First posted
- 23 May 2018 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (1 listed)
- all adult patients undergoing surgery
Key exclusion criteria (1 listed)
- none
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Hypotension Probability IndicatorThe 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. | Diagnostic test | – |
Primary outcome
Sensitivity of the HPI algorithm
Time frame: three minutes prior to the hypotensive event
Sensitivity
Specifity of the HPI algorithm
Time frame: three minutes prior to the hypotensive event
Specifity
24 secondary outcomes
Predictive positive value of the HPI algorithm
Time frame: one minute prior to the hypotensive event
Predictive positive value of the HPI algorithm
Time frame: two minutes prior to the hypotensive event
Predictive positive value of the HPI algorithm
Time frame: three minutes prior to the hypotensive event
Predictive positive value of the HPI algorithm
Time frame: four minutes prior to the hypotensive event
Predictive positive value of the HPI algorithm
Time frame: five minutes prior to the hypotensive event
Predictive positive value of the HPI algorithm
Time frame: ten minutes prior to the hypotensive event
Predictive positive value of the HPI algorithm
Time frame: 15 minutes prior to the hypotensive event
Negative predictive value of the HPI algorithm
Time frame: one minute prior to the hypotensive event
Negative predictive value of the HPI algorithm
Time frame: two minutes prior to the hypotensive event
Negative predictive value of the HPI algorithm
Time frame: four minutes prior to the hypotensive event
Negative predictive value of the HPI algorithm
Time frame: five minutes prior to the hypotensive event
Negative predictive value of the HPI algorithm
Time frame: ten minutes prior to the hypotensive event
Negative predictive value of the HPI algorithm
Time frame: 15 minutes prior to the hypotensive event
Time from HPI alarm to hypotensive event during surgery
Time frame: 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)
Sensitivity of the HPI algorithm
Time frame: one minute prior to the hypotensive event
Sensitivity of the HPI algorithm
Time frame: two minutes prior to the hypotensive event
Sensitivity of the HPI algorithm
Time frame: five minutes prior to the hypotensive event
Sensitivity of the HPI algorithm
Time frame: ten minutes prior to the hypotensive event
Sensitivity of the HPI algorithm
Time frame: 15 minutes prior to the hypotensive event
Specifity of the HPI algorithm
Time frame: one minute prior to the hypotensive event
Specifity of the HPI algorithm
Time frame: two minutes prior to the hypotensive event
Specifity of the HPI algorithm
Time frame: five minutes prior to the hypotensive event
Specifity of the HPI algorithm
Time frame: ten minutes prior to the hypotensive event
Specifity of the HPI algorithm
Time frame: 15 minutes prior to the hypotensive event
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 23 May 2018.
- Derived from this study: PubMed 33927105Wijnberge 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.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
Questions about this trial
What is this clinical trial studying?
An observational study of Blood Pressure and Prediction Models, run by Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA).
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 23 May 2018. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Sensitivity of the HPI algorithm (time frame: three minutes prior to the hypotensive event). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT03533205, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 23 May 2018; AzTrials last refreshed its copy on 6 Nov 2026.