- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT05117151
Ability of Hypotension Prediction Index to Predict Hypotension Events in Thoracic Surgery
April 28, 2025 updated by: National Taiwan University Hospital
Ability of Hypotension Prediction Index to Predict Future Hypotension Events in Each Phase of Thoracic Surgery: an Observational Pilot Study
Investigators design this prospective observational study to exam the ability of hypotension prediction index in predicting intraoperative hypotension events in each phase of thoracic surgery
Study Overview
Status
Completed
Conditions
Intervention / Treatment
Detailed Description
Hypotension prediction index is a novel introduced monitoring-tool that aims to predict hypotension events before their occurence, and has been reported to have superior predictive ability than the commonly measured perioperative hemodynamic variables.
To date, there is no information regarding its ability to predict hypotension events in each phase of thoracic surgery.
Study Type
Observational
Enrollment (Actual)
40
Contacts and Locations
This section provides the contact details for those conducting the study, and information on where this study is being conducted.
Study Locations
-
-
-
Taipei, Taiwan, 100
- National Taiwan University Hospital
-
-
Participation Criteria
Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.
Eligibility Criteria
Ages Eligible for Study
20 years and older (Adult, Older Adult)
Accepts Healthy Volunteers
No
Sampling Method
Non-Probability Sample
Study Population
adults undergoing scheduled VATS surgery
Description
Inclusion Criteria:
- adults undergoing scheduled VATS surgery
Exclusion Criteria:
- pregnant women
- patients in intensive care units
- patients with the underlying disease including respiratory failure(FEV1/FVC < 70 % and FEV1 < 50%), heart failure(NYHA score =III、IV), kidney failure(eGFR< 60 ml.min-1.1.73m-2), liver failure
- patients with ongoing infection
Study Plan
This section provides details of the study plan, including how the study is designed and what the study is measuring.
How is the study designed?
Design Details
- Observational Models: Cohort
- Time Perspectives: Prospective
Cohorts and Interventions
Group / Cohort |
Intervention / Treatment |
|---|---|
|
Observation
monitor intraoperative hypotension prediction index as well as hemodynamic variables to exam the ability in predicting hypotension events of each variable
|
monitor intraoperative hypotension prediction index as well as hemodynamic variables to exam the ability in predicting hypotension events of each variable
|
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
ability of hypotension prediction index in predicting hypotension events
Time Frame: 3 hours
|
to calculate the sensitivity/specificity and AUROC of hypotension prediction index in predicting hypotension events
|
3 hours
|
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
ability of dynamic parameters in predicting hypotension events
Time Frame: 3 hours
|
to calculate the sensitivity/specificity and AUROC of dynamic parameters in predicting hypotension events
|
3 hours
|
Collaborators and Investigators
This is where you will find people and organizations involved with this study.
Investigators
- Study Chair: Chun-Yu Wu, Ph.D., National Taiwan University Hospital
Publications and helpful links
The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.
General Publications
- Hatib F, Jian Z, Buddi S, Lee C, Settels J, Sibert K, Rinehart J, Cannesson M. Machine-learning Algorithm to Predict Hypotension Based on High-fidelity Arterial Pressure Waveform Analysis. Anesthesiology. 2018 Oct;129(4):663-674. doi: 10.1097/ALN.0000000000002300.
- Wijnberge M, Geerts BF, Hol L, Lemmers N, Mulder MP, Berge P, Schenk J, Terwindt LE, Hollmann MW, Vlaar AP, Veelo DP. Effect of a Machine Learning-Derived Early Warning System for Intraoperative Hypotension vs Standard Care on Depth and Duration of Intraoperative Hypotension During Elective Noncardiac Surgery: The HYPE Randomized Clinical Trial. JAMA. 2020 Mar 17;323(11):1052-1060. doi: 10.1001/jama.2020.0592.
- Monk TG, Bronsert MR, Henderson WG, Mangione MP, Sum-Ping ST, Bentt DR, Nguyen JD, Richman JS, Meguid RA, Hammermeister KE. Association between Intraoperative Hypotension and Hypertension and 30-day Postoperative Mortality in Noncardiac Surgery. Anesthesiology. 2015 Aug;123(2):307-19. doi: 10.1097/ALN.0000000000000756. Erratum In: Anesthesiology. 2016 Mar;124(3):741-2. doi: 10.1097/01.anes.0000479922.33134.89.
Study record dates
These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.
Study Major Dates
Study Start (Actual)
March 23, 2022
Primary Completion (Actual)
August 2, 2023
Study Completion (Actual)
August 2, 2023
Study Registration Dates
First Submitted
October 31, 2021
First Submitted That Met QC Criteria
October 31, 2021
First Posted (Actual)
November 11, 2021
Study Record Updates
Last Update Posted (Actual)
May 1, 2025
Last Update Submitted That Met QC Criteria
April 28, 2025
Last Verified
April 1, 2025
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
- 202109061RIND
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
UNDECIDED
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
No
Studies a U.S. FDA-regulated device product
No
product manufactured in and exported from the U.S.
No
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
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