Artificial Intelligence - to Predict and Prevent Hypotension During Surgery

January 26, 2024 updated by: Region Stockholm

The goal of this medtech clinical trial is to develop and evaluate a machine learning algoritm to predict low blood pressure episodes during major surgery. The main questions it aims to answer are:

  • Could a novel method for cardiac output estimation through alterations in carbon dioxide improve the performance of a blood pressure based algoritm in order to predict low blood pressure episodes during major abdominal surgery?
  • Will the predictive performance of the algoritm improve with the addition of other patient specific data?
  • Do the estimated cardiac output and central venous saturation by the novel method agree with our invasive arterial pressure method for cardiac output, and samples via a central venous line, respectively? 300 participants will be anesthetized with total intravenous anesthesia and ventilated with the novel carbon dioxide based method, and arterial and central venous blood gases will be taken regularly throughout the operation. All physiological data will be stored for later analyses and development of the algoritm by machine learning methods. No other invasive interventions will be performed outside our standard clinical peroperative protocol.

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

Detailed Description

Every year, approximately 800,000 patients are operated on in Sweden. Of them, a relatively high proportion suffer from serious complications after surgery, such as heart and kidney damage, stroke and even death. There is a demonstrated connection between the above complications and blood pressure drops during surgery, which means that in any case some of the complications are potentially avoidable if the blood pressure is kept at a stable and adequate level based on each individual patient's conditions.

Unfortunately, episodes of a drop in blood pressure are difficult to predict with the standard monitoring methods available and, therefore unfortunately, often occur during surgery. At Karolinska University Hospital, we are now building a new system that, with the help of artificial intelligence, can sound the alarm even before the drop in blood pressure occurs. In the study, we collect and combine data from the standard monitoring during surgery; pulse, blood pressure, oxygenation, cardiac output, ECG, lab values from blood gases on patients undergoing abdominal surgery which is a group of patients who have a higher risk of suffering from postoperative complications. To this information, we will also add relevant data from a novel method for circulation monitoring based on variations in exhaled carbon dioxide. We will also record the reason why blood pressure has dropped, such as bleeding, drug impact, dehydration, with the aim that the AI algorithms should be able to distinguish between different causes of low blood pressure because they require different measures. The AI algorithms will initially be developed by collecting data from a large group of patients and then evaluating on a smaller group of other, representative patients

Data from our standard monitoring in connection with surgery are supplemented with estimates of cardiac output based on the capnodynamic method. The research subjects will be put to sleep and ventilated via a modified intensive care ventilator (servo-i(R) Getinge). This ventilator has a software for research use that is CE marked. The software modifies the ventilator breathing pattern by to add slightly extended pauses after three out of nine breaths. This breathing pattern results in a mild variation in the level of exhaled carbon dioxide of about 1 kPa. The ventilator has been used in several large animal studies and clinical studies, two of which were conducted by our research group. Because an intensive care ventilator will to be used, the patients are anesthetized with a so-called total intravenous anesthesia (TIVA) instead of gas anesthesia. This is different from our current routine in this type of surgery. However, TIVA is used in several other types of surgery and we are well versed in the method. At several hospitals in the country and abroad, TIVA is used as first-line method even in large cancer surgery of the abdomen because there is data that suggests that patients receives minor relapses (metastasis) in the aftermath. During the course of care, the events that are recorded as occurs in order to then be added to the material for qualitative analyzes. Examples of such events are: administration of drugs and fluids, as well as probable cause of hypotension; bleeding body position changes, etc. To be able to compare the capno method's calculations of mixed venous saturation, blood samples will be drawn from the central venous catheter every hour during surgery. Each sample contains about 2 ml of blood. In a five-hour operation, this corresponds to about 1 cl of blood. Simultaneously with the vein samples, arterial blood gases will be taken with the same blood volumes. These samples are taken approximately: Each/every two hours during surgery in clinical routine but in the study protocol they will be taken every hour in sync with the samples from the central venous catheter.

Also clinical outcome such as length of care and complications are saved so that the dose of hypotension can be linked to complications. What is described above are things that we measure and register in clinical routine and thus do not imply any further impact on the research subjects. Analysis of data is done afterwards by our researchers on our computers and servers. Through artificial intelligence (AI) and machine learning (ML) we will train AI algorithms with the goal of constructing Effective and accurate methods for predicting low blood pressure preventively. Because recorded data is used we can retrieve the outcome of the same data and compare our algorithms with what actually happened.

Study Type

Interventional

Enrollment (Estimated)

300

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Contact Backup

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

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Description

Inclusion Criteria:

patients scheduled for elective major abdominal surgery at Karolinska University Hospital.

Exclusion Criteria:

Cardiac arrytmias, such as atrial fibrillation Severe pulmonary diseases, including severe chronic obstructive pulmonary disease Patient unable to understand or speak swedish and thereby dificulties to give informed concent.

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

  • Primary Purpose: Supportive Care
  • Allocation: N/A
  • Interventional Model: Single Group Assignment
  • Masking: None (Open Label)

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Other: Capnodynamic method arm
Only one arm will be used since all patients will be ventilated using the novel capnodynamic method. The algoritm will then be developed by including or omitting the capnodynamic data in addition to the curve data from the arterial line.
Device: Capnodynamic method
All patients will be ventilated using the novel capnodynamic method, incorporated in a modified Maquet servo I ventilator. For this reason, all patients will be anesthetized using total intravenous anesthesia.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Accuracy of prediction of hypotension 5 minutes ahead of time using capnodynamics and continuous arterial blood pressure measurement.
Time Frame: 6-12 hours
Specified as the area under receiver operating characteristic curve (AUROC).
6-12 hours

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Agreement (precision, bias) for the capnodynamic method vs our standard perioperative method for cardiac output
Time Frame: 6-12 hours
RMSE of the capnodynamic method, based on modified capnography measurements, compared to the pulse contour method for cardiac output. Measured using Bland Altman statistics
6-12 hours
Agreement (precision, bias) for the capnodynamic method vs our standard perioperative method for central venous satuation.
Time Frame: 6-12 hours
RMSE of the capnodynamic method, based on modified capnography measurements, compared to the standard method for central venous saturation (samples from a central venous line) Measured using Bland Altman statistics
6-12 hours

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Region Stockholm

Collaborators

KTH Royal Institute of Technology
Getinge Group

Investigators

  • Principal Investigator: Greg Winski, Dr, Karolinska 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

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 (Estimated)

February 1, 2024

Primary Completion (Estimated)

December 1, 2026

Study Completion (Estimated)

December 1, 2026

Study Registration Dates

First Submitted

March 7, 2023

First Submitted That Met QC Criteria

January 26, 2024

First Posted (Actual)

February 2, 2024

Study Record Updates

Last Update Posted (Actual)

February 2, 2024

Last Update Submitted That Met QC Criteria

January 26, 2024

Last Verified

January 1, 2024

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • CIP_AI_hypotension_study_V01

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

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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