Monitoring, IntraoperativeNCT06240234
Artificial Intelligence - to Predict and Prevent Hypotension During Surgery
A study testing Capnodynamic method in people with Monitoring, Intraoperative, run by Region Stockholm.
Trial summary
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… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Monitoring, Intraoperative
- What is being tested?
- Capnodynamic method
- Type of treatment
- Injection or infusion, Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 300 participants
- Where
- No sites listed yet
- Study dates
- Feb 2024to Dec 2026
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
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
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- patients scheduled for elective major abdominal surgery at Karolinska University Hospital.
Who cannot join
- 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.
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
About this study
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.
Read the 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.
- Conditions
- Monitoring, Intraoperative
- Being tested
- Capnodynamic method
- Keywords
- Hypotension, Artificial intelligence, Capnodynamics
- Medical subject headings
- Hypotension
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Capnodynamic methodDevice
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.
| Study group | Role | What they receive |
|---|---|---|
| Capnodynamic method arm | Other | 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. Receives: Device: Capnodynamic method |
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy of prediction of hypotension 5 minutes ahead of time using capnodynamics and continuous arterial blood pressure measurement.
Specified as the area under receiver operating characteristic curve (AUROC).
Measured: 6-12 hours
Secondary outcomes (2)
Agreement (precision, bias) for the capnodynamic method vs our standard perioperative method for cardiac output
RMSE of the capnodynamic method, based on modified capnography measurements, compared to the pulse contour method for cardiac output. Measured using Bland Altman statistics
Measured: 6-12 hours
Agreement (precision, bias) for the capnodynamic method vs our standard perioperative method for central venous satuation.
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
Measured: 6-12 hours
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted2 Feb 2024
- Study startFeb 2024 (estimated)
- Primary completionDec 2026 (estimated)
- Study completionDec 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (9)Papers the sponsor has linked to this study.
- Other trials for Monitoring, IntraoperativeStudies of the same condition, open and under way.
- More trials run by Region StockholmThe 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
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Supportive care
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 300 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Region Stockholm
- Sponsor type
- Other government
- Collaborators
- KTH Royal Institute of Technology, Getinge Group
- Responsible party
- Sponsor
- Investigators
- Greg Winski, Dr, Principal investigator, Karolinska University Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06240234
- Sponsor's protocol ID
- CIP_AI_hypotension_study_V01
- Status last verified
- Jan 2024
- First submitted
- 7 Mar 2023
- Last updated
- 2 Feb 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Chong MA, Wang Y, Berbenetz NM, McConachie I. Does goal-directed haemodynamic and fluid therapy improve peri-operative outcomes?: A systematic review and meta-analysis. Eur J Anaesthesiol. 2018 Jul;35(7):469-483. doi: 10.1097/EJA.0000000000000778.
PubMed 29369117Hamilton MA, Cecconi M, Rhodes A. A systematic review and meta-analysis on the use of preemptive hemodynamic intervention to improve postoperative outcomes in moderate and high-risk surgical patients. Anesth Analg. 2011 Jun;112(6):1392-402. doi: 10.1213/ANE.0b013e3181eeaae5. Epub 2010 Oct 21.
PubMed 20966436Sun Y, Chai F, Pan C, Romeiser JL, Gan TJ. Effect of perioperative goal-directed hemodynamic therapy on postoperative recovery following major abdominal surgery-a systematic review and meta-analysis of randomized controlled trials. Crit Care. 2017 Jun 12;21(1):141. doi: 10.1186/s13054-017-1728-8.
PubMed 28602158Yuan J, Sun Y, Pan C, Li T. Goal-directed fluid therapy for reducing risk of surgical site infections following abdominal surgery - A systematic review and meta-analysis of randomized controlled trials. Int J Surg. 2017 Mar;39:74-87. doi: 10.1016/j.ijsu.2017.01.081. Epub 2017 Jan 23.
PubMed 28126672Wijnberge M, Schenk J, Bulle E, Vlaar AP, Maheshwari K, Hollmann MW, Binnekade JM, Geerts BF, Veelo DP. Association of intraoperative hypotension with postoperative morbidity and mortality: systematic review and meta-analysis. BJS Open. 2021 Jan 8;5(1):zraa018. doi: 10.1093/bjsopen/zraa018.
PubMed 33609377Hallsjo Sander C, Hallback M, Wallin M, Emtell P, Oldner A, Bjorne H. Novel continuous capnodynamic method for cardiac output assessment during mechanical ventilation. Br J Anaesth. 2014 May;112(5):824-31. doi: 10.1093/bja/aet486. Epub 2014 Feb 18.
PubMed 24554544Sigmundsson TS, Ohman T, Hallback M, Redondo E, Sipmann FS, Wallin M, Oldner A, Hallsjo Sander C, Bjorne H. Performance of a capnodynamic method estimating effective pulmonary blood flow during transient and sustained hypercapnia. J Clin Monit Comput. 2018 Apr;32(2):311-319. doi: 10.1007/s10877-017-0021-3. Epub 2017 May 11.
PubMed 28497180Sigmundsson TS, Ohman T, Hallback M, Suarez-Sipmann F, Wallin M, Oldner A, Hallsjo-Sander C, Bjorne H. Comparison between capnodynamic and thermodilution method for cardiac output monitoring during major abdominal surgery: An observational study. Eur J Anaesthesiol. 2021 Dec 1;38(12):1242-1252. doi: 10.1097/EJA.0000000000001566.
PubMed 34155171Hallsjo Sander C, Hallback M, Suarez Sipmann F, Wallin M, Oldner A, Bjorne H. A novel continuous capnodynamic method for cardiac output assessment in a porcine model of lung lavage. Acta Anaesthesiol Scand. 2015 Sep;59(8):1022-31. doi: 10.1111/aas.12559. Epub 2015 Jun 4.
PubMed 26041115
Study at a glance
From the registry record, last updated by the sponsor on 2 Feb 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence - to Predict and Prevent Hypotension During Surgery
- ClinicalTrials.gov ID
- NCT06240234
- Acronym / sponsor ID
- CIP_AI_hypotension_study_V01
- Conditions
- Monitoring, Intraoperative
- Keywords
- Hypotension, Artificial intelligence, Capnodynamics
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 300 participants (estimated)
- Lead sponsor
- Region StockholmOther government
- Collaborators
- KTH Royal Institute of Technology, Getinge Group
- Study start
- Feb 2024 (estimated)
- Primary completion
- Dec 2026 (estimated)
- Study completion
- Dec 2026 (estimated)
- First posted
- 2 Feb 2024 (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.
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.
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 design
- Primary purpose
- Supportive care
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Capnodynamic method arm | Other | Device: Capnodynamic methodOnly 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (1 listed)
- patients scheduled for elective major abdominal surgery at Karolinska University Hospital.
Key exclusion criteria (1 listed)
- 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.
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 |
|---|---|---|
| Capnodynamic methodAll 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. | Device | Capnodynamic method arm |
Primary outcome
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).
2 secondary outcomes
Agreement (precision, bias) for the capnodynamic method vs our standard perioperative method for cardiac output
Time frame: 6-12 hours
Agreement (precision, bias) for the capnodynamic method vs our standard perioperative method for central venous satuation.
Time frame: 6-12 hours
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 2 Feb 2024.
- Background: PubMed 29369117Chong MA, Wang Y, Berbenetz NM, McConachie I. Does goal-directed haemodynamic and fluid therapy improve peri-operative outcomes?: A systematic review and meta-analysis. Eur J Anaesthesiol. 2018 Jul;35(7):469-483. doi: 10.1097/EJA.0000000000000778.
- Background: PubMed 20966436Hamilton MA, Cecconi M, Rhodes A. A systematic review and meta-analysis on the use of preemptive hemodynamic intervention to improve postoperative outcomes in moderate and high-risk surgical patients. Anesth Analg. 2011 Jun;112(6):1392-402. doi: 10.1213/ANE.0b013e3181eeaae5. Epub 2010 Oct 21.
- Background: PubMed 28602158Sun Y, Chai F, Pan C, Romeiser JL, Gan TJ. Effect of perioperative goal-directed hemodynamic therapy on postoperative recovery following major abdominal surgery-a systematic review and meta-analysis of randomized controlled trials. Crit Care. 2017 Jun 12;21(1):141. doi: 10.1186/s13054-017-1728-8.
- Background: PubMed 28126672Yuan J, Sun Y, Pan C, Li T. Goal-directed fluid therapy for reducing risk of surgical site infections following abdominal surgery - A systematic review and meta-analysis of randomized controlled trials. Int J Surg. 2017 Mar;39:74-87. doi: 10.1016/j.ijsu.2017.01.081. Epub 2017 Jan 23.
- Background: PubMed 33609377Wijnberge M, Schenk J, Bulle E, Vlaar AP, Maheshwari K, Hollmann MW, Binnekade JM, Geerts BF, Veelo DP. Association of intraoperative hypotension with postoperative morbidity and mortality: systematic review and meta-analysis. BJS Open. 2021 Jan 8;5(1):zraa018. doi: 10.1093/bjsopen/zraa018.
- Background: PubMed 24554544Hallsjo Sander C, Hallback M, Wallin M, Emtell P, Oldner A, Bjorne H. Novel continuous capnodynamic method for cardiac output assessment during mechanical ventilation. Br J Anaesth. 2014 May;112(5):824-31. doi: 10.1093/bja/aet486. Epub 2014 Feb 18.
- Background: PubMed 28497180Sigmundsson TS, Ohman T, Hallback M, Redondo E, Sipmann FS, Wallin M, Oldner A, Hallsjo Sander C, Bjorne H. Performance of a capnodynamic method estimating effective pulmonary blood flow during transient and sustained hypercapnia. J Clin Monit Comput. 2018 Apr;32(2):311-319. doi: 10.1007/s10877-017-0021-3. Epub 2017 May 11.
- Background: PubMed 34155171Sigmundsson TS, Ohman T, Hallback M, Suarez-Sipmann F, Wallin M, Oldner A, Hallsjo-Sander C, Bjorne H. Comparison between capnodynamic and thermodilution method for cardiac output monitoring during major abdominal surgery: An observational study. Eur J Anaesthesiol. 2021 Dec 1;38(12):1242-1252. doi: 10.1097/EJA.0000000000001566.
- Background: PubMed 26041115Hallsjo Sander C, Hallback M, Suarez Sipmann F, Wallin M, Oldner A, Bjorne H. A novel continuous capnodynamic method for cardiac output assessment in a porcine model of lung lavage. Acta Anaesthesiol Scand. 2015 Sep;59(8):1022-31. doi: 10.1111/aas.12559. Epub 2015 Jun 4.
- Individual participant dataSharing planned: No
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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.
Greg Winski, Dr principal investigator
Karolinska University Hospital
Questions about this trial
What is this clinical trial studying?
A study testing Capnodynamic method in people with Monitoring, Intraoperative, run by Region Stockholm.
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: opening soon, as of the sponsor's last update on 2 Feb 2024. 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?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Greg Winski, Dr (+46812380000, greg.winski@regionstockholm.se), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy of prediction of hypotension 5 minutes ahead of time using capnodynamics and continuous arterial blood pressure measurement. (time frame: 6-12 hours). 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 NCT06240234, which also lists 9 linked publications.
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 2 Feb 2024; AzTrials last refreshed its copy on 3 Nov 2026.