Artificial Intelligence - to Predict and Prevent Hypotension During Surgery

Monitoring, Intraoperative Clinical Trial

Official title:

Artificial Intelligence - to Predict and Prevent Hypotension During Surgery

Clinical Trial Details — Status: Not yet recruiting

Administrative data

• NCT number: NCT06240234
• Other study ID #: CIP_AI_hypotension_study_V01
• Status: Not yet recruiting
• Phase: N/A
• Start date: February 2024
• Est. completion date: December 2026

Study information

• Verified date: January 2024
• Source: Region Stockholm
• Contact: Greg Winski, Dr
• Phone: +46812380000
• Email: greg.winski[@]regionstockholm.se
• Is FDA regulated: No
• Study type: Interventional

Clinical 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 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.

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.

Recruitment information / eligibility

• Status: Not yet recruiting
• Enrollment: 300
• Est. completion date: December 2026
• Est. primary completion date: December 2026
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

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.

Related Conditions & MeSH terms

• Hypotension
• Monitoring, Intraoperative

Intervention

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.

Locations

Country	Name	City	State
n/a

Sponsors (3)

Lead Sponsor	Collaborator
Region Stockholm	Getinge Group, KTH Royal Institute of Technology

References & Publications (9)

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. — View Citation

Hallsjo 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. — View Citation

Hallsjo 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. — View Citation

Hamilton 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. — View Citation

Sigmundsson 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. — View Citation

Sigmundsson 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. — View Citation

Sun 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. — View Citation

Wijnberge 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. — View Citation

Yuan 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. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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).	6-12 hours
Secondary	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	6-12 hours
Secondary	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	6-12 hours