Monitoring, Intraoperative Clinical Trial
Official title:
Artificial Intelligence - to Predict and Prevent Hypotension During Surgery
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.
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. |
Country | Name | City | State |
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n/a |
Lead Sponsor | Collaborator |
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Region Stockholm | Getinge Group, KTH Royal Institute of Technology |
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
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 |
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