Clinical trial NCT04014010: Machine Learning Modeling of Intraoperative Hemodynamic Predictors of Postoperative Outcomes
Official title: Machine Learning Modeling of Intraoperative Hemodynamic Predictors of 30-day Mortality and Major In-hospital Morbidity After Noncardiac Surgery: a Retrospective Population Cohort Study
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Perioperative/Postoperative Complications
Morbidity, Multiple
Surgery
Anesthesia
Death
Keywords: Perioperative
Anesthesia
Hemodynamics
Machine Learning
Non-cardiac Surgery
Lead sponsor: Nova Scotia Health Authority
Sponsor class: OTHER
Collaborators: name: Dalhousie University
name: Harvard University
name: University of Toronto
name: University of Ottawa
First posted: 2019-07-10
Enrollment: 35000
Sex: ALL
Minimum age (years): 45
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: For data analysis in summer 2019, we have access to mortality data up to December 31, 2017. We chose December 1, 2017, as the last surgery date to be included, to allow for a complete data set 30 days after surgery. January 1, 2013 was chosen to obtain a study period of 5 years.

Brief summary: With population aging and limited resources, strategies to improve outcomes after surgery are ever more important. There is a limited understanding of what ranges of hemodynamic variables under anesthesia are associated with better outcomes. This retrospective cohort study will analyze how hemodynamic variables during surgeries predict mortality, morbidity, Intensive Care Unit admission, length of hospital stay, and hospital readmission. The use of machine learning in a large, broad surgery population dataset could detect new relationships and strategies that may inform current practice, and generate ideas for future research.

Detailed description: Lay Summary

Introduction: The World Health Organization estimates that 270-360 million operations are performed every year worldwide. Death and complications after surgery are a big challenge. In Canada, out of every 1000 major surgeries, 16 patients die in hospital after surgery. In the United States, for every 1000 operations, 67 patients unexpectedly need life support in the Intensive Care Unit. With population aging and limited resources, strategies to improve health after surgery are ever more important.

Vital signs, such as blood pressure and heart rate, show how the body is doing. Vital signs change during surgery because of patient, surgical, and anesthetic factors. Anesthesiologists can change vital signs with medications. However, medical professionals are only starting to understand which, and what ranges of, vital signs under anesthesia are associated with better health. Machine learning is a tool that can provide new ways to understand data. With better understanding, medical professionals can work to improve outcomes after surgery.

Objective: This study will analyze vital signs during surgeries for their links to death, complications (heart, lung, kidney, brain, infection), Intensive Care Unit admission, length of hospital stay, and hospital readmission. This study will determine which, and what levels of, vital signs may be harmful. The investigators predict that blood pressure, heart rate, oxygen level, carbon dioxide level, and the need for medications to change blood pressure will interact to be associated with death after surgery.

Methods: After obtaining Research Ethics Board approval, the investigators will analyze data from all patients who are at least 45 years old and had an operation (with the exception of heart surgery) with an overnight stay at the Queen Elizabeth II health centre (Halifax, Canada) from January 1, 2013 to December 1, 2017. There are approximately eligible 35,000 patients. The investigators will use machine learning to model the data and test how well our model explains outcomes after surgery.

Significance: The use of machine learning in a large, broad surgery population dataset could detect new relationships and strategies that may inform current practice, and generate ideas for future research. A better understanding of the impact of vital signs during surgeries may unveil methods to improve outcomes and resource allocation after surgery. The results may suggest ways to identify high-risk patients who should be monitored more closely after surgery. If the model performs well, it may motivate other researchers to use machine learning in health data research.

Please see full protocol for details.

May 2020 update (prior to dataset aggregation and analysis)

1. Added secondary outcome (days alive and out of hospital at 30 days postoperatively)
2. Improved hemodynamic variable artifact processing algorithm
3. Added sub-study: machine learning for invasive blood pressure artifact removal algorithm

Eligibility criteria: Inclusion Criteria:

* All patients ages ≥ 45 receiving their index (i.e. first) non-cardiac surgery with an overnight stay at the Nova Scotia Health Authority Queen Elizabeth II (QEII) hospitals (Victoria General and Halifax Infirmary) Halifax, Canada, from January 1, 2013 to December 1, 2017.
* For patients who had multiple surgeries, only the first non-cardiac surgery with an overnight stay at QEII will be included to avoid confounding from previous surgical admissions (i.e. one surgical admission per patient).

Exclusion Criteria:

* No intraoperative anesthetic records
* Cardiac surgery patients
* Deceased organ donation

Primary outcomes:
measure: Mortality
timeFrame: 30 days after date of surgery
Secondary outcomes:
measure: In-hospital Morbidity: Any
timeFrame: 30 days after date of surgery
measure: In-hospital Morbidity: Cardiac
timeFrame: 30 days after date of surgery
measure: In-hospital Morbidity: Respiratory
timeFrame: 30 days after date of surgery
measure: In-hospital Morbidity: Acute Kidney Injury
timeFrame: 30 days after date of surgery
measure: In-hospital Morbidity: Cerebrovascular
timeFrame: 30 days after date of surgery
measure: In-hospital Morbidity: Delirium
timeFrame: 30 days after date of surgery
measure: In-hospital Morbidity: Septic Shock
timeFrame: 30 days after date of surgery
measure: Postoperative ICU admission
timeFrame: 30 days after date of surgery
measure: Prolonged Postoperative Length of Stay (LOS)
timeFrame: 30 days after date of surgery
measure: Hospital readmission
timeFrame: 30 days after date of surgery
measure: Intraoperative mortality
timeFrame: 30 days after date of surgery
measure: Days alive and out of hospital at 30 days postoperatively
timeFrame: 30 days after date of surgery
Other outcomes:
Locations:
Study officials:
References:
citation: Ke JXC, McIsaac DI, George RB, Branco P, Cook EF, Beattie WS, Urquhart R, MacDonald DB. Postoperative mortality risk prediction that incorporates intraoperative vital signs: development and internal validation in a historical cohort. Can J Anaesth. 2022 Sep;69(9):1086-1098. doi: 10.1007/s12630-022-02287-0. Epub 2022 Aug 22.