Surgery Clinical Trial
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
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.
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
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