NCT04014010

Machine Learning Modeling of Intraoperative Hemodynamic Predictors of Postoperative Outcomes

Machine Learning Modeling of Intraoperative Hemodynamic Predictors of 30-day Mortality and Major In-hospital Morbidity After Noncardiac Surgery: a Retrospective Population Cohort Study

• Status: Completed
• Phase: —
• Study type: Observational
• Enrollment: 35,000 (estimated)
• Sponsor: Janny Xue Chen Ke · Academic / Other
• Sex: All
• Age: 45 Years
• Healthy volunteers: Not accepted

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

Conditions

• Perioperative/Postoperative Complications
• Morbidity, Multiple
• Surgery
• Anesthesia
• Death

Interventions

Type	Name	Description
OTHER	Blood pressure	Systolic Blood Pressure (SBP) 1. Maximum change from preoperative SBP, in a) absolute change (mmHg), and b) relative change (%)(emergency and elective cases analyzed separately) 2. Cumulative duration (minutes) \>=20% below preoperative SBP 3. Longest single episode (minutes) below a) 80, b) 90, and c)100 mmHg 4. Cumulative duration (minutes) below a) 80, b) 90, and c)100 mmHg Mean Arterial Pressure (MAP) 1. Maximum change from preoperative MAP, in a) absolute change (mmHg), and b) relative change (%) (emergency and elective cases analyzed separately) 2. Cumulative duration (minutes) \>=20% below preoperative MAP 3. Longest single episode (minutes) below a) 60, b) 65, c) 70, and d) 80mmHg 4. Cumulative duration (minutes) below a) 60, b) 65, c) 70, and d) 80mmHg
OTHER	Heart rate	1. Maximum change (beats per minute, BPM) from preoperative heart rate (positive and negative) 2. Relative change (%) from preoperative heart rate (positive and negative) 3. Maximum pulse variation (maximum heart rate minus minimum heart rate) 4. Longest single episode (minutes) a) below 60, and b) above 100BPM 5. Cumulative duration (minutes) a) below 60, and b) above 100BPM
OTHER	Use of hemodynamic medications (i.e. special medications for blood pressure)	1. Vasopressor/inotrope use (yes vs. no): phenylephrine, norepinephrine, epinephrine, vasopressin, dobutamine, or milrinone 2. Infusion of any vasopressor/inotropes above (yes vs. no) (identified by unit of weight over time) 3. Phenylephrine/ephedrine bolus (yes vs. no) (identified by unit of weight only) 4. Vasodilator use (yes vs. no): labetalol, esmolol, nitroglycerin, nitroprusside 5. Infusion of any vasodilator above (yes vs. no) (identified by unit of weight over time)
OTHER	Oxygen saturation by pulse oximetry (SpO2)	1. Longest single episode (minutes) below a) 88, and b) 90% 2. Cumulative duration (minutes) below a) 88, and b) 90%
OTHER	End-tidal Carbon dioxide (EtCO2)	1. Longest single episode (minutes) a) below 30, and b) above 45mmHg 2. Cumulative duration (minutes) a) below 30, and b) above 45mmHg

Timeline

• Start date: 2013-01-01
• Primary completion: 2017-12-31
• Completion: 2017-12-31
• First posted: 2019-07-10
• Last updated: 2020-06-30