Machine Learning Modeling of Intraoperative Hemodynamic Predictors of Postoperative Outcomes
- Conditions
- Perioperative/Postoperative ComplicationsDeathMorbidity, MultipleAnesthesiaSurgery
- Interventions
- Other: Blood pressureOther: Heart rateOther: Use of hemodynamic medications (i.e. special medications for blood pressure)Other: Oxygen saturation by pulse oximetry (SpO2)Other: End-tidal Carbon dioxide (EtCO2)
- Registration Number
- NCT04014010
- Lead Sponsor
- Janny Xue Chen Ke
- 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
Recruitment & Eligibility
- Status
- COMPLETED
- Sex
- All
- Target Recruitment
- 35000
- 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).
- No intraoperative anesthetic records
- Cardiac surgery patients
- Deceased organ donation
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Arm && Interventions
Group Intervention Description Cohort Blood pressure 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 will be included. Patients under going cardiac surgery or deceased organ donation will be excluded. Patients without an electronic anesthetic record during surgery will also be excluded. Preliminary analysis of the intraoperative database estimates approximately 35,000 patients in this cohort. Cohort End-tidal Carbon dioxide (EtCO2) 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 will be included. Patients under going cardiac surgery or deceased organ donation will be excluded. Patients without an electronic anesthetic record during surgery will also be excluded. Preliminary analysis of the intraoperative database estimates approximately 35,000 patients in this cohort. Cohort Heart rate 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 will be included. Patients under going cardiac surgery or deceased organ donation will be excluded. Patients without an electronic anesthetic record during surgery will also be excluded. Preliminary analysis of the intraoperative database estimates approximately 35,000 patients in this cohort. Cohort Use of hemodynamic medications (i.e. special medications for blood pressure) 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 will be included. Patients under going cardiac surgery or deceased organ donation will be excluded. Patients without an electronic anesthetic record during surgery will also be excluded. Preliminary analysis of the intraoperative database estimates approximately 35,000 patients in this cohort. Cohort Oxygen saturation by pulse oximetry (SpO2) 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 will be included. Patients under going cardiac surgery or deceased organ donation will be excluded. Patients without an electronic anesthetic record during surgery will also be excluded. Preliminary analysis of the intraoperative database estimates approximately 35,000 patients in this cohort.
- Primary Outcome Measures
Name Time Method Mortality 30 days after date of surgery All-cause postoperative mortality (yes/no)
- Secondary Outcome Measures
Name Time Method In-hospital Morbidity: Delirium 30 days after date of surgery Delirium (yes/no)
Intraoperative mortality 30 days after date of surgery Intraoperative mortality (yes/no)
In-hospital Morbidity: Acute Kidney Injury 30 days after date of surgery Acute Kidney Injury (yes/no)
In-hospital Morbidity: Cerebrovascular 30 days after date of surgery Composite of strokes and transient ischemic attacks (yes/no)
Prolonged Postoperative Length of Stay (LOS) 30 days after date of surgery Greater than vs. less than or equal to Canadian Institute of Health Information Expected Length of Stay (ELOS) as assigned by the Case Mix Grouping
In-hospital Morbidity: Any 30 days after date of surgery Any complications in terms of cardiac, respiratory, renal, cerebrovascular, delirium, or septic shock (yes/no)
In-hospital Morbidity: Cardiac 30 days after date of surgery Composite of acute myocardial infarction, cardiac arrest, ventricular tachycardia, congestive heart failure, pulmonary edema, complete heart block, shock excluding septic shock (yes/no)
In-hospital Morbidity: Respiratory 30 days after date of surgery Composite of pneumonia, pulmonary embolism, acute respiratory failure, respiratory arrest, Mechanical Ventilation \>= 96 hours (yes/no)
In-hospital Morbidity: Septic Shock 30 days after date of surgery Septic Shock (yes/no)
Postoperative ICU admission 30 days after date of surgery ICU admission (yes/no)
Hospital readmission 30 days after date of surgery Hospital readmission (yes/no)
Days alive and out of hospital at 30 days postoperatively 30 days after date of surgery Number of days