Condition or disease | Intervention/treatment |
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Perioperative/Postoperative Complications Morbidity, Multiple Surgery Anesthesia Death | Other: Blood pressure Other: Heart rate Other: Use of hemodynamic medications (i.e. special medications for blood pressure) Other: Oxygen saturation by pulse oximetry (SpO2) Other: End-tidal Carbon dioxide (EtCO2) |
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)
Study Type : | Observational |
Estimated Enrollment : | 35000 participants |
Observational Model: | Cohort |
Time Perspective: | Retrospective |
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 |
Actual Study Start Date : | January 1, 2013 |
Actual Primary Completion Date : | December 31, 2017 |
Actual Study Completion Date : | December 31, 2017 |
Group/Cohort | Intervention/treatment |
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Cohort
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.
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Other: Blood pressure
Systolic Blood Pressure (SBP)
Mean Arterial Pressure (MAP)
Other: Heart rate
Other: Use of hemodynamic medications (i.e. special medications for blood pressure)
Other: Oxygen saturation by pulse oximetry (SpO2)
Other: End-tidal Carbon dioxide (EtCO2)
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Ages Eligible for Study: | 45 Years and older (Adult, Older Adult) |
Sexes Eligible for Study: | All |
Accepts Healthy Volunteers: | No |
Sampling Method: | Non-Probability Sample |
Inclusion Criteria:
Exclusion Criteria:
No Contacts or Locations Provided
Tracking Information | |||||
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First Submitted Date | July 7, 2019 | ||||
First Posted Date | July 10, 2019 | ||||
Last Update Posted Date | June 30, 2020 | ||||
Actual Study Start Date | January 1, 2013 | ||||
Actual Primary Completion Date | December 31, 2017 (Final data collection date for primary outcome measure) | ||||
Current Primary Outcome Measures |
Mortality [ Time Frame: 30 days after date of surgery ] All-cause postoperative mortality (yes/no)
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Original Primary Outcome Measures | Same as current | ||||
Change History | |||||
Current Secondary Outcome Measures |
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Original Secondary Outcome Measures |
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Current Other Pre-specified Outcome Measures | Not Provided | ||||
Original Other Pre-specified Outcome Measures | Not Provided | ||||
Descriptive Information | |||||
Brief Title | 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 | ||||
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)
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Study Type | Observational | ||||
Study Design | Observational Model: Cohort Time Perspective: Retrospective |
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Target Follow-Up Duration | Not Provided | ||||
Biospecimen | Not Provided | ||||
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. | ||||
Condition |
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Intervention |
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Study Groups/Cohorts | Cohort
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.
Interventions:
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Publications * | Not Provided | ||||
* Includes publications given by the data provider as well as publications identified by ClinicalTrials.gov Identifier (NCT Number) in Medline. |
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Recruitment Information | |||||
Recruitment Status | Completed | ||||
Estimated Enrollment |
35000 | ||||
Original Estimated Enrollment | Same as current | ||||
Actual Study Completion Date | December 31, 2017 | ||||
Actual Primary Completion Date | December 31, 2017 (Final data collection date for primary outcome measure) | ||||
Eligibility Criteria |
Inclusion Criteria:
Exclusion Criteria:
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Sex/Gender |
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Ages | 45 Years and older (Adult, Older Adult) | ||||
Accepts Healthy Volunteers | No | ||||
Contacts | Contact information is only displayed when the study is recruiting subjects | ||||
Listed Location Countries | Not Provided | ||||
Removed Location Countries | |||||
Administrative Information | |||||
NCT Number | NCT04014010 | ||||
Other Study ID Numbers | 1024251 | ||||
Has Data Monitoring Committee | No | ||||
U.S. FDA-regulated Product |
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IPD Sharing Statement |
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Responsible Party | Janny Xue Chen Ke, Nova Scotia Health Authority | ||||
Study Sponsor | Janny Xue Chen Ke | ||||
Collaborators |
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Investigators | Not Provided | ||||
PRS Account | Nova Scotia Health Authority | ||||
Verification Date | June 2020 |