Perioperative/Postoperative ComplicationsNCT04014010
Machine Learning Modeling of Intraoperative Hemodynamic Predictors of Postoperative Outcomes
An observational study of Perioperative/Postoperative Complications and Morbidity, Multiple, run by Janny Xue Chen Ke.
Trial 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. Read more about this study
- Who is it for?
- Ages 45 years and older
- Conditions the study is for
- Perioperative/Postoperative Complications, Morbidity, Multiple, Surgery
- What is being tested?
- Blood pressure, Heart rate and Use of hemodynamic medications (i.e. special medications for blood pressure)
- Type of treatment
- Injection or infusion
- How many people
- 35,000 participants
- Where
- No sites listed yet
- Study dates
- 1 Jan 2013to 31 Dec 2017
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 45 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
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.
Who may be able to join
- 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).
Who cannot join
- No intraoperative anesthetic records
- Cardiac surgery patients
- Deceased organ donation
About this study
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.
Read the 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)
- Added secondary outcome (days alive and out of hospital at 30 days postoperatively)
- Improved hemodynamic variable artifact processing algorithm
- Added sub-study: machine learning for invasive blood pressure artifact removal algorithm
- Conditions
- Perioperative/Postoperative Complications, Morbidity, Multiple, Surgery, Anesthesia, Death
- Being tested
- Blood pressure, Heart rate, Use of hemodynamic medications (i.e. special medications for blood pressure), Oxygen saturation by pulse oximetry (SpO2), End-tidal Carbon dioxide (EtCO2)
- Keywords
- Perioperative, Anesthesia, Hemodynamics, Machine Learning, Non-cardiac Surgery
- Medical subject headings
- Postoperative Complications, Death
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Blood pressureOther
Systolic Blood Pressure (SBP)
- Maximum change from preoperative SBP, in a) absolute change (mmHg), and b) relative change (%)(emergency and elective cases analyzed separately)
- Cumulative duration (minutes) >=20% below preoperative SBP
- Longest single episode (minutes) below a) 80, b) 90, and c)100 mmHg
- Cumulative duration (minutes) below a) 80, b) 90, and c)100 mmHg
Mean Arterial Pressure (MAP)
- Maximum change from preoperative MAP, in a) absolute change (mmHg), and b) relative change (%) (emergency and elective cases analyzed separately)
- Cumulative duration (minutes) >=20% below preoperative MAP
- Longest single episode (minutes) below a) 60, b) 65, c) 70, and d) 80mmHg
- Cumulative duration (minutes) below a) 60, b) 65, c) 70, and d) 80mmHg
Heart rateOther
- Maximum change (beats per minute, BPM) from preoperative heart rate (positive and negative)
- Relative change (%) from preoperative heart rate (positive and negative)
- Maximum pulse variation (maximum heart rate minus minimum heart rate)
- Longest single episode (minutes) a) below 60, and b) above 100BPM
- Cumulative duration (minutes) a) below 60, and b) above 100BPM
Use of hemodynamic medications (i.e. special medications for blood pressure)Other
- Vasopressor/inotrope use (yes vs. no): phenylephrine, norepinephrine, epinephrine, vasopressin, dobutamine, or milrinone
- Infusion of any vasopressor/inotropes above (yes vs. no) (identified by unit of weight over time)
- Phenylephrine/ephedrine bolus (yes vs. no) (identified by unit of weight only)
- Vasodilator use (yes vs. no): labetalol, esmolol, nitroglycerin, nitroprusside
- Infusion of any vasodilator above (yes vs. no) (identified by unit of weight over time)
Oxygen saturation by pulse oximetry (SpO2)Other
- Longest single episode (minutes) below a) 88, and b) 90%
- Cumulative duration (minutes) below a) 88, and b) 90%
End-tidal Carbon dioxide (EtCO2)Other
- Longest single episode (minutes) a) below 30, and b) above 45mmHg
- Cumulative duration (minutes) a) below 30, and b) above 45mmHg
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: 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) |
What is being measured
The primary outcome is the main question the study is designed to answer.
Mortality
All-cause postoperative mortality (yes/no)
Measured: 30 days after date of surgery
Secondary outcomes (12)
In-hospital Morbidity: Any
Any complications in terms of cardiac, respiratory, renal, cerebrovascular, delirium, or septic shock (yes/no)
Measured: 30 days after date of surgery
In-hospital Morbidity: Cardiac
Composite of acute myocardial infarction, cardiac arrest, ventricular tachycardia, congestive heart failure, pulmonary edema, complete heart block, shock excluding septic shock (yes/no)
Measured: 30 days after date of surgery
In-hospital Morbidity: Respiratory
Composite of pneumonia, pulmonary embolism, acute respiratory failure, respiratory arrest, Mechanical Ventilation >= 96 hours (yes/no)
Measured: 30 days after date of surgery
In-hospital Morbidity: Acute Kidney Injury
Acute Kidney Injury (yes/no)
Measured: 30 days after date of surgery
In-hospital Morbidity: Cerebrovascular
Composite of strokes and transient ischemic attacks (yes/no)
Measured: 30 days after date of surgery
In-hospital Morbidity: Delirium
Delirium (yes/no)
Measured: 30 days after date of surgery
In-hospital Morbidity: Septic Shock
Septic Shock (yes/no)
Measured: 30 days after date of surgery
Postoperative ICU admission
ICU admission (yes/no)
Measured: 30 days after date of surgery
Prolonged Postoperative Length of Stay (LOS)
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
Measured: 30 days after date of surgery
Hospital readmission
Hospital readmission (yes/no)
Measured: 30 days after date of surgery
Intraoperative mortality
Intraoperative mortality (yes/no)
Measured: 30 days after date of surgery
Days alive and out of hospital at 30 days postoperatively
Number of days
Measured: 30 days after date of surgery
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted10 Jul 2019
- Study start1 Jan 2013
- Primary completion31 Dec 2017
- Study completion31 Dec 2017
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Perioperative/Postoperative ComplicationsStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 35,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Janny Xue Chen Ke
- Sponsor type
- University, hospital or other
- Collaborators
- Dalhousie University, Harvard University, University of Toronto, University of Ottawa
- Responsible party
- Sponsor-investigator, Janny Xue Chen Ke, Nova Scotia Health Authority
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04014010
- Sponsor's protocol ID
- 1024251
- Status last verified
- Jun 2020
- First submitted
- 7 Jul 2019
- Last updated
- 30 Jun 2020
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
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.
PubMed 35996071
Study at a glance
From the registry record, last updated by the sponsor on 30 Jun 2020. Recruitment status is as the sponsor reported it: confirm with the study team.
- 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
- ClinicalTrials.gov ID
- NCT04014010
- Acronym / sponsor ID
- 1024251
- Conditions
- Perioperative/Postoperative Complications, Morbidity, Multiple, Surgery, Anesthesia, Death
- Keywords
- Perioperative, Anesthesia, Hemodynamics, Machine Learning, Non-cardiac Surgery
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 35,000 participants (estimated)
- Lead sponsor
- Janny Xue Chen KeUniversity, hospital or other
- Collaborators
- Dalhousie University, Harvard University, University of Toronto, University of Ottawa
- Study start
- 1 Jan 2013 (actual)
- Primary completion
- 31 Dec 2017 (actual)
- Study completion
- 31 Dec 2017 (actual)
- First posted
- 10 Jul 2019 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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)
- Added secondary outcome (days alive and out of hospital at 30 days postoperatively)
- Improved hemodynamic variable artifact processing algorithm
- Added sub-study: machine learning for invasive blood pressure artifact removal algorithm
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Cohort | – | 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)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. |
Who may qualify
- Ages
- 45 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (2 listed)
- 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).
Key exclusion criteria (3 listed)
- No intraoperative anesthetic records
- Cardiac surgery patients
- Deceased organ donation
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Blood pressureSystolic 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 | Cohort |
| Heart rate1. 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 | Cohort |
| 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 | Cohort |
| 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 | Cohort |
| 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 | Other | Cohort |
Primary outcome
Mortality
Time frame: 30 days after date of surgery
All-cause postoperative mortality (yes/no)
12 secondary outcomes
In-hospital Morbidity: Any
Time frame: 30 days after date of surgery
In-hospital Morbidity: Cardiac
Time frame: 30 days after date of surgery
In-hospital Morbidity: Respiratory
Time frame: 30 days after date of surgery
In-hospital Morbidity: Acute Kidney Injury
Time frame: 30 days after date of surgery
In-hospital Morbidity: Cerebrovascular
Time frame: 30 days after date of surgery
In-hospital Morbidity: Delirium
Time frame: 30 days after date of surgery
In-hospital Morbidity: Septic Shock
Time frame: 30 days after date of surgery
Postoperative ICU admission
Time frame: 30 days after date of surgery
Prolonged Postoperative Length of Stay (LOS)
Time frame: 30 days after date of surgery
Hospital readmission
Time frame: 30 days after date of surgery
Intraoperative mortality
Time frame: 30 days after date of surgery
Days alive and out of hospital at 30 days postoperatively
Time frame: 30 days after date of surgery
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 30 Jun 2020.
- Derived from this study: PubMed 35996071Ke 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.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
Questions about this trial
What is this clinical trial studying?
An observational study of Perioperative/Postoperative Complications and Morbidity, Multiple, run by Janny Xue Chen Ke.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 30 Jun 2020. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Mortality (time frame: 30 days after date of surgery). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04014010, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 30 Jun 2020; AzTrials last refreshed its copy on 6 Nov 2026.