Colorectal CancerNCT05150548
Predictive Time-to-Event Model for Major Medical Complications After Colectomy
An observational study of Colorectal Cancer and Inflammatory Bowel Diseases, run by University of British Columbia at 1 site in Canada.
Trial summary
Purpose: The purpose of this study is to create prediction models for when major complications occur after elective colectomy surgery. Justification: After surgery, patients can have multiple complications. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Colorectal Cancer, Inflammatory Bowel Diseases, Diverticulitis
- Type of treatment
- Injection or infusion
- How many people
- 130,000 participants
- Where
- 1 siteCanada
- Study dates
- 1 Dec 2021to 31 Dec 2022
- 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 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: The study will include all patients who are 18 years or older undergoing elective colectomy, whose data has been collected in the NSQIP® Procedure Targeted Colectomy dataset from 2014-2019, inclusively.
Who may be able to join
- undergoing elective colectomy
- data has been collected in the NSQIP® Procedure Targeted Colectomy dataset from 2014-2019
Who cannot join
- American Society of Anesthesiologists (ASA) Physical Status (PS) V (defined as "5-Moribund") (ASA PS 6 - organ donation is not included within NSQIP)
- undergoing urgent or emergency surgery
- indication for colectomy consisting of "Acute diverticulitis", "Enterocolitis (e.g. C. Difficile)", and "Volvulus" due to the non-elective nature of these pathologies
- patient with disseminated cancer
- wound infection (i.e. potentially recent surgery)
- systemic sepsis
- ventilator-dependence preoperatively
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Development and Internal Validation of Models to Predict Time-to-event for Major Medical Complications Within 30-days After Planned Colectomy: a Retrospective Population Cohort Study
Purpose: The purpose of this study is to create prediction models for when major complications occur after elective colectomy surgery.
Justification: After surgery, patients can have multiple complications. Accurate risk prediction after surgery is important for determining an appropriate level of monitoring and facilitating patient recovery at home.
Read more
Objectives: Investigators aim to develop and internally validate prediction models to predict time-to-complication for each individual major medical complications (pneumonia, myocardial infarction (MI) (i.e. heart attacks), cerebral vascular event (CVA) (i.e. stroke), venous thromboembolism (VTE) (i.e. clots), acute renal failure (ARF) (i.e. kidney failure), and sepsis (i.e. severe infections)) or adverse outcomes (mortality, readmission) within 30-days after elective colectomy.
Data analysis: Investigators will be analyzing a data set provided by the National Surgical Quality Improvement Program (NSQIP). Descriptive statistics will be performed. Cox proportional hazard and machine learning models will be created for each complication and outcome outlined in "Objectives". The performances of the models will be assessed and compared to each other.
Read the detailed description
Background: Planned (elective or time sensitive) colectomy are performed for indications including cancer, inflammatory bowel disease (IBD), and diverticulitis. After colectomy, patients are at risk of a variety of major medical complications, including pneumonia, myocardial infarction (MI), cerebral vascular event (CVA), venous thromboembolism (VTE), acute renal failure (ARF), and sepsis. However, different complications tend to happen at different times after surgery. Accurate risk prediction, not only whether a complication may occur in a patient, but also when, is crucial for patient education, monitoring, and disposition planning. While several studies have explored the incidence and binary risk prediction for major complications after surgeries, there has been scarce literature on time-to-complication prediction modeling in recent population cohort data.
Objectives
- To develop and internally validate Cox proportional hazards models to predict time-to-complication for each individual major medical complication captured in the American College of Surgeons National Surgical Quality Improvement Program (NSQIP) dataset (pneumonia, myocardial infarction (MI), cerebral vascular event (CVA), venous thromboembolism (VTE), acute renal failure (ARF), and sepsis) or adverse outcomes (mortality, readmission), that started within 30-days after elective colectomy.
- To develop and internally validate machine learning models to predict time-to-complication for major medical complications and adverse outcomes (same as in objective 1) within 30-days after elective colectomy in NSQIP. The best machine learning model for each complication will be compared to the Cox proportional hazards model in terms of discrimination, and calibration.
Methods: Investigators will conduct a time-to-event survival analysis in a retrospective cohort using NSQIP®, a prospectively-collected multicentre dataset with more than 150 clinical variables within 30 days after surgery. This dataset includes information on whether the patient was diagnosed with major complications (in- or out-of-hospital) as well as the number of postoperative days to the diagnoses of complications, as defined by a standardized criteria within the NSQIP operations manual. The general dataset will be linked with the NSQIP® Procedure Targeted Colectomy dataset, which contains additional colectomy-specific variables.
The study will be reported according to the Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) guidelines and Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research.
- Conditions
- Colorectal Cancer, Inflammatory Bowel Diseases, Diverticulitis, Colectomy, Predictive Model, Complications, Postoperative
- Being tested
- No Intervention
- Medical subject headings
- Colorectal Neoplasms, Inflammatory Bowel Diseases, Diverticulitis, Postoperative Complications
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
No InterventionOther
Not applicable, non-interventional study
| Study group | Role | What they receive |
|---|---|---|
| Entire Cohort | Patients undergoing elective colectomy with data that has been collected in the NSQIP® Procedure Targeted Colectomy dataset from 2014-2019 with American Society of Anesthesiologists (ASA) Physical Status I-IV.
Patients will not be included in this cohort with urgent or emergency colectomy or indication for colectomy consisting of "Acute diverticulitis", "Enterocolitis (e.g. C. Difficile)", and "Volvulus", patients with disseminated cancer, wound infection, systemic sepsis or ventilator-dependence preoperatively. Receives: Other: No Intervention |
What is being measured
The primary outcome is the main question the study is designed to answer.
Pneumonia
Occurrence of pneumonia within 30 days post-operatively.
Measured: Within 30 days post-operatively
Myocardial Infarction (MI)
Occurrence of Myocardial Infarction within 30 days post-operatively.
Measured: Within 30 days post-operatively
Cerebral Vascular Event (CVA)
Occurrence of Myocardial Infarction within 30 days post-operatively.
Measured: Within 30 days post-operatively
Venous Thromboembolism (VTE)
Occurrence of Venous Thromboembolism within 30 days post-operatively.
Measured: Within 30 days post-operatively
Acute Renal Failure (ARF)
Occurrence of Acute Renal Failure within 30 days post-operatively.
Measured: Within 30 days post-operatively
Sepsis or septic shock
Occurrence of sepsis or septic shock within 30 days post-operatively.
Measured: Within 30 days post-operatively
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted9 Dec 2021
- Study start1 Dec 2021
- Primary completion31 Dec 2022 (estimated)
- Study completion31 Dec 2022 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (6)Papers the sponsor has linked to this study.
- Other trials for Colorectal CancerStudies of the same condition, open and under way.
- More trials run by University of British ColumbiaThe sponsor's other registered studies.
- 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
- 130,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- University of British Columbia
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Janny Ke, University of British Columbia
- Investigators
- Janny Xue Chen Ke, MD, Principal investigator, University of British Columbia
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05150548
- Sponsor's protocol ID
- H21-02670
- Status last verified
- Mar 2022
- First submitted
- 25 Nov 2021
- Last updated
- 22 Mar 2022
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Morris MS, Deierhoi RJ, Richman JS, Altom LK, Hawn MT. The relationship between timing of surgical complications and hospital readmission. JAMA Surg. 2014 Apr;149(4):348-54. doi: 10.1001/jamasurg.2013.4064.
PubMed 24522747Scarborough JE, Schumacher J, Kent KC, Heise CP, Greenberg CC. Associations of Specific Postoperative Complications With Outcomes After Elective Colon Resection: A Procedure-Targeted Approach Toward Surgical Quality Improvement. JAMA Surg. 2017 Feb 15;152(2):e164681. doi: 10.1001/jamasurg.2016.4681. Epub 2017 Feb 15.
PubMed 27926773Thompson JS, Baxter BT, Allison JG, Johnson FE, Lee KK, Park WY. Temporal patterns of postoperative complications. Arch Surg. 2003 Jun;138(6):596-602; discussion 602-3. doi: 10.1001/archsurg.138.6.596.
PubMed 12799329Luo W, Phung D, Tran T, Gupta S, Rana S, Karmakar C, Shilton A, Yearwood J, Dimitrova N, Ho TB, Venkatesh S, Berk M. Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View. J Med Internet Res. 2016 Dec 16;18(12):e323. doi: 10.2196/jmir.5870.
PubMed 27986644Riley RD, Snell KI, Ensor J, Burke DL, Harrell FE Jr, Moons KG, Collins GS. Minimum sample size for developing a multivariable prediction model: PART II - binary and time-to-event outcomes. Stat Med. 2019 Mar 30;38(7):1276-1296. doi: 10.1002/sim.7992. Epub 2018 Oct 24.
PubMed 30357870Ke JXC, Jen TTH, Gao S, Ngo L, Wu L, Flexman AM, Schwarz SKW, Brown CJ, Gorges M. Development and internal validation of time-to-event risk prediction models for major medical complications within 30 days after elective colectomy. PLoS One. 2024 Dec 2;19(12):e0314526. doi: 10.1371/journal.pone.0314526. eCollection 2024.
PubMed 39621640
Study at a glance
From the registry record, last updated by the sponsor on 22 Mar 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development and Internal Validation of Models to Predict Time-to-event for Major Medical Complications Within 30-days After Planned Colectomy: a Retrospective Population Cohort Study
- ClinicalTrials.gov ID
- NCT05150548
- Acronym / sponsor ID
- H21-02670
- Conditions
- Colorectal Cancer, Inflammatory Bowel Diseases, Diverticulitis, Colectomy, Predictive Model, Complications, Postoperative
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 130,000 participants (estimated)
- Lead sponsor
- University of British ColumbiaUniversity, hospital or other
- Study start
- 1 Dec 2021 (actual)
- Primary completion
- 31 Dec 2022 (estimated)
- Study completion
- 31 Dec 2022 (estimated)
- First posted
- 9 Dec 2021 (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.
Purpose: The purpose of this study is to create prediction models for when major complications occur after elective colectomy surgery.
Justification: After surgery, patients can have multiple complications. Accurate risk prediction after surgery is important for determining an appropriate level of monitoring and facilitating patient recovery at home.
Objectives: Investigators aim to develop and internally validate prediction models to predict time-to-complication for each individual major medical complications (pneumonia, myocardial infarction (MI) (i.e. heart attacks), cerebral vascular event (CVA) (i.e. stroke), venous thromboembolism (VTE) (i.e. clots), acute renal failure (ARF) (i.e. kidney failure), and sepsis (i.e. severe infections)) or adverse outcomes (mortality, readmission) within 30-days after elective colectomy.
Data analysis: Investigators will be analyzing a data set provided by the National Surgical Quality Improvement Program (NSQIP). Descriptive statistics will be performed. Cox proportional hazard and machine learning models will be created for each complication and outcome outlined in "Objectives". The performances of the models will be assessed and compared to each other.
Detailed description
Background: Planned (elective or time sensitive) colectomy are performed for indications including cancer, inflammatory bowel disease (IBD), and diverticulitis. After colectomy, patients are at risk of a variety of major medical complications, including pneumonia, myocardial infarction (MI), cerebral vascular event (CVA), venous thromboembolism (VTE), acute renal failure (ARF), and sepsis. However, different complications tend to happen at different times after surgery. Accurate risk prediction, not only whether a complication may occur in a patient, but also when, is crucial for patient education, monitoring, and disposition planning. While several studies have explored the incidence and binary risk prediction for major complications after surgeries, there has been scarce literature on time-to-complication prediction modeling in recent population cohort data.
Objectives
- To develop and internally validate Cox proportional hazards models to predict time-to-complication for each individual major medical complication captured in the American College of Surgeons National Surgical Quality Improvement Program (NSQIP) dataset (pneumonia, myocardial infarction (MI), cerebral vascular event (CVA), venous thromboembolism (VTE), acute renal failure (ARF), and sepsis) or adverse outcomes (mortality, readmission), that started within 30-days after elective colectomy.
- To develop and internally validate machine learning models to predict time-to-complication for major medical complications and adverse outcomes (same as in objective 1) within 30-days after elective colectomy in NSQIP. The best machine learning model for each complication will be compared to the Cox proportional hazards model in terms of discrimination, and calibration.
Methods: Investigators will conduct a time-to-event survival analysis in a retrospective cohort using NSQIP®, a prospectively-collected multicentre dataset with more than 150 clinical variables within 30 days after surgery. This dataset includes information on whether the patient was diagnosed with major complications (in- or out-of-hospital) as well as the number of postoperative days to the diagnoses of complications, as defined by a standardized criteria within the NSQIP operations manual. The general dataset will be linked with the NSQIP® Procedure Targeted Colectomy dataset, which contains additional colectomy-specific variables.
The study will be reported according to the Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) guidelines and Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Entire Cohort | – | Other: No InterventionPatients undergoing elective colectomy with data that has been collected in the NSQIP® Procedure Targeted Colectomy dataset from 2014-2019 with American Society of Anesthesiologists (ASA) Physical Status I-IV. Patients will not be included in this cohort with urgent or emergency colectomy or indication for colectomy consisting of "Acute diverticulitis", "Enterocolitis (e.g. C. Difficile)", and "Volvulus", patients with disseminated cancer, wound infection, systemic sepsis or ventilator-dependence preoperatively. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- The study will include all patients who are 18 years or older undergoing elective colectomy, whose data has been collected in the NSQIP® Procedure Targeted Colectomy dataset from 2014-2019, inclusively.
Key inclusion criteria (2 listed)
- undergoing elective colectomy
- data has been collected in the NSQIP® Procedure Targeted Colectomy dataset from 2014-2019
Key exclusion criteria (7 listed)
- American Society of Anesthesiologists (ASA) Physical Status (PS) V (defined as "5-Moribund") (ASA PS 6 - organ donation is not included within NSQIP)
- undergoing urgent or emergency surgery
- indication for colectomy consisting of "Acute diverticulitis", "Enterocolitis (e.g. C. Difficile)", and "Volvulus" due to the non-elective nature of these pathologies
- patient with disseminated cancer
- wound infection (i.e. potentially recent surgery)
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 |
|---|---|---|
| No InterventionNot applicable, non-interventional study | Other | Entire Cohort |
Primary outcome
Pneumonia
Time frame: Within 30 days post-operatively
Occurrence of pneumonia within 30 days post-operatively.
Myocardial Infarction (MI)
Time frame: Within 30 days post-operatively
Occurrence of Myocardial Infarction within 30 days post-operatively.
Cerebral Vascular Event (CVA)
Time frame: Within 30 days post-operatively
Occurrence of Myocardial Infarction within 30 days post-operatively.
Venous Thromboembolism (VTE)
Time frame: Within 30 days post-operatively
Occurrence of Venous Thromboembolism within 30 days post-operatively.
Acute Renal Failure (ARF)
Time frame: Within 30 days post-operatively
Occurrence of Acute Renal Failure within 30 days post-operatively.
Sepsis or septic shock
Time frame: Within 30 days post-operatively
Occurrence of sepsis or septic shock within 30 days post-operatively.
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 22 Mar 2022.
- Background: PubMed 24522747Morris MS, Deierhoi RJ, Richman JS, Altom LK, Hawn MT. The relationship between timing of surgical complications and hospital readmission. JAMA Surg. 2014 Apr;149(4):348-54. doi: 10.1001/jamasurg.2013.4064.
- Background: PubMed 27926773Scarborough JE, Schumacher J, Kent KC, Heise CP, Greenberg CC. Associations of Specific Postoperative Complications With Outcomes After Elective Colon Resection: A Procedure-Targeted Approach Toward Surgical Quality Improvement. JAMA Surg. 2017 Feb 15;152(2):e164681. doi: 10.1001/jamasurg.2016.4681. Epub 2017 Feb 15.
- Background: PubMed 12799329Thompson JS, Baxter BT, Allison JG, Johnson FE, Lee KK, Park WY. Temporal patterns of postoperative complications. Arch Surg. 2003 Jun;138(6):596-602; discussion 602-3. doi: 10.1001/archsurg.138.6.596.
- Background: PubMed 27986644Luo W, Phung D, Tran T, Gupta S, Rana S, Karmakar C, Shilton A, Yearwood J, Dimitrova N, Ho TB, Venkatesh S, Berk M. Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View. J Med Internet Res. 2016 Dec 16;18(12):e323. doi: 10.2196/jmir.5870.
- Background: PubMed 30357870Riley RD, Snell KI, Ensor J, Burke DL, Harrell FE Jr, Moons KG, Collins GS. Minimum sample size for developing a multivariable prediction model: PART II - binary and time-to-event outcomes. Stat Med. 2019 Mar 30;38(7):1276-1296. doi: 10.1002/sim.7992. Epub 2018 Oct 24.
- Derived from this study: PubMed 39621640Ke JXC, Jen TTH, Gao S, Ngo L, Wu L, Flexman AM, Schwarz SKW, Brown CJ, Gorges M. Development and internal validation of time-to-event risk prediction models for major medical complications within 30 days after elective colectomy. PLoS One. 2024 Dec 2;19(12):e0314526. doi: 10.1371/journal.pone.0314526. eCollection 2024.
- 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.
Janny Xue Chen Ke, MD principal investigator
University of British Columbia
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Colorectal Cancer and Inflammatory Bowel Diseases, run by University of British Columbia at 1 site in Canada.
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 7 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: status not confirmed, as of the sponsor's last update on 22 Mar 2022. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Canada. Whether a site is taking part right now should be confirmed with that site. See every site.
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?
Results are not available in the source record.
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?
Pneumonia (time frame: Within 30 days post-operatively). The study lists 6 primary outcomes in all. 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 NCT05150548, which also lists 6 linked publications.
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 22 Mar 2022.