Predictive Time-to-Event Model for Major Medical Complications After Colectomy

March 7, 2022 updated by: Janny Ke, University of British Columbia

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.

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.

Study Overview

Status

Active, not recruiting

Conditions

Intervention / Treatment

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

  1. 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.
  2. 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 Type

Observational

Enrollment (Anticipated)

130000

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • Canada
    • British Columbia
      • Vancouver, British Columbia, Canada, V6Z 1Y6
        • St. Paul's Hospital

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

18 years and older (Adult, Older Adult)

Accepts Healthy Volunteers

Yes

Genders Eligible for Study

All

Sampling Method

Probability Sample

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.

Description

Inclusion Criteria:

  • undergoing elective colectomy
  • data has been collected in the NSQIP® Procedure Targeted Colectomy dataset from 2014-2019

Exclusion Criteria:

  • 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

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
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.
Other: No Intervention
Not applicable, non-interventional study

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Pneumonia
Time Frame: Within 30 days post-operatively
Occurrence of pneumonia within 30 days post-operatively.
Within 30 days post-operatively
Myocardial Infarction (MI)
Time Frame: Within 30 days post-operatively
Occurrence of Myocardial Infarction within 30 days post-operatively.
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.
Within 30 days post-operatively
Venous Thromboembolism (VTE)
Time Frame: Within 30 days post-operatively
Occurrence of Venous Thromboembolism within 30 days post-operatively.
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.
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.
Within 30 days post-operatively

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

University of British Columbia

Investigators

  • Principal Investigator: Janny Xue Chen Ke, MD, University of British Columbia

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

December 1, 2021

Primary Completion (Anticipated)

December 31, 2022

Study Completion (Anticipated)

December 31, 2022

Study Registration Dates

First Submitted

November 25, 2021

First Submitted That Met QC Criteria

November 25, 2021

First Posted (Actual)

December 9, 2021

Study Record Updates

Last Update Posted (Actual)

March 22, 2022

Last Update Submitted That Met QC Criteria

March 7, 2022

Last Verified

March 1, 2022

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • H21-02670

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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