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

Colorectal Cancer Clinical Trial

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

Clinical Trial Details — Status: Active, not recruiting

Administrative data

• NCT number: NCT05150548
• Other study ID #: H21-02670
• Status: Active, not recruiting
• Start date: December 1, 2021
• Est. completion date: December 31, 2022

Study information

• Verified date: March 2022
• Source: University of British Columbia
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical 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. 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.

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.

Recruitment information / eligibility

• Status: Active, not recruiting
• Enrollment: 130000
• Est. completion date: December 31, 2022
• Est. primary completion date: December 31, 2022
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years and older

Eligibility

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

Related Conditions & MeSH terms

• Colectomy
• Colorectal Cancer
• Complications, Postoperative
• Diverticulitis
• Inflammatory Bowel Diseases
• Postoperative Complications
• Predictive Model

Intervention

Other:
• No Intervention
Not applicable, non-interventional study

Locations

Country	Name	City	State
Canada	St. Paul's Hospital	Vancouver	British Columbia

Sponsors (1)

Lead Sponsor	Collaborator
University of British Columbia

Country where clinical trial is conducted

Canada, 

References & Publications (5)

Luo 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. — View Citation

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. — View Citation

Riley 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. Erratum in: Stat Med. 2019 Dec 30;38(30):5672. — View Citation

Scarborough 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. — View Citation

Thompson 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. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Pneumonia	Occurrence of pneumonia within 30 days post-operatively.	Within 30 days post-operatively
Primary	Myocardial Infarction (MI)	Occurrence of Myocardial Infarction within 30 days post-operatively.	Within 30 days post-operatively
Primary	Cerebral Vascular Event (CVA)	Occurrence of Myocardial Infarction within 30 days post-operatively.	Within 30 days post-operatively
Primary	Venous Thromboembolism (VTE)	Occurrence of Venous Thromboembolism within 30 days post-operatively.	Within 30 days post-operatively
Primary	Acute Renal Failure (ARF)	Occurrence of Acute Renal Failure within 30 days post-operatively.	Within 30 days post-operatively
Primary	Sepsis or septic shock	Occurrence of sepsis or septic shock within 30 days post-operatively.	Within 30 days post-operatively