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

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.

Recruitment Status

UNKNOWN

Total Enrollment

130000 participants

Study Classification

OBSERVATIONAL

Study Start Date

2021-12-01

Study Completion Date

2022-12-31

Brief Summary

Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.

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.

Related Clinical Trials

Explore similar clinical trials based on study characteristics and research focus.

Same-Day Colectomy: is it Safe for Patients?

NCT07176715

Colorectal Colectomy Colectomy Left/Right/Total Under Laparotomy

RECRUITING NA

Intestinal Microbiota and Postoperative Ileus After Colorectal Surgery

NCT05512741

Postoperative Ileus Surgery-Complications

COMPLETED

Inflammatory Markers After COloRectal Surgery)

NCT01510314

Infectious Complications After Colorectal Surgery

COMPLETED NA

Surgical Decision Making Among People With Inflammatory Bowel Disease

NCT00438711

Inflammatory Bowel Disease

COMPLETED

Colonography Versus Colonoscopy in High Risk Patient

NCT00748449

Colorectal Cancer

TERMINATED NA

Detailed Description

Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.

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.

Conditions

See the medical conditions and disease areas that this research is targeting or investigating.

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

Study Design

Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.

Observational Model Type

COHORT

Study Time Perspective

RETROSPECTIVE

Study Groups

Review each arm or cohort in the study, along with the interventions and objectives associated with them.

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.

No Intervention

Intervention Type OTHER

Not applicable, non-interventional study

Interventions

Learn about the drugs, procedures, or behavioral strategies being tested and how they are applied within this trial.

No Intervention

Not applicable, non-interventional study

Intervention Type OTHER

Eligibility Criteria

Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.

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

Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes