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

Study Overview
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Brief Summary
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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
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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.

Official Title
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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

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

Intervention / Treatment
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* Other: No Intervention


Participation Criteria
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Eligibility Criteria
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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

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Entire Cohort<br>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<br>* Not applicable, non-interventional study<br>|

What is the study measuring?
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Primary Outcome Measures

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