Clinical trial NCT05150548: Predictive Time-to-Event Model for Major Medical Complications After Colectomy
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
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Colorectal Cancer
Inflammatory Bowel Diseases
Diverticulitis
Colectomy
Predictive Model
Complications, Postoperative
Lead sponsor: University of British Columbia
Sponsor class: OTHER
First posted: 2021-12-09
Enrollment: 130000
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: RETROSPECTIVE
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.

Brief 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.

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.

Eligibility criteria: 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

Primary outcomes:
measure: Pneumonia
timeFrame: Within 30 days post-operatively
measure: Myocardial Infarction (MI)
timeFrame: Within 30 days post-operatively
measure: Cerebral Vascular Event (CVA)
timeFrame: Within 30 days post-operatively
measure: Venous Thromboembolism (VTE)
timeFrame: Within 30 days post-operatively
measure: Acute Renal Failure (ARF)
timeFrame: Within 30 days post-operatively
measure: Sepsis or septic shock
timeFrame: Within 30 days post-operatively
Secondary outcomes:
Other outcomes:
Locations:
city: Vancouver
country: Canada
facility: St. Paul's Hospital
state: British Columbia
Study officials:
affiliation: University of British Columbia
name: Janny Xue Chen Ke, MD
role: PRINCIPAL_INVESTIGATOR
References:
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.
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.
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. doi: 10.1001/archsurg.138.6.596.
citation: 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. doi: 10.2196/jmir.5870.
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.