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

ABSTRACT: 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.

DISEASE(S): Predictive Model,Diverticulitis,Inflammatory Bowel Diseases,Colorectal Cancer,Colectomy,Complications, Postoperative,Postoperative Complications

PROVIDER: 2397814 | ecrin-mdr-crc |

REPOSITORIES: ECRIN MDR