Prediction of Ureteral Injury During Colorectal Surgery Using Machine Learning.

ABSTRACT:

Background

Ureteral injury (UI) is a rare but devastating complication during colorectal surgery. Ureteral stents may reduce UI but carry risks themselves. Risk predictors for UI could help target the use of stents, but previous efforts have relied on logistic regression (LR), shown moderate accuracy, and used intraoperative variables. We sought to use an emerging approach in predictive analytics, machine learning, to create a model for UI.

Methods

Patients who underwent colorectal surgery were identified in the National Surgical Quality Improvement Program (NSQIP) database. Patients were split into training, validation, and test sets. The primary outcome was UI. Three machine learning approaches were tested including random forest (RF), gradient boosting (XGB), and neural n

SUBMITTER: Chen KA 

PROVIDER: S-EPMC10622328 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature