Machine-learning prediction of postoperative complications after high tibial osteotomy for canine cranial cruciate ligament disease.

ABSTRACT:

Objective

The aim of this study was to develop and internally validate a machine-learning algorithm, PROSPECT (Predicting Risk Of Surgical complications aftEr CCWO and TPLO), using clinical variables to predict postoperative complications in dogs undergoing high tibial osteotomy for cranial cruciate ligament disease (CrCLD).

Study design

Retrospective multivariable prediction model development.

Sample population

Stifles (n = 670) and dogs (n = 555).

Methods

Complication data with a minimum follow up of 28 days were collected. Clinical variables were preprocessed for machine learning and interaction features were engineered. A multioutput eXtreme Gradient Boosting model was trained on 80% of the sample to predict minor, surgical, and medical complications indepe

SUBMITTER: Low D 

PROVIDER: S-EPMC12528822 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature