Development of Machine Learning Algorithms to Predict Being Lost to Follow-up After Hip Arthroscopy for Femoroacetabular Impingement Syndrome.

ABSTRACT:

Purpose

To determine factors predictive of patients who are at risk for being lost to follow-up after hip arthroscopy for femoroacetabular impingement syndrome (FAIS).

Methods

A prospective clinical repository was queried between January 2012 and October 2017 and all patients who underwent hip arthroscopy for primary or revision FAIS with minimum 2-year follow-up were included. A total of 27 potential risk factors for loss to follow-up were available and tested for predictive value. An 80:20 random sample split of all patients was performed to create training and testing sets. Cross-validation, minimum Bayes information criteria, and adaptive machine-learning algorithms were used to develop the predictive model. The model with the best predictive performance was selected bas

SUBMITTER: Kunze KN 

PROVIDER: S-EPMC7588627 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature