Development and comparison of 1-year survival models in patients with primary bone sarcomas: External validation of a Bayesian belief network model and creation and external validation of a new gradient boosting machine model.

Background: Bone sarcomas often present late with advanced stage at diagnosis and an according, varying short-term survival. In 2016, Nandra et al. generated a Bayesian belief network model for 1-year survival in patients with bone sarcomas.

A Machine-Learning Algorithm to Predict the Likelihood of Prolonged Opioid Use Following Arthroscopic Hip Surgery.

Purpose: To develop a machine-learning algorithm and clinician-friendly tool predicting the likelihood of prolonged opioid use (>90 days) following hip arthroscopy.

Methods: The Military Data Repository was queried for all adult patients undergoing arthroscopic hip surgery between 2012 and 2017. Demographic, health history, and prescription records were extracted for all included patients.

Utilization of the Department of Defense Peer-Reviewed Orthopaedic Research Program (PRORP): Combating Musculoskeletal Disease With PRORP.

Introduction: Established in 2009, the Department of Defense (DoD) Peer-Reviewed Orthopaedic Research Program (PRORP) is an annual funding program for orthopaedic research that seeks to develop evidence for new clinical practice guidelines, procedures, technologies, and drugs. The aim was to help reduce the burden of injury for wounded Service members, Veterans, and civilians and to increase return-to-duty and return-to-work rates. Relative to its burden of disease, musculoskeletal injuries (MSKIs) are one of the most disproportionately underfunded conditions.

Can Predictive Modeling Tools Identify Patients at High Risk of Prolonged Opioid Use After ACL Reconstruction?

Background: Machine-learning methods such as the Bayesian belief network, random forest, gradient boosting machine, and decision trees have been used to develop decision-support tools in other clinical settings. Opioid abuse is a problem among civilians and military service members, and it is difficult to anticipate which patients are at risk for prolonged opioid use.

Questions/purposes: (1) To build a cross-validated model that predicts risk of prolonged opioid use after a specific orthopaedic procedure (ACL reconstruction), (2) To describe the relationships between prognostic and outcome variables, and (3) To determine the clinical utility of a predictive model using a decision curve analysis (as measured by our predictive system's ability to effectively identify high-risk patients and allow for preventative measures to be taken to ensure a successful procedure process).