J Orthop Res 2020 Jun 3. Epub 2020 Jun 3.
Orthopaedics, USU-Walter Reed Department of Surgery, 4301 Jones Bridge Rd, Bethesda, MD, 20814, USA.
Treatment decisions in patients with metastatic bone disease rely on accurate survival estimation. We developed the original PATHFx models using expensive, proprietary software and now seek to provide a more cost effective solution. Using open source machine learning software to create PATHFx version 2.0, we asked whether PATHFx 2.0 could be created using open source methods and externally validated in two unique patient populations. The training set of a well-characterized, database records of 189 patients and the bnlearn package within R© Version 3.5.1 (R Foundation for Statistical Computing), was used to establish a series of Bayesian belief network models designed to predict survival at 1, 3, 6, 12, 18 and 24 months. Each was externally validated in both a Scandinavian (n=815 patients) and a Japanese (n=261 patients) dataset. Brier scores and receiver operating characteristic (ROC) curves to assessed discriminatory ability. Decision curve analysis (DCA) evaluated whether models should be used clinically. DCA showed that the model should be used clinically at all time points in the Scandinavian dataset. For the 1-month time point, DCA of the Japanese dataset suggested expect better outcomes assuming all patients will survive greater than one month. Brier scores for each curve demonstrate that the models are accurate at each time point. Statement of Clinical Significance- We successfully transitioned to PATHFx 2.0 using open source software and externally validated it in two unique patient populations, which can be used as a cost effective option to guide surgical decisions in patients with metastatic bone disease. This article is protected by copyright. All rights reserved.