Shinji Takahashi1, Hidetomi Terai2, Masatoshi Hoshino2, Tadao Tsujio3, Minori Kato4, Hiromitsu Toyoda4, Akinobu Suzuki4, Koji Tamai4, Akito Yabu4, Hiroaki Nakamura4. 1. Department of Orthopaedic Surgery, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahi-Machi, Abeno-Ku, Osaka, 545-8585, Japan. stakahashi@omu.ac.jp. 2. Department of Orthopaedic Surgery, Osaka City General Hospital, 2-15-16, Miyakojima Hon-Dori, Miyakojima-Ku, Osaka, Japan. 3. Department of Orthopaedic Surgery, Shiraniwa Hospital, 6-10-1. Shiraniwadai, Ikoma City, Nara, Japan. 4. Department of Orthopaedic Surgery, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahi-Machi, Abeno-Ku, Osaka, 545-8585, Japan.
Abstract
PURPOSE: An osteoporotic vertebral fracture (OVF) is a common disease that causes disabilities in elderly patients. In particular, patients with nonunion following an OVF often experience severe back pain and require surgical intervention. However, nonunion diagnosis generally takes more than six months. Although several studies have advocated the use of magnetic resonance imaging (MRI) observations as predictive factors, they exhibit insufficient accuracy. The purpose of this study was to create a predictive model for OVF nonunion using machine learning (ML). METHODS: We used datasets from two prospective cohort studies for OVF nonunion prediction based on conservative treatment. Among 573 patients with acute OVFs exceeding 65 years in age enrolled in this study, 505 were analyzed. The demographic data, fracture type, and MRI observations of both studies were analyzed using ML. The ML architecture utilized in this study included a logistic regression model, decision tree, extreme gradient boosting (XGBoost), and random forest (RF). The datasets were processed using Python. RESULTS: The two ML algorithms, XGBoost and RF, exhibited higher area under the receiver operating characteristic curves (AUCs) than the logistic regression and decision tree models (AUC = 0.860 and 0.845 for RF and XGBoost, respectively). The present study found that MRI findings, anterior height ratio, kyphotic angle, BMI, VAS, age, posterior wall injury, fracture level, and smoking habit ranked as important features in the ML algorithms. CONCLUSION: ML-based algorithms might be more effective than conventional methods for nonunion prediction following OVFs.
PURPOSE: An osteoporotic vertebral fracture (OVF) is a common disease that causes disabilities in elderly patients. In particular, patients with nonunion following an OVF often experience severe back pain and require surgical intervention. However, nonunion diagnosis generally takes more than six months. Although several studies have advocated the use of magnetic resonance imaging (MRI) observations as predictive factors, they exhibit insufficient accuracy. The purpose of this study was to create a predictive model for OVF nonunion using machine learning (ML). METHODS: We used datasets from two prospective cohort studies for OVF nonunion prediction based on conservative treatment. Among 573 patients with acute OVFs exceeding 65 years in age enrolled in this study, 505 were analyzed. The demographic data, fracture type, and MRI observations of both studies were analyzed using ML. The ML architecture utilized in this study included a logistic regression model, decision tree, extreme gradient boosting (XGBoost), and random forest (RF). The datasets were processed using Python. RESULTS: The two ML algorithms, XGBoost and RF, exhibited higher area under the receiver operating characteristic curves (AUCs) than the logistic regression and decision tree models (AUC = 0.860 and 0.845 for RF and XGBoost, respectively). The present study found that MRI findings, anterior height ratio, kyphotic angle, BMI, VAS, age, posterior wall injury, fracture level, and smoking habit ranked as important features in the ML algorithms. CONCLUSION: ML-based algorithms might be more effective than conventional methods for nonunion prediction following OVFs.
Authors: S Takahashi; M Hoshino; K Takayama; K Iseki; R Sasaoka; T Tsujio; H Yasuda; T Sasaki; F Kanematsu; H Kono; H Toyoda; H Nakamura Journal: Osteoporos Int Date: 2016-06-25 Impact factor: 4.507
Authors: S Takahashi; M Hoshino; K Takayama; K Iseki; R Sasaoka; T Tsujio; H Yasuda; T Sasaki; F Kanematsu; H Kono; H Toyoda; H Nakamura Journal: Osteoporos Int Date: 2016-08-30 Impact factor: 4.507
Authors: D M Kado; T Duong; K L Stone; K E Ensrud; M C Nevitt; G A Greendale; S R Cummings Journal: Osteoporos Int Date: 2003-06-24 Impact factor: 4.507