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Machine-learning-based approach for nonunion prediction following osteoporotic vertebral fractures

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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.

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Funding

This study was supported by grants from the Health and Labour Sciences Research Grants for Comprehensive Research on Aging and Health, Japan.

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Authors and Affiliations

  1. Department of Orthopaedic Surgery, Osaka Metropolitan University Graduate School of Medicine, 1-4-3 Asahi-Machi, Abeno-Ku, Osaka, 545-8585, Japan

    Shinji Takahashi, Minori Kato, Hiromitsu Toyoda, Akinobu Suzuki, Koji Tamai, Akito Yabu & Hiroaki Nakamura

  2. Department of Orthopaedic Surgery, Osaka City General Hospital, 2-15-16, Miyakojima Hon-Dori, Miyakojima-Ku, Osaka, Japan

    Hidetomi Terai & Masatoshi Hoshino

  3. Department of Orthopaedic Surgery, Shiraniwa Hospital, 6-10-1. Shiraniwadai, Ikoma City, Nara, Japan

    Tadao Tsujio

Authors
  1. Shinji Takahashi
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  2. Hidetomi Terai
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  3. Masatoshi Hoshino
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Correspondence to Shinji Takahashi.

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Takahashi, S., Terai, H., Hoshino, M. et al. Machine-learning-based approach for nonunion prediction following osteoporotic vertebral fractures. Eur Spine J 32, 3788–3796 (2023). https://doi.org/10.1007/s00586-022-07431-4

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  • DOI: https://doi.org/10.1007/s00586-022-07431-4

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