A Predictive Model to Identify Treatment-related Risk Factors for Odontoid Fracture Nonunion Using Machine Learning

Author:

Leister Iris1234ORCID,Haider Thomas5,Vogel Matthias12,Vastmans Jan1,Langthaler Patrick467,Mattiassich Georg8910,Christ Alexandra5,Etschmaier Martin9,Eijkenboom Alexander1,Burghuber Julia10,Kindermann Harald11,Mach Orpheus12,Maier Doris12,Högel Florian12

Affiliation:

1. Spinal Cord Injury Center, BG Trauma Center Murnau, Murnau, Germany

2. ParaMove, SCI Research Unit, BG Trauma Center Murnau, Murnau

3. Institute of Molecular Regenerative Medicine, Paracelsus Medical University, Salzburg, Austria

4. Spinal Cord Injury and Tissue Regeneration Center Salzburg (SCI-TReCS), Paracelsus Medical University, Salzburg, Austria

5. Department of Orthopedics and Trauma Surgery, Medical University of Vienna, Vienna, Austria

6. Department of Neurology, Christian Doppler University Hospital, Paracelsus Medical University and Centre for Cognitive Neuroscience, Austria

7. Department of Mathematics, Paris Lodron University, Salzburg, Austria

8. Department of Orthopedics and Trauma Surgery, Klinik Diakonissen Schladming, Schladming, Austria

9. Department of Trauma Surgery, AUVA Trauma Center Graz, Graz, Austria

10. Department of Trauma Surgery, AUVA Trauma Center Linz, Linz, Austria

11. Department of Marketing and Electronic Business, University of Applied Sciences Upper Austria, Steyr, Austria

Abstract

Study Design. Multicenter retrospective analysis of routinely collected data. Objective. The underlying aim of this study was to identify potential treatment-related risk factors for odontoid fracture nonunion while accounting for known patient- and injury-related risk factors. Summary of Background Data. Type II and III odontoid fractures represent the most common cervical spine fracture in elderly patients and are associated with a relatively high nonunion rate. The management of odontoid fractures is controversial and treatment strategies range from conservative treatment to extensive surgical stabilization and fusion. Methods. A total of 415 individuals who sustained odontoid fracture and were treated in either of four tertiary referral centers in Austria and Germany were included in the study. We included the following potential contributing factors for fracture nonunion in cross-validated extreme gradient boosted (XGBoost) and binary logistic regression models: age, gender, fracture displacement, mechanism of injury (high vs. low energy), fracture classification (Anderson II vs. III), presence of comorbidities (Charlson comorbidity index), and treatment (conservative, anterior screw fixation with one or two screws, posterior C1/C2 spondylodesis, cervico-occipital C0–C4 fusion). Results. In our cohort, 187 (45%) had radiologically confirmed odontoid nonunion six months postinjury. The odds for nonunion increase significantly with age, and are lower in type III compared to type II fractures. Also, odds for nonunion are significantly lower in posterior C1/C2 spondylodesis, and C0–C4 fusion compared to conservative treatment. Importantly, odds are not statistically significantly lower in the group treated with anterior screw fixation compared to conservative treatment. The factors gender, fracture displacement, mechanism of injury, and the presence of comorbidities did not produce significant odds. Conclusion. Higher age, type II fractures, and conservative treatment are the main risk factors for odontoid nonunion. Anterior screw fixation did not differ significantly from conservative treatment in terms of fracture union. Level of Evidence. 3.

Publisher

Ovid Technologies (Wolters Kluwer Health)

Subject

Neurology (clinical),Orthopedics and Sports Medicine

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