Development of a validated computer-based preoperative predictive model for pseudarthrosis with 91% accuracy in 336 adult spinal deformity patients

假关节 医学 接收机工作特性 脊柱侧凸 骨不连 矢状面 射线照相术 脊柱融合术 外科 畸形 后凸 放射科 内科学
作者
Justin K. Scheer,Tae-Min Oh,Justin S. Smith,Christopher I. Shaffrey,Alan H. Daniels,Daniel M. Sciubba,D. Kojo Hamilton,Themistocles S. Protopsaltis,Peter G. Passias,Robert A. Hart,Douglas C. Burton,Shay Bess,Renaud Lafage,Virginie Lafage,Frank J. Schwab,Eric O. Klineberg,Christopher P. Ames,_ _
出处
期刊:Neurosurgical Focus [American Association of Neurological Surgeons]
卷期号:45 (5): E11-E11 被引量:34
标识
DOI:10.3171/2018.8.focus18246
摘要

OBJECTIVE Pseudarthrosis can occur following adult spinal deformity (ASD) surgery and can lead to instrumentation failure, recurrent pain, and ultimately revision surgery. In addition, it is one of the most expensive complications of ASD surgery. Risk factors contributing to pseudarthrosis in ASD have been described; however, a preoperative model predicting the development of pseudarthrosis does not exist. The goal of this study was to create a preoperative predictive model for pseudarthrosis based on demographic, radiographic, and surgical factors. METHODS A retrospective review of a prospectively maintained, multicenter ASD database was conducted. Study inclusion criteria consisted of adult patients (age ≥ 18 years) with spinal deformity and surgery for the ASD. From among 82 variables assessed, 21 were used for model building after applying collinearity testing, redundancy, and univariable predictor importance ≥ 0.90. Variables included demographic data along with comorbidities, modifiable surgical variables, baseline coronal and sagittal radiographic parameters, and baseline scores for health-related quality of life measures. Patients groups were determined according to their Lenke radiographic fusion type at the 2-year follow-up: bilateral or unilateral fusion (union) or pseudarthrosis (nonunion). A decision tree was constructed, and internal validation was accomplished via bootstrapped training and testing data sets. Accuracy and the area under the receiver operating characteristic curve (AUC) were calculated to evaluate the model. RESULTS A total of 336 patients were included in the study (nonunion: 105, union: 231). The model was 91.3% accurate with an AUC of 0.94. From 82 initial variables, the top 21 covered a wide range of areas including preoperative alignment, comorbidities, patient demographics, and surgical use of graft material. CONCLUSIONS A model for predicting the development of pseudarthrosis at the 2-year follow-up was successfully created. This model is the first of its kind for complex predictive analytics in the development of pseudarthrosis for patients with ASD undergoing surgical correction and can aid in clinical decision-making for potential preventative strategies.
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