The prevalence of mechanical complications, with radiologic and clinical manifestations, after surgery for adult spinal deformities is reported to be 30%, and more than 50% of these patients undergo revision surgery for treatment. (8) Soroceanu et al. reported that radiographic and implant-related complications accounted for 31.7%, and in 52.6% of these complications, reoperation for mechanical correction was required. (9) There are many aspects of ASD surgery with notable variability, including the occurrence of complications and outcomes. (10) GAPB is a system that is used to predict mechanical complications that occur after ASD surgery, including both patient-specific and radiological factors. (6) In this study, we constructed a model to predict mechanical complications after ASD surgery using GAPB factors.
Recently, several studies using deep learning algorithms, such as random forest, gradient boosting, and neural networks, have been conducted for the spine. (11) Yagi et al. created a post-surgical complication prediction model for ASD surgery in adults using spinal alignment, demographic data, and surgical invasiveness; 170 participants were enrolled in this study. A decision tree for 2-year postoperative complications was constructed and confirmed by splitting data in a 7:3 ratio for training and testing, (12) with the external validation of 25 ASD patients who underwent surgery at different hospitals. (12) For the test sample, the predictive model was 92% accurate, the AUC was 0.963, and the external validation was 84% accurate.
Lafage created a machine learning model to determine the upper vertebra in ASD surgery. (13) The samples were stratified into three groups: 70% for training, 15% for validation, and 15% for performance testing. A neural network model was used, and the results showed an accuracy of 81.0%, precision of 87.5%, and recall of 87.5%.
Pellise et al. created a model to predict the incidence of adverse events after ASD surgery using a random forest model. (14) The model was trained using 80% of the data for the training set and 20% for the test set and showed adequate predictive accuracy, with AUCs ranging from 0.67 to 0.92. (14) Durand et al. created a model for predicting blood transfusion following surgery for adult spinal deformities. (15) A total of 1029 patients were analyzed and divided into datasets for training (n = 824) and validation (n = 205). The random forest model showed an AUC of 0.85 (95% confidence interval 0.80–0.90) and was reported to show better predictive ability than single-decision tree models. (15) Ames et al. created a model to predict the cost of surgery for ASD. The regression tree and random forest models were used to predict the occurrence of treatment costs exceeding $100,000. (16) The results of the regression tree analysis using CTREE resulted in an adjusted R2 value of 56% at 90 days and 35.6% at 2 years of direct cost forecasting. Random C-forest regression analysis showed an adjusted R2 value of 57.4% at 90 days and 28.8% at 2 years of direct cost forecasts. Peng et al. created a model to predict proximal junctional kyphosis after surgery in adolescent patients with idiopathic scoliosis. (17) The random forest has great value for predicting the individual risk of developing proximal junctional kyphosis after long instrumentation and fusion surgery in patients with Lenke 5 adolescent idiopathic scoliosis. Jain created a model to predict discharge delay, medical complications, and readmission within 90 days after long-segment posterior lumbar spine fusion surgery (18) using logistic regression, random forest, and elastic net.
In our study, we created a model to predict the mechanical complications that occur after ASD surgery. We used logistic regression, gradient boosting, random forest, and deep neural networks. Important factors were BMD, BMI, relative lumbar lordosis score, lordosis distribution index score, and relative sagittal alignment score. The patients were randomly divided into training (70%) and test (30%) datasets. In the training set, the AUC for random forest was 1.000 and the accuracy was 1.000. In the test set, the AUC for random forest was 0.81 and the accuracy was 0.732. Random forest achieved the best predictive performance on the training and test dataset.
This study has several limitations. Because our models were built using retrospective data, future efforts to update these models are required. Additionally, the reasons for mechanical complications after ASD correction are multifactorial. Many factors affect the outcome of surgery, including the surgical method, upper level instrumentation, muscle mass, and various underlying conditions. These factors were excluded when the model was created.
However, the GAPB system is helpful in predicting mechanical complications after ASD surgery. (6) Noh et al. reported that the GAPB system was more meaningful in the moderately disproportioned and severely disproportioned GAP groups. We believe that it will be helpful to develop models that predict mechanical complications through machine learning.