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

OBJECTIVEPseudarthrosis 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.METHODSA 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.RESULTSA 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.CONCLUSIONSA 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.

Potential of predictive computer models for preoperative patient selection to enhance overall quality-adjusted life years gained at 2-year follow-up: a simulation in 234 patients with adult spinal deformity.

OBJECTIVE Patients with adult spinal deformity (ASD) experience significant quality of life improvements after surgery. Treatment, however, is expensive and complication rates are high. Predictive analytics has the potential to use many variables to make accurate predictions in large data sets. A validated minimum clinically important difference (MCID) model has the potential to assist in patient selection, thereby improving outcomes and, potentially, cost-effectiveness. METHODS The present study was a retrospective analysis of a multiinstitutional database of patients with ASD. Inclusion criteria were as follows: age ≥ 18 years, radiographic evidence of ASD, 2-year follow-up, and preoperative Oswestry Disability Index (ODI) > 15. Forty-six variables were used for model training: demographic data, radiographic parameters, surgical variables, and results on the health-related quality of life questionnaire. Patients were grouped as reaching a 2-year ODI MCID (+MCID) or not (-MCID). An ensemble of 5 different bootstrapped decision trees was constructed using the C5.0 algorithm. Internal validation was performed via 70:30 data split for training/testing. Model accuracy and area under the curve (AUC) were calculated. The mean quality-adjusted life years (QALYs) and QALYs gained at 2 years were calculated and discounted at 3.5% per year. The QALYs were compared between patients in the +MCID and -MCID groups. RESULTS A total of 234 patients met inclusion criteria (+MCID 129, -MCID 105). Sixty-nine patients (29.5%) were included for model testing. Predicted versus actual results were 50 versus 40 for +MCID and 19 versus 29 for -MCID (i.e., 10 patients were misclassified). Model accuracy was 85.5%, with 0.96 AUC. Predicted results showed that patients in the +MCID group had significantly greater 2-year mean QALYs (p = 0.0057) and QALYs gained (p = 0.0002). CONCLUSIONS A successful model with 85.5% accuracy and 0.96 AUC was constructed to predict which patients would reach ODI MCID. The patients in the +MCID group had significantly higher mean 2-year QALYs and QALYs gained. This study provides proof of concept for using predictive modeling techniques to optimize patient selection in complex spine surgery.

How does the cervical spine respond to hyperkyphosis correction in Scheuermann's disease?

OBJECTIVE: This study aimed to quantify the response of the cervical spine to the surgical correction of Scheuermann's kyphosis (SK) and to postoperative proximal junctional kyphosis (PJK). METHODS: Fifty-nine patients (mean age 14.6 ± 2.3 years) were enrolled in the study: 35 patients in a thoracic SK (T-SK) group and 24 in a thoracolumbar SK (TL-SK) group. The mean follow-up period was 47.2 ± 17.6 months. Radiographic data, PJK-related complications, and patient-reported outcomes were compared between groups. RESULTS: The global kyphosis significantly decreased postoperatively, and similar correction rates were observed between the two groups (mean 47.1% ± 8.6% [T-SK] vs 45.8% ± 9.4% [TL-SK], p = 0.585). The cervical lordosis (CL) in the T-SK group notably decreased from 21.4° ± 13.3° to 13.1° ± 12.4° after surgery and was maintained at 14.9° ± 10.7° at the latest follow-up, whereas in the TL-SK group, CL considerably increased from 7.2° ± 10.7° to 11.7° ± 11.1° after surgery and to 13.8° ± 8.9° at the latest follow-up. PJK was identified in 16 patients (27.1%). Its incidence in the TL-SK group was notably higher than it was in the T-SK group (41.6% [n = 10] vs 17.1% [n = 6], p = 0.037). Compared with non-PJK patients, PJK patients had greater CL and lower pain scores on the Scoliosis Research Society-22 questionnaire (p < 0.05). CONCLUSIONS: Hyperkyphosis correction eventually resulted in reciprocal changes in the cervical spine, with CL notably decreased in the T-SK group but significantly increased in the TL-SK group. Patients developing PJK have increased CL, which seems to have a negative effect on patients' health-related quality of life.