Development of a preoperative predictive model for major complications following adult spinal deformity surgery

作者：Scheer Justin K^*; Smith Justin S; Schwab Frank; Lafage Virginie; Shaffrey Christopher I; Bess Shay; Daniels Alan H; Hart Robert A; Protopsaltis Themistocles S; Mundis Gregory M Jr; Sciubba Daniel M; Ailon Tamir; Burton Douglas C; Klineberg Eric; Ames Christopher P

来源：Journal of Neurosurgery-Spine, 2017, 26(6): 736-743.

DOI：10.3171/2016.10.SPINE16197

• complications
• adult spinal deformity
• ASD
• predictive modeling
• scoliosis
• sagittal malalignment
• decision tree

OBJECTIVE The operative management of patients with adult spinal deformity (ASD) has a high complication rate and it remains unknown whether baseline patient characteristics and surgical variables can predict early complications (intra-operative and perioperative [within 6 weeks]). The development of an accurate preoperative predictive model can aid in patient counseling, shared decision making, and improved surgical planning. The purpose of this study was to develop a model based on baseline demographic, radiographic, and surgical factors that can predict if patients will sustain an intra-operative or perioperative major complication.
METHODS This study was a retrospective analysis of a prospective, multicenter ASD database. The inclusion criteria were age 18 years and the presence of ASD. In total, 45 variables were used in the initial training of the model including demographic data, comorbidities, modifiable surgical variables, baseline health-related quality of life, and coronal and sagittal radiographic parameter's. Patients were grouped as either having at least 1 major intraoperative or perioperative complication (COMP group) or not (NOCOMP group). An ensemble of decision trees was constructed utilizing the C5.0 algorithm with 5 different bootstrapped models. Internal validation was accomplished via a 70/30 data split for training and testing each model, respectively. Overall accuracy, the area under the receiver operating characteristic (AUROC) curve, and predictor importance were calculated.
RESULTS Five hundred fifty-seven patients were included: 409 (73.4%) in the NOCOMP group, and 148 (26.6%) in the COMP group. The overall model accuracy was 87.6% correct with an AUROC curve of 0.89 indicating a very good model fit. Twenty variables were determined to be the top predictors (importance >= 0.90 as determined by the model) and included (in decreasing importance): age, leg pain, Oswestry Disability Index, number of decompression levels, number of interbody fusion levels, Physical Component Summary of the SF-36, Scoliosis Research Society (SRS)-Schwab coronal curve type, Charlson Comorbidity Index, SRS activity, T-1 pelvic angle, American Society of Anesthesiologists grade, presence of osteoporosis, pelvic tilt, sagittal vertical axis, primary versus revision surgery, SRS pain, SRS total, use of bone morphogenetic protein, use of iliac crest graft, and pelvic incidence lumbar lordosis mismatch.
CONCLUSIONS A successful model (87% accuracy, 0.89 AUROC curve) was built predicting major intraoperative or perioperative complications following ASD surgery. This model can provide the foundation toward improved education and point-of-care decision making for patients undergoing ASD surgery.

• 出版日期2017-6