Surgical risk stratification based on preoperative risk factors in adult spinal deformity

Abstract BACKGROUND CONTEXT Corrective surgery for adult spinal deformity (ASD) improves health-related quality of life but has high complication rates. Predicting a patient's risk of perioperative and late postoperative complications is difficult, although several potential risk factors have been reported. PURPOSE To establish an accurate, ASD-specific model for predicting the risk of postoperative complications, based on baseline demographic, radiographic, and surgical invasiveness data in a retrospective case series. STUDY DESIGN/SETTING Multicentered retrospective review and the surgical risk stratification. PATIENT SAMPLE One hundred fifty-one surgically treated ASD at our hospital for risk analysis and model building and 89 surgically treated ASD at 2 other our hospitals for model validation. OUTCOME MEASURES HRQoL measures and surgical complications. METHODS We analyzed demographic and medical data, including complications, for 151 adults with ASD who underwent surgery at our hospital and were followed for at least 2 years. Each surgical risk factor identified by univariate analyses was assigned a value based on its odds ratio, and the values of all risk factors were summed to obtain a surgical risk score (range 0–20). We stratified risk scores into grades (A–D) and analyzed their correlations with complications. We validated the model using data from 89 patients who underwent ASD surgery at two other hospitals. RESULTS Complications developed in 48% of the patients in the model-building cohort. Univariate analyses identified 10 demographic, physical, and surgical risk indicators, with odds ratios from 5.4 to 1.4, for complications. Our risk-grading system showed good calibration and discrimination in the validation cohort. The complication rate increased with and correlated well with the risk grade using receiver operating characteristic curves. CONCLUSIONS This simple, ASD-specific model uses readily accessible indicators to predict a patient's risk of perioperative and postoperative complications and can help surgeons adjust treatment strategies for best outcomes in high-risk patients.