Tools and techniquesThe Seattle spine score: Predicting 30-day complication risk in adult spinal deformity surgery

•The statistical model could predict complications with 75% accuracy, based on routinely collected preoperative variables.•The statistical model significantly outperformed senior clinicians in predicting complication likelihood.•The decision support system significantly improved the ability of clinicians to predict postoperative complications.•Applying data-driven decision support tools to surgical practice can improve decision making quality and patient safety.

BackgroundComplication rates in complex spine surgery range from 25% to 80% in published studies. Numerous studies have shown that surgeons are not able to accurately predict whether patients are likely to face post-operative complications, in part due to biases based on individual experience. The purpose of this study was to develop and evaluate a predictive risk model and decision support system that could accurately predict the likelihood of 30-day postoperative complications in complex spine surgery based on routinely measured preoperative variables.MethodsPreoperative and postoperative data were collected for 136 patients by reviewing medical records. Logistic regression analysis (LRA) was applied to develop the predictive algorithm based on patient demographic parameters, including age, gender, and co-morbidities, including obesity, diabetes, hypertension and anemia. We additionally compared the performance of the predictive model to a spine surgeon's ability to predict patient complications using signal detection theory statistics representing sensitivity and response bias (A′ and B″ respectively). We developed a decision support system tool, based on the LRA predictive algorithm, that was able to provide a numeric probabilistic likelihood statistic representing an individual patient's risk of developing a complication within the first 30 days after surgery.ResultsThe predictive model was significant (χ2 = 16.242, p < 0.05), showed good fit, and was calibrated by using area under the receiver operating characteristics curve analysis (AUROC = 0.712, p < 0.01). The model yielded a predictive accuracy of 75.0%. It was validated by splitting the data set, comparing subset models, and testing them with unknown data. Validation also involved comparing the classification of cases by experts with the classification of cases by the model. The model significantly improved the classification accuracy of physicians involved in the delivery of complex spine surgical care.ConclusionsThe application of technology and data-driven tools to advanced surgical practice has the potential to improve decision making quality, service quality and patient safety.