The Seattle spine score: Predicting 30-day complication risk in adult spinal deformity surgery

Quinlan D. Buchlak, Vijay Yanamadala, Jean Christophe Leveque, Alicia Edwards, Kellen Nold, Rajiv Sethi

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

Abstract

Background Complication 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. Methods Preoperative 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. Results The 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. Conclusions The application of technology and data-driven tools to advanced surgical practice has the potential to improve decision making quality, service quality and patient safety.

Original languageEnglish (US)
Pages (from-to)247-255
Number of pages9
JournalJournal of Clinical Neuroscience
Volume43
DOIs
StatePublished - Sep 2017
Externally publishedYes

Keywords

  • Predictive clinical decision support
  • Risk stratification
  • Spine surgery
  • Surgical complications

ASJC Scopus subject areas

  • Surgery
  • Neurology
  • Clinical Neurology
  • Physiology (medical)

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