Development of a Risk Prediction Model With Improved Clinical Utility in Elective Cervical and Lumbar Spine Surgery. Issue 9 (1st May 2020)

• Record Type: Journal Article
• Authors: Broda, Andrew; Sanford, Zachary; Turcotte, Justin; Patton, Chad
• Is Part Of: Spine. Volume 45:Issue 9(2020)
• Publication Date: 2020-05-01
• Subjects: cervical fusion -- complications -- CPT code -- lumbar fusion -- outcomes -- predictive modeling -- risk stratification -- spine surgery; Spine -- Abnormalities -- Periodicals; Spine -- Diseases -- Periodicals; Spine -- Surgery -- Periodicals; 616.73005
• DOI: 10.1097/BRS.0000000000003317
• Languages: English
• ISSNs: 0362-2436

Abstract

Abstract : Study Design: Retrospective cohort. Objective: We present a universal model of risk prediction for patients undergoing elective cervical and lumbar spine surgery. Summary of Background Data: Previous studies illustrate predictive risk models as possible tools to identify individuals at increased risk for postoperative complications and high resource utilization following spine surgery. Many are specific to one condition or procedure, cumbersome to calculate, or include subjective variables limiting applicability and utility. Methods: A retrospective cohort of 177, 928 spine surgeries (lumbar (L) Ln = 129, 800; cervical (C) Cn = 48, 128) was constructed from the 2012 to 2016 American College of Surgeons National Surgical Quality Improvement Project (ACS-NSQIP) database. Cases were identified by Current Procedural Terminology (CPT) codes for cervical fusion, lumbar fusion, and lumbar decompression laminectomy. Significant preoperative risk factors for postoperative complications were identified and included in logistic regression. Sum of odds ratios from each factor was used to develop the Universal Spine Surgery (USS) score. Model performance was assessed using receiver-operating characteristic (ROC) curves and tested on 20% of the total sample. Results: Eighteen risk factors were identified, including sixteen found to be significant outcomes predictors. At least one complication was present among 11.1% of patients, the most common of which included bleeding requiring transfusion (4.86%), surgical site infection (1.54%), and urinary tract infection (1.08%). Complication rate increased as a function of the model score and ROC area under the curve analyses demonstrated fair predictive accuracy (lumbar = 0.741; cervical = 0.776). There were no significant deviations between score development and testing datasets. Conclusion: We present the Universal Spine Surgery score as a robust, easily administered, and cross-validated instrument to quickly identify spine surgery candidates at increased risk for postoperative complications and high resource utilization without need for algorithmic software. This may serve as a useful adjunct in preoperative patient counseling and perioperative resource allocation. Level of Evidence: 3 Abstract : Supplemental Digital Content is available in the textPrevious studies illustrate risk models to identify individuals at risk for postoperative complications and high resource utilization following spine surgery. Many are specific to one condition or procedure or too cumbersome to calculate manually. We present a universal risk prediction model for patients undergoing elective cervical and lumbar spine surgery.