• Alessandro Boaro, Michael Wells, John Chi, Yi Lu, Timothy R Smith, Michael W Groff, and Hasan Zaidi.
• Computational Neuroscience Outcomes Center, Department of Neurosurgery, Brigham and Women's Hospital, Harvard University, Boston, Massachusetts, USA; Department of Neurosurgery, Brigham and Women's Hospital, Harvard University, Boston, Massachusetts, USA. Electronic address: aboaro@bwh.harvard.edu.
• World Neurosurg. 2020 Aug 1; 140: e203-e211.

BackgroundMajor complications after spine metastasis surgery are prioritized in the literature with little consideration of the more frequent minor events such as pneumonia or urinary tract infection. We analyzed incidence and risk factors of postsurgical complications in patients with spinal metastasis extracted from the National Surgical Quality Improvement Program (NSQIP). We also developed a useful predictive model to estimate the probability of occurrence of complications.MethodsA total of 1176 patients diagnosed with spinal metastasis were extracted from NSQIP. Variables screened included age, sex, tumor location, patient's functional status, comorbidities, laboratory values, and case urgency. Two multivariate logistic regression models were designed to evaluate risk factors and likelihood of event occurrence.ResultsMinor events occurred twice as frequently compared with major complications (36% vs. 18% of patients). The most common major event was death (10%); the most frequent minor event was need for postoperative transfusion (29.4%). In the multivariate analysis, elderly age, emergency case, preoperative leukocytosis, and smoking status retained significance for major complications; American Society of Anesthesiologists classes 4-5, low hematocrit levels, and intradural extramedullary location of the tumor retained significance for minor complications. The predictive models designed explained 72% of the variability in major complications occurrence and 67% for minor events.ConclusionsSmoking status and emergent surgery were found to be the strongest independent predictors of major complications, whereas higher American Society of Anesthesiologists class showed a greater association with minor events. The predictive models produced can be a useful aid for surgeons to identify those patients who are at greater risk of developing postoperative adverse events.Copyright © 2020 Elsevier Inc. All rights reserved.

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