BACKGROUND: Complications rates vary across spinal surgery procedures and are difficult to predict due to heterogeneity in patient characteristics, surgical methods, and hospital volume. Incorporation of predictive models for complications may guide surgeon decision making and improve outcomes. METHODS: We evaluate current independently validated predictive models for complications in spinal surgery with respect to study design and model generation, accuracy, reliability, and utility. We conducted our search using Preferred Reporting Items for Systematic Review and Meta-analysis guidelines and the Participants, Intervention, Comparison, Outcomes, Study Design model through the PubMed and Ovid Medline databases. RESULTS: A total of 18 articles met inclusion criteria including 30 validated predictive models of complications after adult spinal surgery. National registry databases were used in 12 studies. Validation cohorts were used in seven studies for verification; three studies used other methods including random sample bootstrapping techniques or cross-validation. Reported area under the curve (AUC) values ranged from 0.37 to 1.0. Studies described treatment for deformity, degenerative conditions, inclusive spinal surgery (neoplasm, trauma, infection, deformity, degenerative), and miscellaneous (disk herniation, spinal epidural abscess). The most commonly cited risk factors for complications included in predictive models included age, body mass index, diabetes, sex, and smoking. Those models in the deformity subset that included radiographic and anatomical grading features reported higher AUC values than those that included patient demographics or medical comorbidities alone. CONCLUSIONS: We identified a cohort of 30 validated predictive models of complications following spinal surgery for degenerative conditions, deformity, infection, and trauma. Accurate evidence-based predictive models may enhance shared decision making, improve rehabilitation, reduce adverse events, and inform best practices. Thieme. All rights reserved.
BACKGROUND: Complications rates vary across spinal surgery procedures and are difficult to predict due to heterogeneity in patient characteristics, surgical methods, and hospital volume. Incorporation of predictive models for complications may guide surgeon decision making and improve outcomes. METHODS: We evaluate current independently validated predictive models for complications in spinal surgery with respect to study design and model generation, accuracy, reliability, and utility. We conducted our search using Preferred Reporting Items for Systematic Review and Meta-analysis guidelines and the Participants, Intervention, Comparison, Outcomes, Study Design model through the PubMed and Ovid Medline databases. RESULTS: A total of 18 articles met inclusion criteria including 30 validated predictive models of complications after adult spinal surgery. National registry databases were used in 12 studies. Validation cohorts were used in seven studies for verification; three studies used other methods including random sample bootstrapping techniques or cross-validation. Reported area under the curve (AUC) values ranged from 0.37 to 1.0. Studies described treatment for deformity, degenerative conditions, inclusive spinal surgery (neoplasm, trauma, infection, deformity, degenerative), and miscellaneous (disk herniation, spinal epidural abscess). The most commonly cited risk factors for complications included in predictive models included age, body mass index, diabetes, sex, and smoking. Those models in the deformity subset that included radiographic and anatomical grading features reported higher AUC values than those that included patient demographics or medical comorbidities alone. CONCLUSIONS: We identified a cohort of 30 validated predictive models of complications following spinal surgery for degenerative conditions, deformity, infection, and trauma. Accurate evidence-based predictive models may enhance shared decision making, improve rehabilitation, reduce adverse events, and inform best practices. Thieme. All rights reserved.