Developing Mixed-effects Models to Optimize Prediction of Postoperative Outcomes in a Modern Sample of Over 450,000 Patients Undergoing Elective Cervical Spine Fusion Surgery

Abstract

Study Design: A retrospective cohort. Objective: We utilize big data and modeling techniques to create optimized comorbidity indices for predicting postoperative outcomes following cervical spine fusion surgery. Summary of Background Data: Cervical spine decompression and fusion surgery are commonly used to treat degenerative cervical spine pathologies. However, there is a paucity of high-quality data defining the optimal comorbidity indices specifically in patients undergoing cervical spine fusion surgery. Methods: Using data from 2016 to 2019, we queried the Nationwide Readmissions Database (NRD) to identify individuals who had received cervical spine fusion surgery. The Johns Hopkins Adjusted Clinical Groups (JHACG) frailty-defining indicator was used to assess frailty. To measure the level of comorbidity, Elixhauser Comorbidity Index (ECI) scores were queried. Receiver operating characteristic curves were developed utilizing comorbidity indices as predictor variables for pertinent complications such as mortality, nonroutine discharge, top-quartile cost, top-quartile length of stay, and 1-year readmission. Results: A total of 453,717 patients were eligible. Nonroutine discharges occurred in 93,961 (20.7%) patients. The mean adjusted all-payer cost for the procedure was $22,573.14±18,274.86 (top quartile: $26,775.80) and the mean length of stay was 2.7±4.4 days (top quartile: 4.7 d). There were 703 (0.15%) mortalities and 58,254 (12.8%) readmissions within 1 year postoperatively. Models using frailty+ECI as primary predictors consistently outperformed the ECI-only model with statistically significant P-values for most of the complications assessed. Cost and mortality were the only outcomes for which this was not the case, as frailty outperformed both ECI and frailty+ECI in cost (P<0.0001 for all) and frailty+ECI performed as well as ECI alone in mortality (P=0.10). Conclusions: Our data suggest that frailty+ECI may most accurately predict clinical outcomes in patients receiving cervical spine fusion surgery. These models may be used to identify high-risk populations and patients who may necessitate greater resource utilization following elective cervical spinal fusion.