174 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

INTRODUCTION: Cervical spine decompression and fusion surgery is 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 2016–2019, 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 patient frailty. To measure the level of comorbidity, Elixhauser Comorbidity Index (ECI) scores were queried. ROC curves were developed utilizing comorbidity indices as predictor variables for pertinent complications such as death, non-routine discharge, top-quartile cost, top-quartile length of stay (LOS), and one-year readmission. RESULTS: A total of 453,717 patients were eligible for analysis. Non-routine 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 LOS was 2.7 days ± 4.4 days (Top Quartile: 4.7 days). After surgery, there were 703 (0.15%) mortalities and 58,254 (12.8%) readmissions within one year post-operatively. Models using Frailty+ECI as the primary predictor consistently outperformed the ECI only model with statistically significant p-values, as determined by comparing respective AUCs, 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 preformed as well as ECI alone in mortality (p = 0.10). CONCLUSIONS: Our data suggests that Frailty+ECI may most accurately predict clinical outcomes in patients receiving cervical spine surgery. These models may be used to identify high risk populations and patients who may necessitate greater resource utilization.