Developing Mixed-Effects Models to Compare the Predictive Ability of Various Comorbidity Indices in a Contemporary Cohort of Patients Undergoing Lumbar Fusion.

Abstract

As incidence of operative spinal pathology continues to grow, so do the rates of lumbar spinal fusion procedures. Comorbidity indices can be used preoperatively to predict potential complications. However, there is a paucity of research defining the optimal comorbidity indices in patients undergoing spinal fusion surgery. We aimed to use modeling strategies to evaluate the predictive validity of various comorbidity indices and combinations thereof. Patients who underwent spinal fusion were queried using data from the Nationwide Readmissions Database for the years 2016 through 2019. Using comorbidity indices as predictor variables, receiver operating characteristic curves were developed for pertinent complications such as mortality, nonroutine discharge, top-quartile cost, top-quartile length of stay, and 30-day readmission. A total of 750 183 patients were included. Nonroutine discharges occurred in 161 077 (21.5%) patients. The adjusted all-payer cost for the procedure was $37 616.97 ± $27 408.86 (top quartile: $45 409.20), and the length of stay was 4.1 ± 4.4 days (top quartile: 8.1 days). By comparing receiver operating characteristics of various models, it was found that models using Frailty + Elixhauser Comorbidity Index (ECI) as the primary predictor performed better than other models with statistically significant P -values on post hoc testing. However, for prediction of mortality, the model using Frailty + ECI was not better than the model using ECI alone ( P = .23), and for prediction of all-payer cost, the ECI model outperformed the models using frailty alone ( P < .0001) and the model using Frailty + ECI ( P < .0001). This investigation is the first to use big data and modeling strategies to delineate the relative predictive utility of the ECI and Johns Hopkins Adjusted Clinical Groups comorbidity indices for the prognostication of patients undergoing lumbar fusion surgery. With the knowledge gained from our models, spine surgeons, payers, and hospitals may be able to identify vulnerable patients more effectively within their practice who may require a higher degree of resource utilization.