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

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.