Is the Centers for Medicare and Medicaid Services Hierarchical Condition Category Risk Adjustment Model Satisfactory for Quantifying Risk After Spine Surgery?

Andrew K Chan; Shane Shahrestani; Alexander M Ballatori; Katie O Orrico; Geoffrey T Manley; Phiroz E Tarapore; Michael Huang; Sanjay S Dhall; Dean Chou; Praveen V Mummaneni; Anthony M DiGiorgio

doi:10.1227/neu.0000000000001980

Is the Centers for Medicare and Medicaid Services Hierarchical Condition Category Risk Adjustment Model Satisfactory for Quantifying Risk After Spine Surgery?

Neurosurgery. 2022 Jul 1;91(1):123-131. doi: 10.1227/neu.0000000000001980. Epub 2022 May 16.

Authors

Affiliations

¹ Department of Neurological Surgery, University of California, San Francisco, California, USA.
² Department of Neurosurgery, Duke University, Durham, North Carolina, USA.
³ Department of Medical Engineering, California Institute of Technology, Pasadena, California, USA.
⁴ Keck School of Medicine, University of Southern California, Los Angeles, California, USA.
⁵ American Association of Neurological Surgeons/Congress of Neurological Surgeons Washington Office, Washington, District of Columbia, USA.

Abstract

Background: The Centers for Medicare and Medicaid Services (CMS) hierarchical condition category (HCC) coding is a risk adjustment model that allows for the estimation of risk-and cost-associated with health care provision. Current models may not include key factors that fully delineate the risk associated with spine surgery.

Objective: To augment CMS HCC risk adjustment methodology with socioeconomic data to improve its predictive capabilities for spine surgery.

Methods: The National Inpatient Sample was queried for spinal fusion, and the data was merged with county-level coverage and socioeconomic status variables obtained from the Brookings Institute. We predicted outcomes (death, nonroutine discharge, length of stay [LOS], total charges, and perioperative complication) with pairs of hierarchical, mixed effects logistic regression models-one using CMS HCC score alone and another augmenting CMS HCC scores with demographic and socioeconomic status variables. Models were compared using receiver operating characteristic curves. Variable importance was assessed in conjunction with Wald testing for model optimization.

Results: We analyzed 653 815 patients. Expanded models outperformed models using CMS HCC score alone for mortality, nonroutine discharge, LOS, total charges, and complications. For expanded models, variable importance analyses demonstrated that CMS HCC score was of chief importance for models of mortality, LOS, total charges, and complications. For the model of nonroutine discharge, age was the most important variable. For the model of total charges, unemployment rate was nearly as important as CMS HCC score.

Conclusion: The addition of key demographic and socioeconomic characteristics substantially improves the CMS HCC risk-adjustment models when modeling spinal fusion outcomes. This finding may have important implications for payers, hospitals, and policymakers.

Publication types

Research Support, Non-U.S. Gov't
Research Support, N.I.H., Extramural

MeSH terms

Aged
Centers for Medicare and Medicaid Services, U.S.
Humans
Length of Stay
Medicare
Risk Adjustment* / methods
Spinal Fusion*
United States / epidemiology