Faculty Bibliography

OBJECTIVE:To build the Neiman Imaging Comorbidity Index (NICI), based on variables available in claims datasets, which provides good discrimination of an individual's chance of receiving advanced imaging (CT, MR, PET), and thus, utility as a control variable in research. METHODS:This retrospective study used national commercial claims data from Optum's deidentified Clinformatics Data Mart database from the period January 1, 2018 to December 31, 2019. Individuals with continuous enrollment during this 2-year study period were included. Lasso (least absolute shrinkage and selection operator) regression was used to predict the chance of receiving advanced imaging in 2019 based on the presence of comorbidities in 2018. A numerical index was created in a development cohort (70% of the total dataset) using weights assigned to each comorbidity, based on regression β coefficients. Internal validation of assigned scores was performed in the remaining 30% of claims, with comparison to the commonly used Charlson Comorbidity Index. RESULTS:The final sample (development and validation cohorts) included 10,532,734 beneficiaries, of whom 2,116,348 (20.1%) received advanced imaging. After model development, the NICI included nine comorbidities. In the internal validation set, the NICI achieved good discrimination of receipt of advanced imaging with a C statistic of 0.709 (95% confidence interval [CI] 0.708-0.709), which predicted advanced imaging better than the CCI (C 0.692, 95% CI 0.691-0.692). Controlling for age and sex yielded better discrimination (C 0.748, 95% CI 0.748-0.749). DISCUSSION/CONCLUSIONS:The NICI is an easily calculated measure of comorbidity burden that can be used to adjust for patients' chances of receiving advanced imaging. Future work should explore external validation of the NICI.