Comprehensive Code List Associated with CDC-Defined High-Risk Underlying Medical Conditions Leading to Potential Progression to Severe COVID-19 Outcomes

ABSTRACT

Objectives: This analysis was conducted to develop a comprehensive list of ICD-10 CM codes for underlying conditions identified by the CDC as being associated with high-risk of developing severe COVID-19 and assessed the consistency of these codes when applied to large US based datasets. Method(s) The comprehensive list of ICD 10-CM codes for CDC-defined high-risk underlying conditions were mapped from CDC references and FDA Sentinel code lists. These codes were subsequently applied to Optum's de-identified Clinformatics Data Mart Database (claims) and the Optum de-identified Electronic Health Record (EHR) database across 3 years (2018, 2019 and 2020) among continuously enrolled subjects >= 12 years of age to determine the performance and consistency in identifying these high-risk underlying conditions annually over these years. Result(s) A total of 10,276 ICD-10 codes were mapped to 21 underlying conditions. Within the claims data, 62.7% of subjects >= 12 years had >= 1 CDC-defined high-risk condition (excluding age) with 26.6% of patients >= 65 years while in the EHR data 38% had >= 1 high-risk underlying condition (excluding age) with 14.4% >= 65 years. These results were similar and consistent in both datasets across all years. Patients aged 12-64 years in the claims data had a higher rate of >=1 high risk underlying condition relative to the EHR data, 49.3% and 34%, respectively. The top 5 conditions among the >= 65 were identical across both databases hypertension, immunocompromised status, heart conditions, diabetes (type 1 or 2), and overweight/obesity. The top 5 conditions among the 12-64 age group were also similar among the databases and included immunocompromised status, hypertension, overweight/obesity, smoking (current or former), and mental health conditions. Conclusion(s) These findings present a comprehensive list of codes that can be used by researchers, clinicians and policy makers to identify and characterize patients that may be at high-risk for severe COVID-19 outcomes.Copyright © 2023