Abstract
The Charlson Comorbidity Index (CCI) is used to characterize risk in populations, and is typically calculated using ICD code algorithms from real-world data (RWD) sources, mainly claims data. Electronic health record (EHR) data are becoming a more common source of RWD for research, and are themselves a source of ICD codes. Using data captured in a specialty Oncology EHR, this study compared the accuracy of the CCI calculated using ICD codes versus abstracted data. We included patients with advanced non-small cell lung cancer from the Flatiron Health EHR-derived Database who received platinum doublet induction and pemetrexed maintenance therapies in the community oncology setting. Data recency was through April 30, 2016. We identified comorbidities used in the calculation of the modified CCI, not including cancer or metastasis, using structured data (ICD-9/10-CM codes) and unstructured data (technology-enabled chart abstraction) from an Oncology EHR. We assessed sensitivity, specificity, and negative predictive value (NPV) of presence of any comorbidity (CCI >= 1) as well as each individual comorbidity, considering the unstructured data to be the gold standard for purposes of calculation. In 715 patients, the sensitivity of CCI >=1 from structured data was low (9%, 95% confidence interval: 5-14%), whereas specificity and NPV were high (99%, 98-100% and 72%, 69-75%, respectively). A similar pattern was observed across individual comorbidities: sensitivity < 30%, specificity 99-100%, and NPV 75-100%. Using data from an Oncology EHR, ICD codes were not sufficient for identification of comorbidities as compared to abstracted information from clinician notes, and may not capture CCI-relevant comorbidities that are documented elsewhere in the EHR. Comorbidity data abstracted from an Oncology EHR may be incomplete and not an ideal gold standard. Next steps include combining Oncology EHR data with additional data sources to increase the completeness of data for use in CCI calculation.
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