Discrepancies in ICD-9/ICD-10-based codes used to identify three common diseases in cancer patients in real-world settings and their implications for disease classification in breast cancer patients and patients without cancer: a literature review and descriptive study

Nora Tu; Mackenzie Henderson; Meera Sundararajan; Maribel Salas

doi:10.3389/fonc.2023.1016389

Discrepancies in ICD-9/ICD-10-based codes used to identify three common diseases in cancer patients in real-world settings and their implications for disease classification in breast cancer patients and patients without cancer: a literature review and descriptive study

Front Oncol. 2023 Sep 6:13:1016389. doi: 10.3389/fonc.2023.1016389. eCollection 2023.

Authors

Nora Tu¹, Mackenzie Henderson¹, Meera Sundararajan¹, Maribel Salas^{1

2}

Affiliations

¹ Epidemiology, Clinical Safety and Pharmacovigilance, Daiichi Sankyo, Inc., Basking Ridge, NJ, United States.
² Center for Real-World Effectiveness and Safety of Therapeutics (CREST), University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States.

Abstract

Background: International Classification of Diseases, Ninth/Tenth revisions, clinical modification (ICD-9-CM, ICD-10-CM) are frequently used in the U.S. by health insurers and disease registries, and are often recorded in electronic medical records. Due to their widespread use, ICD-based codes are a valuable source of data for epidemiology studies, but there are challenges related to their accuracy and reliability. This study aims to 1) identify ICD-9/ICD-10-based codes reported in literature/web sources to identify three common diseases in elderly patients with cancer (anemia, hypertension, arthritis), 2) compare codes identified in the literature/web search to SEER-Medicare's 27 CCW Chronic Conditions Algorithm ("gold-standard") to determine their discordance, and 3) determine sensitivity of the literature/web search codes compared to the gold standard.

Methods: A literature search was performed (Embase, Medline) to find sources reporting ICD codes for at least one disease of interest. Articles were screened in two levels (title/abstract; full text). Analysis was performed in SAS Version 9.4.

Results: Of 106 references identified, 29 were included that reported 884 codes (155 anemia, 80 hypertension, 649 arthritis). Overall discordance between the gold standard and literature/web search code list was 32.9% (22.2% for ICD-9; 35.7% for ICD-10). The gold standard contained codes not found in literature/web sources, including codes for hypertensive retinopathy/encephalopathy, Page Kidney, spondylosis/spondylitis, juvenile arthritis, thalassemia, sickle cell disorder, autoimmune anemias, and erythroblastopenia. Among a cohort of non-cancer patients (N=684,376), the gold standard identified an additional 129 patients with anemia, 33,683 with arthritis, and 510 with hypertension compared to the literature/web search. Among a cohort of breast cancer patients (N=303,103), the gold standard identified an additional 59 patients with anemia, 10,993 with arthritis, and 163 with hypertension. Sensitivity of the literature/web search code list was 91.38-99.96% for non-cancer patients, and 93.01-99.96% for breast cancer patients.

Conclusion: Discrepancies in codes used to identify three common diseases resulted in variable differences in disease classification. In all cases, the gold standard captured patients missed using the literature/web search codes. Researchers should use standardized, validated coding algorithms when available to increase consistency in research and reduce risk of misclassification, which can significantly alter the findings of a study.

Keywords: Validation study; breast cancer; comorbidities; international classification of diseases (ICD); methodology; validation.