RESUMO
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.
RESUMO
OBJECTIVE: To identify comorbidity indices that have been validated in cancer populations, with a focus on breast cancer and human epidermal growth factor receptor-2-positive (HER2+) breast cancer. STUDY DESIGN AND SETTING: A systematic review of the literature on the use of comorbidity indices in any cancer, breast cancer, and HER2+ breast cancer using Ovid and PubMed. RESULTS: The final data set comprised 252 articles (252 any cancer, 39 breast cancer, 7 HER2+ breast cancer). The most common cancers assessed were hematologic and breast, and the most common comorbidity index used was the Charlson Comorbidity Index (CCI) or a CCI derivative. Most validity testing of comorbidity indices used predictive validity based on survival outcomes. Hazard ratios for survival outcomes generally found that a higher comorbidity burden (measured by CCI) increased mortality risk in patients with breast cancer. All breast-cancer studies that validated comorbidity indices used CCI-based indices. Only one article validated a comorbidity index in HER2+ breast cancer. CONCLUSION: CCI-based indices are the most appropriate indices to use in the general breast-cancer population. There is insufficient validation of any comorbidity index in HER2+ breast cancer to provide a recommendation, indicating a future need to validate these instruments in this population.