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
BackgroundInternational 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 v...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-09-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2023.1016389/full |
| _version_ | 1797691052721700864 |
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| author | Nora Tu Mackenzie Henderson Meera Sundararajan Maribel Salas Maribel Salas |
| author_facet | Nora Tu Mackenzie Henderson Meera Sundararajan Maribel Salas Maribel Salas |
| author_sort | Nora Tu |
| collection | DOAJ |
| description | BackgroundInternational 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.MethodsA 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.ResultsOf 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.ConclusionDiscrepancies 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. |
| first_indexed | 2024-03-12T02:07:56Z |
| format | Article |
| id | doaj.art-2104cf3c0fdf4c0a9109d07827de74a3 |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-03-12T02:07:56Z |
| publishDate | 2023-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-2104cf3c0fdf4c0a9109d07827de74a32023-09-06T19:31:29ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2023-09-011310.3389/fonc.2023.10163891016389Discrepancies 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 studyNora Tu0Mackenzie Henderson1Meera Sundararajan2Maribel Salas3Maribel Salas4Epidemiology, Clinical Safety and Pharmacovigilance, Daiichi Sankyo, Inc., Basking Ridge, NJ, United StatesEpidemiology, Clinical Safety and Pharmacovigilance, Daiichi Sankyo, Inc., Basking Ridge, NJ, United StatesEpidemiology, Clinical Safety and Pharmacovigilance, Daiichi Sankyo, Inc., Basking Ridge, NJ, United StatesEpidemiology, Clinical Safety and Pharmacovigilance, Daiichi Sankyo, Inc., Basking Ridge, NJ, United StatesCenter for Real-World Effectiveness and Safety of Therapeutics (CREST), University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United StatesBackgroundInternational 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.MethodsA 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.ResultsOf 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.ConclusionDiscrepancies 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.https://www.frontiersin.org/articles/10.3389/fonc.2023.1016389/fullvalidationinternational classification of diseases (ICD)comorbiditiesbreast cancermethodologyValidation study |
| spellingShingle | Nora Tu Mackenzie Henderson Meera Sundararajan Maribel Salas Maribel Salas 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 Frontiers in Oncology validation international classification of diseases (ICD) comorbidities breast cancer methodology Validation study |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | validation international classification of diseases (ICD) comorbidities breast cancer methodology Validation study |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2023.1016389/full |
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