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Performance of claims-based algorithms for identifying incident thyroid cancer in commercial health plan enrollees receiving antidiabetic drug therapies
BACKGROUND: Thyroid cancer incidence is increasing in the United States (US) and many other countries. The objective of this study was to develop and evaluate algorithms using administrative medical claims data for identification of incident thyroid cancer. METHODS: This effort was part of a prospec...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5420112/ https://www.ncbi.nlm.nih.gov/pubmed/28476125 http://dx.doi.org/10.1186/s12913-017-2259-3 |
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author | Funch, Donnie Ross, Douglas Gardstein, Betsey M. Norman, Heather S. Sanders, Lauren A. Major-Pedersen, Atheline Gydesen, Helge Dore, David D. |
author_facet | Funch, Donnie Ross, Douglas Gardstein, Betsey M. Norman, Heather S. Sanders, Lauren A. Major-Pedersen, Atheline Gydesen, Helge Dore, David D. |
author_sort | Funch, Donnie |
collection | PubMed |
description | BACKGROUND: Thyroid cancer incidence is increasing in the United States (US) and many other countries. The objective of this study was to develop and evaluate algorithms using administrative medical claims data for identification of incident thyroid cancer. METHODS: This effort was part of a prospective cohort study of adults initiating therapy on antidiabetic drugs and used administrative data from a large commercial health insurer in the US. Patients had at least 6 months of continuous enrollment prior to initiation during 2009–2013, with follow-up through March, 2014 or until disenrollment. Potential incident thyroid cancers were identified using International Classification of Diseases, 9(th) Revision (ICD-9) diagnosis code 193 (malignant neoplasm of the thyroid gland). Medical records were adjudicated by a thyroid cancer specialist. Several clinical variables (e.g., hospitalization, treatments) were considered as predictors of case status. Positive predictive values (PPVs) and 95% confidence intervals (CIs) were calculated to evaluate the performance of two primary algorithms. RESULTS: Charts were requested for 170 patients, 150 (88%) were received and 141 (80%) had sufficient information to adjudicate. Of the 141 potential cases identified using ≥1 ICD-9 diagnosis code 193, 72 were confirmed as incident thyroid cancer (PPV of 51% (95% CI 43–60%)). Adding the requirement for thyroid surgery increased the PPV to 68% (95% CI 58-77%); including the presence of other therapies (chemotherapy, radio-iodine therapy) had no impact. When cases were required to have thyroid surgery during follow-up and ≥2 ICD-9 193 codes within 90 days of this surgery, the PPV was 91% (95% CI 81-96%); 62 (82%) of the true cases were identified and 63 (91%) of the non-cases were removed from consideration by the algorithm as potential cases. CONCLUSIONS: These findings suggest a significant degree of misclassification results from relying only on ICD-9 diagnosis codes to detect thyroid cancer. An administrative claims-based algorithm was developed that performed well to identify true incident thyroid cancer cases. |
format | Online Article Text |
id | pubmed-5420112 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-54201122017-05-08 Performance of claims-based algorithms for identifying incident thyroid cancer in commercial health plan enrollees receiving antidiabetic drug therapies Funch, Donnie Ross, Douglas Gardstein, Betsey M. Norman, Heather S. Sanders, Lauren A. Major-Pedersen, Atheline Gydesen, Helge Dore, David D. BMC Health Serv Res Research Article BACKGROUND: Thyroid cancer incidence is increasing in the United States (US) and many other countries. The objective of this study was to develop and evaluate algorithms using administrative medical claims data for identification of incident thyroid cancer. METHODS: This effort was part of a prospective cohort study of adults initiating therapy on antidiabetic drugs and used administrative data from a large commercial health insurer in the US. Patients had at least 6 months of continuous enrollment prior to initiation during 2009–2013, with follow-up through March, 2014 or until disenrollment. Potential incident thyroid cancers were identified using International Classification of Diseases, 9(th) Revision (ICD-9) diagnosis code 193 (malignant neoplasm of the thyroid gland). Medical records were adjudicated by a thyroid cancer specialist. Several clinical variables (e.g., hospitalization, treatments) were considered as predictors of case status. Positive predictive values (PPVs) and 95% confidence intervals (CIs) were calculated to evaluate the performance of two primary algorithms. RESULTS: Charts were requested for 170 patients, 150 (88%) were received and 141 (80%) had sufficient information to adjudicate. Of the 141 potential cases identified using ≥1 ICD-9 diagnosis code 193, 72 were confirmed as incident thyroid cancer (PPV of 51% (95% CI 43–60%)). Adding the requirement for thyroid surgery increased the PPV to 68% (95% CI 58-77%); including the presence of other therapies (chemotherapy, radio-iodine therapy) had no impact. When cases were required to have thyroid surgery during follow-up and ≥2 ICD-9 193 codes within 90 days of this surgery, the PPV was 91% (95% CI 81-96%); 62 (82%) of the true cases were identified and 63 (91%) of the non-cases were removed from consideration by the algorithm as potential cases. CONCLUSIONS: These findings suggest a significant degree of misclassification results from relying only on ICD-9 diagnosis codes to detect thyroid cancer. An administrative claims-based algorithm was developed that performed well to identify true incident thyroid cancer cases. BioMed Central 2017-05-05 /pmc/articles/PMC5420112/ /pubmed/28476125 http://dx.doi.org/10.1186/s12913-017-2259-3 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Funch, Donnie Ross, Douglas Gardstein, Betsey M. Norman, Heather S. Sanders, Lauren A. Major-Pedersen, Atheline Gydesen, Helge Dore, David D. Performance of claims-based algorithms for identifying incident thyroid cancer in commercial health plan enrollees receiving antidiabetic drug therapies |
title | Performance of claims-based algorithms for identifying incident thyroid cancer in commercial health plan enrollees receiving antidiabetic drug therapies |
title_full | Performance of claims-based algorithms for identifying incident thyroid cancer in commercial health plan enrollees receiving antidiabetic drug therapies |
title_fullStr | Performance of claims-based algorithms for identifying incident thyroid cancer in commercial health plan enrollees receiving antidiabetic drug therapies |
title_full_unstemmed | Performance of claims-based algorithms for identifying incident thyroid cancer in commercial health plan enrollees receiving antidiabetic drug therapies |
title_short | Performance of claims-based algorithms for identifying incident thyroid cancer in commercial health plan enrollees receiving antidiabetic drug therapies |
title_sort | performance of claims-based algorithms for identifying incident thyroid cancer in commercial health plan enrollees receiving antidiabetic drug therapies |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5420112/ https://www.ncbi.nlm.nih.gov/pubmed/28476125 http://dx.doi.org/10.1186/s12913-017-2259-3 |
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