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4486 Assessing the Validity of an ICD-9 and ICD-10 Coding Algorithm for Identifying Cervical Premalignant Lesions Using Administrative Claims Data

OBJECTIVES/GOALS: We compared the validity of an International Classification of Diseases, Clinical Modification (ICD) algorithm for identifying high-grade cervical intraepithelial neoplasia and adenocarcinoma in situ (together referred to as CIN2+) from ICD 9th revision (ICD-9) and 10th revision (I...

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Autores principales: Shing, Jaimie Zhi, Griffin, Marie, Slaughter, James C, Pemmaraju, Manideepthi, Mitchel, Edward F, Chang, Rachel S, Hull, Pamela C
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cambridge University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8823207/
http://dx.doi.org/10.1017/cts.2020.167
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author Shing, Jaimie Zhi
Griffin, Marie
Slaughter, James C
Pemmaraju, Manideepthi
Mitchel, Edward F
Chang, Rachel S
Hull, Pamela C
author_facet Shing, Jaimie Zhi
Griffin, Marie
Slaughter, James C
Pemmaraju, Manideepthi
Mitchel, Edward F
Chang, Rachel S
Hull, Pamela C
author_sort Shing, Jaimie Zhi
collection PubMed
description OBJECTIVES/GOALS: We compared the validity of an International Classification of Diseases, Clinical Modification (ICD) algorithm for identifying high-grade cervical intraepithelial neoplasia and adenocarcinoma in situ (together referred to as CIN2+) from ICD 9th revision (ICD-9) and 10th revision (ICD-10) codes. METHODS/STUDY POPULATION: Using Tennessee Medicaid data, we identified cervical diagnostic procedures in 2008-2017 among females aged 18-39 years in Davidson County, TN. Gold-standard cases were pathology-confirmed CIN2+ diagnoses validated by HPV-IMPACT, a population-based surveillance project in catchment areas of five US states. Procedures in the ICD transition year (2015) were excluded to account for implementation lag. We pre-grouped diagnosis and procedure codes by theme. We performed feature selection using least absolute shrinkage and selection operator (LASSO) logistic regression with 10-fold cross validation and validated models by ICD-9 era (2008-2014, N = 6594) and ICD-10 era (2016-2017, N = 1270). RESULTS/ANTICIPATED RESULTS: Of 7864 cervical diagnostic procedures, 880 (11%) were true CIN2+ cases. LASSO logistic regression selected the strongest features of case status: Having codes for a CIN2+ tissue diagnosis, non-specific CIN tissue diagnosis, high-grade squamous intraepithelial lesion, receiving a cervical treatment procedure, and receiving a cervical/vaginal biopsy. Features of non-case status were codes for a CIN1 tissue diagnosis, Pap test, and HPV DNA test. The ICD-9 vs ICD-10 algorithms predicted case status with 68% vs 63% sensitivity, 95% vs 94% specificity, 63% vs 64% positive predictive value, 96% vs 94% negative predictive value, 92% vs 89% accuracy, and C-indices of 0.95 vs 0.92, respectively. DISCUSSION/SIGNIFICANCE OF IMPACT: Overall, the algorithm’s validity for identifying CIN2+ case status was similar between coding versions. ICD-9 had slightly better discriminative ability. Results support a prior study concluding that ICD-10 implementation has not substantially improved the quality of administrative data from ICD-9.
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spelling pubmed-88232072022-02-18 4486 Assessing the Validity of an ICD-9 and ICD-10 Coding Algorithm for Identifying Cervical Premalignant Lesions Using Administrative Claims Data Shing, Jaimie Zhi Griffin, Marie Slaughter, James C Pemmaraju, Manideepthi Mitchel, Edward F Chang, Rachel S Hull, Pamela C J Clin Transl Sci Data Science/Biostatistics/Informatics OBJECTIVES/GOALS: We compared the validity of an International Classification of Diseases, Clinical Modification (ICD) algorithm for identifying high-grade cervical intraepithelial neoplasia and adenocarcinoma in situ (together referred to as CIN2+) from ICD 9th revision (ICD-9) and 10th revision (ICD-10) codes. METHODS/STUDY POPULATION: Using Tennessee Medicaid data, we identified cervical diagnostic procedures in 2008-2017 among females aged 18-39 years in Davidson County, TN. Gold-standard cases were pathology-confirmed CIN2+ diagnoses validated by HPV-IMPACT, a population-based surveillance project in catchment areas of five US states. Procedures in the ICD transition year (2015) were excluded to account for implementation lag. We pre-grouped diagnosis and procedure codes by theme. We performed feature selection using least absolute shrinkage and selection operator (LASSO) logistic regression with 10-fold cross validation and validated models by ICD-9 era (2008-2014, N = 6594) and ICD-10 era (2016-2017, N = 1270). RESULTS/ANTICIPATED RESULTS: Of 7864 cervical diagnostic procedures, 880 (11%) were true CIN2+ cases. LASSO logistic regression selected the strongest features of case status: Having codes for a CIN2+ tissue diagnosis, non-specific CIN tissue diagnosis, high-grade squamous intraepithelial lesion, receiving a cervical treatment procedure, and receiving a cervical/vaginal biopsy. Features of non-case status were codes for a CIN1 tissue diagnosis, Pap test, and HPV DNA test. The ICD-9 vs ICD-10 algorithms predicted case status with 68% vs 63% sensitivity, 95% vs 94% specificity, 63% vs 64% positive predictive value, 96% vs 94% negative predictive value, 92% vs 89% accuracy, and C-indices of 0.95 vs 0.92, respectively. DISCUSSION/SIGNIFICANCE OF IMPACT: Overall, the algorithm’s validity for identifying CIN2+ case status was similar between coding versions. ICD-9 had slightly better discriminative ability. Results support a prior study concluding that ICD-10 implementation has not substantially improved the quality of administrative data from ICD-9. Cambridge University Press 2020-07-29 /pmc/articles/PMC8823207/ http://dx.doi.org/10.1017/cts.2020.167 Text en © The Association for Clinical and Translational Science 2020 https://creativecommons.org/licenses/by/4.0/This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Data Science/Biostatistics/Informatics
Shing, Jaimie Zhi
Griffin, Marie
Slaughter, James C
Pemmaraju, Manideepthi
Mitchel, Edward F
Chang, Rachel S
Hull, Pamela C
4486 Assessing the Validity of an ICD-9 and ICD-10 Coding Algorithm for Identifying Cervical Premalignant Lesions Using Administrative Claims Data
title 4486 Assessing the Validity of an ICD-9 and ICD-10 Coding Algorithm for Identifying Cervical Premalignant Lesions Using Administrative Claims Data
title_full 4486 Assessing the Validity of an ICD-9 and ICD-10 Coding Algorithm for Identifying Cervical Premalignant Lesions Using Administrative Claims Data
title_fullStr 4486 Assessing the Validity of an ICD-9 and ICD-10 Coding Algorithm for Identifying Cervical Premalignant Lesions Using Administrative Claims Data
title_full_unstemmed 4486 Assessing the Validity of an ICD-9 and ICD-10 Coding Algorithm for Identifying Cervical Premalignant Lesions Using Administrative Claims Data
title_short 4486 Assessing the Validity of an ICD-9 and ICD-10 Coding Algorithm for Identifying Cervical Premalignant Lesions Using Administrative Claims Data
title_sort 4486 assessing the validity of an icd-9 and icd-10 coding algorithm for identifying cervical premalignant lesions using administrative claims data
topic Data Science/Biostatistics/Informatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8823207/
http://dx.doi.org/10.1017/cts.2020.167
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