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Supplementing Claims Data with Electronic Medical Records to Improve Estimation and Classification of Rheumatoid Arthritis Disease Activity: A Machine Learning Approach

OBJECTIVE: Previous attempts to estimate rheumatoid arthritis (RA) disease activity using claims data only did not yield high performance. We aimed to assess whether supplementing claims data with readily available electronic medical record (EMR) data might result in improvement. METHODS: We used a...

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Autores principales: Feldman, Candace H., Yoshida, Kazuki, Xu, Chang, Frits, Michelle L., Shadick, Nancy A., Weinblatt, Michael E., Connolly, Sean E., Alemao, Evo, Solomon, Daniel H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6857973/
https://www.ncbi.nlm.nih.gov/pubmed/31777839
http://dx.doi.org/10.1002/acr2.11068
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author Feldman, Candace H.
Yoshida, Kazuki
Xu, Chang
Frits, Michelle L.
Shadick, Nancy A.
Weinblatt, Michael E.
Connolly, Sean E.
Alemao, Evo
Solomon, Daniel H.
author_facet Feldman, Candace H.
Yoshida, Kazuki
Xu, Chang
Frits, Michelle L.
Shadick, Nancy A.
Weinblatt, Michael E.
Connolly, Sean E.
Alemao, Evo
Solomon, Daniel H.
author_sort Feldman, Candace H.
collection PubMed
description OBJECTIVE: Previous attempts to estimate rheumatoid arthritis (RA) disease activity using claims data only did not yield high performance. We aimed to assess whether supplementing claims data with readily available electronic medical record (EMR) data might result in improvement. METHODS: We used a subset of the Brigham and Women's Hospital Rheumatoid Arthritis Sequential Study (BRASS) that had linked Medicare claims. The disease activity score in 28 joints with C‐reactive protein (DAS28‐CRP) was considered the gold standard of measure. Variables in the linked Medicare claims, as well as EMR recorded in the preceding one‐year period were used as potential explanatory variables. We constructed three models: “Claims‐Only,” “Claims + Medications,” and “Claims + Medications + Labs (laboratory data from EMR). We selected variables via adaptive LASSO. Model performance was measured with adjusted R2 for continuous DAS28‐CRP and C‐statistics for binary category classification (high/moderate vs low disease activity/remission). RESULTS: We identified 300 patients with laboratory data and linked Medicare claims. The mean age was 68 years and 80% were female. The mean (SD) DAS28‐CRP was 3.6 (1.6) and 51% had high or moderate DAS28‐CRP. For the continuous estimation, the adjusted R2 was 0.02 for Claims‐Only, 0.09 for Claims + Medications, and 0.18 for Claims + Medications + Labs. The C‐statistics for discriminating the binary categories were 0.61 for Claims‐Only, 0.68 for Claims + Medications, and 0.76 for Claims + Medications + Labs. CONCLUSION: Adding EMR‐derived variables to claims‐derived variables resulted in modest improvement. Even with EMR variables, we were unable to estimate continuous DAS28‐CRP satisfactorily. However, in claims‐EMR models, we were able to discriminate between binary categories of disease activity with reasonable accuracy.
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spelling pubmed-68579732019-11-27 Supplementing Claims Data with Electronic Medical Records to Improve Estimation and Classification of Rheumatoid Arthritis Disease Activity: A Machine Learning Approach Feldman, Candace H. Yoshida, Kazuki Xu, Chang Frits, Michelle L. Shadick, Nancy A. Weinblatt, Michael E. Connolly, Sean E. Alemao, Evo Solomon, Daniel H. ACR Open Rheumatol Original Articles OBJECTIVE: Previous attempts to estimate rheumatoid arthritis (RA) disease activity using claims data only did not yield high performance. We aimed to assess whether supplementing claims data with readily available electronic medical record (EMR) data might result in improvement. METHODS: We used a subset of the Brigham and Women's Hospital Rheumatoid Arthritis Sequential Study (BRASS) that had linked Medicare claims. The disease activity score in 28 joints with C‐reactive protein (DAS28‐CRP) was considered the gold standard of measure. Variables in the linked Medicare claims, as well as EMR recorded in the preceding one‐year period were used as potential explanatory variables. We constructed three models: “Claims‐Only,” “Claims + Medications,” and “Claims + Medications + Labs (laboratory data from EMR). We selected variables via adaptive LASSO. Model performance was measured with adjusted R2 for continuous DAS28‐CRP and C‐statistics for binary category classification (high/moderate vs low disease activity/remission). RESULTS: We identified 300 patients with laboratory data and linked Medicare claims. The mean age was 68 years and 80% were female. The mean (SD) DAS28‐CRP was 3.6 (1.6) and 51% had high or moderate DAS28‐CRP. For the continuous estimation, the adjusted R2 was 0.02 for Claims‐Only, 0.09 for Claims + Medications, and 0.18 for Claims + Medications + Labs. The C‐statistics for discriminating the binary categories were 0.61 for Claims‐Only, 0.68 for Claims + Medications, and 0.76 for Claims + Medications + Labs. CONCLUSION: Adding EMR‐derived variables to claims‐derived variables resulted in modest improvement. Even with EMR variables, we were unable to estimate continuous DAS28‐CRP satisfactorily. However, in claims‐EMR models, we were able to discriminate between binary categories of disease activity with reasonable accuracy. John Wiley and Sons Inc. 2019-09-04 /pmc/articles/PMC6857973/ /pubmed/31777839 http://dx.doi.org/10.1002/acr2.11068 Text en © 2019 The Authors. ACR Open Rheumatology published by Wiley Periodicals, Inc. on behalf of American College of Rheumatology. This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
spellingShingle Original Articles
Feldman, Candace H.
Yoshida, Kazuki
Xu, Chang
Frits, Michelle L.
Shadick, Nancy A.
Weinblatt, Michael E.
Connolly, Sean E.
Alemao, Evo
Solomon, Daniel H.
Supplementing Claims Data with Electronic Medical Records to Improve Estimation and Classification of Rheumatoid Arthritis Disease Activity: A Machine Learning Approach
title Supplementing Claims Data with Electronic Medical Records to Improve Estimation and Classification of Rheumatoid Arthritis Disease Activity: A Machine Learning Approach
title_full Supplementing Claims Data with Electronic Medical Records to Improve Estimation and Classification of Rheumatoid Arthritis Disease Activity: A Machine Learning Approach
title_fullStr Supplementing Claims Data with Electronic Medical Records to Improve Estimation and Classification of Rheumatoid Arthritis Disease Activity: A Machine Learning Approach
title_full_unstemmed Supplementing Claims Data with Electronic Medical Records to Improve Estimation and Classification of Rheumatoid Arthritis Disease Activity: A Machine Learning Approach
title_short Supplementing Claims Data with Electronic Medical Records to Improve Estimation and Classification of Rheumatoid Arthritis Disease Activity: A Machine Learning Approach
title_sort supplementing claims data with electronic medical records to improve estimation and classification of rheumatoid arthritis disease activity: a machine learning approach
topic Original Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6857973/
https://www.ncbi.nlm.nih.gov/pubmed/31777839
http://dx.doi.org/10.1002/acr2.11068
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