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Incorporating Breast Cancer Recurrence Events Into Population-Based Cancer Registries Using Medical Claims: Cohort Study

BACKGROUND: There is a need for automated approaches to incorporate information on cancer recurrence events into population-based cancer registries. OBJECTIVE: The aim of this study is to determine the accuracy of a novel data mining algorithm to extract information from linked registry and medical...

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Autores principales: A'mar, Teresa, Beatty, J David, Fedorenko, Catherine, Markowitz, Daniel, Corey, Thomas, Lange, Jane, Schwartz, Stephen M, Huang, Bin, Chubak, Jessica, Etzioni, Ruth
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7459434/
https://www.ncbi.nlm.nih.gov/pubmed/32804084
http://dx.doi.org/10.2196/18143
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author A'mar, Teresa
Beatty, J David
Fedorenko, Catherine
Markowitz, Daniel
Corey, Thomas
Lange, Jane
Schwartz, Stephen M
Huang, Bin
Chubak, Jessica
Etzioni, Ruth
author_facet A'mar, Teresa
Beatty, J David
Fedorenko, Catherine
Markowitz, Daniel
Corey, Thomas
Lange, Jane
Schwartz, Stephen M
Huang, Bin
Chubak, Jessica
Etzioni, Ruth
author_sort A'mar, Teresa
collection PubMed
description BACKGROUND: There is a need for automated approaches to incorporate information on cancer recurrence events into population-based cancer registries. OBJECTIVE: The aim of this study is to determine the accuracy of a novel data mining algorithm to extract information from linked registry and medical claims data on the occurrence and timing of second breast cancer events (SBCE). METHODS: We used supervised data from 3092 stage I and II breast cancer cases (with 394 recurrences), diagnosed between 1993 and 2006 inclusive, of patients at Kaiser Permanente Washington and cases in the Puget Sound Cancer Surveillance System. Our goal was to classify each month after primary treatment as pre- versus post-SBCE. The prediction feature set for a given month consisted of registry variables on disease and patient characteristics related to the primary breast cancer event, as well as features based on monthly counts of diagnosis and procedure codes for the current, prior, and future months. A month was classified as post-SBCE if the predicted probability exceeded a probability threshold (PT); the predicted time of the SBCE was taken to be the month of maximum increase in the predicted probability between adjacent months. RESULTS: The Kaplan-Meier net probability of SBCE was 0.25 at 14 years. The month-level receiver operating characteristic curve on test data (20% of the data set) had an area under the curve of 0.986. The person-level predictions (at a monthly PT of 0.5) had a sensitivity of 0.89, a specificity of 0.98, a positive predictive value of 0.85, and a negative predictive value of 0.98. The corresponding median difference between the observed and predicted months of recurrence was 0 and the mean difference was 0.04 months. CONCLUSIONS: Data mining of medical claims holds promise for the streamlining of cancer registry operations to feasibly collect information about second breast cancer events.
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spelling pubmed-74594342020-09-03 Incorporating Breast Cancer Recurrence Events Into Population-Based Cancer Registries Using Medical Claims: Cohort Study A'mar, Teresa Beatty, J David Fedorenko, Catherine Markowitz, Daniel Corey, Thomas Lange, Jane Schwartz, Stephen M Huang, Bin Chubak, Jessica Etzioni, Ruth JMIR Cancer Original Paper BACKGROUND: There is a need for automated approaches to incorporate information on cancer recurrence events into population-based cancer registries. OBJECTIVE: The aim of this study is to determine the accuracy of a novel data mining algorithm to extract information from linked registry and medical claims data on the occurrence and timing of second breast cancer events (SBCE). METHODS: We used supervised data from 3092 stage I and II breast cancer cases (with 394 recurrences), diagnosed between 1993 and 2006 inclusive, of patients at Kaiser Permanente Washington and cases in the Puget Sound Cancer Surveillance System. Our goal was to classify each month after primary treatment as pre- versus post-SBCE. The prediction feature set for a given month consisted of registry variables on disease and patient characteristics related to the primary breast cancer event, as well as features based on monthly counts of diagnosis and procedure codes for the current, prior, and future months. A month was classified as post-SBCE if the predicted probability exceeded a probability threshold (PT); the predicted time of the SBCE was taken to be the month of maximum increase in the predicted probability between adjacent months. RESULTS: The Kaplan-Meier net probability of SBCE was 0.25 at 14 years. The month-level receiver operating characteristic curve on test data (20% of the data set) had an area under the curve of 0.986. The person-level predictions (at a monthly PT of 0.5) had a sensitivity of 0.89, a specificity of 0.98, a positive predictive value of 0.85, and a negative predictive value of 0.98. The corresponding median difference between the observed and predicted months of recurrence was 0 and the mean difference was 0.04 months. CONCLUSIONS: Data mining of medical claims holds promise for the streamlining of cancer registry operations to feasibly collect information about second breast cancer events. JMIR Publications 2020-08-17 /pmc/articles/PMC7459434/ /pubmed/32804084 http://dx.doi.org/10.2196/18143 Text en ©Teresa A'mar, J David Beatty, Catherine Fedorenko, Daniel Markowitz, Thomas Corey, Jane Lange, Stephen M Schwartz, Bin Huang, Jessica Chubak, Ruth Etzioni. Originally published in JMIR Cancer (http://cancer.jmir.org), 17.08.2020. https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Cancer, is properly cited. The complete bibliographic information, a link to the original publication on http://cancer.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
A'mar, Teresa
Beatty, J David
Fedorenko, Catherine
Markowitz, Daniel
Corey, Thomas
Lange, Jane
Schwartz, Stephen M
Huang, Bin
Chubak, Jessica
Etzioni, Ruth
Incorporating Breast Cancer Recurrence Events Into Population-Based Cancer Registries Using Medical Claims: Cohort Study
title Incorporating Breast Cancer Recurrence Events Into Population-Based Cancer Registries Using Medical Claims: Cohort Study
title_full Incorporating Breast Cancer Recurrence Events Into Population-Based Cancer Registries Using Medical Claims: Cohort Study
title_fullStr Incorporating Breast Cancer Recurrence Events Into Population-Based Cancer Registries Using Medical Claims: Cohort Study
title_full_unstemmed Incorporating Breast Cancer Recurrence Events Into Population-Based Cancer Registries Using Medical Claims: Cohort Study
title_short Incorporating Breast Cancer Recurrence Events Into Population-Based Cancer Registries Using Medical Claims: Cohort Study
title_sort incorporating breast cancer recurrence events into population-based cancer registries using medical claims: cohort study
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7459434/
https://www.ncbi.nlm.nih.gov/pubmed/32804084
http://dx.doi.org/10.2196/18143
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