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Inferring pregnancy episodes and outcomes within a network of observational databases

Administrative claims and electronic health records are valuable resources for evaluating pharmaceutical effects during pregnancy. However, direct measures of gestational age are generally not available. Establishing a reliable approach to infer the duration and outcome of a pregnancy could improve...

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Autores principales: Matcho, Amy, Ryan, Patrick, Fife, Daniel, Gifkins, Dina, Knoll, Chris, Friedman, Andrew
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5794136/
https://www.ncbi.nlm.nih.gov/pubmed/29389968
http://dx.doi.org/10.1371/journal.pone.0192033
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author Matcho, Amy
Ryan, Patrick
Fife, Daniel
Gifkins, Dina
Knoll, Chris
Friedman, Andrew
author_facet Matcho, Amy
Ryan, Patrick
Fife, Daniel
Gifkins, Dina
Knoll, Chris
Friedman, Andrew
author_sort Matcho, Amy
collection PubMed
description Administrative claims and electronic health records are valuable resources for evaluating pharmaceutical effects during pregnancy. However, direct measures of gestational age are generally not available. Establishing a reliable approach to infer the duration and outcome of a pregnancy could improve pharmacovigilance activities. We developed and applied an algorithm to define pregnancy episodes in four observational databases: three US-based claims databases: Truven MarketScan(®) Commercial Claims and Encounters (CCAE), Truven MarketScan(®) Multi-state Medicaid (MDCD), and the Optum ClinFormatics(®) (Optum) database and one non-US database, the United Kingdom (UK) based Clinical Practice Research Datalink (CPRD). Pregnancy outcomes were classified as live births, stillbirths, abortions and ectopic pregnancies. Start dates were estimated using a derived hierarchy of available pregnancy markers, including records such as last menstrual period and nuchal ultrasound dates. Validation included clinical adjudication of 700 electronic Optum and CPRD pregnancy episode profiles to assess the operating characteristics of the algorithm, and a comparison of the algorithm’s Optum pregnancy start estimates to starts based on dates of assisted conception procedures. Distributions of pregnancy outcome types were similar across all four data sources and pregnancy episode lengths found were as expected for all outcomes, excepting term lengths in episodes that used amenorrhea and urine pregnancy tests for start estimation. Validation survey results found highest agreement between reviewer chosen and algorithm operating characteristics for questions assessing pregnancy status and accuracy of outcome category with 99–100% agreement for Optum and CPRD. Outcome date agreement within seven days in either direction ranged from 95–100%, while start date agreement within seven days in either direction ranged from 90–97%. In Optum validation sensitivity analysis, a total of 73% of algorithm estimated starts for live births were in agreement with fertility procedure estimated starts within two weeks in either direction; ectopic pregnancy 77%, stillbirth 47%, and abortion 36%. An algorithm to infer live birth and ectopic pregnancy episodes and outcomes can be applied to multiple observational databases with acceptable accuracy for further epidemiologic research. Less accuracy was found for start date estimations in stillbirth and abortion outcomes in our sensitivity analysis, which may be expected given the nature of the outcomes.
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spelling pubmed-57941362018-02-16 Inferring pregnancy episodes and outcomes within a network of observational databases Matcho, Amy Ryan, Patrick Fife, Daniel Gifkins, Dina Knoll, Chris Friedman, Andrew PLoS One Research Article Administrative claims and electronic health records are valuable resources for evaluating pharmaceutical effects during pregnancy. However, direct measures of gestational age are generally not available. Establishing a reliable approach to infer the duration and outcome of a pregnancy could improve pharmacovigilance activities. We developed and applied an algorithm to define pregnancy episodes in four observational databases: three US-based claims databases: Truven MarketScan(®) Commercial Claims and Encounters (CCAE), Truven MarketScan(®) Multi-state Medicaid (MDCD), and the Optum ClinFormatics(®) (Optum) database and one non-US database, the United Kingdom (UK) based Clinical Practice Research Datalink (CPRD). Pregnancy outcomes were classified as live births, stillbirths, abortions and ectopic pregnancies. Start dates were estimated using a derived hierarchy of available pregnancy markers, including records such as last menstrual period and nuchal ultrasound dates. Validation included clinical adjudication of 700 electronic Optum and CPRD pregnancy episode profiles to assess the operating characteristics of the algorithm, and a comparison of the algorithm’s Optum pregnancy start estimates to starts based on dates of assisted conception procedures. Distributions of pregnancy outcome types were similar across all four data sources and pregnancy episode lengths found were as expected for all outcomes, excepting term lengths in episodes that used amenorrhea and urine pregnancy tests for start estimation. Validation survey results found highest agreement between reviewer chosen and algorithm operating characteristics for questions assessing pregnancy status and accuracy of outcome category with 99–100% agreement for Optum and CPRD. Outcome date agreement within seven days in either direction ranged from 95–100%, while start date agreement within seven days in either direction ranged from 90–97%. In Optum validation sensitivity analysis, a total of 73% of algorithm estimated starts for live births were in agreement with fertility procedure estimated starts within two weeks in either direction; ectopic pregnancy 77%, stillbirth 47%, and abortion 36%. An algorithm to infer live birth and ectopic pregnancy episodes and outcomes can be applied to multiple observational databases with acceptable accuracy for further epidemiologic research. Less accuracy was found for start date estimations in stillbirth and abortion outcomes in our sensitivity analysis, which may be expected given the nature of the outcomes. Public Library of Science 2018-02-01 /pmc/articles/PMC5794136/ /pubmed/29389968 http://dx.doi.org/10.1371/journal.pone.0192033 Text en © 2018 Matcho et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Matcho, Amy
Ryan, Patrick
Fife, Daniel
Gifkins, Dina
Knoll, Chris
Friedman, Andrew
Inferring pregnancy episodes and outcomes within a network of observational databases
title Inferring pregnancy episodes and outcomes within a network of observational databases
title_full Inferring pregnancy episodes and outcomes within a network of observational databases
title_fullStr Inferring pregnancy episodes and outcomes within a network of observational databases
title_full_unstemmed Inferring pregnancy episodes and outcomes within a network of observational databases
title_short Inferring pregnancy episodes and outcomes within a network of observational databases
title_sort inferring pregnancy episodes and outcomes within a network of observational databases
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5794136/
https://www.ncbi.nlm.nih.gov/pubmed/29389968
http://dx.doi.org/10.1371/journal.pone.0192033
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