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Validating Claims-Based Algorithms Determining Pregnancy Outcomes and Gestational Age Using a Linked Claims-Electronic Medical Record Database
INTRODUCTION: Pregnancy outcome identification and precise estimates of gestational age (GA) are critical in drug safety studies of pregnant women. Validated pregnancy outcome algorithms based on the International Classification of Diseases, Tenth Revision, Clinical Modification/Procedure Coding Sys...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8481319/ https://www.ncbi.nlm.nih.gov/pubmed/34591264 http://dx.doi.org/10.1007/s40264-021-01113-8 |
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author | Moll, Keran Wong, Hui Lee Fingar, Kathryn Hobbi, Shayan Sheng, Minya Burrell, Timothy A. Eckert, Linda O. Munoz, Flor M. Baer, Bethany Shoaibi, Azadeh Anderson, Steven |
author_facet | Moll, Keran Wong, Hui Lee Fingar, Kathryn Hobbi, Shayan Sheng, Minya Burrell, Timothy A. Eckert, Linda O. Munoz, Flor M. Baer, Bethany Shoaibi, Azadeh Anderson, Steven |
author_sort | Moll, Keran |
collection | PubMed |
description | INTRODUCTION: Pregnancy outcome identification and precise estimates of gestational age (GA) are critical in drug safety studies of pregnant women. Validated pregnancy outcome algorithms based on the International Classification of Diseases, Tenth Revision, Clinical Modification/Procedure Coding System (ICD-10-CM/PCS) have not previously been published. METHODS: We developed algorithms to classify pregnancy outcomes and estimate GA using ICD-10-CM/PCS and service codes on claims in the 2016–2018 IBM(®) MarketScan(®) Explorys(®) Claims-EMR Data Set and compared the results with ob-gyn adjudication of electronic medical records (EMRs). Obstetric services were grouped into episodes using hierarchical and spacing requirements. GA was based on evidence with the highest clinical accuracy. Among pregnancies with obstetric EMRs, 100 full-term live births (FTBs), 100 preterm live births (PTBs), 100 spontaneous abortions (SAs), and 24 stillbirths were selected for review. Physicians adjudicated cases using Global Alignment of Immunization safety Assessment in pregnancy (GAIA) definitions applied to structured EMRs. RESULTS: The claims-based algorithms identified 34,204 pregnancies, of which 9.9% had obstetric EMRs. Of sampled pregnancies, 92 FTBs, 93 PTBs, 75 SAs, and 24 stillbirths were adjudicated. Among these pregnancies, the percent agreement was 97.8%, 62.4%, 100.0%, and 70.8% for FTBs, PTBs, SAs, and stillbirths, respectively. The percent agreement on GA within 7 and 28 days, respectively, was 85.9% and 100.0% for FTBs, 81.7% and 98.9% for PTBs, 61.3% and 94.7% for SAs, and 66.7% and 79.2% for stillbirths. CONCLUSIONS: The pregnancy outcome algorithms had high agreement with physician adjudication of EMRs and may inform post-market maternal safety surveillance. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40264-021-01113-8. |
format | Online Article Text |
id | pubmed-8481319 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-84813192021-09-30 Validating Claims-Based Algorithms Determining Pregnancy Outcomes and Gestational Age Using a Linked Claims-Electronic Medical Record Database Moll, Keran Wong, Hui Lee Fingar, Kathryn Hobbi, Shayan Sheng, Minya Burrell, Timothy A. Eckert, Linda O. Munoz, Flor M. Baer, Bethany Shoaibi, Azadeh Anderson, Steven Drug Saf Original Research Article INTRODUCTION: Pregnancy outcome identification and precise estimates of gestational age (GA) are critical in drug safety studies of pregnant women. Validated pregnancy outcome algorithms based on the International Classification of Diseases, Tenth Revision, Clinical Modification/Procedure Coding System (ICD-10-CM/PCS) have not previously been published. METHODS: We developed algorithms to classify pregnancy outcomes and estimate GA using ICD-10-CM/PCS and service codes on claims in the 2016–2018 IBM(®) MarketScan(®) Explorys(®) Claims-EMR Data Set and compared the results with ob-gyn adjudication of electronic medical records (EMRs). Obstetric services were grouped into episodes using hierarchical and spacing requirements. GA was based on evidence with the highest clinical accuracy. Among pregnancies with obstetric EMRs, 100 full-term live births (FTBs), 100 preterm live births (PTBs), 100 spontaneous abortions (SAs), and 24 stillbirths were selected for review. Physicians adjudicated cases using Global Alignment of Immunization safety Assessment in pregnancy (GAIA) definitions applied to structured EMRs. RESULTS: The claims-based algorithms identified 34,204 pregnancies, of which 9.9% had obstetric EMRs. Of sampled pregnancies, 92 FTBs, 93 PTBs, 75 SAs, and 24 stillbirths were adjudicated. Among these pregnancies, the percent agreement was 97.8%, 62.4%, 100.0%, and 70.8% for FTBs, PTBs, SAs, and stillbirths, respectively. The percent agreement on GA within 7 and 28 days, respectively, was 85.9% and 100.0% for FTBs, 81.7% and 98.9% for PTBs, 61.3% and 94.7% for SAs, and 66.7% and 79.2% for stillbirths. CONCLUSIONS: The pregnancy outcome algorithms had high agreement with physician adjudication of EMRs and may inform post-market maternal safety surveillance. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40264-021-01113-8. Springer International Publishing 2021-09-30 2021 /pmc/articles/PMC8481319/ /pubmed/34591264 http://dx.doi.org/10.1007/s40264-021-01113-8 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by-nc/4.0/Open AccessThis article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which permits any non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) . |
spellingShingle | Original Research Article Moll, Keran Wong, Hui Lee Fingar, Kathryn Hobbi, Shayan Sheng, Minya Burrell, Timothy A. Eckert, Linda O. Munoz, Flor M. Baer, Bethany Shoaibi, Azadeh Anderson, Steven Validating Claims-Based Algorithms Determining Pregnancy Outcomes and Gestational Age Using a Linked Claims-Electronic Medical Record Database |
title | Validating Claims-Based Algorithms Determining Pregnancy Outcomes and Gestational Age Using a Linked Claims-Electronic Medical Record Database |
title_full | Validating Claims-Based Algorithms Determining Pregnancy Outcomes and Gestational Age Using a Linked Claims-Electronic Medical Record Database |
title_fullStr | Validating Claims-Based Algorithms Determining Pregnancy Outcomes and Gestational Age Using a Linked Claims-Electronic Medical Record Database |
title_full_unstemmed | Validating Claims-Based Algorithms Determining Pregnancy Outcomes and Gestational Age Using a Linked Claims-Electronic Medical Record Database |
title_short | Validating Claims-Based Algorithms Determining Pregnancy Outcomes and Gestational Age Using a Linked Claims-Electronic Medical Record Database |
title_sort | validating claims-based algorithms determining pregnancy outcomes and gestational age using a linked claims-electronic medical record database |
topic | Original Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8481319/ https://www.ncbi.nlm.nih.gov/pubmed/34591264 http://dx.doi.org/10.1007/s40264-021-01113-8 |
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