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Hospital discharge data is not accurate enough to monitor the incidence of postpartum hemorrhage
INTRODUCTION: Postpartum hemorrhage remains a leading cause of maternal morbidity and mortality worldwide. Therefore, cumulative incidence of postpartum hemorrhage and severe postpartum hemorrhage are commonly monitored within and compared across maternity hospitals or countries for obstetrical safe...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7857548/ https://www.ncbi.nlm.nih.gov/pubmed/33534862 http://dx.doi.org/10.1371/journal.pone.0246119 |
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author | Walther, Diana Halfon, Patricia Tanzer, Romain Burnand, Bernard Robertson, Moira Vial, Yvan Desseauve, David Le Pogam, Marie-Annick |
author_facet | Walther, Diana Halfon, Patricia Tanzer, Romain Burnand, Bernard Robertson, Moira Vial, Yvan Desseauve, David Le Pogam, Marie-Annick |
author_sort | Walther, Diana |
collection | PubMed |
description | INTRODUCTION: Postpartum hemorrhage remains a leading cause of maternal morbidity and mortality worldwide. Therefore, cumulative incidence of postpartum hemorrhage and severe postpartum hemorrhage are commonly monitored within and compared across maternity hospitals or countries for obstetrical safety improvement. These indicators are usually based on hospital discharge data though their accuracy is seldom assessed. We aimed to measure postpartum hemorrhage and severe postpartum hemorrhage using electronic health records and hospital discharge data separately and compare the detection accuracy of these methods to manual chart review, and to examine the temporal trends in cumulative incidence of these potentially avoidable adverse outcomes. MATERIALS AND METHODS: We analyzed routinely collected data of 7904 singleton deliveries from a large Swiss university hospital for a three year period (2014–2016). We identified postpartum hemorrhage and severe postpartum hemorrhage in electronic health records by text mining discharge letters and operative reports and calculating drop in hemoglobin from laboratory tests. Diagnostic and procedure codes were used to identify cases in hospital discharge data. A sample of 334 charts was reviewed manually to provide a reference-standard and evaluate the accuracy of the other detection methods. RESULTS: Sensitivities of detection algorithms based on electronic health records and hospital discharge data were 95.2% (95% CI: 92.6% 97.8%) and 38.2% (33.3% to 43.0%), respectively for postpartum hemorrhage, and 87.5% (85.2% to 89.8%) and 36.2% (26.3% to 46.1%) for severe postpartum hemorrhage. Postpartum hemorrhage cumulative incidence based on electronic health records decreased from 15.6% (13.1% to 18.2%) to 8.5% (6.7% to 10.5%) from the beginning of 2014 to the end of 2016, with an average of 12.5% (11.8% to 13.3%). The cumulative incidence of severe postpartum hemorrhage remained at approximately 4% (3.5% to 4.4%). Hospital discharge data-based algorithms provided significantly underestimated incidences. CONCLUSIONS: Hospital discharge data is not accurate enough to assess the incidence of postpartum hemorrhage at hospital or national level. Instead, automated algorithms based on structured and textual data from electronic health records should be considered, as they provide accurate and timely estimates for monitoring and improvement in obstetrical safety. Furthermore, they have the potential to better code for postpartum hemorrhage thus improving hospital reimbursement. |
format | Online Article Text |
id | pubmed-7857548 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-78575482021-02-11 Hospital discharge data is not accurate enough to monitor the incidence of postpartum hemorrhage Walther, Diana Halfon, Patricia Tanzer, Romain Burnand, Bernard Robertson, Moira Vial, Yvan Desseauve, David Le Pogam, Marie-Annick PLoS One Research Article INTRODUCTION: Postpartum hemorrhage remains a leading cause of maternal morbidity and mortality worldwide. Therefore, cumulative incidence of postpartum hemorrhage and severe postpartum hemorrhage are commonly monitored within and compared across maternity hospitals or countries for obstetrical safety improvement. These indicators are usually based on hospital discharge data though their accuracy is seldom assessed. We aimed to measure postpartum hemorrhage and severe postpartum hemorrhage using electronic health records and hospital discharge data separately and compare the detection accuracy of these methods to manual chart review, and to examine the temporal trends in cumulative incidence of these potentially avoidable adverse outcomes. MATERIALS AND METHODS: We analyzed routinely collected data of 7904 singleton deliveries from a large Swiss university hospital for a three year period (2014–2016). We identified postpartum hemorrhage and severe postpartum hemorrhage in electronic health records by text mining discharge letters and operative reports and calculating drop in hemoglobin from laboratory tests. Diagnostic and procedure codes were used to identify cases in hospital discharge data. A sample of 334 charts was reviewed manually to provide a reference-standard and evaluate the accuracy of the other detection methods. RESULTS: Sensitivities of detection algorithms based on electronic health records and hospital discharge data were 95.2% (95% CI: 92.6% 97.8%) and 38.2% (33.3% to 43.0%), respectively for postpartum hemorrhage, and 87.5% (85.2% to 89.8%) and 36.2% (26.3% to 46.1%) for severe postpartum hemorrhage. Postpartum hemorrhage cumulative incidence based on electronic health records decreased from 15.6% (13.1% to 18.2%) to 8.5% (6.7% to 10.5%) from the beginning of 2014 to the end of 2016, with an average of 12.5% (11.8% to 13.3%). The cumulative incidence of severe postpartum hemorrhage remained at approximately 4% (3.5% to 4.4%). Hospital discharge data-based algorithms provided significantly underestimated incidences. CONCLUSIONS: Hospital discharge data is not accurate enough to assess the incidence of postpartum hemorrhage at hospital or national level. Instead, automated algorithms based on structured and textual data from electronic health records should be considered, as they provide accurate and timely estimates for monitoring and improvement in obstetrical safety. Furthermore, they have the potential to better code for postpartum hemorrhage thus improving hospital reimbursement. Public Library of Science 2021-02-03 /pmc/articles/PMC7857548/ /pubmed/33534862 http://dx.doi.org/10.1371/journal.pone.0246119 Text en © 2021 Walther 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 Walther, Diana Halfon, Patricia Tanzer, Romain Burnand, Bernard Robertson, Moira Vial, Yvan Desseauve, David Le Pogam, Marie-Annick Hospital discharge data is not accurate enough to monitor the incidence of postpartum hemorrhage |
title | Hospital discharge data is not accurate enough to monitor the incidence of postpartum hemorrhage |
title_full | Hospital discharge data is not accurate enough to monitor the incidence of postpartum hemorrhage |
title_fullStr | Hospital discharge data is not accurate enough to monitor the incidence of postpartum hemorrhage |
title_full_unstemmed | Hospital discharge data is not accurate enough to monitor the incidence of postpartum hemorrhage |
title_short | Hospital discharge data is not accurate enough to monitor the incidence of postpartum hemorrhage |
title_sort | hospital discharge data is not accurate enough to monitor the incidence of postpartum hemorrhage |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7857548/ https://www.ncbi.nlm.nih.gov/pubmed/33534862 http://dx.doi.org/10.1371/journal.pone.0246119 |
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