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Leveraging electronic health records to identify risk factors for recurrent pregnancy loss across two medical centers: a case-control study
Recurrent pregnancy loss (RPL), defined as 2 or more pregnancy losses, affects 5–6% of ever-pregnant individuals. Approximately half of these cases have no identifiable explanation. To generate hypotheses about RPL etiologies, we implemented a case-control study comparing the history of over 1,600 d...
Autores principales: | , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Journal Experts
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10055527/ https://www.ncbi.nlm.nih.gov/pubmed/36993325 http://dx.doi.org/10.21203/rs.3.rs-2631220/v2 |
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author | Roger, Jacquelyn Xie, Feng Costello, Jean Tang, Alice Liu, Jay Oskotsky, Tomiko Woldemariam, Sarah Kosti, Idit Le, Brian Snyder, Michael P. Giudice, Linda C. Torgerson, Dara Shaw, Gary M. Stevenson, David K. Rajkovic, Aleksandar Glymour, M. Maria Aghaeepour, Nima Cakmak, Hakan Lathi, Ruth B. Sirota, Marina |
author_facet | Roger, Jacquelyn Xie, Feng Costello, Jean Tang, Alice Liu, Jay Oskotsky, Tomiko Woldemariam, Sarah Kosti, Idit Le, Brian Snyder, Michael P. Giudice, Linda C. Torgerson, Dara Shaw, Gary M. Stevenson, David K. Rajkovic, Aleksandar Glymour, M. Maria Aghaeepour, Nima Cakmak, Hakan Lathi, Ruth B. Sirota, Marina |
author_sort | Roger, Jacquelyn |
collection | PubMed |
description | Recurrent pregnancy loss (RPL), defined as 2 or more pregnancy losses, affects 5–6% of ever-pregnant individuals. Approximately half of these cases have no identifiable explanation. To generate hypotheses about RPL etiologies, we implemented a case-control study comparing the history of over 1,600 diagnoses between RPL and live-birth patients, leveraging the University of California San Francisco (UCSF) and Stanford University electronic health record databases. In total, our study included 8,496 RPL (UCSF: 3,840, Stanford: 4,656) and 53,278 Control (UCSF: 17,259, Stanford: 36,019) patients. Menstrual abnormalities and infertility-associated diagnoses were significantly positively associated with RPL in both medical centers. Age-stratified analysis revealed that the majority of RPL-associated diagnoses had higher odds ratios for patients <35 compared with 35+ patients. While Stanford results were sensitive to control for healthcare utilization, UCSF results were stable across analyses with and without utilization. Intersecting significant results between medical centers was an effective filter to identify associations that are robust across center-specific utilization patterns. |
format | Online Article Text |
id | pubmed-10055527 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | American Journal Experts |
record_format | MEDLINE/PubMed |
spelling | pubmed-100555272023-03-30 Leveraging electronic health records to identify risk factors for recurrent pregnancy loss across two medical centers: a case-control study Roger, Jacquelyn Xie, Feng Costello, Jean Tang, Alice Liu, Jay Oskotsky, Tomiko Woldemariam, Sarah Kosti, Idit Le, Brian Snyder, Michael P. Giudice, Linda C. Torgerson, Dara Shaw, Gary M. Stevenson, David K. Rajkovic, Aleksandar Glymour, M. Maria Aghaeepour, Nima Cakmak, Hakan Lathi, Ruth B. Sirota, Marina Res Sq Article Recurrent pregnancy loss (RPL), defined as 2 or more pregnancy losses, affects 5–6% of ever-pregnant individuals. Approximately half of these cases have no identifiable explanation. To generate hypotheses about RPL etiologies, we implemented a case-control study comparing the history of over 1,600 diagnoses between RPL and live-birth patients, leveraging the University of California San Francisco (UCSF) and Stanford University electronic health record databases. In total, our study included 8,496 RPL (UCSF: 3,840, Stanford: 4,656) and 53,278 Control (UCSF: 17,259, Stanford: 36,019) patients. Menstrual abnormalities and infertility-associated diagnoses were significantly positively associated with RPL in both medical centers. Age-stratified analysis revealed that the majority of RPL-associated diagnoses had higher odds ratios for patients <35 compared with 35+ patients. While Stanford results were sensitive to control for healthcare utilization, UCSF results were stable across analyses with and without utilization. Intersecting significant results between medical centers was an effective filter to identify associations that are robust across center-specific utilization patterns. American Journal Experts 2023-03-31 /pmc/articles/PMC10055527/ /pubmed/36993325 http://dx.doi.org/10.21203/rs.3.rs-2631220/v2 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use. https://creativecommons.org/licenses/by/4.0/License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License (https://creativecommons.org/licenses/by/4.0/) |
spellingShingle | Article Roger, Jacquelyn Xie, Feng Costello, Jean Tang, Alice Liu, Jay Oskotsky, Tomiko Woldemariam, Sarah Kosti, Idit Le, Brian Snyder, Michael P. Giudice, Linda C. Torgerson, Dara Shaw, Gary M. Stevenson, David K. Rajkovic, Aleksandar Glymour, M. Maria Aghaeepour, Nima Cakmak, Hakan Lathi, Ruth B. Sirota, Marina Leveraging electronic health records to identify risk factors for recurrent pregnancy loss across two medical centers: a case-control study |
title | Leveraging electronic health records to identify risk factors for recurrent pregnancy loss across two medical centers: a case-control study |
title_full | Leveraging electronic health records to identify risk factors for recurrent pregnancy loss across two medical centers: a case-control study |
title_fullStr | Leveraging electronic health records to identify risk factors for recurrent pregnancy loss across two medical centers: a case-control study |
title_full_unstemmed | Leveraging electronic health records to identify risk factors for recurrent pregnancy loss across two medical centers: a case-control study |
title_short | Leveraging electronic health records to identify risk factors for recurrent pregnancy loss across two medical centers: a case-control study |
title_sort | leveraging electronic health records to identify risk factors for recurrent pregnancy loss across two medical centers: a case-control study |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10055527/ https://www.ncbi.nlm.nih.gov/pubmed/36993325 http://dx.doi.org/10.21203/rs.3.rs-2631220/v2 |
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