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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...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Journal Experts 2023
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.
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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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