EHRs could clarify drug safety in pregnant people

Challa, Anup P.; Lavieri, Robert R.; Lippmann, Ethan S.; Goldstein, Jeffrey A.; Bastarache, Lisa; Pulley, Jill M.; Aronoff, David M.

doi:10.1038/s41591-020-0925-1

Cited by 5 publications

(8 citation statements)

References 7 publications

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“…Studies involving EHR-based models for the prediction of gestational diabetes mellitus (GDM), preeclampsia, and PTB illustrate how such data sources are relevant in the context of reproductive health [38][39][40]. EHRs can also be used to characterize the currently unknown pharmacological effects that a broad range of drugs might have on the physiology of pregnancy [41,42]. Well-established measures of health such as hematology panels and urinalyses continue to provide useful information when assessing maternal and fetal health.…”

Section: Clinical and Social Determinants Of Maternal And Neonatal Hementioning

confidence: 99%

Data-Driven Modeling of Pregnancy-Related Complications

Espinosa

Becker

Marić

et al. 2021

Trends in Molecular Medicine

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A multitude of clinical, biological, environmental, and demographic factors influence the trajectory of a pregnancy. Maternal genetics, environment, stress, nutrition, medical history, socioeconomic status, and racial and ethnic background all play a role in determining the success of a pregnancy. Diverse data sources are available for the study of pregnancy and prediction of adverse outcomes, including electronic health records (EHRs) and administrative claims data, high-throughput multiomics data for characterizing biological systems, and more complex sources like time series, imaging and video data, and text. Recent advances in multiview, multitask, and deep learning allow joint modeling across data sources as well as across outcomes and demonstrate the vast potential of such integrated approaches.

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Section: Clinical and Social Determinants Of Maternal And Neonatal Hementioning

confidence: 99%

Data-Driven Modeling of Pregnancy-Related Complications

Espinosa

Becker

Marić

et al. 2021

Trends in Molecular Medicine

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“…Collectively, these factors allow us to advance the ontological reliability and epistemological robustness of data-driven studies of adverse pregnancy outcomes 8 .…”

Section: Introductionmentioning

confidence: 99%

“…Such an opportunity for discovery of new drug safety insights for pregnant patients may be available through strategic analysis of large numbers of existing healthcare documents like electronic health records (EHRs) that were collected during routine patient care. Collectively, EHRs can uniquely replicate the natural history of pregnancy by linking medical information of pregnant patients and their neonates, such as mothers' prescriptions (while expectant) and the perinatal diseases of their children [7][8][9] . This information allows for the creation of a unique framework of relational knowledge generation.…”

Section: Introductionmentioning

confidence: 99%

“…This information allows for the creation of a unique framework of relational knowledge generation. Namely, a surrogate for RCTs in pregnant patients can result from stratifying EHR data into distinct cohorts by patients' documented exposure-or lack thereof-to a drug of interest and subsequently developing an inferential model to relate incidences of maternal drug exposure and neonatal disease 8 . While these experiments are not a replacement for prospective clinical studies, such a platform of clinical trial emulation presents an ethical way of studying the effects of drug exposure in pregnant people with human data, on a significant scale and across all drug classes.…”

Section: Introductionmentioning

confidence: 99%

“…Technical innovation in this project also rests within the rigor of the analytical methods we employ (Figure 1, below) 8 . We apply a mode of systematic, relational inference to maternal drug exposure and perinatal disease that we believe is more directly and appropriately aligned with the etiology of drug-associated birth defects, compared to the highly coincidental frameworks that dominate the literature.…”

Section: Introductionmentioning

confidence: 99%

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Clinical trial emulation can identify new opportunities to enhance the regulation of drug safety in pregnancy

Challa

Niu

Garrison

et al. 2021

Preprint

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From the perspective of most regulatory agencies, it is usually unethical to perform interventional clinical trials on pregnant people. While this policy recognizes the vulnerability of an expectant mother and unborn child, it has created a public health emergency for millions of pregnant patients through a dearth of robust safety data for many common drugs. To address this problem, we harnessed an enterprise collection of 2.8M electronic health records (EHRs) originally collected from routine primary care, leveraging the data linkage between mothers and their babies to create a surrogate for randomized, controlled drug trials in this population. To demonstrate the feasibility of our clinical trial emulation platform to stimulate new hypotheses for post-market drug surveillance, we identified 1,054 drugs historically prescribed to pregnant patients and developed a medication history-wide association study and follow-up evidence synthesis platform—leveraging expert clinician review and real-world data analysis—to test the effects of maternal exposure to these drugs on the incidence of neurodevelopmental defects in their children. Our results replicate known teratogenic risks and existing knowledge on drug structure-related teratogenic risks. Herein, we highlight 5 common drug classes that we believe warrant further assessment of their safety in pregnancy. We also discuss our efforts to develop a discovery-to-regulatory framework that could allow for pragmatic translation of our results to enhanced regulatory policy. Collectively, our work presents a simple approach to evaluating the utility of EHRs in guiding new regulatory review programs focused on improving the delicate equipoise of accuracy and ethics inherent to assessing drug safety in an extremely vulnerable patient population.

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Big Data in healthcare from a sex and gender perspective

Subirats

Piella

2022

Sex and Gender Bias in Technology and Artificial Intelligence

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EHRs could clarify drug safety in pregnant people

Cited by 5 publications

References 7 publications

Data-Driven Modeling of Pregnancy-Related Complications

Data-Driven Modeling of Pregnancy-Related Complications

Clinical trial emulation can identify new opportunities to enhance the regulation of drug safety in pregnancy

Big Data in healthcare from a sex and gender perspective

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