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Importance: Preeclampsia poses a significant threat to women's long-term health. However, what diseases are affected and at what level they are affected by PE needs a thorough investigation.
Objective: To conduct the first large-scale, non-hypothesis-driven study using EHR data from multiple medical centers to comprehensively explore adverse health outcomes after preeclampsia.
Design: Retrospective multi-cohort case-control study.
Participants: We analyzed 3,592 preeclampsia patients and 23,040 non-preeclampsia controls from the University of Michigan Healthcare System. We externally validated the findings using UK Biobank data (443 cases, 14,870 controls) and Cedar Sinai data(2755 cases, 60,305 controls).
Main Outcomes: We showed that six complications are significantly affected by PE. We demonstrate the effect of race as well as preeclampsia severity on these complications.
Results: PE significantly increases the risk of later hypertension, uncomplicated and complicated diabetes, renal failure and obesity, after careful confounder adjustment. We also identified that hypothyroidism risks are significantly reduced in PE patients, particularly among African Americans. Severe PE affects hypertension, renal failure, uncomplicated diabetes and obesity more than mild PE, as expected. Caucasians are affected more negatively than African Americans by PE on future hypertension, uncomplicated and complicated diabetes and obesity.
Conclusion: This study fills a gap in the comprehensive assessment of preeclampsia's long-term effects using large-scale EHR data and rigorous statistical methods. Our findings emphasize the need for extended monitoring and tailored interventions for women with a history of preeclampsia, by considering pre-existing conditions, preeclampsia severity, and racial differences.
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http://dx.doi.org/10.1101/2023.12.05.23299296 | DOI Listing |
JMIR Cancer
September 2025
Department of Health Outcomes and Biomedical Informatics, University of Florida, 1889 Museum Road, Suite 7000, Gainesville, FL, 32611, United States, 1 352 294-5969.
Background: Disparities in cancer burden between transgender and cisgender individuals remain an underexplored area of research.
Objective: This study aimed to examine the cumulative incidence and associated risk factors for cancer and precancerous conditions among transgender individuals compared with matched cisgender individuals.
Methods: We conducted a retrospective cohort study using patient-level electronic health record (EHR) data from the University of Florida Health Integrated Data Repository between 2012 and 2023.
Ann Intern Med
September 2025
Department of Medicine, Johns Hopkins University School of Medicine, and Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland (J.B.S.).
Electronic health record (EHR) data are increasingly used to develop prediction models that guide clinical decision making at the point of care. These include algorithms that use high-frequency data, like in sepsis prediction, as well as simpler equations, such as the Pooled Cohort Equations for cardiovascular outcome prediction. Although EHR data used in prediction models are often highly granular and more current than other data, there is systematic and nonsystematic missingness in EHR data as there is with most data.
View Article and Find Full Text PDFJAMIA Open
October 2025
Applied Clinical Research Center, Children's Hospital of Philadelphia, Philadelphia, PA 19104, United States.
Objective: To develop a natural language processing (NLP) pipeline for unstructured electronic health record (EHR) data to identify symptoms and functional impacts associated with Long COVID in children.
Materials And Methods: We analyzed 48 287 outpatient progress notes from 10 618 pediatric patients from 12 institutions. We evaluated notes obtained 28 to 179 days after a COVID-19 diagnosis or positive test.
JAMIA Open
October 2025
Department of Biomedical Informatics, University of Utah, Salt Lake City, UT 84108, United States.
Objectives: Type 2 diabetes (T2D) is a growing public health burden with persistent racial and ethnic disparities. . This study assessed the completeness of social determinants of health (SdoH) data for patients with T2D in Epic Cosmos, a nationwide, cross-institutional electronic health recors (EHR) database.
View Article and Find Full Text PDFNpj Health Syst
September 2025
Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN USA.
Most clinical AI solutions center on patient data-particularly those from electronic health records (EHR)-while overlooking the clinician activities that shape care and signal patient outcomes. EHR use metadata, which capture fine-grained clinician-EHR interactions, represent a powerful yet underutilized resource that complements patient data. This perspective highlights the opportunity of integrating EHR use metadata with patient data to advance the development, evaluation, and real-world impact of clinical AI.
View Article and Find Full Text PDF