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  1. Article ; Online: Providing Data to Empower Pregnant Patients to Make Evidence-Based Choices.

    Gray, Kathryn J

    JAMA network open

    2021  Volume 4, Issue 4, Page(s) e215359

    MeSH term(s) Female ; Humans ; Pregnancy
    Language English
    Publishing date 2021-04-01
    Publishing country United States
    Document type Journal Article ; Comment
    ISSN 2574-3805
    ISSN (online) 2574-3805
    DOI 10.1001/jamanetworkopen.2021.5359
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  2. Article ; Online: Maternal COVID-19 vaccine antibody response and passage into cord blood.

    Gray, Kathryn J

    The Journal of pediatrics

    2021  Volume 236, Page(s) 325–328

    MeSH term(s) Antibodies, Viral ; Antibody Formation ; COVID-19 ; COVID-19 Vaccines ; Fetal Blood/immunology ; Humans ; SARS-CoV-2
    Chemical Substances Antibodies, Viral ; COVID-19 Vaccines
    Language English
    Publishing date 2021-08-16
    Publishing country United States
    Document type Journal Article ; Comment
    ZDB-ID 3102-1
    ISSN 1097-6833 ; 0022-3476
    ISSN (online) 1097-6833
    ISSN 0022-3476
    DOI 10.1016/j.jpeds.2021.06.055
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    Um IV Zs.116: Show issues Location:
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  3. Article: Deep Survival Analysis for Interpretable Time-Varying Prediction of Preeclampsia Risk.

    Eberhard, Braden W / Gray, Kathryn J / Bates, David W / Kovacheva, Vesela P

    medRxiv : the preprint server for health sciences

    2024  

    Abstract: Objective: Survival analysis is widely utilized in healthcare to predict the timing of disease onset. Traditional methods of survival analysis are usually based on Cox Proportional Hazards model and assume proportional risk for all subjects. However, ... ...

    Abstract Objective: Survival analysis is widely utilized in healthcare to predict the timing of disease onset. Traditional methods of survival analysis are usually based on Cox Proportional Hazards model and assume proportional risk for all subjects. However, this assumption is rarely true for most diseases, as the underlying factors have complex, non-linear, and time-varying relationships. This concern is especially relevant for pregnancy, where the risk for pregnancy-related complications, such as preeclampsia, varies across gestation. Recently, deep learning survival models have shown promise in addressing the limitations of classical models, as the novel models allow for non-proportional risk handling, capturing nonlinear relationships, and navigating complex temporal dynamics.
    Methods: We present a methodology to model the temporal risk of preeclampsia during pregnancy and investigate the associated clinical risk factors. We utilized a retrospective dataset including 66,425 pregnant individuals who delivered in two tertiary care centers from 2015-2023. We modeled the preeclampsia risk by modifying DeepHit, a deep survival model, which leverages neural network architecture to capture time-varying relationships between covariates in pregnancy. We applied time series k-means clustering to DeepHit's normalized output and investigated interpretability using Shapley values.
    Results: We demonstrate that DeepHit can effectively handle high-dimensional data and evolving risk hazards over time with performance similar to the Cox Proportional Hazards model, achieving an area under the curve (AUC) of 0.78 for both models. The deep survival model outperformed traditional methodology by identifying time-varied risk trajectories for preeclampsia, providing insights for early and individualized intervention. K-means clustering resulted in patients delineating into low-risk, early-onset, and late-onset preeclampsia groups- notably, each of those has distinct risk factors.
    Conclusion: This work demonstrates a novel application of deep survival analysis in time-varying prediction of preeclampsia risk. Our results highlight the advantage of deep survival models compared to Cox Proportional Hazards models in providing personalized risk trajectory and demonstrating the potential of deep survival models to generate interpretable and meaningful clinical applications in medicine.
    Language English
    Publishing date 2024-01-19
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2024.01.18.24301456
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  4. Article ; Online: Adaptive immunity, chronic inflammation and the clock.

    Gray, Kathryn J / Gibbs, Julie E

    Seminars in immunopathology

    2022  Volume 44, Issue 2, Page(s) 209–224

    Abstract: The adaptive arm of the immune system facilitates recognition of specific foreign pathogens and, via the action of T and B lymphocytes, induces a fine-tuned response to target the pathogen and develop immunological memory. The functionality of the ... ...

    Abstract The adaptive arm of the immune system facilitates recognition of specific foreign pathogens and, via the action of T and B lymphocytes, induces a fine-tuned response to target the pathogen and develop immunological memory. The functionality of the adaptive immune system exhibits daily 24-h variation both in homeostatic processes (such as lymphocyte trafficking and development of T lymphocyte subsets) and in responses to challenge. Here, we discuss how the circadian clock exerts influence over the function of the adaptive immune system, considering the roles of cell intrinsic clockwork machinery and cell extrinsic rhythmic signals. Inappropriate or misguided actions of the adaptive immune system can lead to development of autoimmune diseases such as rheumatoid arthritis, ulcerative colitis and multiple sclerosis. Growing evidence indicates that disturbance of the circadian clock has negative impact on development and progression of these chronic inflammatory diseases and we examine current understanding of clock-immune interactions in the setting of these inflammatory conditions. A greater appreciation of circadian control of adaptive immunity will facilitate further understanding of mechanisms driving daily variation in disease states and drive improvements in the diagnosis and treatment of chronic inflammatory diseases.
    MeSH term(s) Adaptive Immunity ; Circadian Clocks/physiology ; Circadian Rhythm/physiology ; Humans ; Inflammation ; T-Lymphocyte Subsets
    Language English
    Publishing date 2022-03-01
    Publishing country Germany
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Review
    ZDB-ID 2316828-6
    ISSN 1863-2300 ; 1863-2297
    ISSN (online) 1863-2300
    ISSN 1863-2297
    DOI 10.1007/s00281-022-00919-7
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    Zs.A 1425: Show issues Location:
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    Jg. 1995 - 2021: Lesesall (1.OG)
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  5. Article ; Online: Cell-Free RNA Transcriptome and Prediction of Adverse Pregnancy Outcomes.

    Gray, Kathryn J / Hemberg, Martin / Karumanchi, S Ananth

    Clinical chemistry

    2022  Volume 68, Issue 11, Page(s) 1358–1360

    MeSH term(s) Pregnancy ; Female ; Humans ; Transcriptome ; Cell-Free Nucleic Acids ; Pregnancy Outcome ; Gene Expression Profiling ; Amniotic Fluid ; RNA/genetics
    Chemical Substances Cell-Free Nucleic Acids ; RNA (63231-63-0)
    Language English
    Publishing date 2022-08-15
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 80102-1
    ISSN 1530-8561 ; 0009-9147
    ISSN (online) 1530-8561
    ISSN 0009-9147
    DOI 10.1093/clinchem/hvac109
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    Ud II Zs.171: Show issues Location:
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  6. Article ; Online: Correspondence on "Points to consider in the practice of postmortem genetic testing: A statement of the American College of Medical Genetics and Genomics (ACMG)" by Deignan, et al.

    Alkuraya, Fowzan S / Gray, Kathryn J / Prakash, Siddharth K / Wojcik, Monica H / Lin, Angela E

    Genetics in medicine : official journal of the American College of Medical Genetics

    2023  Volume 25, Issue 10, Page(s) 100904

    Language English
    Publishing date 2023-06-29
    Publishing country United States
    Document type Letter
    ZDB-ID 1455352-1
    ISSN 1530-0366 ; 1098-3600
    ISSN (online) 1530-0366
    ISSN 1098-3600
    DOI 10.1016/j.gim.2023.100904
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    Zs.A 5059: Show issues Location:
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  7. Article ; Online: Aspiring toward equitable benefits from genomic advances to individuals of ancestrally diverse backgrounds.

    Wang, Ying / He, Yixuan / Shi, Yue / Qian, David C / Gray, Kathryn J / Winn, Robert / Martin, Alicia R

    American journal of human genetics

    2024  Volume 111, Issue 5, Page(s) 809–824

    Abstract: Advancements in genomic technologies have shown remarkable promise for improving health trajectories. The Human Genome Project has catalyzed the integration of genomic tools into clinical practice, such as disease risk assessment, prenatal testing and ... ...

    Abstract Advancements in genomic technologies have shown remarkable promise for improving health trajectories. The Human Genome Project has catalyzed the integration of genomic tools into clinical practice, such as disease risk assessment, prenatal testing and reproductive genomics, cancer diagnostics and prognostication, and therapeutic decision making. Despite the promise of genomic technologies, their full potential remains untapped without including individuals of diverse ancestries and integrating social determinants of health (SDOHs). The NHGRI launched the 2020 Strategic Vision with ten bold predictions by 2030, including "individuals from ancestrally diverse backgrounds will benefit equitably from advances in human genomics." Meeting this goal requires a holistic approach that brings together genomic advancements with careful consideration to healthcare access as well as SDOHs to ensure that translation of genetics research is inclusive, affordable, and accessible and ultimately narrows rather than widens health disparities. With this prediction in mind, this review delves into the two paramount applications of genetic testing-reproductive genomics and precision oncology. When discussing these applications of genomic advancements, we evaluate current accessibility limitations, highlight challenges in achieving representativeness, and propose paths forward to realize the ultimate goal of their equitable applications.
    MeSH term(s) Humans ; Genomics/methods ; Precision Medicine/methods ; Genome, Human ; Genetic Testing ; Neoplasms/genetics ; Health Services Accessibility
    Language English
    Publishing date 2024-04-19
    Publishing country United States
    Document type Journal Article ; Review ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 219384-x
    ISSN 1537-6605 ; 0002-9297
    ISSN (online) 1537-6605
    ISSN 0002-9297
    DOI 10.1016/j.ajhg.2024.04.002
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    Zs.A 107: Show issues Location:
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  8. Article: Prediction of Preeclampsia from Clinical and Genetic Risk Factors in Early and Late Pregnancy Using Machine Learning and Polygenic Risk Scores.

    Kovacheva, Vesela P / Eberhard, Braden W / Cohen, Raphael Y / Maher, Matthew / Saxena, Richa / Gray, Kathryn J

    medRxiv : the preprint server for health sciences

    2023  

    Abstract: Background: Preeclampsia, a pregnancy-specific condition associated with new-onset hypertension after 20 weeks gestation, is a leading cause of maternal and neonatal morbidity and mortality. Predictive tools to understand which individuals are most at ... ...

    Abstract Background: Preeclampsia, a pregnancy-specific condition associated with new-onset hypertension after 20 weeks gestation, is a leading cause of maternal and neonatal morbidity and mortality. Predictive tools to understand which individuals are most at risk are needed.
    Methods: We identified a cohort of N=1,125 pregnant individuals who delivered between 05/2015-05/2022 at Mass General Brigham hospitals with available electronic health record (EHR) data and linked genetic data. Using clinical EHR data and systolic blood pressure polygenic risk scores (SBP PRS) derived from a large genome-wide association study, we developed machine learning (xgboost) and linear regression models to predict preeclampsia risk.
    Results: Pregnant individuals with an SBP PRS in the top quartile had higher blood pressures throughout pregnancy compared to patients within the lowest quartile SBP PRS. In the first trimester, the most predictive model was xgboost, with an area under the curve (AUC) of 0.73. Adding the SBP PRS to the models improved the performance only of the linear regression model from AUC 0.70 to 0.71; the predictive power of other models remained unchanged. In late pregnancy, with data obtained up to the delivery admission, the best performing model was xgboost using clinical variables, which achieved an AUC of 0.91.
    Conclusions: Integrating clinical and genetic factors into predictive models can inform personalized preeclampsia risk and achieve higher predictive power than the current practice. In the future, personalized tools can be implemented in clinical practice to identify high-risk patients for preventative therapies and timely intervention to improve adverse maternal and neonatal outcomes.
    Language English
    Publishing date 2023-02-07
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2023.02.03.23285385
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  9. Article ; Online: Special issue on "Feto-Maternal Genomic Medicine": a decade of incredible advances.

    Gray, Kathryn J / Wilkins-Haug, Louise

    Human genetics

    2020  Volume 139, Issue 9, Page(s) 1119–1120

    MeSH term(s) Congenital Abnormalities/diagnosis ; Female ; Fetal Diseases/diagnosis ; Genetic Testing/methods ; Humans ; Maternal-Fetal Exchange ; Pregnancy ; Prenatal Diagnosis/methods
    Language English
    Publishing date 2020-08-16
    Publishing country Germany
    Document type Editorial
    ZDB-ID 223009-4
    ISSN 1432-1203 ; 0340-6717
    ISSN (online) 1432-1203
    ISSN 0340-6717
    DOI 10.1007/s00439-020-02217-4
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    Zs.A 256: Show issues Location:
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  10. Article ; Online: Preeclampsia Prediction Using Machine Learning and Polygenic Risk Scores From Clinical and Genetic Risk Factors in Early and Late Pregnancies.

    Kovacheva, Vesela P / Eberhard, Braden W / Cohen, Raphael Y / Maher, Matthew / Saxena, Richa / Gray, Kathryn J

    Hypertension (Dallas, Tex. : 1979)

    2023  Volume 81, Issue 2, Page(s) 264–272

    Abstract: Background: Preeclampsia, a pregnancy-specific condition associated with new-onset hypertension after 20-weeks gestation, is a leading cause of maternal and neonatal morbidity and mortality. Predictive tools to understand which individuals are most at ... ...

    Abstract Background: Preeclampsia, a pregnancy-specific condition associated with new-onset hypertension after 20-weeks gestation, is a leading cause of maternal and neonatal morbidity and mortality. Predictive tools to understand which individuals are most at risk are needed.
    Methods: We identified a cohort of N=1125 pregnant individuals who delivered between May 2015 and May 2022 at Mass General Brigham Hospitals with available electronic health record data and linked genetic data. Using clinical electronic health record data and systolic blood pressure polygenic risk scores derived from a large genome-wide association study, we developed machine learning (XGBoost) and logistic regression models to predict preeclampsia risk.
    Results: Pregnant individuals with a systolic blood pressure polygenic risk score in the top quartile had higher blood pressures throughout pregnancy compared with patients within the lowest quartile systolic blood pressure polygenic risk score. In the first trimester, the most predictive model was XGBoost, with an area under the curve of 0.74. In late pregnancy, with data obtained up to the delivery admission, the best-performing model was XGBoost using clinical variables, which achieved an area under the curve of 0.91. Adding the systolic blood pressure polygenic risk score to the models did not improve the performance significantly based on De Long test comparing the area under the curve of models with and without the polygenic score.
    Conclusions: Integrating clinical factors into predictive models can inform personalized preeclampsia risk and achieve higher predictive power than the current practice. In the future, personalized tools can be implemented to identify high-risk patients for preventative therapies and timely intervention to improve adverse maternal and neonatal outcomes.
    MeSH term(s) Female ; Infant, Newborn ; Pregnancy ; Humans ; Pre-Eclampsia/diagnosis ; Pre-Eclampsia/epidemiology ; Pre-Eclampsia/genetics ; Genetic Risk Score ; Genome-Wide Association Study ; Predictive Value of Tests ; Machine Learning ; Risk Factors
    Language English
    Publishing date 2023-10-30
    Publishing country United States
    Document type Journal Article
    ZDB-ID 423736-5
    ISSN 1524-4563 ; 0194-911X ; 0362-4323
    ISSN (online) 1524-4563
    ISSN 0194-911X ; 0362-4323
    DOI 10.1161/HYPERTENSIONAHA.123.21053
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    Zs.A 1488: Show issues Location:
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