Cardiac electrical and structural alterations in preeclampsia

Aksu, Ekrem; Çuğlan, Bilal; Tok, Abdullah; Çelik, Enes; Doğaner, Adem; Sökmen, Abdullah; Sökmen, Gülizar

doi:10.1080/14767058.2021.1895742

Cited by 15 publications

(6 citation statements)

References 34 publications

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“…There was a decrease in AUC (0.79) when ECGs in the month prior to diagnosis were excluded. While this could mean that markers of preeclampsia manifest more within the 30 days closer to the diagnosis ( 12 , 20 ), our model still operates well up to 90 days prior to diagnosis, at earlier stages of pregnancy, with increased potential for patient monitoring and clinical follow-ups ( 12 , 16 , 28 ).…”

Section: Discussionmentioning

confidence: 99%

AI-based preeclampsia detection and prediction with electrocardiogram data

Butler,

Gunturkun,

Chinthala

et al. 2024

Front. Cardiovasc. Med.

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IntroductionMore than 76,000 women die yearly from preeclampsia and hypertensive disorders of pregnancy. Early diagnosis and management of preeclampsia can improve outcomes for both mother and baby. In this study, we developed artificial intelligence models to detect and predict preeclampsia from electrocardiograms (ECGs) in point-of-care settings.MethodsTen-second 12-lead ECG data was obtained from two large health care settings: University of Tennessee Health Science Center (UTHSC) and Atrium Health Wake Forest Baptist (AHWFB). UTHSC data was split into 80% training and 20% holdout data. The model used a modified ResNet convolutional neural network, taking one-dimensional raw ECG signals comprising 12 channels as an input, to predict risk of preeclampsia. Sub-analyses were performed to assess the predictive accuracy for preeclampsia prediction within 30, 60, or 90 days before diagnosis.ResultsThe UTHSC cohort included 904 ECGs from 759 females (78.8% African American) with a mean ± sd age of 27.3 ± 5.0 years. The AHWFB cohort included 817 ECGs from 141 females (45.4 African American) with a mean ± sd age of 27.4 ± 5.9 years. The cross-validated ECG-AI model yielded an AUC (95% CI) of 0.85 (0.77-0.93) on UTHSC holdout data, and an AUC (95% CI) of 0.81 (0.77-0.84) on AHWFB data. The sub-analysis of different time windows before preeclampsia prediction resulted in AUCs (95% CI) of 0.92 (0.84-1.00), 0.89 (0.81-0.98) and 0.90 (0.81-0.98) when tested on ECGs 30 days, 60 days and 90 days, respectively, before diagnosis. When assessed on early onset preeclampsia (preeclampsia diagnosed at <34 weeks of pregnancy), the model's AUC (95% CI) was 0.98 (0.89-1.00).DiscussionWe conclude that preeclampsia can be identified with high accuracy via application of AI models to ECG data.

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Section: Discussionmentioning

confidence: 99%

AI-based preeclampsia detection and prediction with electrocardiogram data

Butler,

Gunturkun,

Chinthala

et al. 2024

Front. Cardiovasc. Med.

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“…The aforementioned study’s authors defined “abnormal electrocardiograms” as those that displayed at least one of the following: the presence of isolated infarct, Q waves, new left bundle branch block (LBBB), higher or lower ST than normal, or T wave inversion. The traditional common CIMT measurement does not demonstrate this, unlike measuring the individual CCA intima and media thicknesses that indicate increased vascular risk [ 59 ].…”

Section: Pregnancy Complicationsmentioning

confidence: 99%

Is There a Relationship between Adverse Pregnancy Outcomes and Future Development of Atherosclerosis?

Poznyak,

Khotina,

Zhigmitova

et al. 2023

Biomedicines

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Cardiovascular disease is one of the main death causes globally. Effective cardiovascular risk management requires a thorough understanding of the mechanisms underlying the disorder. Establishing early markers of the disease allows a timely intervention and prevention of further atherosclerosis development. Multiple studies confirm the correlation between pregnancy disorders and cardiovascular disease in the postpartum period. Moreover, over 30% of women experience adverse pregnancy outcomes. Thus, the examination of the links between these conditions and atherosclerotic cardiovascular disease may help to identify gender-specific risk factors. In this review, we will explore the association between several adverse pregnancy outcome conditions and atherosclerosis. The current analysis is based on the data from several recent studies on the mechanisms behind gestational diabetes, hypertensive disorders of pregnancy, miscarriages, and stillbirths and their implications for the female cardiovascular system.

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“…On electrocardiograms performed during the pregnancy, fQRS morphology was found in 76.5% patients with preeclampsia compared to 27.3% healthy controls. [9] In a study comparing early onset preeclampsia (defined as <34 weeks at diagnosis), late onset pre-eclampsia (>34 weeks at diagnosis, and healthy controls, patients diagnosed earlier in their pregnancy had prolonged QT intervals compared to healthy patients. [10] Although these studies did not include patients with known cardiac disease, arrhythmias, or previous cardiac surgery, they do suggest a relationship between pre-eclampsia and structural cardiac changes that can predispose a patient to arrhythmia.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Pregnancy Outcomes in Women with Arrhythmias following Surgical Repair of Cardiac Defects

et al. 2022

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Objective: The goal of this study was to investigate whether pre-existing cardiac arrhythmias are associated with adverse obstetrical outcomes in women with a history of open cardiac surgery. Study Design: This was a retrospective cohort study of women with a history of open cardiac surgery who delivered at MedStar Washington Hospital Center (Washington, DC) from January 2007 through December 2018. Women with isolated percutaneous cardiac surgical repair were excluded. Maternal and neonatal outcomes were compared between patients with pre-existing cardiac arrhythmias and patients without pre-existing cardiac arrythmias. Maternal outcomes studied were intensive care unit (ICU) admission, postpartum blood loss greater than 1000 mL, congestive heart failure (CHF) development, pre-eclampsia with severe features, postpartum readmission, postpartum cardiac events, and postpartum length of stay >5 days. Neonatal outcomes investigated were low birth weight <2500g, Apgar scores <7 at 5 minutes, and neonatal intensive care unit (NICU) admission. Multivariate logistic regression model was used calculate adjusted odds ratio (aOR) and 95% confidence intervals (95% CI). Results: The outcomes for sixety-nine deliveries from fifty-six women with a history of open cardiac surgery were examined. Thirty-three women (48%) had arrhythmias after cardiac surgery with fourteen (20%) requiring Implantable Cardiac Defibrillators (ICDs). Two women (6%) with pre-existing arrhythmias after cardiac surgery developed postpartum volume overload requiring readmission (p=0.06). After controlling for age, gestational age at delivery and BMI, pre-eclampsia with severe features (p=0.02) and low birth weight neonates (p=0.02, aOR 2.26 (0.56-9.03)) remained statistically more like to occur in patients with pre-existing cardiac arrythmias than in patients without pre-existing arrhythmias. Conclusion: Women with a history of open cardiac surgery and pre-existing cardiac arrhythmias prior to pregnancy are more likely to develop pre-eclampsia with severe features and have low birth weight neonates compared to women with a history of open cardiac surgery without pre-existing cardiac arrhythmias.

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Cardiac electrical and structural alterations in preeclampsia

Cited by 15 publications

References 34 publications

AI-based preeclampsia detection and prediction with electrocardiogram data

AI-based preeclampsia detection and prediction with electrocardiogram data

Is There a Relationship between Adverse Pregnancy Outcomes and Future Development of Atherosclerosis?

Pregnancy Outcomes in Women with Arrhythmias following Surgical Repair of Cardiac Defects

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