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Pre-Procedural Guidelines for Assessing Blood Pressure01:10

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Accurate blood pressure assessment is crucial for diagnosing and managing various health conditions. To ensure the reliability of these measurements, healthcare professionals must adhere to standardized pre-procedural guidelines. These guidelines enhance patient safety and improve the overall quality of healthcare. The following steps are essential for obtaining accurate and consistent blood pressure readings, from using the appropriate tools to ensuring effective communication with the...
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First-Trimester Machine Learning to Predict Preeclampsia in Normotensive Pregnancies by American Heart Association

Rebecca Horgan1, Erkan Kalafat2, Elena Sinkovskaya1

  • 1Eastern Virginia Medical School, Old Dominion University, Norfolk, Virginia, United States.

American Journal of Perinatology
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Machine learning identified a high-risk subgroup among pregnant individuals with normal first-trimester blood pressure, showing increased preeclampsia and small-for-gestational-age risks. This highlights potential early vascular risks missed by conventional blood pressure thresholds.

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Area of Science:

  • Obstetrics and Gynecology
  • Cardiovascular Health in Pregnancy
  • Computational Medicine

Background:

  • Preeclampsia is a leading cause of maternal and fetal morbidity.
  • Current first-trimester blood pressure guidelines may not adequately identify all individuals at risk.
  • Early identification of high-risk pregnancies is crucial for timely intervention.

Purpose of the Study:

  • To investigate the utility of unsupervised machine learning in identifying distinct hemodynamic phenotypes.
  • To determine if these phenotypes predict preeclampsia risk in individuals with normal first-trimester blood pressure.
  • To assess the association of identified subgroups with hypertensive disorders of pregnancy and small-for-gestational-age neonates.

Main Methods:

  • Secondary analysis of a prospective cohort study.
  • Application of k-means clustering to first-trimester systolic, diastolic, and mean arterial pressure in individuals with American Heart Association (AHA)-defined normal blood pressure.
  • Exclusion of participants with pre-existing chronic hypertension or major fetal/placental abnormalities.

Main Results:

  • Machine learning identified a high-risk cluster (7.4%) within the normotensive group.
  • The high-risk cluster exhibited significantly higher rates of preeclampsia (25.0% vs. 3.1%) and hypertensive disorders of pregnancy (28.6% vs. 5.7%).
  • Adjusted analysis revealed an 8-fold increased hazard for preeclampsia and increased risk for small-for-gestational-age neonates in the high-risk cluster.

Conclusions:

  • Unsupervised clustering can identify a subgroup with normal first-trimester blood pressure at significantly elevated risk for preeclampsia and small-for-gestational-age.
  • Conventional blood pressure thresholds may underestimate early vascular risk.
  • Machine learning offers a promising approach for enhanced risk stratification in pregnancy.