# Optimizing decision-making to improve maternal outcomes in high-risk pregnant patients with hypertensive disorders - Resubmission - 1 > **NIH NIH K08** · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · 2024 · $170,420 ## Abstract PROJECT SUMMARY/ ABSTRACT Hypertensive disorders of pregnancy (HDP) are common conditions that cause significant maternal morbidity during the birthing process. Guidelines encourage routine delivery management with induction of labor, but my prior work demonstrated many patients across the US are not receiving this care, and instead have high rates of cesarean delivery. This variation may occur because current guidelines lack specificity for patients with HDP complications or factors that increase risk of induction of labor, where it is unknown if induction of labor continues to be favored. My preliminary data from NYU reports up to 20% of those undergoing induction for HDP develop abnormal vital signs, lab abnormalities, or symptoms consistent with progression of HDP. However, it is unknown how the risks of induction of labor compare to those of immediate cesarean delivery. Without a more nuanced understanding of how delivery decisions and clinical factors influence outcomes, too many may be receiving a potentially unnecessary cesarean delivery (when induction of labor would have improved outcomes), or experiencing potential excess morbidity from induction of labor, when immediate cesarean delivery may have been favored. This proposal will characterize the risks of each delivery decision with more precision and potentially develop a decision model to guide individual-level decision making. To accomplish this, in Aim 1 we will use a large representative dataset to compare maternal health outcomes among comparable newly diagnosed patients with HDP based on delivery decision (induction of labor versus immediate cesarean delivery). In Aim 2 we will potentially develop a decision model for delivery decisions between induction of labor and immediate CD, incorporating patient preferences and long-term health outcomes. In Aim 3 we will identify perceived clinical, structural, and cognitive factors informing delivery through clinician focus groups and patient interviews. I have assembled a mentorship team, led by Dr. Scott Braithwaite, with the expertise to supervise this research proposal and its corresponding training objectives. My training objectives are to 1) develop advanced skills in perinatal epidemiology and causal inference techniques 2) acquire expertise in decision science incorporating advanced risk prediction and 3) train in qualitative and mixed methods, conducting focus groups and patient interviews, coding, and analyzing qualitative data. My long-term career goal is to become an independent physician scientist with a research program that focuses on improving risk prediction and clinical decision-making in hypertensive disorders of pregnancy. The proposed training and research plan will prepare me to succeed as an independent investigator. ## Key facts - **NIH application ID:** 10808647 - **Project number:** 1K08HD111687-01A1 - **Recipient organization:** NEW YORK UNIVERSITY SCHOOL OF MEDICINE - **Principal Investigator:** Christina Penfield - **Activity code:** K08 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2024 - **Award amount:** $170,420 - **Award type:** 1 - **Project period:** 2024-09-03 → 2029-08-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10808647 ## Citation > US National Institutes of Health, RePORTER application 10808647, Optimizing decision-making to improve maternal outcomes in high-risk pregnant patients with hypertensive disorders - Resubmission - 1 (1K08HD111687-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10808647. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*