Maternal Obstetric Monitoring System (MOMS): a Postpartum Hemorrhage (PPH) Wearable Monitor
- Institution
- University of California-Irvine, CA
- Principal investigator
- Michelle Khine
- Funding
- $393K
- Duration
- 2025–2026
Moonbase score
Still being scored
Abstract
PROJECT SUMMARY/ABSTRACT This proposed project is responsive to the continued need for improved early detection of postpartum hemorrhage (PPH). PPH is the leading causes of death in pregnant women. Data from multiple published studies strongly suggest that vital signs alone are not actionable during the early stages of PPH. Blood volume loss as great as 30% can occur with current standard vital signs remaining clinically normal. In these stages, compensatory mechanisms for volume loss result in delayed changes in vital signs. Changes in vital signs such as blood pressure often occur only when cardiovascular collapse is imminent, which is often too late to provide a life-saving intervention as the victim enters a state of circulatory shock. The objective of the proposed grant is to develop a robust wearable system, which we call Maternal Obstetrics Monitoring System (MOMS), that can measure high-quality physiological waveforms, from multiple sensors integrated into one easy to wear device, for hospital as well as ambulatory monitoring. We will test the hypothesis that continuous monitoring and analysis of vital sign waveforms with MOMS is a more accurate, precise, and robust early indicator of PPH than current, commercially available technologies. The R61 Specific Aims are 1) to complete and validate the alpha prototype sensor integrating the MEMS-based blood pressure sensor and the CSI sensor and 2) to determine design requirements of the R33 beta prototype from design inputs obtained from shadowing, interviews, and focus groups. The R33 Specific Aims are 1) to design and develop the beta prototype per design specifications of R61 Aim 2, 2) to test this beta prototype with a validated lower-body negative pressure protocol of hypovolemia, and 3) to build multimodal machine learning (MMML) algorithms integrating the multimodal data streams for predictive capabilities and perform explainability analysis. To achieve these aims, we have assembled a multidisciplinary research team with expertise in biomedical device design, biophotonics, clinical obstetrics and gynecology, and machine learning. With the successful completion of the proposed research, we will possess a validated MOMS sensor technology that is ready for use during delivery and postpartum care, which in turn will lead to increased maternal survivability.
Research team
- Bernard ChoiPI
- Judith ChungPI
- Michelle KhinePI
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