Early Warning system for detecting pre-eclampsia

Award Information
Agency:
Department of Health and Human Services
Branch
n/a
Amount:
$149,403.00
Award Year:
2013
Program:
STTR
Phase:
Phase I
Contract:
1R41HD075550-01
Award Id:
n/a
Agency Tracking Number:
R41HD075550
Solicitation Year:
2013
Solicitation Topic Code:
NICHD
Solicitation Number:
PA12-089
Small Business Information
107 SW 140th Terr Suite 1, NEWBERRY, FL, 32669-3367
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
176698624
Principal Investigator:
TAMMY EULIANO
(352) 273-6782
TEuliano@anest.ufl.edu
Business Contact:
NEIL EULIANO
(352) 378-4899
neil@conveng.com
Research Institute:
UNIVERSITY OF FLORIDA

UNIVERSITY OF FLORIDA
219 Grinter Hall
GAINESVILLE, FL, 32611-5500
() -
Nonprofit college or university
Abstract
DESCRIPTION (provided by applicant): Preeclampsia, a hypertensive disorder of pregnancy, is a leading cause of maternal and neonatal morbidity and mortality around the world, responsible for approximately 76,000 maternal and 500,000 infant deaths per year.As a heterogeneous and poorly understood disorder, its pathogenesis and possible treatments are an area of active research. While the majority of morbidity and mortality from the disorder can be prevented with close monitoring and treatment in a tertiarycare center, atypical presentations with delayed diagnosis and inability to accurately predict which women will progress to a severe form complicates management. Additionally, many women are treated unnecessarily at untold cost in prolonged antepartum admissions, monitoring, and complications from misguided therapy. Technology to accurately diagnose preeclampsia, or better yet predict which women will develop the disease, would enhance treatment and save lives. Current research focuses on an array of biomarkers in the bloodstream; a viable, if expensive, opportunity for both prediction and improved diagnostic accuracy. The current proposal seeks a much less costly alternative. The vascular derangement that underlies preeclampsia develops months before the onset of symptoms. Early work shows these changes are detectable by analyzing features of the maternal cardiovascular system. The current project will develop and test an inexpensive, reusable, non-invasive device that accurately diagnoses preeclampsia. Thedevice employs a single ECG lead and a pulse oximetry probe. Software algorithms extract more than 80 features from these two technologies. The phases of the project include 1) collect data on 100 preeclamptic and 100 control parturients in labor and delivery with known diagnosis, using a bench-top system; 2) refine preliminary algorithms with regression and nonlinear techniques; 3) develop a miniaturized prototype of the device and validate performance. For Phase II the device will be employed in two additional studies: (1) longitudinal data collection in prenatal clinic patients to refine algorithms for prediction of preeclampsia months before onset and (2) large-scale data collection in delivery wards for development of algorithms that distinguish mild preeclamptics from those who will go on to develop severe disease. The eventual goal is a small, inexpensive, reusable, non-invasive device for use in clinics, emergency departments and delivery wards to predict and diagnose preeclampsia and differentiate future mild and severe disease. The cost of the device allows for a per use cost of as little as five cents. It will support better decision making regarding patient management including level of monitoring, delivery planning and therapeutic interventions.PUBLIC HEALTH RELEVANCE PUBLIC HEALTH RELEVANCE: The proposed project will develop a device to detect and predict preeclampsia, a disorder of pregnancy responsible for 76,000 maternal and 500,000 infant deaths per year. It will inform healthcareproviders of the risk and severity of impending preeclampsia, enabling them to implement appropriate and timely therapy, including transfer to a high-risk facility with appropriate resources for the management of potential complications.

* information listed above is at the time of submission.

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