Karen Fairchild
University of Virginia, VA·Medicine & Clinical Research
Premature babies born at very low birth weight face two dangerous illnesses in the Neonatal Intensive Care Unit (NICU): sepsis, a bloodstream infection that can spread through the body, and necrotizing enterocolitis (NEC), in which intestinal tissue becomes inflamed and dies. Treating either illness early improves survival.
- Fields
Medicine & Clinical Research
Biomedical Engineering
Computer Science
- Topics
neonatal intensive care unit
premature infants
very low birth weight
- Grants
- 1 known grant, through Apr 2027
What they work on
- Refine and expand algorithms that predict sepsis and necrotizing enterocolitis (NEC) in very low birth weight infants, applying time series analysis and machine learning to continuous heart rate and pulse oximetry oxygen data from four NICUs.
- Determine how patient demographics and differences between hospitals affect infant outcomes and how well the sepsis and NEC prediction algorithms perform, using large multi-center vital sign and clinical datasets.
- Build on an existing platform to share multi-center NICU vital sign data and analysis tools openly worldwide, supporting rigorous, reproducible work on predictive monitoring.
More about the research
In the early stages, vital signs such as heart rate and blood oxygen change in subtle ways. Today's monitors and even experienced clinicians cannot see these changes. This long-running, multi-center project builds early warning systems that read those hidden signals in continuous monitoring data.
Why it matters
Sepsis and NEC remain major causes of illness and death in premature infants. Algorithms that flag these conditions earlier from routine bedside vital signs could help NICU clinicians start treatment sooner and improve survival and outcomes. Because the algorithms are tested across several hospitals, they may work broadly across NICUs. An open data and analysis platform could also support reproducible research by other groups.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Heather Siefkes
University of California-Davis, CA·Medicine & Clinical Research·Early-career
Externally validate a machine learning algorithm that combines pulse oximetry features, including oxygen saturation, perfusion index, and pulse delay, for detecting critical congenital heart disease in hospitalized newborns.
Sabrina Malone Jenkins
University of Utah, UT·Medicine & Clinical Research·Early-career
Build logic models mapping barriers and facilitators to adopting rapid whole genome sequencing in Level 4 (metropolitan, well-resourced) and Level 3 (rural, lower-resourced) NICUs.
Camilia Martin
Weill Medical College of Cornell University, NY·Medicine & Clinical Research
Measure the pharmacokinetics and virus-blocking power of an anti-CMV neutralizing antibody added to CMV-positive human milk, testing whether it can reduce infection and immune cell infiltration.
The school
University of Virginia: 43 labs on PI Pages, $814M a year on research, 413 research doctorates a year.
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