Dr. C. Nataraj Awarded Funding to Study PVL in Infants

Dr. C. Nataraj, Chair of the Department of Mechanical Engineering

In collaboration with the Children’s Hospital of Philadelphia (CHOP) and the University of Pennsylvania, Dr. C. Nataraj, Professor and Chair of the Department of Mechanical Engineering, has secured a five-year, $1.9 million research grant from the National Institutes of Health to investigate strategies for predicting and preventing fatal cases of periventricular leukomalacia (PVL) in infants. Ranked as the top pediatric hospital in the nation by U.S. News and World Report, CHOP is world-renowned for pediatric cardiac care, cancer care, and fetal therapy.

PVL, a common brain injury that affects babies who have cardiac issues, can cause serious speech, cognition, and motor skills problems if left untreated. In the worst cases, PVL can be fatal. “I have had a career-long interest in the physics of complex systems, and I’m thrilled that my knowledge in this area could help save lives,” says Dr. Nataraj.

Dr. Nataraj and Ali Jalali, a PhD student, will apply a machinery diagnostic approach to studying PVL data from CHOP to help medical staff predict which infants may be at risk. “Just as we might use this approach to determine when a component within a complex system fails – or will fail – we can adapt these same principles to the concept of PVL detection,” he says.

Tracking complex signals from hemodynamic data from blood pressure, heart rate, and gas concentration readings, as well as MRI studies, they have developed algorithms to predict when PVL may occur. Now, they will conduct more elaborate studies with more patients to develop complex data for use in predicting the severity of a PVL case so that medical staff can take preventative measures or apply effective treatments.

Dr. Nataraj is also in talks with CHOP’s intensive cardiac unit, which houses infants with the most life-threatening conditions, to apply a similar approach to help the staff pinpoint which children’s conditions may worsen overnight, based on daily data analysis.