Ohio State researchers developing better methods to diagnose chronic middle ear infections
Researchers at The Ohio State University are using computational techniques to develop better diagnostic tests for chronic middle ear infections, known as otitis media, and determine the most effective treatment for a given patient.
Samir Ghadiali, associate professor of biomedical engineering at Ohio State, has been awarded a $1.2 million grant from the National Institute of Health (NIH) to create a library of computational models that will help clinicians diagnose and treat chronic ear infections. The five-year award is part of a Clinical Research Center Grant led by the University of Pittsburgh.
Approximately fifty percent of children in the U.S. under age three experience at least one episode of otitis media at some point in their lives, according to research published in the April 2013 issue of Otolaryngology-Head and Neck Surgery. Approximately 15 percent of those patients suffer from chronic episodes of the disease.
"Our goal is to build a library of models, based on a child's age and other factors, that clinicians could use to diagnose chronic otitis media and determine which treatments would be most effective in individual patients," said Ghadiali.
While researchers have known for some time that chronic otitis media is linked to dysfunction of the Eustachian tube, an upper respiratory airway, the anatomical and functional structure of this airway varies from patient to patient. This makes diagnosing and treating the disease difficult.
"In our urgent care centers, clinics and surgery centers here at Nationwide Children's Hospital, we see an average of 17,000 patients each year for ear infections," said Gregory Wiet, MD, FACS, FAAP, a physician in Otolaryngology at Nationwide Children's Hospital and a faculty member at The Ohio State University College of Medicine. "Current diagnostic tests are not able to identify patients at risk for developing chronic otitis media and many therapies for treating it are only minimally effective. Research in this field is critical to help us find a solution for these patients and, ultimately, prevent ear infections altogether."
Chronic middle ear infections are currently treated with either antibiotics or the surgical insertion of tympanostomy tubes, but those treatments don't work on all patients, said Ghadiali. Other treatment options exist, but have low effectiveness rates across the spectrum of patients.
"These kids really suffer a lot because they have these hearing problems at such a young age, which can lead to developmental issues," Ghadiali said.
Ghadiali, who is also an investigator in Ohio State's Davis Heart and Lung Research Institute, and his team will first analyze and characterize human subject data obtained from patients with ear infections supplied by the University of Pittsburgh. That information will be used to create patient-specific computational models that can successfully diagnose and provide personalized treatment strategies for chronic otitis media. Moving forward, the research team will track the models' effectiveness related to diagnostic accuracy and treatment outcomes for specific patient groups.
The research builds on previous work Ghadiali completed with Charles Bluestone and William Doyle, professors of otolaryngology at the University of Pittsburgh, to develop computational models of the Eustachian tube. Those models helped illustrate how the Eustachian tube functions structurally, both in normal adults and in kids with ear infections.
A previous R01 NIH grant funded research to develop and refine computational models that simulate Eustachian tube dysfunction in several defined groups of patients who develop the chronic disease. Results of that research showed the highly personalized nature of the disease, where even patients who were in the same group reacted very differently to simulated treatments.
Research reported in this press release was supported by National Institute for Deafness and Communication Disorders of the National Institutes of Health under award numbers RO1DC007230 and P50DC007667.
The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.