Tuesday, November 28 | 8:20 a.m.-8:30 a.m. | T1-SSCH05-4 | Room S404
Chest CT body composition data -- specifically, findings that show a patient's body fat has decreased -- helps predict outcomes in patients with idiopathic pulmonary fibrosis (IPF), according to a group of researchers from the University of South Alabama Health in Mobile.
"A significant decrease in body fat in the first-year disease course, derived from chest CT images by deep learning-based technique, [is] a poor prognostic factor in patients with IPF," wrote a team led by Ji Young Lee, MD, PhD.
Using chest CT and a deep-learning technique, Lee's group sought to investigate any associations between body composition changes during the first year of IPF disease course and outcomes. The team conducted a study that included 307 patients diagnosed with IPF between January 2010 and December 2020 who had undergone chest CT and pulmonary function tests at diagnosis and at one-year follow-up. The group calculated fat and muscle area at the T12 to L1 vertebrae level using commercially available deep-learning body composition analysis software.
Lee and colleagues found that, during the first year after diagnosis, average muscle and fat areas decreased by 1 cm2 and 15.4 cm2, respectively, and body mass index decreased by 0.4kg/m2. They also discovered a significant decrease in fat area during the first-year disease course were predictive for poor patient outcomes, with a hazard ratio of 1.617 (with 1 as reference).
There are measures that can be taken to support IPF patients, however, according to Lee's team.
"During the disease courses, nutritional support based on the CT-derived body composition analysis can improve outcomes in patients with IPF," the group concluded.
Attend this session to get all of the details.
