Thursday, November 21, 2024
11/21/2024
ABSTRACT Prematurity is the largest single cause of death in children under five in the world and lower respiratory tract infections (LRTI) are the top cause of hospitalization and mortality in premature infants. Clinical tools to predict the risk and assess the severity of LRTI in premature babies are critically needed to allow early interventions to decrease the high morbidity and mortality in this patient group. Our goal is to improve clinical practice by developing an objective framework to predict the risk and assess the severity of respiratory disease in premature babies using non-invasive low-radiation X-ray imaging biomarkers and clinical parameters. In the Phase I of this project, our multidisciplinary team of pulmonologists, neonatologists and imaging and machine learning specialists developed an imaging software technology called Lung Aeration and Irregular opacities Radiological analyzer (LungAIR). Our accomplishments include: 1) establishing a curated ground truth of focal findings in chest X-Ray (CXR) of premature babies; 2) developing a machine learning algorithm to automatically localize and quantify CXR-based prematurity lung disease signatures (fibrosis/interstitial opacities, cystic changes and hyperinflation); 3) creating a graphical user interface for clinical deployment; and 4) evaluating our imaging software technology in an independent cohort. We also demonstrated that the imaging biomarkers obtained by LungAIR correlate strongly with the severity of bronchopulmonary dysplasia (BPD)—the most common respiratory complication of prematurity-- and the cumulative exposure to supplemental O2 and mechanical ventilation in the neonatal intensive care unit (NICU) (p<0.001). Importantly, our preliminary results indicated that the combination of imaging and clinical markers (BPD severity) provide an accurate predictive model for LRTI-related complications in the first year of life (AUC=74, p<0.01). This Phase II project builds on the findings and methodology developed in Phase I. In Specific Aim 1, we will incorporate a model of lung disease risk factors in LungAIR platform. Our software will ingest respiratory support information daily during NICU hospitalization and integrate the data with CXR analysis. In Specific Aim 2, we will extend LungAIR to perform longitudinal analyses during hospitalization with the potential to accelerate the prediction of health risks. We will also integrate our results with the electronic health record of the patient for improve the clinical workflow. In Specific Aim 3 we will conduct a clinical study to prospectively evaluate the LungAIR clinical platform functionality. The proposal includes the business model and a path to commercializing LungAIR. The early identification of premature babies at high risk for BPD and severe LRTI should improve their outcome, reduce hospitalization times and inherent clinical costs, and decrease infant mortality. In addition, the ability to objectively quantify and track lung imaging biomarkers will also guide therapy and clinical trials, as well as improve the longitudinal monitoring of infants.
HHS’ Tracking Accountability in Government Grants System (TAGGS) website provides access to detailed descriptions of grants, loans, aggregated direct payments and other types of financial assistance awarded by the HHS Operating Divisions (OPDIVs) and the Office of the Secretary Staff Divisions (STAFFDIVs).
