Andy Kusta
Research Mentor: Sardar Ansari
Mentor Department: Weil Institute for Critical Care Research and Innovation, Medicine
Author(s): Loc Cao Quynh, Andy Kusta, Nina Swier, Sardar Ansari
Session: Session 2 (10:00 AM – 10:50 AM)
Presentation Type: Poster 126
Abstract
The acute respiratory distress syndrome (ARDS) lab aims to create models that can more accurately diagnose and assess the severity of ARDS for patients using gas chromatography readings. Acute Respiratory Distress Syndrome results from an acute lung injury that develops into severe inflammation. The onset of ARDS is rapid and carries a high mortality rate, as there are few effective therapies available (Diamond, 2024). Analyzing volatile organic compounds (VOCs) from breath readings is important in the clinical field with certain diseases/syndromes such as COVID-19, asthma, and even ARDS (L. Cao et al, 2023). This lab utilizes gas chromatography to assess the severity of ARDS in a patient. VOCs are biomarkers analyzed from breath that can provide insight into the health of a patient (Moura, 2023). In this study, they include pentane and osoprene. Signaling from VOCs on chromatography readings is inconsistent, meaning the peaks occur at different times and differ from each other. This is due to varying signaling timing, the distance from which breath is measured, temperature, and other environmental factors. Because these signals vary, a MATLAB GUI interface designed to help first-time users is used to manually align the signals by matching large reference peaks to corresponding unaligned peaks on the chromatography readings. Using data from hundreds of signals and breath samples from 300+ patients, the end goal is to train an annotator that can process large amounts of information and efficiently diagnose ARDS in patients.
