449 Does incorporation of plasma biomarkers to the Lung Injury Prediction Score improve the predictive value for development of acute respiratory distress syndrome?

• Published in: Journal of clinical and translational science Vol. 8; no. s1; p. 133
• Main Authors: Chase, Aaron, Almuntashiri, Sultan, Bennett, Andrew, Sikora, Andrea, Zhang, Duo
• Format: Journal Article
• Language: English
• Published: Cambridge Cambridge University Press 01-04-2024
• Subjects: Biomarkers; Clinical trials; Congestive heart failure; Diagnosis; End-stage renal disease; Enzyme-linked immunosorbent assay; Heart diseases; Interleukin 8; Kidney diseases; Learning algorithms; Lungs; Machine learning; Matrix metalloproteinase; Metalloproteinase; Patients; Plasma; Population studies; Precision Medicine/Health; Prediction models; Protein C; Proteins; Renal failure; Respiratory distress syndrome; Stromelysin 1
• ISSN: 2059-8661; 2059-8661
• DOI: 10.1017/cts.2024.384

OBJECTIVES/GOALS: To determine if incorporating specific laboratory values and plasma biomarkers (club cell secretory protein (CC16), matrix metalloproteinase 3 (MMP3), interleukin 8 (IL-8), protein C) to the Lung Injury Prediction (LIP) Score improves the predictive value for development of acute respiratory distress syndrome (ARDS) in ICU patients. METHODS/STUDY POPULATION: Adult patients admitted to the ICU on supplemental oxygen over baseline requirement with a LIP Score ≥6 will be included. Patients admitted to the ICU >24 hours, end-stage renal disease, decompensated heart failure, or <100 µL plasma available will be excluded. Whole blood will be collected from the core lab, centrifuged, and plasma will be stored at -80°C. Protein biomarkers will be measured using enzyme-linked immunosorbent assay. Baseline characteristics, laboratory values, ventilator parameters, and clinical outcomes will be collected from the medical record. ARDS will be defined by the Berlin criteria. Machine learning methods will be used to identify the model with the highest predictive accuracy. Area under the receiver operating characteristic curve of each model will be compared to the LIP Score. RESULTS/ANTICIPATED RESULTS: Research is in progress. Plasma samples and clinical data have been collected for 148 of the 160 samples required to achieve power. Biomarker analysis will take place after sample collection is complete. We anticipate a machine learning model incorporating laboratory values and one or more plasma biomarkers into the LIP Score will outperform the baseline LIP Score for prediction of ARDS development. DISCUSSION/SIGNIFICANCE: Delayed diagnosis and intervention contribute to poor ARDS outcomes. Current predictive models for ARDS have low accuracy and enriching these models with plasma biomarkers may increase their predictive value. Development of accurate models may facilitate earlier ARDS diagnosis and intervention as well as enrichment strategies for ARDS trials.