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

Published online by Cambridge University Press:  03 April 2024

Aaron Chase
Affiliation:
University of Georgia College of Pharmacy Emory University Augusta University Medical Center
Sultan Almuntashiri
Affiliation:
University of Georgia College of Pharmacy
Andrew Bennett
Affiliation:
University of Georgia College of Pharmacy
Andrea Sikora
Affiliation:
University of Georgia College of Pharmacy
Duo Zhang
Affiliation:
University of Georgia College of Pharmacy
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Abstract

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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.

Type
Precision Medicine/Health
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
© The Author(s), 2024. The Association for Clinical and Translational Science