Blood-based Biomarkers of Acute Lung Injury/Acute Respiratory Distress Syndrome
Conditions: ARDS (Acute Respiratory Distress Syndrome)
Sponsor: University of Pennsylvania

Title of the Study: Biomarkers of Lung Injury 
NCT Number: Not Assigned Yet 
Date of Submission: 30th May 2025 
Date of Revision: 6th Aug 2025 
STUDY PROTOCOL: 
Peripheral blood mononuclear cells (PBMC) will be isolated from human blood (obtained 
from ARDS subjects recruited for this study) using a Ficoll gradient (Ficoll-Paque 
Premium). The isolated PBMCs are the immune cells that will be used in this study. These 
immune cells will be studied in vitro. PBMC will be checked for activation (by monitoring 
ROS production). At the same time, clinical parameters of ARDS (ARDS severity and 
ARDS score that are routinely monitored daily for each patient) will be accessed and 
correlation analysis carried out to check if immune cell activation matches the ARDS 
severity. In separate in vitro experiments, the regulation of PBMC activation will be 
monitored by addition of novel agent PIP-2 to the immune cell culture.  
ANALYSIS PLAN:  
The association between the ROS, cytokine, oxidative stress levels with clinical 
parameters of ARDS (i.ARDS severity measures and ii. ARDS severity score) will be 
studied in two steps. As outcome variables associated with clinical parameters are both 
discrete and continuous, we will utilize multiple procedures to develop and test discrete, 
binary models and continuous models. In the first step, point biserial correlation will be 
used for dichotomous ARDS clinical parameters and Pearsons correlation coefficients or 
Spearman's rank correlation coefficients will be calculated for continuous outcome 
variables. Second, using multivariable linear and logistic regression procedures, several 
discrete, binary class prediction models will be developed to stratify the efficacy of PIP-2 
on ARDS patients. Briefly, for each of the class prediction models, patients will be 
assigned to class 0, 1, or 2 based on ARDS severity (moderate, severe) and a weighted 
voting classification algorithm will be utilized in which each clinical parameter will 
contribute a vote, which will be summed to create a winning class. Different combinations 
of clinical parameters will be evaluated to determine combinations capable of 
stratifyingPIP-2 efficacy (low, moderate, high, very high responders to PIP-2) in immune 
cell samples from ARDS patients.