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Updated: Aug 6, 2026
Real-Time High Throughput Technique to Quantify Neutrophil Extracellular Traps Formation in Human Neutrophils
Published on: December 1, 2023
Development of a Predictive Model for Transfusion-Related Acute Lung Injury Based on Neutrophil Extracellular Traps
Qiong Wang1, Zhenyang Li2, Junliang Shao1
1Department of Blood Transfusion, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi, Jiangsu, 214023, China, njmu.edu.cn.
Background:
Patients receiving massive transfusion after acute hemorrhage are at risk for transfusion-related acute lung injury (TRALI), a severe complication. Neutrophil extracellular traps (NETs) play a key role in acute lung injury. This study aimed to explore the link between NETs and TRALI and to develop a risk-prediction model using machine learning for early detection and intervention.
Methods:
In this multicenter prospective study, 513 patients with acute massive hemorrhage who underwent transfusion therapy (March 2020-February 2025) were consecutively recruited. All biomarker assays, sampling time points, and the statistical analysis plan were prespecified and ethically approved before study initiation. Based on TRALI occurrence after transfusion, they were divided into an injured group (n = 42) and a uninjured group (n = 471). Clinical features and NET-related markers were compared. LASSO regression was used for variable selection, followed by random forest for importance ranking. Multivariate logistic regression identified independent predictors, and a nomogram model was built and evaluated using ROC analysis, calibration, and decision curve analysis.
Results:
The injured group had significantly higher rates of smoking history, total infusion volume, perioperative transfusion volume, and transfusion frequency (all p < 0.05). Levels of citrullinated histone H3 (citH3), myeloperoxidase (MPO), neutrophil elastase (NE), interleukin-6 (IL-6), interleukin-1β (IL-1β), tumor necrosis factor-α (TNF-α), and interleukin-8 (IL-8) were also elevated (all p < 0.05). LASSO identified seven key variables, with citH3 and MPO showing high importance. Multivariate analysis confirmed citH3 (OR = 1.142), MPO (OR = 5.017), and NE (OR = 1.014) as independent predictors of TRALI (all p < 0.05). The combined model achieved an AUC of 0.85 (95% CI: 0.78-0.92), indicating strong predictive performance.
Conclusion:
TRALI risk in acute massive hemorrhage patients is associated with NET-related markers, particularly citH3, MPO, and NE. A model integrating these indicators provides valuable early identification of TRALI, aiding clinical decision-making.
