ORIGINAL RESEARCH article
Front. Med.
Sec. Intensive Care Medicine and Anesthesiology
Associations of serum sTREM-1 and sTREM-2 with mortality and neurological prognosis in patients resuscitated from cardiac arrest: A machine learning-based approach
Ling Wang 1
Peiyan Chen 2
Yushu Chen 2
Zheyuan Fan 1
Dongping Yu 3
Wei Zhang 1
Bao Fu 1
Ping Gong 4
1. Affiliated Hospital of Zunyi Medical University, Zunyi, China
2. The First Affiliated Hospital of Dalian Medical University, Dalian, China
3. The Second Hospital of Dalian Medical University, Dalian, China
4. Shenzhen People's Hospital, Shenzhen, China
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Abstract
Background: Patients resuscitated from cardiac arrest (CA) commonly have poor outcomes with a high mortality rate. We aimed to determine the predictive values of serum soluble triggering receptor expressed on myeloid cells 1 and 2 (sTREM-1 and sTREM-2) in patients after return of spontaneous circulation (ROSC) and to develop machine learning (ML) prediction models. Methods: We prospectively enrolled adult CA patients successfully resuscitated after cardiopulmonary resuscitation between November, 2021 to December, 2023. Serum sTREM-1, sTREM-2 and other biomarkers were measured on days 1, 3 and 5 after ROSC. The primary outcome was 28-day all-cause mortality. The secondary outcome was 3-month neurological prognosis. The performance of serum sTREM-1, sTREM-2, as well as the developed ML prediction models, to predict 28-day all-cause mortality and 3-month neurological prognosis were studied. Results: The study enrolled 120 patients, including 32 survivors and 88 non-survivors, with 30 healthy volunteers. Both sTREM-1 and sTREM-2 levels increased in patients after ROSC, with a larger increase in the non-survivors than survivors. Moreover, eleven features, including sTREM-1 and sTREM-2, were ultimately identified to build ML models. Among other ML models, the eXtreme Gradient Boosting (XGBoost) and Random Forest (RF) models showed strong performances for predicting 28-day all-cause mortality and 3-month neurological prognosis, respectively. Conclusions: Serum sTREM-1 performed better than sTREM-2 to predict mortality and neurological outcome after ROSC. Furthermore, the newly developed XGBoost and RF models incorporating sTREM-1 and/or sTREM-2 demonstrated superior predictive accuracy compared to conventional clinical scoring systems.
Summary
Keywords
Cardiac arrest, machine learning, prognosis, STREM-1, sTREM-2
Received
03 October 2025
Accepted
20 February 2026
Copyright
© 2026 Wang, Chen, Chen, Fan, Yu, Zhang, Fu and Gong. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Ping Gong
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