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Effective risk stratification in pulmonary embolism: evaluating current tools and identifying research gaps
Wilhelmina J E Stenger1, Corstiaan A den Uil2, Wim J R Rietdijk3,4
1Thrombosis and Haemostasis, Leiden University Medical Centre, Leiden, the Netherlands w.j.e.stenger@lumc.nl.
Heart (British Cardiac Society)
|July 22, 2025
Summary
International guidelines use risk stratification for acute pulmonary embolism (PE) to guide treatment. Current algorithms vary, highlighting a need for updated evidence and research to refine patient categorization and care.
Area of Science:
- Cardiology
- Pulmonology
- Medical Guidelines
Background:
- Risk stratification is crucial for guiding acute pulmonary embolism (PE) treatment decisions.
- Key prognostic factors include clinical presentation, comorbidities, biomarkers, and hemodynamic status.
- Existing guidelines, like the 2019 ESC/ERS, categorize PE patients into risk levels (high, intermediate, low), with outcomes correlating to risk.
Purpose of the Study:
- To summarize current PE risk stratification algorithms and treatment recommendations.
- To identify knowledge gaps in PE management.
- To outline future research priorities for improved risk stratification.
Main Methods:
- Review of existing international guidelines on PE risk stratification.
- Analysis of prognostic factors and their role in current algorithms.
- Identification of inconsistencies and evidence limitations across guidelines.
Main Results:
- Significant variation exists among international guidelines regarding PE risk stratification and treatment.
- Lack of high-quality evidence (Grade/Level 1A) hinders definitive treatment recommendations for certain PE risk profiles.
- Ongoing trials on novel reperfusion strategies may influence future risk stratification categories.
Conclusions:
- Current PE risk stratification methods show inconsistencies across major guidelines.
- Further high-quality research is needed to refine risk categories and optimize treatment strategies.
- Emerging evidence from new trials may lead to improved patient classification and management protocols.
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