Non-invasive diagnostic methods improve patient outcomes and risk assessment in pulmonary embolism, indicating their critical role.
Pulmonary embolism represents a major cause of morbidity and mortality worldwide, and remains a diagnostic challenge due to its highly non-specific clinical presentation. Early recognition is critical because timely diagnosis not only improves prognosis but also guides risk stratification, which is essential for therapeutic decision-making. Non-invasive diagnostic modalities – including clinical prediction rules, biomarkers, imaging techniques and bedside assessments – have become indispensable tools for rapid and accurate identification of patients with suspected pulmonary embolism. These methods enable clinicians to stratify risk, predict outcomes and tailor treatment strategies to individual patients, reducing both underdiagnosis and overtreatment. Despite their significant impact, limitations persist, such as access disparities, overdiagnosis of subsegmental events and interpretation challenges in special populations. Ongoing advances, including artificial intelligence and novel biomarkers, hold promise for refining diagnostic accuracy and personalised risk assessment. Ultimately, the integration of non-invasive tests into structured algorithms ensures earlier detection, better prognostic evaluation and improved clinical outcomes for patients with acute pulmonary embolism.
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Torremocha et al. (2025) studied this question.
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