Diagnostic accuracy of the wells score and the Geneva score for the prediction of pulmonary embolism: a systematic review

Introduction: Pulmonary embolism (PE) is a potentially life-threatening condition that requires timely and accurate diagnosis. Clinical prediction tools, such as the Wells score and the Geneva score, are widely used to estimate the pre-test probability of PE, guiding subsequent diagnostic decisions. Objective: To evaluate and compare the diagnostic accuracy of the Wells score and the Geneva score for the prediction of PE in patients with clinical suspicion of this condition. Materials and Methods: A systematic review was conducted comprising studies published between 2019 and 2024 in databases such as PubMed, Scopus, and Web of Science. Included studies were cohort or diagnostic accuracy studies that evaluated the sensitivity, specificity, and predictive values of both scores, using pulmonary CT angiography or ventilation-perfusion (V/Q) scintigraphy as the reference standard. Study selection and analysis followed PRISMA guidelines. Methodological quality was assessed using the QUADAS-2 tool. Results: A comparative analysis of 20 studies revealed significant variability in diagnostic performance depending on the population and clinical setting. The combination of clinical scores with D-dimer (DD) testing notably improved diagnostic accuracy, particularly lowering the negative likelihood ratio (LR−), thus enhancing the ability to rule out PE. Notably, 10% of studies reported very high positive likelihood ratios (LR+ = 123.75 and 57.6), indicating excellent diagnostic confirmation power when results are positive. Conclusions: The combined use of clinical prediction rules and DD testing—especially Geneva+DD and Wells+DD—provides the highest diagnostic accuracy for PE, with high LR+ and low LR− values, effectively confirming or ruling out the diagnosis. However, their utility is context-dependent. Therefore, the choice of clinical prediction tool should take into account the clinical setting, availability of complementary diagnostic tests, and patient characteristics to optimize decision-making.