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The contribution of clinical assessments to the diagnostic algorithm of pulmonary embolism

Authors :
Türkan Günay
Ayse Turan
Deniz Turgut
Atila Akkoclu
Onur Turan
Erkan Yilmaz
Publication Year :
2017

Abstract

BACKGROUND Pulmonary thromboembolism (PE) is a major disease in respiratory emergencies. Thoracic CT angiography (CTA) is an important method of visualizing PE. Because of the high radiation and contrast exposure, the method should be performed selectively in patients in whom PE is suspected. OBJECTIVES The aim of the study was to identify the role of clinical scoring systems utilizing CTA results to diagnose PE. MATERIAL AND METHODS The study investigated 196 patients referred to the hospital emergency service in whom PE was suspected and CTA performed. They were evaluated by empirical, Wells, Geneva and Miniati assessments and classified as low, intermediate and high clinical probability. They were also classified according to serum D-dimer levels. The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were calculated and evaluated according to CTA findings. RESULTS Empirical scoring was found to have the highest sensitivity, while the Wells system had the highest specificity. When low D-dimer levels and "low probabilty" were evaluated together for each scoring system, the sensitivity was found to be 100% for all methods. Wells scoring with a cut-off score of 4 had the highest specificity (56.1%). CONCLUSIONS Clinical scoring systems may be guides for patients in whom PE is suspected in the emergency department. The empirical and Wells scoring systems are effective methods for patient selection. Adding evaluation of D-dimer serum levels to the clinical scores could identify patients in whom CTA should be performed. Since CTA can only be used conservatively, the use of clinical scoring systems in conjunction with D-dimer levels can be a useful guide for patient selection.

Details

Language :
English
Database :
OpenAIRE
Accession number :
edsair.doi.dedup.....6a4c755990bb5e950cc8d4407091f90d