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A Journal on Nuclear Medicine and Molecular Imaging
Affiliated to the and to the International Research Group of Immunoscintigraphy
Indexed/Abstracted in: Current Contents/Clinical Medicine, EMBASE, PubMed/MEDLINE, Science Citation Index (SciSearch), Scopus
Impact Factor 2,413
Online ISSN 1827-1936
Guest Editors: Pistolesi M. and Pupi A.
Miniati M., Pistolesi M. *
From the Institute of Clinical Physiology “Consiglio Nazionale delle Ricerche (CNR)”, Pisa, Italy
*Department of Section of Nuclear Medicine Critical Care University of Florence, Florence, Italy
Clinical assessment is a cornerstone of the recently validated diagnostic strategies for pulmonary embolism (PE). Although the diagnostic yield of individual symptoms, signs, and common laboratory tests is limited, the combination of these variables, either by empirical assessment or by a prediction rule, can be used to express a clinical probability of PE. The latter may serve as pretest probability to predict the probability of PE after further objective testing (posterior or post-test probability). Over the last few years, attempts have been made to develop structured prediction models for PE. In a Canadian multicenter prospective study, the clinical probability of PE was rated as low, intermediate, or high according to a model which included assessment of presenting symptoms and signs, risk factors, and presence or absence of an alternative diagnosis at least as likely as PE. The prevalence of PE in the low, intermediate, and high pretest probability categories was 3, 28, and 78%, respectively. This model relies heavily on the clinician’s subjective judgement as to whether an alternative diagnosis is as likely as or more likely than PE, and, as such, it can be hardly standardized. Furthermore, the inherent complexity of the model may limit its applicability in daily clinical practice. Recently, a simple clinical score was developed to stratify outpatients with suspected PE into groups with low, intermediate, or high clinical probability. Logistic regression was used to predict parameters associated with PE. A score ≤4 identified patients with low probability of whom 10% had PE. The prevalence of PE in patients with intermediate (score 5-8) and high probability (score ≥9) was 38 and 81%, respectively. As opposed to the Canadian model, this clinical score is standardized. The predictor variables identified in the model, however, were derived from a data base of emergency ward patients. This model may, therefore, not be valid in assessing the clinical probability of PE in inpatients. In the PISA-PED study, a clinical diagnostic algorithm was developed which rests on the identification of three relevant clinical symptoms and on their association with electrocardiographic and/or radiographic abnormalities specific for PE. Among patients who, according to the model, had been rated as having a high clinical probability, the prevalence of proven PE was 97%, while it was 3% in those with low probability. The prevalence of PE in patients with intermediate clinical probability was 41%. These results underscore the importance of incorporating the standardized reading of the electrocardiogram and of the chest radiograph into the clinical evaluation of patients with suspected PE. The interpretation of these laboratory data, however, requires experience. Future research is needed to develop standardized models, of varying degree of complexity, which may find application in different clinical settings to predict the probability of PE.