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Simple and Accurate Prediction of the Clinical Probability of Pulmonary Embolism

¹ Dipartimento di Area Critica Medico-Chirurgica, Università degli Studi di Firenze, Firenze, Italy; ² Arnold School of Public Health, University of South Carolina, Columbia, South Carolina; and ³ Istituto di Fisiologia Clinica del Consiglio Nazionale Delle Ricerche, Pisa, Italy

Correspondence and requests for reprints should be addressed to Massimo Miniati, M.D., Dipartimento di Area Critica Medico Chirurgica, Università di Firenze, Viale Morgagni 85, 50134 Firenze, Italy. E-mail: massimo.miniati{at}unifi.it

Rationale: Clinical probability assessment is a fundamental step in the diagnosis of pulmonary embolism.

Objectives: To develop a predictive model for pulmonary embolism based on clinical symptoms, signs, and the interpretation of the electrocardiogram.

Methods: The model was developed from a database of 1,100 patients with suspected pulmonary embolism, of whom 440 had the disease confirmed by angiography or autopsy findings. It was validated in an independent sample of 400 patients with suspected pulmonary embolism (71% were inpatients). Easy-to-use software was developed for computing the clinical probability on palm computers and mobile phones.

Measurements and Main Results: The model comprises 16 variables of which 10 (older age, male sex, prolonged immobilization, history of deep vein thrombosis, sudden-onset dyspnea, chest pain, syncope, hemoptysis, unilateral leg swelling, electrocardiographic signs of acute cor pulmonale) are positively associated, and 6 (prior cardiovascular or pulmonary disease, orthopnea, high fever, wheezes, or crackles on chest auscultation) are negatively associated with pulmonary embolism. In the validation sample, 165 (41%) of 400 patients had pulmonary embolism confirmed by angiography. The prevalence of pulmonary embolism was 2% when the predicted clinical probability was slight (0 to 10%), 28% when moderate (11 to 50%), 67% when substantial (51 to 80%), and 94% when high (81 to 100%). There was no significant difference between inpatients and outpatients with respect to the prevalence of pulmonary embolism in the four probability categories.

Conclusions: The proposed model is simple and accurate, and it may aid physicians when assessing the clinical probability of pulmonary embolism.

Key Words: pulmonary embolism • diagnosis • clinical assessment

AT A GLANCE COMMENTARY Scientific Knowledge on the Subject None of the available diagnostic tests for suspected pulmonary embolism can safely confirm or exclude the diagnosis without independent assessment of the clinical probability of the disease. What This Study Adds to the Field The proposed clinical model is accurate in predicting the probability of pulmonary embolism in both inpatients and outpatients. Easy-to-use software is available for online computation of the clinical probability on palm computers and mobile phones.