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.
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.
Authors: Linda A Russell; Alana E Sigmund; Jackie Szymonifka; Shari T Jawetz; Sarah E Grond; Shirin A Dey; Anne R Bass Journal: J Thromb Thrombolysis Date: 2018-04 Impact factor: 2.300
Authors: Janneke M T Hendriksen; Geert-Jan Geersing; Wim A M Lucassen; Petra M G Erkens; Henri E J H Stoffers; Henk C P M van Weert; Harry R Büller; Arno W Hoes; Karel G M Moons Journal: BMJ Date: 2015-09-08