title: Simple and accurate prediction of the clinical probability of pulmonary embolism.
abstract: BACKGROUND
  Clinical probability assessment is a fundamental step in the diagnosis of pulmonary embolism.
  
  
  OBJECTIVE
  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.
  
  
  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.affiliation: Dipartimento di Area Critica Medico Chirurgica, Università di Firenze, Viale Morgagni 85, 50134 Firenze, Italy. massimo.miniati@unifi.it
authors.forename: Massimo
authors.initials: M
authors.lastname: Miniati
authors.forename: Matteo
authors.initials: M
authors.lastname: Bottai
authors.forename: Simonetta
authors.initials: S
authors.lastname: Monti
authors.forename: Marco
authors.initials: M
authors.lastname: Salvadori
authors.forename: Luca
authors.initials: L
authors.lastname: Serasini
authors.forename: Mirko
authors.initials: M
authors.lastname: Passera
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D003936:Diagnosis, Computer-Assisted; D004562:Electrocardiography; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D016017:Odds Ratio; D010808:Physical Examination; D011655:Pulmonary Embolism / Q000175:diagnosis*; D012372:ROC Curve; D012720:Severity of Illness Index; D012984:Software