title: Derivation and validation of a Bayesian network to predict pretest probability of venous thromboembolism.
abstract: OBJECTIVE
  A Bayesian network can estimate a numeric pretest probability of venous thromboembolism on the basis of values of clinical variables. We determine the accuracy with which a Bayesian network can identify patients with a low pretest probability of venous thromboembolism, defined as less than or equal to 2%.
  
  
  METHODS
  Using commercial software, we derived a population of Bayesian networks from 25 input variables collected on 3,145 emergency department (ED) patients with suspected venous thromboembolism who underwent standardized testing, including pulmonary vascular imaging, and 90-day follow-up (11.0% of patients were venous thromboembolism positive). The best-fit Bayesian network was selected using a genetic algorithm. The selected Bayesian network was tested in a validation population of 1,423 ED patients prospectively evaluated for venous thromboembolism, including 90-day follow-up (8.0% were venous thromboembolism positive). The Bayesian network probability estimate was normalized to a score of 0% to 100%.
  
  
  RESULTS
  Of 1,423 patients in the validation cohort, 711 (50%; 95% confidence interval [CI] 47% to 52%) had a score less than or equal to 2% that predicted a low pretest probability. Of these 711 patients, 700 (98.5%; 95% CI 97.2% to 99.2%) had no venous thromboembolism at follow-up.
  
  
  CONCLUSIONS
  A Bayesian network, derived and independently validated in ED populations, identified half of the validation cohort as having a low pretest probability (< or =2%); 98.5% of these patients were correctly classified by the network.
authors.affiliation: Department of Emergency Medicine, Carolinas Medical Center, Charlotte, NC 28323-2861, USA. jkline@carolina.rr.com
authors.forename: Jeffrey A
authors.initials: JA
authors.lastname: Kline
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Novobilski
authors.forename: Christopher
authors.initials: C
authors.lastname: Kabrhel
authors.forename: Peter B
authors.initials: PB
authors.lastname: Richman
authors.forename: D Mark
authors.initials: DM
authors.lastname: Courtney
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D001499:Bayes Theorem; D003661:Decision Support Techniques; D005260:Female; D005338:Fibrin Fibrinogen Degradation Products / Q000032:analysis; D006801:Humans; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D011446:Prospective Studies; D011655:Pulmonary Embolism / Q000175:diagnosis; D013923:Thromboembolism / Q000097:blood / Q000175:diagnosis*; D020246:Venous Thrombosis / Q000097:blood / Q000175:diagnosis*
chemical_list: D005338:Fibrin Fibrinogen Degradation Products; C036309:fibrin fragment D