Derivation and Validation of a Bayesian Network to Predict Pretest Probability of Venous Thromboembolism - 18/08/11
, Andrew J. Novobilski, PhD, Christopher Kabrhel, MD, Peter B. Richman, MD, D. Mark Courtney, MD
Address for correspondence: Jeffrey A. Kline, MD, Emergency Medicine Research, Department of Emergency Medicine, Carolinas Medical Center, PO Box 32861, Charlotte, NC 28323-2861; 704-355-7092, fax 704-355-7047SEE EDITORIAL, P. 291.
Abstract |
Study 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.
Conclusion |
A Bayesian network, derived and independently validated in ED populations, identified half of the validation cohort as having a low pretest probability (≤2%); 98.5% of these patients were correctly classified by the network.
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| Author contributions: JAK and AJN conceived the study. JAK, PBR, CK, and DMC obtained study funding and supervised data collection, including quality control. JAK, PBR, CK, and DMC contributed substantially to data collection, data entry, and data analysis. AJN constructed and tested the Bayesian network. JAK performed other statistical analysis. JAK drafted the manuscript, and all authors contributed substantially to its revision. JAK takes responsibility for the paper as a whole. Funding and support: The authors report this study did not receive any outside funding or support. Reprints not available from the authors. |
Vol 45 - N° 3
P. 282-290 - mars 2005 Retour au numéro
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