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The Importance of Disease Prevalence in Transporting Clinical Prediction Rules: The Case of Streptococcal Pharyngitis

ROY M. POSES, M.D.; RANDALL D. CEBUL, M.D.; MARJEANNE COLLINS, M.D.; and SAMUEL S. FAGER, M.D.
[+] Article and Author Information

Grant support: in part by grant 1 RO1 LM04321-01 from the National Library of Medicine. Dr. Poses did this work, in part, as a Henry J. Kaiser Family Foundation Fellow in General Internal Medicine at the University of Pennsylvania; Dr. Cebul is a Henry J. Kaiser Family Foundation Faculty Scholar in General Internal Medicine.

▸Requests for reprints should be addressed to Roy M. Poses, M.D.; Department of Medicine, Cooper Hospital/University Medical Center, One Cooper Plaza, Camden, NJ 08103.


Camden, New Jersey; and Philadelphia, Pennsylvania


©1986 American College of PhysiciansAmerican College of Physicians


Ann Intern Med. 1986;105(4):586-591. doi:10.7326/0003-4819-105-4-586
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Because clinical prediction rules often are applied in new settings to calculate the probability of a disease, we evaluated the accuracy of three rules for predicting streptococcal pharyngitis in 310 patients. Use of the rules led to overestimations of disease probability in 47%, 82%, and 93% of the patients. When we used receiver-operating characteristic curve analysis, no rule lost power to discriminate streptococcal from nonstreptococcal causes of pharyngitis. The overestimations in disease probability likely were caused by differences in disease prevalence between our setting (5%) and the settings in which they were developed (15% to 17%). All rules led to accurate predictions when they were adjusted for the disease prevalence found in our setting using a likelihood ratio formulation of Bayes' theorem. The value of prediction rules, like that of other diagnostic tests, is affected by differences in disease prevalence in different settings. Failure to recognize and adjust for these differences may cause poor decision making or the premature dismissal of valid rules.

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