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Ann Intern Med. 2011 Sep 20;155(6):345-52. doi: 10.7326/0003-4819-155-6-201109200-00002.

Improved diagnostic accuracy of group A streptococcal pharyngitis with use of real-time biosurveillance.

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Harvard Medical School, Harvard-MIT Health Sciences and Technology, Boston, MA, USA.



Clinical prediction rules do not incorporate real-time incidence data to adjust estimates of disease risk in symptomatic patients.


To measure the value of integrating local incidence data into a clinical decision rule for diagnosing group A streptococcal (GAS) pharyngitis in patients aged 15 years or older.


Retrospective analysis of clinical and biosurveillance predictors of GAS pharyngitis.


Large U.S.-based retail health chain.


82 062 patient visits for pharyngitis.


Accuracy of the Centor score was compared with that of a biosurveillance-responsive score, which was essentially an adjusted Centor score based on real-time GAS pharyngitis information from the 14 days before a patient's visit: the recent local proportion positive (RLPP).


Increased RLPP correlated with the likelihood of GAS pharyngitis (r(2) = 0.79; P < 0.001). Local incidence data enhanced diagnostic models. For example, when the RLPP was greater than 0.30, managing patients with Centor scores of 1 as if the scores were 2 would identify 62, 537 previously missed patients annually while misclassifying 18, 446 patients without GAS pharyngitis. Decreasing the score of patients with Centor values of 3 by 1 point for an RLPP less than 0.20 would spare unnecessary antibiotics for 166, 616 patients while missing 18, 812 true-positive cases.


Analyses were conducted retrospectively. Real-time regional data on GAS pharyngitis are generally not yet available to clinicians.


Incorporating live biosurveillance data into clinical guidelines for GAS pharyngitis and other communicable diseases should be considered for reducing missed cases when the contemporaneous incidence is elevated and for sparing unnecessary antibiotics when the contemporaneous incidence is low. Delivering epidemiologic data to the point of care will enable the use of real-time pretest probabilities in medical decision making.

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