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Improved Diagnostic Accuracy of Group A Streptococcal Pharyngitis Using Real-Time Biosurveillance
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9 20 2011
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Source: Ann Intern Med. 155(6)
[PDF-869.02 KB]
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Alternative Title:Ann Intern Med
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Description:Background
Clinical prediction rules do not incorporate real time incidence data to adjust estimates of disease risk in symptomatic patients.
Objective
To measure the value of integrating local incidence data into a clinical decision rule for diagnosing group A streptococcal (GAS) pharyngitis in patients age 15 years and older.
Design
Retrospective analysis of clinical and biosurveillance predictors of GAS pharyngitis.
Setting
Large U.S.-based retail-health chain.
Patients
82,062 patient visits for pharyngitis.
Measurements
Accuracy of the Centor score, was compared with that of a biosurveillance-responsive score, essentially an adjusted Centor score based on real-time GAS pharyngitis information from the 14 days prior to a patient’s visit – the recent local proportion positive (RLPP).
Results
Increased RLPP correlated with likelihood of GAS pharyngitis (r2 =0.79, p<0.001). Local incidence data enhanced diagnostic models. For example, when RLPP >0.30, managing patients with Centor scores of 1 as if 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 one point for RLPP <0.20, would spare unnecessary antibiotics for 166,616 patients while missing 18,812 true positives.
Limitations
Analyses were conducted retrospectively. Real time regional GAS pharyngitis data are generally not yet available to clinicians.
Conclusions
Incorporating live biosurveillance data into clinical guidelines for GAS pharyngitis and other communicable diseases should be considered to reduce missed cases when the contemporaneous incidence is elevated and spare unnecessary antibiotics when the contemporaneous incidence is low. Delivering epidemiologic data to the point of care will enable the use of real-time pre-test probabilities in medical decision-making.
Primary Funding Source
The Mentored Public Health Research Scientist Development Award K01 HK000055 from the Centers for Disease Control and Prevention and R01 LM007677 from the National Library of Medicine, National Institutes of Health.
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Pubmed Central ID:PMC3651845
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