Nizet Victor

Group A Streptococcal (GAS) pharyngitis, the most common bacterial cause of acute pharyngitis, causes more than half a billion cases annually worldwide. Treatment with antibiotics provides symptomatic benefit and reduces complications, missed work days and transmission. Physical examination alone is an unreliable way to distinguish GAS from other causes of pharyngitis, so the 4-point Centor score, based on history and physical, is used to classify GAS risk. Still, patients with pharyngitis are often misclassified, leading to inappropriate antibiotic treatment of those with viral disease and to under-treatment of those with bone fide GAS. One key problem, even when clinical guidelines are followed, is that diagnostic accuracy for GAS pharyngitis is affected by earlier probability of disease, which in turn is related to exposure. Point-of-care clinicians rarely have access to valuable biosurveillance-derived contextualizing information when making clinical management decisions.

Objective

The objective of this study was to measure the value of integrating real-time contemporaneous local disease incidence (biosurveillance) data with a clinical score, to more accurately identify patients with GAS pharyngitis.

GAS pharyngitis affects hundreds of millions of individuals globally each year, and over 12 million seek care in the United States annually for sore throat. Clinicians cannot differentiate GAS from other causes of acute pharyngitis based on the oropharynx exam, so consensus guidelines recommend use of clinical scores to classify GAS risk and guide management of adults with acute pharyngitis. When the clinical score is low, consensus guidelines agree patients should neither be tested nor treated for GAS. A prediction model that could identify very-low risk patients prior to an ambulatory visit could reduce low-yield, unnecessary visits for a most common outpatient condition. We recently showed that real-time biosurveillance can further identify patients at low-risk of GAS. With increasing emphasis on patient-centric health care and the well-documented barriers impeding clinicians’ incorporation of prediction models into medical practice, this presents an opportunity to create a patient-centric model for GAS pharyngitis based on history and recent local epidemiology. We refer to this model as the “home score,” because it is designed for use prior to a physical exam.

Objective

1. To derive and validate an accurate clinical prediction model (“home score”) to estimate a patient’s risk of group A streptococcal (GAS) pharyngitis before a health care visit based only on history and real-time local biosurveillance, and to compare its accuracy to traditional clinical prediction models composed of history and physical exam features. 2. To examine the impact of a home score on patient and public health outcomes.