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Evaluating the Diagnostic Paradigm for Group A and Non-Group A Streptococcal Pharyngitis in the College Student
Shannan N Rich1, Mattia Prosperi1, Emily M Klann1
1Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, Florida, USA.
Background:
Acute pharyngitis is a frequent illness presenting in outpatient settings. Antibiotics are only recommended for bacterial pharyngitis caused by group A β-hemolytic streptococci (GAS); however, infections with non-group A β-hemolytic streptococci (NGAS) have similar clinical presentations and are common in young adult populations. The objective of this study was to analyze the performance of a current (expert) diagnostic algorithm for GAS pharyngitis, the Centor score, and compare it to alternative models developed to predict GAS and NGAS in a college student population.
Methods:
Electronic health records were obtained for all patients who received a streptococcal rapid antigen detection test (RADT) and/or a bacterial throat culture (n = 3963) at a southeastern US university in 2014. Bivariate and multivariable regression models (least absolute shrinkage and selection operator [LASSO] and stepwise-selected) were fitted to assess and compare their diagnostic performances for GAS-positive and NGAS-positive infections.
Results:
Prevalence of GAS was 18.8%. In the subset of RADT-negative patients who received bacterial throat cultures (n = 313), growth of NGAS occurred in 34.8%, with group C streptococci the most frequent isolate. Mean Centor score was higher for NGAS (3.2) vs GAS (2.9) infections (P = .0111). The area under the curve (AUC) for GAS prediction was 0.64 using the Centor score and 0.70 using the LASSO model. For NGAS, the most important features were cough, pharyngeal erythema, tonsillar exudate, and gastrointestinal symptoms (AUC = 0.63).
Conclusions:
GAS and NGAS pharyngitis were indistinguishable among college students in this study utilizing a commonly applied decision score. Alternative models using additional clinical criteria may be useful for supporting diagnosis of this common illness.
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