A scoring system for predicting group A streptococcal throat infection

Abstract

Background: Sore throat is very common in general practice and is usually caused by viral infection. Nevertheless, up to 95% of patients may be treated with antibiotics. Previous diagnostic systems have not transferred well from one area to another because of an inability to allow for changing prevalence of streptococcus.

Aim: To measure the occurrence rates of symptoms and signs in sore throat patients with and without streptococcal infection, and to develop a Bayesian scoring system which is easily adapted for prevalence to predict if patients have bacterial infection.

Method: Occurrence rates of symptoms and signs were measured for 206 patients with sore throat symptoms over a 3-year period. Bayesian probability scores (B-scores) for each data item were calculated from the ocurrence rates in the patients with positive throat cultures for group A streptococci and the rates in patients with negative throat cultures. The B-score values were then used to predict the probability of positive culture for each patient.

Results: The streptococcal throat B-score system predicted positive culture with a sensitivity of 71% and a specificity of 71%. In comparison, the unaided general practitioners predicted infection with a sensitivity of 61% and a specificity of 65%. If the B-score prediction had been used to decide on treatment, more patients with streptococci present on culture would have been treated with antibiotic (71% instead of 68%) and appreciably fewer patients with negative streptococcal cultures would have been treated (29% instead of 59%).

Conclusion: Use of the B-score system could result in significant savings in unnecessary antibiotic prescription, and unnecessary throat swab cultures, while achieving better levels of treatment.