Predicting MSSA in Acute Hematogenous Osteomyelitis in a Setting With MRSA Prevalence. Issue 4 (June 2015) - Dietrich, Lindsey N.; Reid, Daniel; Doo, David; Fineberg, Naomi S.; Khoury, Joseph G.; Gilbert, Shawn R.
Abstract : Background: Increased severity of illness in patient with acute hematogenous osteomyelitis (AHO) with methicillin-resistant Staphylococcus aureus (MRSA) necessitates prompt intervention, but overtreatment of methicillin-sensitive S. aureus (MSSA) may contribute to antibiotic resistance. Therefore, predicting methicillin sensitivity in suspected AHO is desirable. A previously published prediction algorithm has not performed well in settings with high prevalence of MRSA. We sought to develop a predictive equation using presenting factors to predict MRSA in our patient population with a predominance of MRSA. Methods: A retrospective chart review was performed. Consecutive cases of AHO with positive blood or bone cultures were identified at a single children's hospital. Presenting features were recorded including duration of symptoms, weight-bearing, prior antibiotic use, vital signs, complete blood count, erythrocyte sedimentation rate (ESR), and C-reactive protein (CRP). Univariate comparison was made between the groups with MRSA and MSSA. Continuous variables were compared with t tests and discrete variables were compared using the Fischer exact test. Logistic regression was performed using a forward stepwise regression to develop a model to predict MRSA. Results: A total of 68 patients formed the study group, and 60% had MRSA (41 MRSA, 27 MSSA). Temperature, respiratory rate, heart rate, white blood cell count, absolute neutrophil count (ANC), ESR), and CRP were significantly higher in MRSA cases, whereas platelets were lower. Logistic regression resulted in a model utilizing temperature, ANC, and CRP. This model correctly predicted 87% of cases (92% of MRSA and 79% of MSSA) with an area under the curve of 0.919±0.035 with a 95% confidence interval of 0.851, 0.987. Conclusion: A logistic regression model incorporating temperature, ANC, and CRP correctly predicts methicillin resistance of S. aureus in 87% of cases. The model differs from one developed at an institution with a low rate of MRSA. Prediction of MRSA could help direct antibiotic management, whereas prediction of MSSA could help prevent overuse of antibiotics directed against MRSA. Level of Evidence: Diagnostic study level IV.