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Updated: May 9, 2026
Subcutaneous Infection of Methicillin Resistant Staphylococcus Aureus (MRSA)
Published on: February 9, 2011
Independent analysis of a clinical predictive algorithm to identify methicillin-resistant Staphylococcus aureus
M Wade Shrader1, Miranda Nowlin, Lee S Segal
1Division of Pediatric Orthopaedic Surgery, Phoenix Children's Hospital, Phoenix, AZ.
Background:
The number of serious, life-threatening musculoskeletal infections in children due to methicillin-resistant Staphylococcus aureus (MRSA) infections is increasing. The early identification of the bacteria causing osteomyelitis is critical to determine the appropriate antibiotic treatment. A recent study proposed a clinical algorithm to predict which infections were caused by MRSA by stratifying basic clinical values at the time of admission for children with osteomyelitis. The purpose of this study is to apply that predictive algorithm on an independent patient population to determine its wider applicability.
Methods:
This was a retrospective chart review at a tertiary care children's hospital. All children who were treated for a culture-positive osteomyelitis were identified over a 3-year period. The previously reported predictors, determined by multivariate regression analysis, of MRSA infection (temperature >38°C, hematocrit <34%, white blood cell count >12,000/µL, and C-reactive protein >13 mg/L) were determined for each patient. The number of positive predictors was then correlated with the percentage of cases that were MRSA positive.
Results:
A total of 58 patients with culture-positive osteomyelitis were identified from 2008 to 2010. Sixteen of the infections were caused by MRSA (overall 26%). The percentage of patients with MRSA osteomyelitis according to the number of risk factors were as follows: all 4 risk factors, 50% (1 out of 2 patients); 3 risk factors, 42% (5 out of 12 patients); 2 risk factors, 21% (4 out of 19 patients); 1 risk factor, 50% (6 out of 12 patients); and 0 risk factor, 0% (0 out of 13 patients).
Conclusions:
The previously reported clinical predictive algorithm had a relatively poor diagnostic performance in this independent patient population. Specifically, the percentages of MRSA were the same for 1 risk factor compared with 4 (50%). Differences in bacteria strain, host responses, and a variety of other confounding variables could be responsible for these differences. Specific genetic markers may be the best early test to identify MRSA infections in the future.
Level Of Evidence:
Level III-case-control series.
Insights
A clinical algorithm to predict methicillin-resistant Staphylococcus aureus (MRSA) osteomyelitis in children showed poor performance. The algorithm
Area of Science:
- Pediatric infectious diseases
- Orthopedic surgery
- Microbiology
Background:
- Increasing incidence of severe pediatric musculoskeletal infections caused by methicillin-resistant Staphylococcus aureus (MRSA).
- Early identification of causative bacteria is crucial for effective antibiotic treatment of osteomyelitis.
- A prior study developed a clinical algorithm to predict MRSA in pediatric osteomyelitis.
Purpose of the Study:
- To validate a previously developed clinical algorithm for predicting MRSA osteomyelitis in an independent cohort of pediatric patients.
- To assess the broader applicability of the MRSA predictive algorithm in a new patient population.
Main Methods:
- Retrospective chart review of culture-positive osteomyelitis cases in children over a 3-year period at a tertiary care hospital.
- Evaluation of previously identified predictors: temperature >38°C, hematocrit <34%, WBC >12,000/µL, and CRP >13 mg/L.
- Correlation of the number of positive predictors with the prevalence of MRSA infection.
Main Results:
- Out of 58 patients with culture-positive osteomyelitis, 16 (26%) were caused by MRSA.
- The algorithm showed variable performance: 50% MRSA with 1 risk factor vs. 50% with 4 risk factors.
- Prevalence of MRSA ranged from 0% (0 risk factors) to 50% (4 risk factors).
Conclusions:
- The clinical predictive algorithm demonstrated poor diagnostic performance in this independent cohort.
- Discrepancies in MRSA prevalence suggest limitations due to bacterial strain, host factors, or other confounders.
- Future research may focus on genetic markers for more accurate early detection of MRSA infections.
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