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General Orthopaedics

The use of CRP within a clinical prediction algorithm for the differentiation of septic arthritis and transient synovitis

British Orthopaedic Association/Irish Orthopaedic Association Annual Congress (BOA/IOA)



Abstract

Background

Establishing the diagnosis in a child presenting with an atraumatic limp can be difficult. Clinical prediction algorithms have been devised to distinguish septic arthritis (SA) from transient synovitis (TS). Within Europe measurement of the Erythrocyte Sedimentation Rate (ESR) has largely been replaced with assessment of C-Reactive Protein (CRP) as an acute phase protein. We produce a prediction algorithm to determine the significance of CRP in distinguishing between TS and SA.

Method

All children with a presentation of ‘atraumatic limp’ and a proven effusion on hip ultrasound between 2004 and 2009 were included. Patient demographics, details of the clinical presentation and laboratory investigations were documented to identify a response to each of the four variables (Weight bearing status, WCC >12,000 cells/m3, CRP >20mg/L and Temperature >38.5°C). SA was defined based upon culture and microscopy of the operative findings.

Results

311 hips were included within the study. Of these 282 were considered to have transient synovitis. 29 patients met criteria to be classified as SA based upon laboratory assessment of the synovial fluid. The introduction of CRP eliminated the need for a four variable model as the use of two variables (CRP and weight bearing status) had similar efficacy. Treating individuals who were non-weight-bearing and a CRP >20mg/L as SA correctly classified 94.8% individuals, with a sensitivity of 75.9%, specificity of 96.8%, positive predictive value of 71.0%, and negative predictive value of 97.5%. CRP was a significant independent predictor of septic arthritis.

Conclusions

CRP was a strong independent risk factor of septic arthritis, and its inclusion within a regression model simplifies the diagnostic algorithm. Nevertheless, this and other models are generally more reliable in excluding SA, than confirming SA, and therefore a clinician's acumen remains important in identifying SA in those individuals with a single abnormal variable.