The use of CRP within a clinical prediction algorithm for the differentiation of septic arthritis and transient synovitis in children
- 1 November 2011
- journal article
- research article
- Published by British Editorial Society of Bone & Joint Surgery in The Journal of Bone and Joint Surgery. British volume
- Vol. 93 (11), 1556-1561
- https://doi.org/10.1302/0301-620x.93b11.26857
Abstract
Clinical prediction algorithms are used to differentiate transient synovitis from septic arthritis. These algorithms typically include the erythrocyte sedimentation rate (ESR), although in clinical practice measurement of the C-reactive protein (CRP) has largely replaced the ESR. We evaluated the use of CRP in a predictive algorithm. The records of 311 children with an effusion of the hip, which was confirmed on ultrasound, were reviewed (mean age 5.3 years (0.2 to 15.1)). Of these, 269 resolved without intervention and without long-term sequelae and were considered to have had transient synovitis. The remaining 42 underwent arthrotomy because of suspicion of septic arthritis. Infection was confirmed in 29 (18 had micro-organisms isolated and 11 had a high synovial fluid white cell count). In the remaining 13 no evidence of infection was found and they were also considered to have had transient synovitis. In total 29 hips were categorised as septic arthritis and 282 as transient synovitis. The temperature, weight-bearing status, peripheral white blood cell count and CRP was reviewed in each patient. A CRP > 20 mg/l was the strongest independent risk factor for septic arthritis (odds ratio 81.9, p < 0.001). A multivariable prediction model revealed that only two determinants (weight-bearing status and CRP > 20 mg/l) were independent in differentiating septic arthritis from transient synovitis. Individuals with neither predictor had a < 1% probability of septic arthritis, but those with both had a 74% probability of septic arthritis. A two-variable algorithm can therefore quantify the risk of septic arthritis, and is an excellent negative predictor.Keywords
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