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Differentiation between septic arthritis and transient synovitis of the hip in children with clinical prediction
Scott J Luhmann1, Angela Jones, Mario Schootman
1St. Louis Children's Hospital, Washington University Medical Center, St Louis, MO 63110, USA. luhmanns@msnotes.wustl.edu.
Insights
Differentiating pediatric septic arthritis from transient synovitis is challenging. A previously developed clinical prediction algorithm showed lower predictive value in this study, suggesting its limited generalizability for diagnosing hip joint infections.
Area of Science:
- Pediatric Orthopedics
- Infectious Diseases
- Rheumatology
Background:
- Distinguishing septic arthritis from transient synovitis in children presents diagnostic challenges.
- A clinical prediction algorithm by Kocher et al. uses fever, non-weight-bearing status, elevated ESR, and high WBC count to predict septic arthritis.
- This study aimed to retrospectively validate Kocher et al.'s algorithm in a different patient cohort.
Purpose of the Study:
- To assess the predictive accuracy of Kocher et al.'s clinical algorithm for septic arthritis in a local pediatric population.
- To identify potential modifications or alternative models for improved diagnostic utility.
Main Methods:
- Retrospective review of 163 children (165 hips) undergoing hip arthrocentesis between 1992 and 2000.
- Patients were categorized into septic arthritis (true/presumed) or transient synovitis groups.
- Kocher et al.'s four-variable algorithm and a modified three-variable model were applied.
Main Results:
- Septic arthritis cases differed significantly from transient synovitis in ESR, WBC count, synovial fluid analysis, gender, prior visits, and fever history.
- Kocher et al.'s algorithm predicted a 59% probability of septic arthritis in this cohort, lower than the 99.6% in the original study.
- A three-variable model (fever, WBC >12000, prior visit) yielded a 71% predictive probability for septic arthritis.
Conclusions:
- The predictive algorithm by Kocher et al. demonstrated reduced accuracy when applied to this patient population.
- A modified three-variable model showed potential but requires further validation.
- The findings suggest that clinical prediction algorithms for septic arthritis may lack universal applicability across different institutions.
Background:
Differentiation between septic arthritis and transient synovitis of the hip in children can be difficult. Kocher et al. recently developed a clinical prediction algorithm for septic arthritis based on four clinical variables: history of fever, non-weight-bearing, an erythrocyte sedimentation rate of >or=40 mm/hr, and a serum white blood-cell count of >12000/mm(3) (>12.0 x 10(9)/L). The purpose of this study was to apply this clinical algorithm retrospectively to determine its predictive value in our patient population.
Methods:
A retrospective review was performed to identify all children who had undergone a hip arthrocentesis for the evaluation of an irritable hip at our institution between 1992 and 2000. One hundred and sixty-three patients with 165 involved hips satisfied the criteria for inclusion in the study and were classified as having true septic arthritis (twenty hips), presumed septic arthritis (twenty-seven hips), or transient synovitis (118 hips).
Results:
Patients with septic arthritis (true and presumed; forty-seven hips) differed significantly (p < 0.05) from patients with transient synovitis (118 hips) with regard to the erythrocyte sedimentation rate, differential of serum white blood-cell count, total white blood-cell count and differential in the synovial fluid, gender, previous health-care visits, and history of fever. If the four independent multivariate predictors of septic arthritis proposed by Kocher et al. were present, the predicted probability of the patient having septic arthritis was 59% in our study, in contrast to the 99.6% predicted probability in the patient population described by Kocher et al. Statistical analyses demonstrated that the best model to describe our patient population was based on three variables: a history of fever, a serum total white blood-cell count of >12000/mm(3) (>12.0 x 10(9)/L), and a previous health-care visit. When all three variables were present, the predicted probability of the patient having septic arthritis was 71%.
Conclusions:
Although the use of a clinical prediction algorithm to differentiate between septic arthritis and transient synovitis may have improved the utility of existing technology and medical care to facilitate the diagnosis at the institution at which the algorithm originated, application of the algorithm proposed by Kocher et al. or of our three-variable model does not appear to be valid at other institutions.