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External Validation of an Algorithm to Predict Adjacent Musculoskeletal Infection in Pediatric Patients With Septic
Sarah Hunter1, Jim Kennedy2, Joseph F Baker3,4
1Department of Orthopaedic Surgery, Waikato Hospital, University of Auckland.
Background:
Septic arthritis (SA) remains a potentially morbid disease in the pediatric population. Magnetic resonance imaging (MRI) is the most sensitive tool for recognizing associated osteomyelitis and intramuscular abscess, but is a limited resource. The aim of this study is to externally validate a previously developed algorithm (Rosenfeld and colleagues) to predict adjacent infection in pediatric patients diagnosed with SA.
Methods:
We identified 120 children under 16 with presumed SA presenting to a tertiary referral center between 2008 and 2018. Patients without confirmed SA, those with insufficient data, and patients who did not receive perioperative MRI were excluded, leaving 53 patients. The previous algorithm suggests that patient age (above 4 y), C-reactive protein (>8.9 mg/L), platelet count (<310×10cells/µL), duration of symptoms (>3 d), and absolute neutrophil count (>7.2×10cells/µL) are risk factors for adjacent infection, with 3 or more variables signifying a "positive" result. Comparing against the gold standard of MRI, the accuracy of the algorithm was validated in terms of sensitivity, specificity, likelihood ratio (LR), and positive and negative predictive value. Discrimination and calibration of this algorithm have been assessed using receiver operating curve analysis and calibration plots.
Results:
The sensitivity and specificity of criteria from Rosenfeld algorithm were 73% and 44%, respectively. Receiver operating curve showed poor discrimination [area under the curve=0.54, confidence interval (CI): 0.26-0.83]. The positive predictive value was 55.9% and the negative predictive value was 63.1% with LR +1.23 (CI: 0.87-1.98) and LR -0.61 (CI 0.28-1.30). Only 53% of patients with 4 or more criteria had an adjacent infection on MRI. Examining our cohort, children with a positive MRI finding had higher mean C-reactive protein (77 vs. 122 mg/L, P=0.04) and were more likely to have waited >72 hours days between symptom onset and hospital presentation (P=0.03).
Conclusion:
Although treatment algorithms are an attractive tool to guide clinicians and resource allocation, they need to take into account the local population characteristics before routine implementation.
Level Of Evidence:
Level IV-retrospective cohort study.
Insights
This study found the Rosenfeld algorithm has limited accuracy in predicting adjacent infections in pediatric septic arthritis. Local data is crucial before implementing such predictive tools.
Area of Science:
- Pediatric Infectious Diseases
- Musculoskeletal Infections
- Diagnostic Accuracy Studies
Background:
- Septic arthritis (SA) poses significant risks in children.
- Magnetic resonance imaging (MRI) is sensitive for complications but resource-limited.
- External validation of predictive algorithms for SA complications is needed.
Purpose of the Study:
- To externally validate the Rosenfeld algorithm for predicting adjacent infections in pediatric septic arthritis.
- To assess the algorithm's performance using sensitivity, specificity, and predictive values.
- To evaluate the algorithm's utility in a real-world clinical setting.
Main Methods:
- Retrospective cohort study of 53 pediatric patients with septic arthritis.
- Validated the Rosenfeld algorithm's criteria (age, CRP, platelets, symptom duration, ANC) against MRI findings.
- Analyzed sensitivity, specificity, likelihood ratios, and receiver operating curve (ROC).
Main Results:
- The Rosenfeld algorithm demonstrated low sensitivity (73%) and specificity (44%).
- Poor discrimination was observed (Area Under Curve = 0.54).
- Positive predictive value was 55.9%, negative predictive value was 63.1%.
Conclusions:
- The Rosenfeld algorithm showed limited accuracy for predicting adjacent infections in pediatric SA.
- Routine implementation requires consideration of local population characteristics.
- Further research may be needed to refine predictive models for pediatric septic arthritis.
