A Clinical Prediction Rule for Bacterial Musculoskeletal Infections in Children with Monoarthritis in Lyme Endemic Regions.

STUDY OBJECTIVE: Children with a bacterial musculoskeletal infection (MSKI) require prompt identification and treatment. In Lyme disease endemic areas, children with an MSKI can present similarly to those with Lyme arthritis. Our goal was to derive a clinical prediction rule to accurately identify children at a low risk for an MSKI. METHODS: We enrolled children with monoarthritis presenting to 1 of 6 Pedi Lyme Net centers and performed a procalcitonin (PCT) and a first-tier Lyme C6 enzyme immunoassay (EIA) test. Our primary outcome was an MSKI (septic arthritis, osteomyelitis, or pyomyositis). Using recursive partitioning with k-fold cross validation, we derived a clinical prediction rule to identify children at a low risk of an MSKI. We calculated the accuracy of our novel rule in a derivation cohort. RESULTS: Of the 735 children in the derivation cohort with an available research biosample, 39 (5%) had an MSKI (18 had septic arthritis, 20 had osteomyelitis, and 1 had pyomyositis), 260 (37%) had Lyme arthritis, and 436 (53%) had other inflammatory arthritis. Children with a PCT level of more than or equal to 0.50 ng/mL and those with a C-reactive protein (CRP) level of more than or equal to 0.6 mg/dL with a negative Lyme C6 EIA were classified as not low risk for an MSKI. Of the 451 (61%) children categorized as low risk, none had an MSKI (sensitivity 100%, 95% confidence interval 91.0% to 100%; specificity 74.2%, 95% confidence interval 70.5% to 77.6%). CONCLUSION: A novel clinical decision rule that includes PCT, CRP, and a first-tier Lyme EIA was highly sensitive for MSKIs. Although broader external validation is required, the application of this rule may safely reduce invasive testing, procedures, and treatment for low risk children.

Novel Uses of Traditional Algorithms for Septic Arthritis.

BACKGROUND: Pediatric musculoskeletal infection (MSKI) is a bacterial infection of the bone, joint, and/or muscle that can be difficult to diagnose. The Kocher and Caird algorithms were developed to distinguish septic arthritis (SA) from transient synovitis (TS) in the hip. These algorithms have been applied to all patients presenting with painful, swollen monoarticular joints regardless of suspicion for SA. The aim of this analysis was to assess the test performance of Kocher and Caird to distinguish MSKI among all pediatric patients presenting with monoarticular joint pain. A secondary aim was to validate the original algorithms. METHODS: We conducted a secondary analysis of a prospective cohort study evaluating the test performance of procalcitonin for suspected SA in a pediatric emergency department. Patients aged 0 to 16 years old who presented with a painful or swollen monoarticular joint were considered for enrollment. We compared the test performance of the traditional algorithms in an expanded population of MSKI versus alternate joint pain using sensitivity, specificity, and area under the curve (AUC). As a sensitivity analysis, missing data for predictors like temperature, erythrocyte sedimentation rate, C-reactive protein, and inability to bear weight were multiply imputed using the Stata program, mi impute, for changed equations. RESULTS: The Caird algorithm had better test performance compared to the Kocher in all populations. Both algorithms were most discriminative in comparing SA to TS in all joints (AUC: 0.84 Caird and 0.75 Kocher). However, the Caird criteria performed almost as well discriminating MSKI from other causes of monoarticular joint pain in all joints (AUC: 0.79; 95% confidence interval: 0.72, 0.85) and nonhip joints (AUC: 0.80; 95% confidence interval: 0.71, 0.88). CONCLUSION: Based on the findings of this study, it is clinically reasonable to apply the Caird algorithm to pediatric patients presenting with monoarticular joint pain of medium and large joints such as the knee, ankle, shoulder, elbow, and wrist, in addition to the hip. LEVEL OF EVIDENCE: Level III-retrospective study of novel applications of SA algorithms.