Vitamin K-Dependent Coagulopathy in Pediatric Osteomyelitis: A Case Report

A Clinical Prediction Algorithm to Stratify Pediatric Musculoskeletal Infection by Severity

OBJECTIVE

There are currently no algorithms for early stratification of pediatric musculoskeletal infection (MSKI) severity that are applicable to all types of tissue involvement. In this study, the authors sought to develop a clinical prediction algorithm that accurately stratifies infection severity based on clinical and laboratory data at presentation to the emergency department.

METHODS

An IRB-approved retrospective review was conducted to identify patients aged 0 to 18 who presented to the pediatric emergency department at a tertiary care children's hospital with concern for acute MSKI over a 5-year period (2008 to 2013). Qualifying records were reviewed to obtain clinical and laboratory data and to classify in-hospital outcomes using a 3-tiered severity stratification system. Ordinal regression was used to estimate risk for each outcome. Candidate predictors included age, temperature, respiratory rate, heart rate, C-reactive protein (CRP), and peripheral white blood cell count. We fit fully specified (all predictors) and reduced models (retaining predictors with a P-value ≤0.2). Discriminatory power of the models was assessed using the concordance (c)-index.

RESULTS

Of the 273 identified children, 191 (70%) met inclusion criteria. Median age was 5.8 years. Outcomes included 47 (25%) children with inflammation only, 41 (21%) with local infection, and 103 (54%) with disseminated infection. Both the full and reduced models accurately demonstrated excellent performance (full model c-index 0.83; 95% confidence interval, 0.79-0.88; reduced model 0.83; 95% confidence interval, 0.78-0.87). Model fit was also similar, indicating preference for the reduced model. Variables in this model included CRP, pulse, temperature, and an interaction term for pulse and temperature. The odds of a more severe outcome increased by 30% for every 10 U increase in CRP.

CONCLUSIONS

Clinical and laboratory data obtained in the emergency department may be used to accurately differentiate pediatric MSKI severity. The predictive algorithm in this study stratifies pediatric MSKI severity at presentation irrespective of tissue involvement and anatomic diagnosis. Prospective studies are needed to validate model performance and clinical utility.

LEVEL OF EVIDENCE

Level II-prognostic study.

A Novel Classification System Based on Dissemination of Musculoskeletal Infection is Predictive of Hospital Outcomes

BACKGROUND

Musculoskeletal infections (MSKIs) are a common cause of pediatric hospitalization. Children affected by MSKI have highly variable hospital courses, which seem to depend on infection severity. Early stratification of infection severity would therefore help to maximize resource utilization and improve patient care. Currently, MSKIs are classified according to primary diagnoses such as osteomyelitis, pyomyositis, etc. These diagnoses, however, do not often occur in isolation and may differ widely in severity. On the basis of this, the authors propose a severity classification system that differentiates patients based on total infection burden and degree of dissemination.

METHODS

The authors developed a classification system with operational definitions for MSKI severity based on the degree of dissemination. The operational definitions were applied retrospectively to a cohort of 202 pediatric patients with MSKI from a tertiary care children's hospital over a 5-year period (2008 to 2013). Hospital outcomes data [length of stay (LOS), number of surgeries, positive blood cultures, duration of antibiotics, intensive care unit LOS, number of days with fever, and number of imaging studies] were collected from the electronic medical record and compared between groups.

RESULTS

Patients with greater infection dissemination were more likely to have worse hospital outcomes for LOS, number of surgeries performed, number of positive blood cultures, duration of antibiotics, intensive care unit LOS, number of days with fever, and number of imaging studies performed. Peak C-reactive protein, erythrocyte sedimentation rate, white blood cell count, and temperature were also higher in patients with more disseminated infection.

CONCLUSIONS

The severity classification system for pediatric MSKI defined in this study correlates with hospital outcomes and markers of inflammatory response. The advantage of this classification system is that it is applicable to different types of MSKI and represents a potentially complementary system to the previous practice of differentiating MSKI based on primary diagnosis. Early identification of disease severity in children with MSKI has the potential to enhance hospital outcomes through more efficient resource utilization and improved patient care.

LEVEL OF EVIDENCE

Level II-prognostic study.

Predicting the Presence of Adjacent Infections in Septic Arthritis in Children

BACKGROUND

The gold standard for treatment of septic arthritis is urgent surgical debridement. Preoperative magnetic resonance imaging (MRI) may identify osteomyelitis, subperiosteal abscesses, and intramuscular abscesses, which frequently occur with septic arthritis. If these adjacent infections are not recognized, initial treatment may be inadequate. The purpose of this study is to develop a prediction algorithm to distinguish septic arthritis with adjacent infections from isolated septic arthritis to determine which patients should undergo preoperative MRI.

METHODS

An IRB-approved retrospective review of 87 children treated for septic arthritis was performed. All patients underwent MRI. Sixteen variables (age, sex, temperature, WBC, CRP, ESR, ANC, hematocrit, platelet count, heart rate, systolic blood pressure, diastolic blood pressure, symptom duration, weight-bearing status, prior antibiotic therapy, and prior hospitalization) from admission were reviewed. Graphical and logistical regression analysis was used to determine variables independently predictive of adjacent infection. Optimal cutoff values were determined for each variable and a prediction algorithm was created. Finally, the model was applied to our patient database and each patient with isolated septic arthritis or adjacent infection was stratified based upon the number of positive predictive factors.

RESULTS

A total of 36 (41%) patients had isolated septic arthritis and 51 (59%) had septic arthritis with adjacent foci. Five variables (age above 3.6 y, CRP>13.8 mg/L, duration of symptoms >3 d, platelets <314×10 cells/μL, and ANC>8.6×10 cells/μL) were found to be predictive of adjacent infection and were included in the algorithm. Patients with ≥3 risk factors were classified as high risk for septic arthritis with adjacent infection (sensitivity: 90%, specificity: 67%, positive predictive value: 80%, negative predictive value: 83%).

CONCLUSIONS

Age, CRP, duration of symptoms, platelet count, and ANC were predictive of adjacent infections. Patients who met ≥3 criteria are at high risk for adjacent infection and may benefit from preoperative MRI.

LEVEL OF EVIDENCE

Level III—retrospective comparative study.