A Clinical Prediction Algorithm to Stratify Pediatric Musculoskeletal Infection by Severity
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Published on: November 6, 2018
Michael A Benvenuti1, Thomas J An1, Megan E Mignemi2
1Vanderbilt University School of Medicine.
Insights
A new algorithm effectively stratifies pediatric musculoskeletal infection (MSKI) severity using emergency department clinical and lab data. This tool aids in early diagnosis and treatment planning for children with MSKI.
Area of Science:
- Pediatric Emergency Medicine
- Infectious Diseases
- Clinical Prediction Modeling
Background:
- Pediatric musculoskeletal infections (MSKI) lack early stratification tools applicable across all tissue involvements.
- Accurate severity assessment is crucial for timely and appropriate management of pediatric MSKI.
Purpose of the Study:
- To develop and validate a clinical prediction algorithm for stratifying pediatric MSKI severity.
- The algorithm aims to utilize readily available clinical and laboratory data from emergency department presentations.
Main Methods:
- Retrospective review of 191 pediatric patients (0-18 years) with suspected acute MSKI.
- Ordinal regression analysis to predict infection severity (inflammation, local, disseminated).
- Model performance assessed using the concordance (c)-index, with predictors including CRP, pulse, and temperature.
Main Results:
- The developed algorithm demonstrated excellent predictive performance (c-index 0.83) for MSKI severity.
- Key predictors identified were C-reactive protein (CRP), pulse, and temperature.
- A 10 U increase in CRP correlated with a 30% increased odds of more severe outcomes.
Conclusions:
- Clinical and laboratory data at emergency department presentation can accurately stratify pediatric MSKI severity.
- The developed algorithm is applicable regardless of tissue involvement or diagnosis.
- Further prospective studies are recommended for validation and to assess clinical utility.
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.
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