Incorporation of biomarkers into a prediction model for paediatric radiographic pneumonia

Sriram Ramgopal; Lilliam Ambroggio; Douglas Lorenz; Samir S Shah; Richard M Ruddy; Todd A Florin

doi:10.1183/23120541.00339-2022

Incorporation of biomarkers into a prediction model for paediatric radiographic pneumonia

ERJ Open Res. 2023 Mar 6;9(2):00339-2022. doi: 10.1183/23120541.00339-2022. eCollection 2023 Mar.

Authors

Sriram Ramgopal¹, Lilliam Ambroggio², Douglas Lorenz³, Samir S Shah⁴, Richard M Ruddy⁵, Todd A Florin¹

Affiliations

¹ Division of Emergency Medicine, Ann & Robert H. Lurie Children's Hospital of Chicago, Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
² Department of Pediatrics, University of Colorado and Sections of Emergency Medicine and Hospital Medicine, Children's Hospital Colorado, Aurora, CO, USA.
³ Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY, USA.
⁴ Division of Hospital Medicine, Cincinnati Children's Hospital Medical Center and the University of Cincinnati College of Medicine, Cincinnati, OH, USA.
⁵ Division of Emergency Medicine, Cincinnati Children's Hospital Medical Center and University of Cincinnati College of Medicine, Cincinnati, OH, USA.

Abstract

Objective: The aim of this study was to evaluate biomarkers to predict radiographic pneumonia among children with suspected lower respiratory tract infections (LRTI).

Methods: We performed a single-centre prospective cohort study of children 3 months to 18 years evaluated in the emergency department with signs and symptoms of LRTI. We evaluated the incorporation of four biomarkers (white blood cell count, absolute neutrophil count, C-reactive protein (CRP) and procalcitonin), in isolation and in combination, with a previously developed clinical model (which included focal decreased breath sounds, age and fever duration) for an outcome of radiographic pneumonia using multivariable logistic regression. We evaluated the improvement in performance of each model with the concordance (c-) index.

Results: Of 580 included children, 213 (36.7%) had radiographic pneumonia. In multivariable analysis, all biomarkers were statistically associated with radiographic pneumonia, with CRP having the greatest adjusted odds ratio of 1.79 (95% CI 1.47-2.18). As an isolated predictor, CRP at a cut-off of 3.72 mg·dL^-1 demonstrated a sensitivity of 60% and a specificity of 75%. The model incorporating CRP demonstrated improved sensitivity (70.0% versus 57.7%) and similar specificity (85.3% versus 88.3%) compared to the clinical model when using a statistically derived cutpoint. In addition, the multivariable CRP model demonstrated the greatest improvement in concordance index (0.780 to 0.812) compared with a model including only clinical variables.

Conclusion: A model consisting of three clinical variables and CRP demonstrated improved performance for the identification of paediatric radiographic pneumonia compared with a model with clinical variables alone.