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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
ERJ Open Research 2022; DOI: 10.1183/23120541.00339-2022
Sriram Ramgopal
1Division of Emergency Medicine, Ann & Robert H. Lurie Children's Hospital of Chicago, Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
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  • For correspondence: sramgopal@luriechildrens.org
Lilliam Ambroggio
2Department of Pediatrics, University of Colorado and Sections of Emergency Medicine and Hospital Medicine, Children's Hospital Colorado, Aurora, CO, USA
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Douglas Lorenz
3Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY
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Samir S. Shah
4Division of Hospital Medicine, Cincinnati Children's Hospital Medical Center and the University of Cincinnati College of Medicine, Cincinnati, OH, USA
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Richard M. Ruddy
5Division of Emergency Medicine, Cincinnati Children's Hospital Medical Center and University of Cincinnati College of Medicine, Cincinnati, OH, USA
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Todd A. Florin
1Division of Emergency Medicine, Ann & Robert H. Lurie Children's Hospital of Chicago, Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
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Abstract

Objective To evaluate biomarkers to predict radiographic pneumonia among children with suspected lower respiratory tract infections (LRTI).

Methods We performed a single-center 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 (WBC), absolute neutrophil count (ANC), 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 cutoff 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 c-index (0.780 to 0.812) compared with a model including only clinical variables.

Conclusion A model consisting of 3 clinical variables and CRP demonstrated improved performance for the identification of radiographic pneumonia compared with a model with clinical variables alone.

Footnotes

This manuscript has recently been accepted for publication in the ERJ Open Research. It is published here in its accepted form prior to copyediting and typesetting by our production team. After these production processes are complete and the authors have approved the resulting proofs, the article will move to the latest issue of the ERJOR online. Please open or download the PDF to view this article.

Conflicts of Interest: The authors have nothing to disclose.

This is a PDF-only article. Please click on the PDF link above to read it.

  • Received July 8, 2022.
  • Accepted October 12, 2022.
  • Copyright ©The authors 2022
http://creativecommons.org/licenses/by-nc/4.0/

This version is distributed under the terms of the Creative Commons Attribution Non-Commercial Licence 4.0. For commercial reproduction rights and permissions contact permissions{at}ersnet.org

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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
ERJ Open Research Jan 2022, 00339-2022; DOI: 10.1183/23120541.00339-2022

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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
ERJ Open Research Jan 2022, 00339-2022; DOI: 10.1183/23120541.00339-2022
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