External validation of a multivariable prediction model for identification of pneumonia and other serious bacterial infections in febrile immunocompromised children

Alexander James Martin, Fabian Johannes Stanislaus Van Der Velden, Ulrich Von Both, Maria N. Tsolia, Werner Zenz, Manfred Sagmeister, Clementien Vermont, Gabriella De Vries, Laura Kolberg, Emma Lim, Marko Pokorn, Dace Zavadska, Federico Martinón-Torres, Irene Rivero-Calle, Nienke N. Hagedoorn, Effua Usuf, Luregn Schlapbach, Taco W. Kuijpers, Andrew J. Pollard, Shunmay YeungColin Fink, Marie Voice, Enitan Carrol, Philipp K.A. Agyeman, Aakash Khanijau, Stephane Paulus, Tisham De, Jethro Adam Herberg, Michael Levin, Michiel Van Der Flier, Ronald De Groot, Ruud Nijman, Marieke Emonts*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)

Abstract

Objective:

To externally validate and update the Feverkids tool clinical prediction model for differentiating bacterial pneumonia and other serious bacterial infections (SBIs) from non-SBI causes of fever in immunocompromised children. 

Design: 

International, multicentre, prospective observational study embedded in PErsonalised Risk assessment in Febrile illness to Optimise Real-life Management across the European Union (PERFORM). 

Setting: 

Fifteen teaching hospitals in nine European countries. 

Participants: 

Febrile immunocompromised children aged 0-18 years. 

Methods: 

The Feverkids clinical prediction model predicted the probability of bacterial pneumonia, other SBI or no SBI. Model discrimination, calibration and diagnostic performance at different risk thresholds were assessed. The model was then re-fitted and updated. 

Results: 

Of 558 episodes, 21 had bacterial pneumonia, 104 other SBI and 433 no SBI. Discrimination was 0.83 (95% CI 0.71 to 0.90) for bacterial pneumonia, with moderate calibration and 0.67 (0.61 to 0.72) for other SBIs, with poor calibration. After model re-fitting, discrimination improved to 0.88 (0.79 to 0.96) and 0.71 (0.65 to 0.76) and calibration improved. Predicted risk <1% ruled out bacterial pneumonia with sensitivity 0.95 (0.86 to 1.00) and negative likelihood ratio (LR) 0.09 (0.00 to 0.32). Predicted risk >10% ruled in bacterial pneumonia with specificity 0.91 (0.88 to 0.94) and positive LR 6.51 (3.71 to 10.3). Predicted risk <10% ruled out other SBIs with sensitivity 0.92 (0.87 to 0.97) and negative LR 0.32 (0.13 to 0.57). Predicted risk >30% ruled in other SBIs with specificity 0.89 (0.86 to 0.92) and positive LR 2.86 (1.91 to 4.25). 

Conclusion: 

Discrimination and calibration were good for bacterial pneumonia but poorer for other SBIs. The rule-out thresholds have the potential to reduce unnecessary investigations and antibiotics in this high-risk group.

Original languageEnglish
Article numberarchdischild-2023-325869
Pages (from-to)58-66
Number of pages9
JournalArchives of Disease in Childhood
Volume109
Issue number1
Early online date27 Aug 2023
DOIs
Publication statusPublished - 1 Jan 2024

Bibliographical note

Publisher Copyright:
© 2024 BMJ Publishing Group. All rights reserved.

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