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

Alexander James Martin ORCID logo ; Fabian Johannes Stanislaus van der Velden ORCID logo ; Ulrich von Both ORCID logo ; Maria N Tsolia ORCID logo ; Werner Zenz ORCID logo ; Manfred Sagmeister ORCID logo ; Clementien Vermont ORCID logo ; Gabriella de Vries ; Laura Kolberg ORCID logo ; Emma Lim ORCID logo ; +24 more... Marko Pokorn ORCID logo ; Dace Zavadska ORCID logo ; Federico Martinón-Torres ORCID logo ; Irene Rivero-Calle ORCID logo ; Nienke N Hagedoorn ORCID logo ; Effua Usuf ORCID logo ; Luregn Schlapbach ORCID logo ; Taco W Kuijpers ORCID logo ; Andrew J Pollard ORCID logo ; Shunmay Yeung ORCID logo ; Colin Fink ORCID logo ; Marie Voice ; Enitan Carrol ORCID logo ; Philipp KA Agyeman ORCID logo ; Aakash Khanijau ; Stephane Paulus ORCID logo ; Tisham De ; Jethro Adam Herberg ORCID logo ; Michael Levin ORCID logo ; Michiel van der Flier ORCID logo ; Ronald de Groot ; Ruud Nijman ORCID logo ; Marieke Emonts ORCID logo ; PERFORM consortium ; (2023) External validation of a multivariable prediction model for identification of pneumonia and other serious bacterial infections in febrile immunocompromised children. Archives of disease in childhood, 109 (1). pp. 58-66. ISSN 0003-9888 DOI: 10.1136/archdischild-2023-325869
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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.


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