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

Martin, AJORCID logo; van der Velden, FJSORCID logo; von Both, UORCID logo; Tsolia, MNORCID logo; Zenz, WORCID logo; Sagmeister, MORCID logo; Vermont, CORCID logo; de Vries, G; Kolberg, LORCID logo; Lim, EORCID logo; +24 more...Pokorn, MORCID logo; Zavadska, DORCID logo; Martinón-Torres, FORCID logo; Rivero-Calle, IORCID logo; Hagedoorn, NNORCID logo; Usuf, EORCID logo; Schlapbach, LORCID logo; Kuijpers, TWORCID logo; Pollard, AJORCID logo; Yeung, SORCID logo; Fink, CORCID logo; Voice, M; Carrol, EORCID logo; Agyeman, PKORCID logo; Khanijau, A; Paulus, SORCID logo; De, T; Herberg, JAORCID logo; Levin, MORCID logo; van der Flier, MORCID logo; de Groot, R; Nijman, RORCID logo; Emonts, MORCID logo; PERFORM consortium and (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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