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.
- Paediatric Emergency Medicine
- Infectious Disease Medicine
- Allergy and Immunology
Data availability statement
Data are available upon reasonable request. Contact corresponding author.
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AJM and FJSvdV contributed equally.
RN and ME contributed equally.
Collaborators For the full list of collaborators see the online supplemental file.
Contributors AJM wrote the original manuscript, performed the statistical analysis and contributed to preparing the database and recruitment. FJSvdV reviewed the manuscript and was responsible for the study dataset and data quality control. GdV was involved in the preparation of the database and patient recruitment. UvB, MNT, WZ, CV, LK, EL, MP, DZ, FM-T, IR-C, NNH, EU, LS, TWK, AJP, SY, CF, MV, EC, PKAA, AK, SP, JAH, ML, MvdF, RdG, RN and ME were responsible for the conduct of the PERFORM study and patient recruitment for their respective sites. TD was responsible for the digital database system and its maintenance. RN and ME supervised the project. ME acts as guarantor. All authors reviewed and approved the final manuscript.
Funding This project received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No 668303. RN is funded by an NIHR academic clinical lectureship award (ACL-2018-21-007). UK enrolment was supported by NIHR Biomedical Research Centres at Imperial College London and Newcastle.
Competing interests None declared.
Provenance and peer review Not commissioned; externally peer reviewed.
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