TY - JOUR
T1 - External validation of a multivariable prediction model for identification of pneumonia and other serious bacterial infections in febrile immunocompromised children
AU - Martin, Alexander James
AU - van der Velden, Fabian Johannes Stanislaus
AU - von Both, Ulrich
AU - Tsolia, Maria N.
AU - Zenz, Werner
AU - Sagmeister, Manfred
AU - Vermont, Clementien
AU - de Vries, Gabriella
AU - Kolberg, Laura
AU - Lim, Emma
AU - Pokorn, Marko
AU - Zavadska, Dace
AU - Martinón-Torres, Federico
AU - Rivero-Calle, Irene
AU - Hagedoorn, Nienke N.
AU - Usuf, Effua
AU - Schlapbach, Luregn
AU - Kuijpers, Taco W.
AU - Pollard, Andrew J.
AU - Yeung, Shunmay
AU - Fink, Colin
AU - Voice, Marie
AU - Carrol, Enitan
AU - Agyeman, Philipp K. A.
AU - Khanijau, Aakash
AU - Paulus, Stephane
AU - de, Tisham
AU - Herberg, Jethro Adam
AU - Levin, Michael
AU - van der Flier, Michiel
AU - de Groot, Ronald
AU - Nijman, Ruud
AU - Emonts, Marieke
N1 - Publisher Copyright: © 2024 BMJ Publishing Group. All rights reserved.
PY - 2024/1/1
Y1 - 2024/1/1
N2 - 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.
AB - 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.
KW - Allergy and Immunology
KW - Infectious Disease Medicine
KW - Paediatric Emergency Medicine
KW - Paediatrics
UR - http://www.scopus.com/inward/record.url?scp=85170665329&partnerID=8YFLogxK
U2 - 10.1136/archdischild-2023-325869
DO - 10.1136/archdischild-2023-325869
M3 - Article
C2 - 37640431
SN - 0003-9888
VL - 109
SP - 58
EP - 66
JO - Archives of disease in childhood
JF - Archives of disease in childhood
IS - 1
M1 - archdischild-2023-325869
ER -