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Clinical prediction models for serious infections in children: external validation in ambulatory care

BACKGROUND: Early distinction between mild and serious infections (SI) is challenging in children in ambulatory care. Clinical prediction models (CPMs), developed to aid physicians in clinical decision-making, require broad external validation before clinical use. We aimed to externally validate fou...

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Autores principales: Bos, David A. G., De Burghgraeve, Tine, De Sutter, An, Buntinx, Frank, Verbakel, Jan Y.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10114467/
https://www.ncbi.nlm.nih.gov/pubmed/37072778
http://dx.doi.org/10.1186/s12916-023-02860-4
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author Bos, David A. G.
De Burghgraeve, Tine
De Sutter, An
Buntinx, Frank
Verbakel, Jan Y.
author_facet Bos, David A. G.
De Burghgraeve, Tine
De Sutter, An
Buntinx, Frank
Verbakel, Jan Y.
author_sort Bos, David A. G.
collection PubMed
description BACKGROUND: Early distinction between mild and serious infections (SI) is challenging in children in ambulatory care. Clinical prediction models (CPMs), developed to aid physicians in clinical decision-making, require broad external validation before clinical use. We aimed to externally validate four CPMs, developed in emergency departments, in ambulatory care. METHODS: We applied the CPMs in a prospective cohort of acutely ill children presenting to general practices, outpatient paediatric practices or emergency departments in Flanders, Belgium. For two multinomial regression models, Feverkidstool and Craig model, discriminative ability and calibration were assessed, and a model update was performed by re-estimation of coefficients with correction for overfitting. For two risk scores, the SBI score and PAWS, the diagnostic test accuracy was assessed. RESULTS: A total of 8211 children were included, comprising 498 SI and 276 serious bacterial infections (SBI). Feverkidstool had a C-statistic of 0.80 (95% confidence interval 0.77–0.84) with good calibration for pneumonia and 0.74 (0.70–0.79) with poor calibration for other SBI. The Craig model had a C-statistic of 0.80 (0.77–0.83) for pneumonia, 0.75 (0.70–0.80) for complicated urinary tract infections and 0.63 (0.39–0.88) for bacteraemia, with poor calibration. The model update resulted in improved C-statistics for all outcomes and good overall calibration for Feverkidstool and the Craig model. SBI score and PAWS performed extremely weak with sensitivities of 0.12 (0.09–0.15) and 0.32 (0.28–0.37). CONCLUSIONS: Feverkidstool and the Craig model show good discriminative ability for predicting SBI and a potential for early recognition of SBI, confirming good external validity in a low prevalence setting of SBI. The SBI score and PAWS showed poor diagnostic performance. TRIAL REGISTRATION: ClinicalTrials.gov, NCT02024282. Registered on 31 December 2013. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12916-023-02860-4.
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spelling pubmed-101144672023-04-20 Clinical prediction models for serious infections in children: external validation in ambulatory care Bos, David A. G. De Burghgraeve, Tine De Sutter, An Buntinx, Frank Verbakel, Jan Y. BMC Med Research Article BACKGROUND: Early distinction between mild and serious infections (SI) is challenging in children in ambulatory care. Clinical prediction models (CPMs), developed to aid physicians in clinical decision-making, require broad external validation before clinical use. We aimed to externally validate four CPMs, developed in emergency departments, in ambulatory care. METHODS: We applied the CPMs in a prospective cohort of acutely ill children presenting to general practices, outpatient paediatric practices or emergency departments in Flanders, Belgium. For two multinomial regression models, Feverkidstool and Craig model, discriminative ability and calibration were assessed, and a model update was performed by re-estimation of coefficients with correction for overfitting. For two risk scores, the SBI score and PAWS, the diagnostic test accuracy was assessed. RESULTS: A total of 8211 children were included, comprising 498 SI and 276 serious bacterial infections (SBI). Feverkidstool had a C-statistic of 0.80 (95% confidence interval 0.77–0.84) with good calibration for pneumonia and 0.74 (0.70–0.79) with poor calibration for other SBI. The Craig model had a C-statistic of 0.80 (0.77–0.83) for pneumonia, 0.75 (0.70–0.80) for complicated urinary tract infections and 0.63 (0.39–0.88) for bacteraemia, with poor calibration. The model update resulted in improved C-statistics for all outcomes and good overall calibration for Feverkidstool and the Craig model. SBI score and PAWS performed extremely weak with sensitivities of 0.12 (0.09–0.15) and 0.32 (0.28–0.37). CONCLUSIONS: Feverkidstool and the Craig model show good discriminative ability for predicting SBI and a potential for early recognition of SBI, confirming good external validity in a low prevalence setting of SBI. The SBI score and PAWS showed poor diagnostic performance. TRIAL REGISTRATION: ClinicalTrials.gov, NCT02024282. Registered on 31 December 2013. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12916-023-02860-4. BioMed Central 2023-04-18 /pmc/articles/PMC10114467/ /pubmed/37072778 http://dx.doi.org/10.1186/s12916-023-02860-4 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Bos, David A. G.
De Burghgraeve, Tine
De Sutter, An
Buntinx, Frank
Verbakel, Jan Y.
Clinical prediction models for serious infections in children: external validation in ambulatory care
title Clinical prediction models for serious infections in children: external validation in ambulatory care
title_full Clinical prediction models for serious infections in children: external validation in ambulatory care
title_fullStr Clinical prediction models for serious infections in children: external validation in ambulatory care
title_full_unstemmed Clinical prediction models for serious infections in children: external validation in ambulatory care
title_short Clinical prediction models for serious infections in children: external validation in ambulatory care
title_sort clinical prediction models for serious infections in children: external validation in ambulatory care
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10114467/
https://www.ncbi.nlm.nih.gov/pubmed/37072778
http://dx.doi.org/10.1186/s12916-023-02860-4
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