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Predictive model for long COVID in children 3 months after a SARS-CoV-2 PCR test

BACKGROUND: To update and internally validate a model to predict children and young people (CYP) most likely to experience long COVID (i.e. at least one impairing symptom) 3 months after SARS-CoV-2 PCR testing and to determine whether the impact of predictors differed by SARS-CoV-2 status. METHODS:...

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Autores principales: Nugawela, Manjula D., Stephenson, Terence, Shafran, Roz, De Stavola, Bianca L., Ladhani, Shamez N., Simmons, Ruth, McOwat, Kelsey, Rojas, Natalia, Dalrymple, Emma, Cheung, Emily Y., Ford, Tamsin, Heyman, Isobel, Crawley, Esther, Pinto Pereira, Snehal M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9708506/
https://www.ncbi.nlm.nih.gov/pubmed/36447237
http://dx.doi.org/10.1186/s12916-022-02664-y
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author Nugawela, Manjula D.
Stephenson, Terence
Shafran, Roz
De Stavola, Bianca L.
Ladhani, Shamez N.
Simmons, Ruth
McOwat, Kelsey
Rojas, Natalia
Dalrymple, Emma
Cheung, Emily Y.
Ford, Tamsin
Heyman, Isobel
Crawley, Esther
Pinto Pereira, Snehal M.
author_facet Nugawela, Manjula D.
Stephenson, Terence
Shafran, Roz
De Stavola, Bianca L.
Ladhani, Shamez N.
Simmons, Ruth
McOwat, Kelsey
Rojas, Natalia
Dalrymple, Emma
Cheung, Emily Y.
Ford, Tamsin
Heyman, Isobel
Crawley, Esther
Pinto Pereira, Snehal M.
author_sort Nugawela, Manjula D.
collection PubMed
description BACKGROUND: To update and internally validate a model to predict children and young people (CYP) most likely to experience long COVID (i.e. at least one impairing symptom) 3 months after SARS-CoV-2 PCR testing and to determine whether the impact of predictors differed by SARS-CoV-2 status. METHODS: Data from a nationally matched cohort of SARS-CoV-2 test-positive and test-negative CYP aged 11–17 years was used. The main outcome measure, long COVID, was defined as one or more impairing symptoms 3 months after PCR testing. Potential pre-specified predictors included SARS-CoV-2 status, sex, age, ethnicity, deprivation, quality of life/functioning (five EQ-5D-Y items), physical and mental health and loneliness (prior to testing) and number of symptoms at testing. The model was developed using logistic regression; performance was assessed using calibration and discrimination measures; internal validation was performed via bootstrapping and the final model was adjusted for overfitting. RESULTS: A total of 7139 (3246 test-positives, 3893 test-negatives) completing a questionnaire 3 months post-test were included. 25.2% (817/3246) of SARS-CoV-2 PCR-positives and 18.5% (719/3893) of SARS-CoV-2 PCR-negatives had one or more impairing symptoms 3 months post-test. The final model contained SARS-CoV-2 status, number of symptoms at testing, sex, age, ethnicity, physical and mental health, loneliness and four EQ-5D-Y items before testing. Internal validation showed minimal overfitting with excellent calibration and discrimination measures (optimism-adjusted calibration slope: 0.96575; C-statistic: 0.83130). CONCLUSIONS: We updated a risk prediction equation to identify those most at risk of long COVID 3 months after a SARS-CoV-2 PCR test which could serve as a useful triage and management tool for CYP during the ongoing pandemic. External validation is required before large-scale implementation. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12916-022-02664-y.
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spelling pubmed-97085062022-11-30 Predictive model for long COVID in children 3 months after a SARS-CoV-2 PCR test Nugawela, Manjula D. Stephenson, Terence Shafran, Roz De Stavola, Bianca L. Ladhani, Shamez N. Simmons, Ruth McOwat, Kelsey Rojas, Natalia Dalrymple, Emma Cheung, Emily Y. Ford, Tamsin Heyman, Isobel Crawley, Esther Pinto Pereira, Snehal M. BMC Med Research Article BACKGROUND: To update and internally validate a model to predict children and young people (CYP) most likely to experience long COVID (i.e. at least one impairing symptom) 3 months after SARS-CoV-2 PCR testing and to determine whether the impact of predictors differed by SARS-CoV-2 status. METHODS: Data from a nationally matched cohort of SARS-CoV-2 test-positive and test-negative CYP aged 11–17 years was used. The main outcome measure, long COVID, was defined as one or more impairing symptoms 3 months after PCR testing. Potential pre-specified predictors included SARS-CoV-2 status, sex, age, ethnicity, deprivation, quality of life/functioning (five EQ-5D-Y items), physical and mental health and loneliness (prior to testing) and number of symptoms at testing. The model was developed using logistic regression; performance was assessed using calibration and discrimination measures; internal validation was performed via bootstrapping and the final model was adjusted for overfitting. RESULTS: A total of 7139 (3246 test-positives, 3893 test-negatives) completing a questionnaire 3 months post-test were included. 25.2% (817/3246) of SARS-CoV-2 PCR-positives and 18.5% (719/3893) of SARS-CoV-2 PCR-negatives had one or more impairing symptoms 3 months post-test. The final model contained SARS-CoV-2 status, number of symptoms at testing, sex, age, ethnicity, physical and mental health, loneliness and four EQ-5D-Y items before testing. Internal validation showed minimal overfitting with excellent calibration and discrimination measures (optimism-adjusted calibration slope: 0.96575; C-statistic: 0.83130). CONCLUSIONS: We updated a risk prediction equation to identify those most at risk of long COVID 3 months after a SARS-CoV-2 PCR test which could serve as a useful triage and management tool for CYP during the ongoing pandemic. External validation is required before large-scale implementation. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12916-022-02664-y. BioMed Central 2022-11-30 /pmc/articles/PMC9708506/ /pubmed/36447237 http://dx.doi.org/10.1186/s12916-022-02664-y Text en © The Author(s) 2022 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
Nugawela, Manjula D.
Stephenson, Terence
Shafran, Roz
De Stavola, Bianca L.
Ladhani, Shamez N.
Simmons, Ruth
McOwat, Kelsey
Rojas, Natalia
Dalrymple, Emma
Cheung, Emily Y.
Ford, Tamsin
Heyman, Isobel
Crawley, Esther
Pinto Pereira, Snehal M.
Predictive model for long COVID in children 3 months after a SARS-CoV-2 PCR test
title Predictive model for long COVID in children 3 months after a SARS-CoV-2 PCR test
title_full Predictive model for long COVID in children 3 months after a SARS-CoV-2 PCR test
title_fullStr Predictive model for long COVID in children 3 months after a SARS-CoV-2 PCR test
title_full_unstemmed Predictive model for long COVID in children 3 months after a SARS-CoV-2 PCR test
title_short Predictive model for long COVID in children 3 months after a SARS-CoV-2 PCR test
title_sort predictive model for long covid in children 3 months after a sars-cov-2 pcr test
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9708506/
https://www.ncbi.nlm.nih.gov/pubmed/36447237
http://dx.doi.org/10.1186/s12916-022-02664-y
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