Nugawela, Manjula D;
Stephenson, Terence;
Shafran, Roz;
Chalder, Trudie;
Dalrymple, Emma;
Ford, Tamsin;
Fox-Smith, Lana;
... Pinto Pereira, Snehal M; + view all
(2024)
Predicting post-COVID-19 condition in children and young people up to 24 months after a positive SARS-CoV-2 PCR-test: the CLoCk study.
BMC Medicine
, 22
(1)
, Article 520. 10.1186/s12916-024-03708-1.
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Abstract
BACKGROUND: Predicting which children and young people (CYP) are at the highest risk of developing post-COVID-19 condition (PCC) could improve care pathways. We aim to develop and validate prediction models for persistent PCC up to 24 months post-infection in CYP. METHODS: CYP who were PCR-positive between September 2020 and March 2021, with follow-up data up to 24-months post-infection, were analysed. Persistent PCC was defined in two ways, as PCC at (a) 3, 6, 12 and 24 months post-infection (N = 943) or (b) 6, 12 and 24 months post-infection (N = 2373). Prediction models were developed using logistic regression; performance was assessed using calibration and discrimination measures; internal validation was performed via bootstrapping; the final model was adjusted for overfitting. RESULTS: While 24.7% (233/943) of CYP met the PCC definition 3 months post-infection, only 7.2% (68/943) continued to meet the PCC definition at all three subsequent timepoints, i.e. at 6, 12 and 24 months. The final models predicting risk of persistent PCC (at 3, 6, 12 and 24 months and at 6, 12 and 24 months) contained sex (female), history of asthma, allergy problems, learning difficulties at school and family history of ongoing COVID-19 problems, with additional variables (e.g. older age at infection and region of residence) in the model predicting PCC at 6, 12 and 24 months. Internal validation showed minimal overfitting of models with good calibration and discrimination measures (optimism-adjusted calibration slope: 1.064–1.142; C-statistic: 0.724–0.755). CONCLUSIONS: To our knowledge, these are the only prediction models estimating the risk of CYP persistently meeting the PCC definition up to 24 months post-infection. The models could be used to triage CYP after infection. CYP with factors predicting longer-term symptomology, may benefit from earlier support.
| Type: | Article |
|---|---|
| Title: | Predicting post-COVID-19 condition in children and young people up to 24 months after a positive SARS-CoV-2 PCR-test: the CLoCk study |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1186/s12916-024-03708-1 |
| Publisher version: | http://doi.org/10.1186/s12916-024-03708-1 |
| Language: | English |
| Additional information: | This 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/. |
| Keywords: | Children and young people, PostCOVID19 condition, Prediction model, Cohort study |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Surgery and Interventional Sci UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Surgery and Interventional Sci > Department of Targeted Intervention |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10200453 |
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