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Emergency teleradiological activity is an epidemiological estimator and predictor of the covid-19 pandemic in mainland France
BACKGROUND: COVID-19 pandemic highlighted the need for real-time monitoring of diseases evolution to rapidly adapt restrictive measures. This prospective multicentric study aimed at investigating radiological markers of COVID-19-related emergency activity as global estimators of pandemic evolution i...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8295630/ https://www.ncbi.nlm.nih.gov/pubmed/34292414 http://dx.doi.org/10.1186/s13244-021-01040-3 |
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author | Crombé, Amandine Lecomte, Jean-Christophe Banaste, Nathan Tazarourte, Karim Seux, Mylène Nivet, Hubert Thomson, Vivien Gorincour, Guillaume |
author_facet | Crombé, Amandine Lecomte, Jean-Christophe Banaste, Nathan Tazarourte, Karim Seux, Mylène Nivet, Hubert Thomson, Vivien Gorincour, Guillaume |
author_sort | Crombé, Amandine |
collection | PubMed |
description | BACKGROUND: COVID-19 pandemic highlighted the need for real-time monitoring of diseases evolution to rapidly adapt restrictive measures. This prospective multicentric study aimed at investigating radiological markers of COVID-19-related emergency activity as global estimators of pandemic evolution in France. We incorporated two sources of data from March to November 2020: an open-source epidemiological dataset, collecting daily hospitalisations, intensive care unit admissions, hospital deaths and discharges, and a teleradiology dataset corresponding to the weekly number of CT-scans performed in 65 emergency centres and interpreted remotely. CT-scans specifically requested for COVID-19 suspicion were monitored. Teleradiological and epidemiological time series were aligned. Their relationships were estimated through a cross-correlation function, and their extremes and breakpoints were compared. Dynamic linear models were trained to forecast the weekly hospitalisations based on teleradiological activity predictors. RESULTS: A total of 100,018 CT-scans were included over 36 weeks, and 19,133 (19%) performed within the COVID-19 workflow. Concomitantly, 227,677 hospitalisations were reported. Teleradiological and epidemiological time series were almost perfectly superimposed (cross-correlation coefficients at lag 0: 0.90–0.92). Maximal number of COVID-19 CT-scans was reached the week of 2020-03-23 (1 086 CT-scans), 1 week before the highest hospitalisations (23,542 patients). The best valid forecasting model combined the number of COVID-19 CT-scans and the number of hospitalisations during the prior two weeks and provided the lowest mean absolute percentage (5.09%, testing period: 2020-11-02 to 2020-11-29). CONCLUSION: Monitoring COVID-19 CT-scan activity in emergencies accurately and instantly predicts hospitalisations and helps adjust medical resources, paving the way for complementary public health indicators. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13244-021-01040-3. |
format | Online Article Text |
id | pubmed-8295630 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-82956302021-07-22 Emergency teleradiological activity is an epidemiological estimator and predictor of the covid-19 pandemic in mainland France Crombé, Amandine Lecomte, Jean-Christophe Banaste, Nathan Tazarourte, Karim Seux, Mylène Nivet, Hubert Thomson, Vivien Gorincour, Guillaume Insights Imaging Original Article BACKGROUND: COVID-19 pandemic highlighted the need for real-time monitoring of diseases evolution to rapidly adapt restrictive measures. This prospective multicentric study aimed at investigating radiological markers of COVID-19-related emergency activity as global estimators of pandemic evolution in France. We incorporated two sources of data from March to November 2020: an open-source epidemiological dataset, collecting daily hospitalisations, intensive care unit admissions, hospital deaths and discharges, and a teleradiology dataset corresponding to the weekly number of CT-scans performed in 65 emergency centres and interpreted remotely. CT-scans specifically requested for COVID-19 suspicion were monitored. Teleradiological and epidemiological time series were aligned. Their relationships were estimated through a cross-correlation function, and their extremes and breakpoints were compared. Dynamic linear models were trained to forecast the weekly hospitalisations based on teleradiological activity predictors. RESULTS: A total of 100,018 CT-scans were included over 36 weeks, and 19,133 (19%) performed within the COVID-19 workflow. Concomitantly, 227,677 hospitalisations were reported. Teleradiological and epidemiological time series were almost perfectly superimposed (cross-correlation coefficients at lag 0: 0.90–0.92). Maximal number of COVID-19 CT-scans was reached the week of 2020-03-23 (1 086 CT-scans), 1 week before the highest hospitalisations (23,542 patients). The best valid forecasting model combined the number of COVID-19 CT-scans and the number of hospitalisations during the prior two weeks and provided the lowest mean absolute percentage (5.09%, testing period: 2020-11-02 to 2020-11-29). CONCLUSION: Monitoring COVID-19 CT-scan activity in emergencies accurately and instantly predicts hospitalisations and helps adjust medical resources, paving the way for complementary public health indicators. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13244-021-01040-3. Springer International Publishing 2021-07-22 /pmc/articles/PMC8295630/ /pubmed/34292414 http://dx.doi.org/10.1186/s13244-021-01040-3 Text en © The Author(s) 2021 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/) . |
spellingShingle | Original Article Crombé, Amandine Lecomte, Jean-Christophe Banaste, Nathan Tazarourte, Karim Seux, Mylène Nivet, Hubert Thomson, Vivien Gorincour, Guillaume Emergency teleradiological activity is an epidemiological estimator and predictor of the covid-19 pandemic in mainland France |
title | Emergency teleradiological activity is an epidemiological estimator and predictor of the covid-19 pandemic in mainland France |
title_full | Emergency teleradiological activity is an epidemiological estimator and predictor of the covid-19 pandemic in mainland France |
title_fullStr | Emergency teleradiological activity is an epidemiological estimator and predictor of the covid-19 pandemic in mainland France |
title_full_unstemmed | Emergency teleradiological activity is an epidemiological estimator and predictor of the covid-19 pandemic in mainland France |
title_short | Emergency teleradiological activity is an epidemiological estimator and predictor of the covid-19 pandemic in mainland France |
title_sort | emergency teleradiological activity is an epidemiological estimator and predictor of the covid-19 pandemic in mainland france |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8295630/ https://www.ncbi.nlm.nih.gov/pubmed/34292414 http://dx.doi.org/10.1186/s13244-021-01040-3 |
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