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Development and validation of multivariable prediction models for adverse COVID-19 outcomes in patients with IBD
OBJECTIVES: Develop an individualised prognostic risk prediction tool for predicting the probability of adverse COVID-19 outcomes in patients with inflammatory bowel disease (IBD). DESIGN AND SETTING: This study developed and validated prognostic penalised logistic regression models using reports to...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8593277/ https://www.ncbi.nlm.nih.gov/pubmed/34772750 http://dx.doi.org/10.1136/bmjopen-2021-049740 |
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author | Sperger, John Shah, Kushal S Lu, Minxin Zhang, Xian Ungaro, Ryan C Brenner, Erica J Agrawal, Manasi Colombel, Jean-Frédéric Kappelman, Michael D Kosorok, Michael R |
author_facet | Sperger, John Shah, Kushal S Lu, Minxin Zhang, Xian Ungaro, Ryan C Brenner, Erica J Agrawal, Manasi Colombel, Jean-Frédéric Kappelman, Michael D Kosorok, Michael R |
author_sort | Sperger, John |
collection | PubMed |
description | OBJECTIVES: Develop an individualised prognostic risk prediction tool for predicting the probability of adverse COVID-19 outcomes in patients with inflammatory bowel disease (IBD). DESIGN AND SETTING: This study developed and validated prognostic penalised logistic regression models using reports to the international Surveillance Epidemiology of Coronavirus Under Research Exclusion for Inflammatory Bowel Disease voluntary registry from March to October 2020. Model development was done using a training data set (85% of cases reported 13 March–15 September 2020), and model validation was conducted using a test data set (the remaining 15% of cases plus all cases reported 16 September–20 October 2020). PARTICIPANTS: We included 2709 cases from 59 countries (mean age 41.2 years (SD 18), 50.2% male). All submitted cases after removing duplicates were included. PRIMARY AND SECONDARY OUTCOME MEASURES: COVID-19 related: (1) Hospitalisation+: composite outcome of hospitalisation, ICU admission, mechanical ventilation or death; (2) Intensive Care Unit+ (ICU+): composite outcome of ICU admission, mechanical ventilation or death; (3) Death. We assessed the resulting models’ discrimination using the area under the curve of the receiver operator characteristic curves and reported the corresponding 95% CIs. RESULTS: Of the submitted cases, a total of 633 (24%) were hospitalised, 137 (5%) were admitted to the ICU or intubated and 69 (3%) died. 2009 patients comprised the training set and 700 the test set. The models demonstrated excellent discrimination, with a test set area under the curve (95% CI) of 0.79 (0.75 to 0.83) for Hospitalisation+, 0.88 (0.82 to 0.95) for ICU+ and 0.94 (0.89 to 0.99) for Death. Age, comorbidities, corticosteroid use and male gender were associated with a higher risk of death, while the use of biological therapies was associated with a lower risk. CONCLUSIONS: Prognostic models can effectively predict who is at higher risk for COVID-19-related adverse outcomes in a population of patients with IBD. A free online risk calculator (https://covidibd.org/covid-19-risk-calculator/) is available for healthcare providers to facilitate discussion of risks due to COVID-19 with patients with IBD. |
format | Online Article Text |
id | pubmed-8593277 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-85932772021-11-16 Development and validation of multivariable prediction models for adverse COVID-19 outcomes in patients with IBD Sperger, John Shah, Kushal S Lu, Minxin Zhang, Xian Ungaro, Ryan C Brenner, Erica J Agrawal, Manasi Colombel, Jean-Frédéric Kappelman, Michael D Kosorok, Michael R BMJ Open Gastroenterology and Hepatology OBJECTIVES: Develop an individualised prognostic risk prediction tool for predicting the probability of adverse COVID-19 outcomes in patients with inflammatory bowel disease (IBD). DESIGN AND SETTING: This study developed and validated prognostic penalised logistic regression models using reports to the international Surveillance Epidemiology of Coronavirus Under Research Exclusion for Inflammatory Bowel Disease voluntary registry from March to October 2020. Model development was done using a training data set (85% of cases reported 13 March–15 September 2020), and model validation was conducted using a test data set (the remaining 15% of cases plus all cases reported 16 September–20 October 2020). PARTICIPANTS: We included 2709 cases from 59 countries (mean age 41.2 years (SD 18), 50.2% male). All submitted cases after removing duplicates were included. PRIMARY AND SECONDARY OUTCOME MEASURES: COVID-19 related: (1) Hospitalisation+: composite outcome of hospitalisation, ICU admission, mechanical ventilation or death; (2) Intensive Care Unit+ (ICU+): composite outcome of ICU admission, mechanical ventilation or death; (3) Death. We assessed the resulting models’ discrimination using the area under the curve of the receiver operator characteristic curves and reported the corresponding 95% CIs. RESULTS: Of the submitted cases, a total of 633 (24%) were hospitalised, 137 (5%) were admitted to the ICU or intubated and 69 (3%) died. 2009 patients comprised the training set and 700 the test set. The models demonstrated excellent discrimination, with a test set area under the curve (95% CI) of 0.79 (0.75 to 0.83) for Hospitalisation+, 0.88 (0.82 to 0.95) for ICU+ and 0.94 (0.89 to 0.99) for Death. Age, comorbidities, corticosteroid use and male gender were associated with a higher risk of death, while the use of biological therapies was associated with a lower risk. CONCLUSIONS: Prognostic models can effectively predict who is at higher risk for COVID-19-related adverse outcomes in a population of patients with IBD. A free online risk calculator (https://covidibd.org/covid-19-risk-calculator/) is available for healthcare providers to facilitate discussion of risks due to COVID-19 with patients with IBD. BMJ Publishing Group 2021-11-12 /pmc/articles/PMC8593277/ /pubmed/34772750 http://dx.doi.org/10.1136/bmjopen-2021-049740 Text en © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) . |
spellingShingle | Gastroenterology and Hepatology Sperger, John Shah, Kushal S Lu, Minxin Zhang, Xian Ungaro, Ryan C Brenner, Erica J Agrawal, Manasi Colombel, Jean-Frédéric Kappelman, Michael D Kosorok, Michael R Development and validation of multivariable prediction models for adverse COVID-19 outcomes in patients with IBD |
title | Development and validation of multivariable prediction models for adverse COVID-19 outcomes in patients with IBD |
title_full | Development and validation of multivariable prediction models for adverse COVID-19 outcomes in patients with IBD |
title_fullStr | Development and validation of multivariable prediction models for adverse COVID-19 outcomes in patients with IBD |
title_full_unstemmed | Development and validation of multivariable prediction models for adverse COVID-19 outcomes in patients with IBD |
title_short | Development and validation of multivariable prediction models for adverse COVID-19 outcomes in patients with IBD |
title_sort | development and validation of multivariable prediction models for adverse covid-19 outcomes in patients with ibd |
topic | Gastroenterology and Hepatology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8593277/ https://www.ncbi.nlm.nih.gov/pubmed/34772750 http://dx.doi.org/10.1136/bmjopen-2021-049740 |
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