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A Definitive Prognostication System for Patients With Thoracic Malignancies Diagnosed With Coronavirus Disease 2019: An Update From the TERAVOLT Registry
INTRODUCTION: Patients with thoracic malignancies are at increased risk for mortality from coronavirus disease 2019 (COVID-19), and a large number of intertwined prognostic variables have been identified so far. METHODS: Capitalizing data from the Thoracic Cancers International COVID-19 Collaboratio...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
International Association for the Study of Lung Cancer. Published by Elsevier Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8804493/ https://www.ncbi.nlm.nih.gov/pubmed/35121086 http://dx.doi.org/10.1016/j.jtho.2021.12.015 |
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author | Whisenant, Jennifer G. Baena, Javier Cortellini, Alessio Huang, Li-Ching Lo Russo, Giuseppe Porcu, Luca Wong, Selina K. Bestvina, Christine M. Hellmann, Matthew D. Roca, Elisa Rizvi, Hira Monnet, Isabelle Boudjemaa, Amel Rogado, Jacobo Pasello, Giulia Leighl, Natasha B. Arrieta, Oscar Aujayeb, Avinash Batra, Ullas Azzam, Ahmed Y. Unk, Mojca Azab, Mohammed A. Zhumagaliyeva, Ardak N. Gomez-Martin, Carlos Blaquier, Juan B. Geraedts, Erica Mountzios, Giannis Serrano-Montero, Gloria Reinmuth, Niels Coate, Linda Marmarelis, Melina Presley, Carolyn J. Hirsch, Fred R. Garrido, Pilar Khan, Hina Baggi, Alice Mascaux, Celine Halmos, Balazs Ceresoli, Giovanni L. Fidler, Mary J. Scotti, Vieri Métivier, Anne-Cécile Falchero, Lionel Felip, Enriqueta Genova, Carlo Mazieres, Julien Tapan, Umit Brahmer, Julie Bria, Emilio Puri, Sonam Popat, Sanjay Reckamp, Karen L. Morgillo, Floriana Nadal, Ernest Mazzoni, Francesca Agustoni, Francesco Bar, Jair Grosso, Federica Avrillon, Virginie Patel, Jyoti D. Gomes, Fabio Ibrahim, Ehab Trama, Annalisa Bettini, Anna C. Barlesi, Fabrice Dingemans, Anne-Marie Wakelee, Heather Peters, Solange Horn, Leora Garassino, Marina Chiara Torri, Valter |
author_facet | Whisenant, Jennifer G. Baena, Javier Cortellini, Alessio Huang, Li-Ching Lo Russo, Giuseppe Porcu, Luca Wong, Selina K. Bestvina, Christine M. Hellmann, Matthew D. Roca, Elisa Rizvi, Hira Monnet, Isabelle Boudjemaa, Amel Rogado, Jacobo Pasello, Giulia Leighl, Natasha B. Arrieta, Oscar Aujayeb, Avinash Batra, Ullas Azzam, Ahmed Y. Unk, Mojca Azab, Mohammed A. Zhumagaliyeva, Ardak N. Gomez-Martin, Carlos Blaquier, Juan B. Geraedts, Erica Mountzios, Giannis Serrano-Montero, Gloria Reinmuth, Niels Coate, Linda Marmarelis, Melina Presley, Carolyn J. Hirsch, Fred R. Garrido, Pilar Khan, Hina Baggi, Alice Mascaux, Celine Halmos, Balazs Ceresoli, Giovanni L. Fidler, Mary J. Scotti, Vieri Métivier, Anne-Cécile Falchero, Lionel Felip, Enriqueta Genova, Carlo Mazieres, Julien Tapan, Umit Brahmer, Julie Bria, Emilio Puri, Sonam Popat, Sanjay Reckamp, Karen L. Morgillo, Floriana Nadal, Ernest Mazzoni, Francesca Agustoni, Francesco Bar, Jair Grosso, Federica Avrillon, Virginie Patel, Jyoti D. Gomes, Fabio Ibrahim, Ehab Trama, Annalisa Bettini, Anna C. Barlesi, Fabrice Dingemans, Anne-Marie Wakelee, Heather Peters, Solange Horn, Leora Garassino, Marina Chiara Torri, Valter |
author_sort | Whisenant, Jennifer G. |
collection | PubMed |
description | INTRODUCTION: Patients with thoracic malignancies are at increased risk for mortality from coronavirus disease 2019 (COVID-19), and a large number of intertwined prognostic variables have been identified so far. METHODS: Capitalizing data from the Thoracic Cancers International COVID-19 Collaboration (TERAVOLT) registry, a global study created with the aim of describing the impact of COVID-19 in patients with thoracic malignancies, we used a clustering approach, a fast-backward step-down selection procedure, and a tree-based model to screen and optimize a broad panel of demographics and clinical COVID-19 and cancer characteristics. RESULTS: As of April 15, 2021, a total of 1491 consecutive eligible patients from 18 countries were included in the analysis. With a mean observation period of 42 days, 361 events were reported with an all-cause case fatality rate of 24.2%. The clustering procedure screened 73 covariates in 13 clusters. A further multivariable logistic regression for the association between clusters and death was performed, resulting in five clusters significantly associated with the outcome. The fast-backward step-down selection procedure then identified the following seven major determinants of death: Eastern Cooperative Oncology Group—performance status (ECOG-PS) (OR = 2.47, 1.87–3.26), neutrophil count (OR = 2.46, 1.76–3.44), serum procalcitonin (OR = 2.37, 1.64–3.43), development of pneumonia (OR = 1.95, 1.48–2.58), C-reactive protein (OR = 1.90, 1.43–2.51), tumor stage at COVID-19 diagnosis (OR = 1.97, 1.46–2.66), and age (OR = 1.71, 1.29–2.26). The receiver operating characteristic analysis for death of the selected model confirmed its diagnostic ability (area under the receiver operating curve = 0.78, 95% confidence interval: 0.75–0.81). The nomogram was able to classify the COVID-19 mortality in an interval ranging from 8% to 90%, and the tree-based model recognized ECOG-PS, neutrophil count, and c-reactive protein as the major determinants of prognosis. CONCLUSIONS: From 73 variables analyzed, seven major determinants of death have been identified. Poor ECOG-PS was found to have the strongest association with poor outcome from COVID-19. With our analysis, we provide clinicians with a definitive prognostication system to help determine the risk of mortality for patients with thoracic malignancies and COVID-19. |
format | Online Article Text |
id | pubmed-8804493 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | International Association for the Study of Lung Cancer. Published by Elsevier Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-88044932022-02-01 A Definitive Prognostication System for Patients With Thoracic Malignancies Diagnosed With Coronavirus Disease 2019: An Update From the TERAVOLT Registry Whisenant, Jennifer G. Baena, Javier Cortellini, Alessio Huang, Li-Ching Lo Russo, Giuseppe Porcu, Luca Wong, Selina K. Bestvina, Christine M. Hellmann, Matthew D. Roca, Elisa Rizvi, Hira Monnet, Isabelle Boudjemaa, Amel Rogado, Jacobo Pasello, Giulia Leighl, Natasha B. Arrieta, Oscar Aujayeb, Avinash Batra, Ullas Azzam, Ahmed Y. Unk, Mojca Azab, Mohammed A. Zhumagaliyeva, Ardak N. Gomez-Martin, Carlos Blaquier, Juan B. Geraedts, Erica Mountzios, Giannis Serrano-Montero, Gloria Reinmuth, Niels Coate, Linda Marmarelis, Melina Presley, Carolyn J. Hirsch, Fred R. Garrido, Pilar Khan, Hina Baggi, Alice Mascaux, Celine Halmos, Balazs Ceresoli, Giovanni L. Fidler, Mary J. Scotti, Vieri Métivier, Anne-Cécile Falchero, Lionel Felip, Enriqueta Genova, Carlo Mazieres, Julien Tapan, Umit Brahmer, Julie Bria, Emilio Puri, Sonam Popat, Sanjay Reckamp, Karen L. Morgillo, Floriana Nadal, Ernest Mazzoni, Francesca Agustoni, Francesco Bar, Jair Grosso, Federica Avrillon, Virginie Patel, Jyoti D. Gomes, Fabio Ibrahim, Ehab Trama, Annalisa Bettini, Anna C. Barlesi, Fabrice Dingemans, Anne-Marie Wakelee, Heather Peters, Solange Horn, Leora Garassino, Marina Chiara Torri, Valter J Thorac Oncol Original Article INTRODUCTION: Patients with thoracic malignancies are at increased risk for mortality from coronavirus disease 2019 (COVID-19), and a large number of intertwined prognostic variables have been identified so far. METHODS: Capitalizing data from the Thoracic Cancers International COVID-19 Collaboration (TERAVOLT) registry, a global study created with the aim of describing the impact of COVID-19 in patients with thoracic malignancies, we used a clustering approach, a fast-backward step-down selection procedure, and a tree-based model to screen and optimize a broad panel of demographics and clinical COVID-19 and cancer characteristics. RESULTS: As of April 15, 2021, a total of 1491 consecutive eligible patients from 18 countries were included in the analysis. With a mean observation period of 42 days, 361 events were reported with an all-cause case fatality rate of 24.2%. The clustering procedure screened 73 covariates in 13 clusters. A further multivariable logistic regression for the association between clusters and death was performed, resulting in five clusters significantly associated with the outcome. The fast-backward step-down selection procedure then identified the following seven major determinants of death: Eastern Cooperative Oncology Group—performance status (ECOG-PS) (OR = 2.47, 1.87–3.26), neutrophil count (OR = 2.46, 1.76–3.44), serum procalcitonin (OR = 2.37, 1.64–3.43), development of pneumonia (OR = 1.95, 1.48–2.58), C-reactive protein (OR = 1.90, 1.43–2.51), tumor stage at COVID-19 diagnosis (OR = 1.97, 1.46–2.66), and age (OR = 1.71, 1.29–2.26). The receiver operating characteristic analysis for death of the selected model confirmed its diagnostic ability (area under the receiver operating curve = 0.78, 95% confidence interval: 0.75–0.81). The nomogram was able to classify the COVID-19 mortality in an interval ranging from 8% to 90%, and the tree-based model recognized ECOG-PS, neutrophil count, and c-reactive protein as the major determinants of prognosis. CONCLUSIONS: From 73 variables analyzed, seven major determinants of death have been identified. Poor ECOG-PS was found to have the strongest association with poor outcome from COVID-19. With our analysis, we provide clinicians with a definitive prognostication system to help determine the risk of mortality for patients with thoracic malignancies and COVID-19. International Association for the Study of Lung Cancer. Published by Elsevier Inc. 2022-05 2022-02-01 /pmc/articles/PMC8804493/ /pubmed/35121086 http://dx.doi.org/10.1016/j.jtho.2021.12.015 Text en © 2022 International Association for the Study of Lung Cancer. Published by Elsevier Inc. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Original Article Whisenant, Jennifer G. Baena, Javier Cortellini, Alessio Huang, Li-Ching Lo Russo, Giuseppe Porcu, Luca Wong, Selina K. Bestvina, Christine M. Hellmann, Matthew D. Roca, Elisa Rizvi, Hira Monnet, Isabelle Boudjemaa, Amel Rogado, Jacobo Pasello, Giulia Leighl, Natasha B. Arrieta, Oscar Aujayeb, Avinash Batra, Ullas Azzam, Ahmed Y. Unk, Mojca Azab, Mohammed A. Zhumagaliyeva, Ardak N. Gomez-Martin, Carlos Blaquier, Juan B. Geraedts, Erica Mountzios, Giannis Serrano-Montero, Gloria Reinmuth, Niels Coate, Linda Marmarelis, Melina Presley, Carolyn J. Hirsch, Fred R. Garrido, Pilar Khan, Hina Baggi, Alice Mascaux, Celine Halmos, Balazs Ceresoli, Giovanni L. Fidler, Mary J. Scotti, Vieri Métivier, Anne-Cécile Falchero, Lionel Felip, Enriqueta Genova, Carlo Mazieres, Julien Tapan, Umit Brahmer, Julie Bria, Emilio Puri, Sonam Popat, Sanjay Reckamp, Karen L. Morgillo, Floriana Nadal, Ernest Mazzoni, Francesca Agustoni, Francesco Bar, Jair Grosso, Federica Avrillon, Virginie Patel, Jyoti D. Gomes, Fabio Ibrahim, Ehab Trama, Annalisa Bettini, Anna C. Barlesi, Fabrice Dingemans, Anne-Marie Wakelee, Heather Peters, Solange Horn, Leora Garassino, Marina Chiara Torri, Valter A Definitive Prognostication System for Patients With Thoracic Malignancies Diagnosed With Coronavirus Disease 2019: An Update From the TERAVOLT Registry |
title | A Definitive Prognostication System for Patients With Thoracic Malignancies Diagnosed With Coronavirus Disease 2019: An Update From the TERAVOLT Registry |
title_full | A Definitive Prognostication System for Patients With Thoracic Malignancies Diagnosed With Coronavirus Disease 2019: An Update From the TERAVOLT Registry |
title_fullStr | A Definitive Prognostication System for Patients With Thoracic Malignancies Diagnosed With Coronavirus Disease 2019: An Update From the TERAVOLT Registry |
title_full_unstemmed | A Definitive Prognostication System for Patients With Thoracic Malignancies Diagnosed With Coronavirus Disease 2019: An Update From the TERAVOLT Registry |
title_short | A Definitive Prognostication System for Patients With Thoracic Malignancies Diagnosed With Coronavirus Disease 2019: An Update From the TERAVOLT Registry |
title_sort | definitive prognostication system for patients with thoracic malignancies diagnosed with coronavirus disease 2019: an update from the teravolt registry |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8804493/ https://www.ncbi.nlm.nih.gov/pubmed/35121086 http://dx.doi.org/10.1016/j.jtho.2021.12.015 |
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