A definitive prognostication system for patients with thoracic malignancies diagnosed with Coronavirus disease 2019: An update from the TERAVOLT registry Journal Article
| Authors: | Whisenant, J. G.; Baena, J.; Cortellini, A.; Huang, L. C.; Lo Russo, G.; Porcu, L.; Wong, S. K.; Bestvina, C. M.; Hellmann, M. D.; Roca, E.; Rizvi, H.; Monnet, I.; Boudjemaa, A.; Rogado, J.; Pasello, G.; Leighl, N. B.; Arrieta, O.; Aujayeb, A.; Batra, U.; Azzam, A. Y.; Unk, M.; Azab, M. A.; Zhumagaliyeva, A. N.; Gomez-Martin, C.; Blaquier, J. B.; Geraedts, E.; Mountzios, G.; Serrano-Montero, G.; Reinmuth, N.; Coate, L.; Marmarelis, M.; Presley, C. J.; Hirsch, F. R.; Garrido, P.; Khan, H.; Baggi, A.; Mascaux, C.; Halmos, B.; Ceresoli, G. L.; Fidler, M. J.; Scotti, V.; Métivier, A. C.; Falchero, L.; Felip, E.; Genova, C.; Mazieres, J.; Tapan, U.; Brahmer, J.; Bria, E.; Puri, S.; Popat, S.; Reckamp, K. L.; Morgillo, F.; Nadal, E.; Mazzoni, F.; Agustoni, F.; Bar, J.; Grosso, F.; Avrillon, V.; Patel, J. D.; Gomes, F.; Ibrahim, E.; Trama, A.; Bettini, A. C.; Barlesi, F.; Dingemans, A. M.; Wakelee, H.; Peters, S.; Horn, L.; Garassino, M. C.; Torri, V.; On behalf of the TERAVOLT study group |
| Article Title: | A definitive prognostication system for patients with thoracic malignancies diagnosed with Coronavirus disease 2019: An update from the TERAVOLT registry |
| Abstract: | 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 In-ternational 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 char-acteristic 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 pro-tein 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.(c) 2022 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved. |
| Keywords: | mortality; risk; lung-cancer; nsclc; multicenter; severity; thoracic; clinical characteristics; registry; procalcitonin; cancer; covid-19; wuhan; teravolt |
| Journal Title: | Journal of Thoracic Oncology |
| Volume: | 17 |
| Issue: | 5 |
| ISSN: | 1556-0864 |
| Publisher: | Elsevier Inc. |
| Date Published: | 2022-05-01 |
| Start Page: | 661 |
| End Page: | 674 |
| Language: | English |
| ACCESSION: | WOS:000808120800012 |
| DOI: | 10.1016/j.jtho.2021.12.015 |
| PROVIDER: | wos |
| PMCID: | PMC8804493 |
| PUBMED: | 35121086 |
| Notes: | Article -- Source: Wos |
Altmetric
Citation Impact
Related MSK Work