1. A Definitive Prognostication System for Patients With Thoracic Malignancies Diagnosed With Coronavirus Disease 2019: An Update From the TERAVOLT Registry.
- Author
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Whisenant JG, Baena J, Cortellini A, Huang LC, Lo Russo G, Porcu L, Wong SK, Bestvina CM, Hellmann MD, Roca E, Rizvi H, Monnet I, Boudjemaa A, Rogado J, Pasello G, Leighl NB, Arrieta O, Aujayeb A, Batra U, Azzam AY, Unk M, Azab MA, Zhumagaliyeva AN, Gomez-Martin C, Blaquier JB, Geraedts E, Mountzios G, Serrano-Montero G, Reinmuth N, Coate L, Marmarelis M, Presley CJ, Hirsch FR, Garrido P, Khan H, Baggi A, Mascaux C, Halmos B, Ceresoli GL, Fidler MJ, Scotti V, Métivier AC, Falchero L, Felip E, Genova C, Mazieres J, Tapan U, Brahmer J, Bria E, Puri S, Popat S, Reckamp KL, Morgillo F, Nadal E, Mazzoni F, Agustoni F, Bar J, Grosso F, Avrillon V, Patel JD, Gomes F, Ibrahim E, Trama A, Bettini AC, Barlesi F, Dingemans AM, Wakelee H, Peters S, Horn L, Garassino MC, and Torri V
- Subjects
- C-Reactive Protein, COVID-19 Testing, Humans, Prognosis, Registries, Retrospective Studies, SARS-CoV-2, COVID-19, Lung Neoplasms diagnosis, Thoracic Neoplasms diagnosis
- 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 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., (Copyright © 2022 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.)
- Published
- 2022
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