Journal of Thoracic Oncology 2022 May [Link]
Jennifer G Whisenant, Javier Baena, Alessio Cortellini, Li-Ching Huang, Giuseppe Lo Russo, Luca Porcu, Selina K Wong, Christine M Bestvina, Matthew D Hellmann, Elisa Roca, Hira Rizvi, Isabelle Monnet, Amel Boudjemaa, Jacobo Rogado, Giulia Pasello, Natasha B Leighl, Oscar Arrieta, Avinash Aujayeb, Ullas Batra, Ahmed Y Azzam, Mojca Unk, Mohammed A Azab, Ardak N Zhumagaliyeva, Carlos Gomez-Martin, Juan B Blaquier 20, Erica Geraedts 21, Giannis Mountzios 22, Gloria Serrano-Montero, Niels Reinmuth, Linda Coate, Melina Marmarelis, Carolyn J Presley, Fred R Hirsch, Pilar Garrido, Hina Khan, Alice Baggi, Celine Mascaux, Balazs Halmos, Giovanni L Ceresoli, Mary J Fidler, Vieri Scotti, Anne-Cécile Métivier, Lionel Falchero, Enriqueta Felip, Carlo Genova, Julien Mazieres, Umit Tapan, Julie Brahmer, Emilio Bria, Sonam Puri, Sanjay Popat, Karen L Reckamp, Floriana Morgillo, Ernest Nadal, Francesca Mazzoni, Francesco Agustoni, Jair Bar, Federica Grosso, Virginie Avrillon, Jyoti D Patel, Fabio Gomes 55, Ehab Ibrahim, Annalisa Trama, Anna C Bettini, Fabrice Barlesi, Anne-Marie Dingemans, Heather Wakelee, Solange Peters, Leora Horn, Marina Chiara Garassino, Valter Torri
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.