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, Pasello, Giulia, 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, Erica, Geraedts, Giannis, Mountzios, 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, Ehab, Ibrahim, Annalisa, Trama, Anna, C Bettini, Fabrice, Barlesi, Anne-Marie, Dingemans, Heather, Wakelee, Solange, Peters, Leora, Horn, Marina Chiara Garassino, Valter, Torri, TERAVOLT study group, RS: GROW - R3 - Innovative Cancer Diagnostics & Therapy, Pulmonologie, MUMC+: MA Med Staf Spec Longziekten (9), and Pulmonary Medicine
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.