A Definitive Prognostication System for Patients With Thoracic Malignancies Diagnosed With Coronavirus Disease 2019: an update from the TERAVOLT registry

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; 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; Anne-Marie Dingemans; TERAVOLT study group

doi:10.1016/j.jtho.2021.12.015

A Definitive Prognostication System for Patients With Thoracic Malignancies Diagnosed With Coronavirus Disease 2019: an update from the TERAVOLT registry

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 AzzamMojca 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, Anne-Marie Dingemans, TERAVOLT study group

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

Abstract

BACKGROUND: Patients with thoracic malignancies are at increased risk for mortality from Coronavirus disease 2019 (COVID-19) and large number of intertwined prognostic variables have been identified so far.

METHODS: Capitalizing data from the 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, clinical COVID-19 and cancer characteristics.

RESULTS: As of April 15, 2021, 1491 consecutive evaluable 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 approximately 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 then identified seven major determinants of death 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 (CRP) (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 ROC analysis for death of the selected model confirmed its diagnostic ability (AUC 0.78; 95%CI: 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 CRP as the major determinants of prognosis.

CONCLUSION: From 73 variables analyzed, seven major determinants of death have been identified. Poor ECOG-PS demonstrated 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.

Original language	English
Pages (from-to)	661-674
Number of pages	14
Journal	Journal of Thoracic Oncology
Volume	17
Issue number	5
Early online date	24 Jan 2022
DOIs	https://doi.org/10.1016/j.jtho.2021.12.015
Publication status	Published - May 2022

Keywords

CLINICAL CHARACTERISTICS
COVID-19
Cancer
LUNG-CANCER
MORTALITY
MULTICENTER
NSCLC
PROCALCITONIN
RISK
Registry
SEVERITY
TERAVOLT
Thoracic
WUHAN

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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., ... TERAVOLT study group (2022). A Definitive Prognostication System for Patients With Thoracic Malignancies Diagnosed With Coronavirus Disease 2019: an update from the TERAVOLT registry. Journal of Thoracic Oncology, 17(5), 661-674. https://doi.org/10.1016/j.jtho.2021.12.015

@article{764ac41ba9e54f0684299ed5e7bde7a4,

title = "A Definitive Prognostication System for Patients With Thoracic Malignancies Diagnosed With Coronavirus Disease 2019: an update from the TERAVOLT registry",

abstract = "BACKGROUND: Patients with thoracic malignancies are at increased risk for mortality from Coronavirus disease 2019 (COVID-19) and large number of intertwined prognostic variables have been identified so far.METHODS: Capitalizing data from the 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, clinical COVID-19 and cancer characteristics.RESULTS: As of April 15, 2021, 1491 consecutive evaluable 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 approximately 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 then identified seven major determinants of death 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 (CRP) (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 ROC analysis for death of the selected model confirmed its diagnostic ability (AUC 0.78; 95%CI: 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 CRP as the major determinants of prognosis.CONCLUSION: From 73 variables analyzed, seven major determinants of death have been identified. Poor ECOG-PS demonstrated 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.",

keywords = "CLINICAL CHARACTERISTICS, COVID-19, Cancer, LUNG-CANCER, MORTALITY, MULTICENTER, NSCLC, PROCALCITONIN, RISK, Registry, SEVERITY, TERAVOLT, Thoracic, WUHAN",

author = "Whisenant, {Jennifer G} and Javier Baena and Alessio Cortellini and Li-Ching Huang and {Lo Russo}, Giuseppe and Luca Porcu and Wong, {Selina K} and Bestvina, {Christine M} and Hellmann, {Matthew D} and Elisa Roca and Hira Rizvi and Isabelle Monnet and Amel Boudjemaa and Jacobo Rogado and Giulia Pasello and Leighl, {Natasha B} and Oscar Arrieta and Avinash Aujayeb and Ullas Batra and Azzam, {Ahmed Y} and Mojca Unk and Azab, {Mohammed A} and Zhumagaliyeva, {Ardak N} and Carlos Gomez-Martin and Blaquier, {Juan B} and Erica Geraedts and Giannis Mountzios and Gloria Serrano-Montero and Niels Reinmuth and Linda Coate and Melina Marmarelis and Presley, {Carolyn J} and Hirsch, {Fred R} and Pilar Garrido and Hina Khan and Alice Baggi and Celine Mascaux and Balazs Halmos and Ceresoli, {Giovanni L} and Fidler, {Mary J} and Vieri Scotti and Anne-C{\'e}cile M{\'e}tivier and Lionel Falchero and Enriqueta Felip and Carlo Genova and Julien Mazieres and Umit Tapan and Julie Brahmer and Emilio Bria and Anne-Marie Dingemans and {TERAVOLT study group}",

year = "2022",

month = may,

doi = "10.1016/j.jtho.2021.12.015",

language = "English",

volume = "17",

pages = "661--674",

journal = "Journal of Thoracic Oncology",

issn = "1556-0864",

publisher = "Elsevier Science",

number = "5",

}

Whisenant, JG, Baena, J, Cortellini, A, Huang, L-C, 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, A-C, Falchero, L, Felip, E, Genova, C, Mazieres, J, Tapan, U, Brahmer, J, Bria, E, Dingemans, A-M & TERAVOLT study group 2022, 'A Definitive Prognostication System for Patients With Thoracic Malignancies Diagnosed With Coronavirus Disease 2019: an update from the TERAVOLT registry', Journal of Thoracic Oncology, vol. 17, no. 5, pp. 661-674. https://doi.org/10.1016/j.jtho.2021.12.015

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 et al.
In: Journal of Thoracic Oncology, Vol. 17, No. 5, 05.2022, p. 661-674.

Research output: Contribution to journal › Article › Academic › peer-review

TY - JOUR

T1 - A Definitive Prognostication System for Patients With Thoracic Malignancies Diagnosed With Coronavirus Disease 2019

T2 - an update from the TERAVOLT registry

AU - Whisenant, Jennifer G

AU - Baena, Javier

AU - Cortellini, Alessio

AU - Huang, Li-Ching

AU - Lo Russo, Giuseppe

AU - Porcu, Luca

AU - Wong, Selina K

AU - Bestvina, Christine M

AU - Hellmann, Matthew D

AU - Roca, Elisa

AU - Rizvi, Hira

AU - Monnet, Isabelle

AU - Boudjemaa, Amel

AU - Rogado, Jacobo

AU - Pasello, Giulia

AU - Leighl, Natasha B

AU - Arrieta, Oscar

AU - Aujayeb, Avinash

AU - Batra, Ullas

AU - Azzam, Ahmed Y

AU - Unk, Mojca

AU - Azab, Mohammed A

AU - Zhumagaliyeva, Ardak N

AU - Gomez-Martin, Carlos

AU - Blaquier, Juan B

AU - Geraedts, Erica

AU - Mountzios, Giannis

AU - Serrano-Montero, Gloria

AU - Reinmuth, Niels

AU - Coate, Linda

AU - Marmarelis, Melina

AU - Presley, Carolyn J

AU - Hirsch, Fred R

AU - Garrido, Pilar

AU - Khan, Hina

AU - Baggi, Alice

AU - Mascaux, Celine

AU - Halmos, Balazs

AU - Ceresoli, Giovanni L

AU - Fidler, Mary J

AU - Scotti, Vieri

AU - Métivier, Anne-Cécile

AU - Falchero, Lionel

AU - Felip, Enriqueta

AU - Genova, Carlo

AU - Mazieres, Julien

AU - Tapan, Umit

AU - Brahmer, Julie

AU - Bria, Emilio

AU - Dingemans, Anne-Marie

AU - TERAVOLT study group

PY - 2022/5

Y1 - 2022/5

N2 - BACKGROUND: Patients with thoracic malignancies are at increased risk for mortality from Coronavirus disease 2019 (COVID-19) and large number of intertwined prognostic variables have been identified so far.METHODS: Capitalizing data from the 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, clinical COVID-19 and cancer characteristics.RESULTS: As of April 15, 2021, 1491 consecutive evaluable 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 approximately 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 then identified seven major determinants of death 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 (CRP) (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 ROC analysis for death of the selected model confirmed its diagnostic ability (AUC 0.78; 95%CI: 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 CRP as the major determinants of prognosis.CONCLUSION: From 73 variables analyzed, seven major determinants of death have been identified. Poor ECOG-PS demonstrated 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.

AB - BACKGROUND: Patients with thoracic malignancies are at increased risk for mortality from Coronavirus disease 2019 (COVID-19) and large number of intertwined prognostic variables have been identified so far.METHODS: Capitalizing data from the 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, clinical COVID-19 and cancer characteristics.RESULTS: As of April 15, 2021, 1491 consecutive evaluable 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 approximately 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 then identified seven major determinants of death 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 (CRP) (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 ROC analysis for death of the selected model confirmed its diagnostic ability (AUC 0.78; 95%CI: 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 CRP as the major determinants of prognosis.CONCLUSION: From 73 variables analyzed, seven major determinants of death have been identified. Poor ECOG-PS demonstrated 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.

KW - CLINICAL CHARACTERISTICS

KW - COVID-19

KW - Cancer

KW - LUNG-CANCER

KW - MORTALITY

KW - MULTICENTER

KW - NSCLC

KW - PROCALCITONIN

KW - RISK

KW - Registry

KW - SEVERITY

KW - TERAVOLT

KW - Thoracic

KW - WUHAN

U2 - 10.1016/j.jtho.2021.12.015

DO - 10.1016/j.jtho.2021.12.015

M3 - Article

C2 - 35121086

SN - 1556-0864

VL - 17

SP - 661

EP - 674

JO - Journal of Thoracic Oncology

JF - Journal of Thoracic Oncology

IS - 5

ER -