Establishment of CORONET, COVID-19 Risk in Oncology Evaluation Tool, to Identify Patients With Cancer at Low Versus High Risk of Severe Complications of COVID-19 Disease On Presentation to Hospital

Rebecca J Lee; Oskar Wysocki; Cong Zhou; Rohan Shotton; Ann Tivey; Louise Lever; Joshua Woodcock; Laurence Albiges; Angelos Angelakas; Dirk Arnold; Theingi Aung; Kathryn Banfill; Mark Baxter; Fabrice Barlesi; Arnaud Bayle; Benjamin Besse; Talvinder Bhogal; Hayley Boyce; Fiona Britton; Antonio Calles; Luis Castelo-Branco; Ellen Copson; Adina E Croitoru; Sourbha S Dani; Elena Dickens; Leonie Eastlake; Paul Fitzpatrick; Stephanie Foulon; Henrik Frederiksen; Hannah Frost; Sarju Ganatra; Spyridon Gennatas; Andreas Glenthøj; Fabio Gomes; Donna M Graham; Christina Hague; Kevin Harrington; Michelle Harrison; Laura Horsley; Richard Hoskins; Prerana Huddar; Zoe Hudson; Lasse H. Jakobsen; Nalinie Joharatnam-Hogan; Sam Khan; Umair T Khan; Khurum Khan; Christophe Massard; Alec Maynard; Hayley McKenzie; Olivier Michielin; Anne C Mosenthal; Berta Obispo; Rushin Patel; George Pentheroudakis; Solange Peters; Kimberly Rieger-Christ; Timothy Robinson; Jacobo Rogado; Emanuela Romano; Michael Rowe; Marina Sekacheva; Roseleen Sheehan; Julie Stevenson; Alexander Stockdale; Anne Thomas; Lance Turtle; David Viñal; Jamie Weaver; Sophie Williams; Caroline Wilson; Carlo Palmieri; Donal Landers; Timothy Cooksley; ESMO Co-Care; Caroline Dive; André Freitas; Anne C Armstrong

doi:10.1200/CCI.21.00177

Establishment of CORONET, COVID-19 Risk in Oncology Evaluation Tool, to Identify Patients With Cancer at Low Versus High Risk of Severe Complications of COVID-19 Disease On Presentation to Hospital

Rebecca J Lee^*, Oskar Wysocki, Cong Zhou, Rohan Shotton, Ann Tivey, Louise Lever, Joshua Woodcock, Laurence Albiges, Angelos Angelakas, Dirk Arnold, Theingi Aung, Kathryn Banfill, Mark Baxter, Fabrice Barlesi, Arnaud Bayle, Benjamin Besse, Talvinder Bhogal, Hayley Boyce, Fiona Britton, Antonio CallesLuis Castelo-Branco, Ellen Copson, Adina E Croitoru, Sourbha S Dani, Elena Dickens, Leonie Eastlake, Paul Fitzpatrick, Stephanie Foulon, Henrik Frederiksen, Hannah Frost, Sarju Ganatra, Spyridon Gennatas, Andreas Glenthøj, Fabio Gomes, Donna M Graham, Christina Hague, Kevin Harrington, Michelle Harrison, Laura Horsley, Richard Hoskins, Prerana Huddar, Zoe Hudson, Lasse H. Jakobsen, Nalinie Joharatnam-Hogan, Sam Khan, Umair T Khan, Khurum Khan, Christophe Massard, Alec Maynard, Hayley McKenzie, Olivier Michielin, Anne C Mosenthal, Berta Obispo, Rushin Patel, George Pentheroudakis, Solange Peters, Kimberly Rieger-Christ, Timothy Robinson, Jacobo Rogado, Emanuela Romano, Michael Rowe, Marina Sekacheva, Roseleen Sheehan, Julie Stevenson, Alexander Stockdale, Anne Thomas, Lance Turtle, David Viñal, Jamie Weaver, Sophie Williams, Caroline Wilson, Carlo Palmieri, Donal Landers, Timothy Cooksley, ESMO Co-Care, Caroline Dive, André Freitas, Anne C Armstrong

^*Corresponding author for this work

Research output: Contribution to journal › Journal article › Research › peer-review

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Abstract

PURPOSE: Patients with cancer are at increased risk of severe COVID-19 disease, but have heterogeneous presentations and outcomes. Decision-making tools for hospital admission, severity prediction, and increased monitoring for early intervention are critical. We sought to identify features of COVID-19 disease in patients with cancer predicting severe disease and build a decision support online tool, COVID-19 Risk in Oncology Evaluation Tool (CORONET).

METHODS: Patients with active cancer (stage I-IV) and laboratory-confirmed COVID-19 disease presenting to hospitals worldwide were included. Discharge (within 24 hours), admission (≥ 24 hours inpatient), oxygen (O2) requirement, and death were combined in a 0-3 point severity scale. Association of features with outcomes were investigated using Lasso regression and Random Forest combined with Shapley Additive Explanations. The CORONET model was then examined in the entire cohort to build an online CORONET decision support tool. Admission and severe disease thresholds were established through pragmatically defined cost functions. Finally, the CORONET model was validated on an external cohort.

RESULTS: The model development data set comprised 920 patients, with median age 70 (range 5-99) years, 56% males, 44% females, and 81% solid versus 19% hematologic cancers. In derivation, Random Forest demonstrated superior performance over Lasso with lower mean squared error (0.801 v 0.807) and was selected for development. During validation (n = 282 patients), the performance of CORONET varied depending on the country cohort. CORONET cutoffs for admission and mortality of 1.0 and 2.3 were established. The CORONET decision support tool recommended admission for 95% of patients eventually requiring oxygen and 97% of those who died (94% and 98% in validation, respectively). The specificity for mortality prediction was 92% and 83% in derivation and validation, respectively. Shapley Additive Explanations revealed that National Early Warning Score 2, C-reactive protein, and albumin were the most important features contributing to COVID-19 severity prediction in patients with cancer at time of hospital presentation.

CONCLUSION: CORONET, a decision support tool validated in health care systems worldwide, can aid admission decisions and predict COVID-19 severity in patients with cancer.

Original language	English
Article number	e2100177
Journal	JCO Clinical Cancer Informatics
Volume	6
ISSN	2473-4276
DOIs	https://doi.org/10.1200/CCI.21.00177
Publication status	Published - 24 May 2022

Bibliographical note

Part of Special Series: COVID-19 and Cancer Care

Keywords

Adolescent
Adult
Aged
Aged, 80 and over
COVID-19/complications
Child
Child, Preschool
Female
Hospitals
Humans
Male
Middle Aged
Neoplasms/complications
Oxygen
SARS-CoV-2
Young Adult

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10.1200/CCI.21.00177Licence: CC BY-NC-ND 4.0

Lee et al. (2022). Establishment of CORONET, COVID-19 Risk in Oncology Evaluation Tool, to Identify Patients With Cancer at Low Versus High Risk of Severe Complications of COVID-19 Disease On Presentation to HospitalFinal published version, 613 KBLicence: CC BY-NC-ND 4.0

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Cite this

Lee, R. J., Wysocki, O., Zhou, C., Shotton, R., Tivey, A., Lever, L., Woodcock, J., Albiges, L., Angelakas, A., Arnold, D., Aung, T., Banfill, K., Baxter, M., Barlesi, F., Bayle, A., Besse, B., Bhogal, T., Boyce, H., Britton, F., ... Armstrong, A. C. (2022). Establishment of CORONET, COVID-19 Risk in Oncology Evaluation Tool, to Identify Patients With Cancer at Low Versus High Risk of Severe Complications of COVID-19 Disease On Presentation to Hospital. JCO Clinical Cancer Informatics, 6, Article e2100177. https://doi.org/10.1200/CCI.21.00177

@article{cd5bd57ce72c462c81fa7938e458bac4,

title = "Establishment of CORONET, COVID-19 Risk in Oncology Evaluation Tool, to Identify Patients With Cancer at Low Versus High Risk of Severe Complications of COVID-19 Disease On Presentation to Hospital",

abstract = "PURPOSE: Patients with cancer are at increased risk of severe COVID-19 disease, but have heterogeneous presentations and outcomes. Decision-making tools for hospital admission, severity prediction, and increased monitoring for early intervention are critical. We sought to identify features of COVID-19 disease in patients with cancer predicting severe disease and build a decision support online tool, COVID-19 Risk in Oncology Evaluation Tool (CORONET).METHODS: Patients with active cancer (stage I-IV) and laboratory-confirmed COVID-19 disease presenting to hospitals worldwide were included. Discharge (within 24 hours), admission (≥ 24 hours inpatient), oxygen (O2) requirement, and death were combined in a 0-3 point severity scale. Association of features with outcomes were investigated using Lasso regression and Random Forest combined with Shapley Additive Explanations. The CORONET model was then examined in the entire cohort to build an online CORONET decision support tool. Admission and severe disease thresholds were established through pragmatically defined cost functions. Finally, the CORONET model was validated on an external cohort.RESULTS: The model development data set comprised 920 patients, with median age 70 (range 5-99) years, 56% males, 44% females, and 81% solid versus 19% hematologic cancers. In derivation, Random Forest demonstrated superior performance over Lasso with lower mean squared error (0.801 v 0.807) and was selected for development. During validation (n = 282 patients), the performance of CORONET varied depending on the country cohort. CORONET cutoffs for admission and mortality of 1.0 and 2.3 were established. The CORONET decision support tool recommended admission for 95% of patients eventually requiring oxygen and 97% of those who died (94% and 98% in validation, respectively). The specificity for mortality prediction was 92% and 83% in derivation and validation, respectively. Shapley Additive Explanations revealed that National Early Warning Score 2, C-reactive protein, and albumin were the most important features contributing to COVID-19 severity prediction in patients with cancer at time of hospital presentation.CONCLUSION: CORONET, a decision support tool validated in health care systems worldwide, can aid admission decisions and predict COVID-19 severity in patients with cancer.",

keywords = "Adolescent, Adult, Aged, Aged, 80 and over, COVID-19/complications, Child, Child, Preschool, Female, Hospitals, Humans, Male, Middle Aged, Neoplasms/complications, Oxygen, SARS-CoV-2, Young Adult",

author = "Lee, {Rebecca J} and Oskar Wysocki and Cong Zhou and Rohan Shotton and Ann Tivey and Louise Lever and Joshua Woodcock and Laurence Albiges and Angelos Angelakas and Dirk Arnold and Theingi Aung and Kathryn Banfill and Mark Baxter and Fabrice Barlesi and Arnaud Bayle and Benjamin Besse and Talvinder Bhogal and Hayley Boyce and Fiona Britton and Antonio Calles and Luis Castelo-Branco and Ellen Copson and Croitoru, {Adina E} and Dani, {Sourbha S} and Elena Dickens and Leonie Eastlake and Paul Fitzpatrick and Stephanie Foulon and Henrik Frederiksen and Hannah Frost and Sarju Ganatra and Spyridon Gennatas and Andreas Glenth{\o}j and Fabio Gomes and Graham, {Donna M} and Christina Hague and Kevin Harrington and Michelle Harrison and Laura Horsley and Richard Hoskins and Prerana Huddar and Zoe Hudson and Jakobsen, {Lasse H.} and Nalinie Joharatnam-Hogan and Sam Khan and Khan, {Umair T} and Khurum Khan and Christophe Massard and Alec Maynard and Hayley McKenzie and Olivier Michielin and Mosenthal, {Anne C} and Berta Obispo and Rushin Patel and George Pentheroudakis and Solange Peters and Kimberly Rieger-Christ and Timothy Robinson and Jacobo Rogado and Emanuela Romano and Michael Rowe and Marina Sekacheva and Roseleen Sheehan and Julie Stevenson and Alexander Stockdale and Anne Thomas and Lance Turtle and David Vi{\~n}al and Jamie Weaver and Sophie Williams and Caroline Wilson and Carlo Palmieri and Donal Landers and Timothy Cooksley and {ESMO Co-Care} and Caroline Dive and Andr{\'e} Freitas and Armstrong, {Anne C}",

note = "Part of Special Series: COVID-19 and Cancer Care",

year = "2022",

month = may,

day = "24",

doi = "10.1200/CCI.21.00177",

language = "English",

volume = "6",

journal = "JCO Clinical Cancer Informatics",

issn = "2473-4276",

publisher = "American Society of Clinical Oncology",

}

Lee, RJ, Wysocki, O, Zhou, C, Shotton, R, Tivey, A, Lever, L, Woodcock, J, Albiges, L, Angelakas, A, Arnold, D, Aung, T, Banfill, K, Baxter, M, Barlesi, F, Bayle, A, Besse, B, Bhogal, T, Boyce, H, Britton, F, Calles, A, Castelo-Branco, L, Copson, E, Croitoru, AE, Dani, SS, Dickens, E, Eastlake, L, Fitzpatrick, P, Foulon, S, Frederiksen, H, Frost, H, Ganatra, S, Gennatas, S, Glenthøj, A, Gomes, F, Graham, DM, Hague, C, Harrington, K, Harrison, M, Horsley, L, Hoskins, R, Huddar, P, Hudson, Z, Jakobsen, LH, Joharatnam-Hogan, N, Khan, S, Khan, UT, Khan, K, Massard, C, Maynard, A, McKenzie, H, Michielin, O, Mosenthal, AC, Obispo, B, Patel, R, Pentheroudakis, G, Peters, S, Rieger-Christ, K, Robinson, T, Rogado, J, Romano, E, Rowe, M, Sekacheva, M, Sheehan, R, Stevenson, J, Stockdale, A, Thomas, A, Turtle, L, Viñal, D, Weaver, J, Williams, S, Wilson, C, Palmieri, C, Landers, D, Cooksley, T, ESMO Co-Care, Dive, C, Freitas, A & Armstrong, AC 2022, 'Establishment of CORONET, COVID-19 Risk in Oncology Evaluation Tool, to Identify Patients With Cancer at Low Versus High Risk of Severe Complications of COVID-19 Disease On Presentation to Hospital', JCO Clinical Cancer Informatics, vol. 6, e2100177. https://doi.org/10.1200/CCI.21.00177

Establishment of CORONET, COVID-19 Risk in Oncology Evaluation Tool, to Identify Patients With Cancer at Low Versus High Risk of Severe Complications of COVID-19 Disease On Presentation to Hospital. / Lee, Rebecca J; Wysocki, Oskar; Zhou, Cong et al.
In: JCO Clinical Cancer Informatics, Vol. 6, e2100177, 24.05.2022.

Research output: Contribution to journal › Journal article › Research › peer-review

TY - JOUR

T1 - Establishment of CORONET, COVID-19 Risk in Oncology Evaluation Tool, to Identify Patients With Cancer at Low Versus High Risk of Severe Complications of COVID-19 Disease On Presentation to Hospital

AU - Lee, Rebecca J

AU - Wysocki, Oskar

AU - Zhou, Cong

AU - Shotton, Rohan

AU - Tivey, Ann

AU - Lever, Louise

AU - Woodcock, Joshua

AU - Albiges, Laurence

AU - Angelakas, Angelos

AU - Arnold, Dirk

AU - Aung, Theingi

AU - Banfill, Kathryn

AU - Baxter, Mark

AU - Barlesi, Fabrice

AU - Bayle, Arnaud

AU - Besse, Benjamin

AU - Bhogal, Talvinder

AU - Boyce, Hayley

AU - Britton, Fiona

AU - Calles, Antonio

AU - Castelo-Branco, Luis

AU - Copson, Ellen

AU - Croitoru, Adina E

AU - Dani, Sourbha S

AU - Dickens, Elena

AU - Eastlake, Leonie

AU - Fitzpatrick, Paul

AU - Foulon, Stephanie

AU - Frederiksen, Henrik

AU - Frost, Hannah

AU - Ganatra, Sarju

AU - Gennatas, Spyridon

AU - Glenthøj, Andreas

AU - Gomes, Fabio

AU - Graham, Donna M

AU - Hague, Christina

AU - Harrington, Kevin

AU - Harrison, Michelle

AU - Horsley, Laura

AU - Hoskins, Richard

AU - Huddar, Prerana

AU - Hudson, Zoe

AU - Jakobsen, Lasse H.

AU - Joharatnam-Hogan, Nalinie

AU - Khan, Sam

AU - Khan, Umair T

AU - Khan, Khurum

AU - Massard, Christophe

AU - Maynard, Alec

AU - McKenzie, Hayley

AU - Michielin, Olivier

AU - Mosenthal, Anne C

AU - Obispo, Berta

AU - Patel, Rushin

AU - Pentheroudakis, George

AU - Peters, Solange

AU - Rieger-Christ, Kimberly

AU - Robinson, Timothy

AU - Rogado, Jacobo

AU - Romano, Emanuela

AU - Rowe, Michael

AU - Sekacheva, Marina

AU - Sheehan, Roseleen

AU - Stevenson, Julie

AU - Stockdale, Alexander

AU - Thomas, Anne

AU - Turtle, Lance

AU - Viñal, David

AU - Weaver, Jamie

AU - Williams, Sophie

AU - Wilson, Caroline

AU - Palmieri, Carlo

AU - Landers, Donal

AU - Cooksley, Timothy

AU - ESMO Co-Care

AU - Dive, Caroline

AU - Freitas, André

AU - Armstrong, Anne C

N1 - Part of Special Series: COVID-19 and Cancer Care

PY - 2022/5/24

Y1 - 2022/5/24

N2 - PURPOSE: Patients with cancer are at increased risk of severe COVID-19 disease, but have heterogeneous presentations and outcomes. Decision-making tools for hospital admission, severity prediction, and increased monitoring for early intervention are critical. We sought to identify features of COVID-19 disease in patients with cancer predicting severe disease and build a decision support online tool, COVID-19 Risk in Oncology Evaluation Tool (CORONET).METHODS: Patients with active cancer (stage I-IV) and laboratory-confirmed COVID-19 disease presenting to hospitals worldwide were included. Discharge (within 24 hours), admission (≥ 24 hours inpatient), oxygen (O2) requirement, and death were combined in a 0-3 point severity scale. Association of features with outcomes were investigated using Lasso regression and Random Forest combined with Shapley Additive Explanations. The CORONET model was then examined in the entire cohort to build an online CORONET decision support tool. Admission and severe disease thresholds were established through pragmatically defined cost functions. Finally, the CORONET model was validated on an external cohort.RESULTS: The model development data set comprised 920 patients, with median age 70 (range 5-99) years, 56% males, 44% females, and 81% solid versus 19% hematologic cancers. In derivation, Random Forest demonstrated superior performance over Lasso with lower mean squared error (0.801 v 0.807) and was selected for development. During validation (n = 282 patients), the performance of CORONET varied depending on the country cohort. CORONET cutoffs for admission and mortality of 1.0 and 2.3 were established. The CORONET decision support tool recommended admission for 95% of patients eventually requiring oxygen and 97% of those who died (94% and 98% in validation, respectively). The specificity for mortality prediction was 92% and 83% in derivation and validation, respectively. Shapley Additive Explanations revealed that National Early Warning Score 2, C-reactive protein, and albumin were the most important features contributing to COVID-19 severity prediction in patients with cancer at time of hospital presentation.CONCLUSION: CORONET, a decision support tool validated in health care systems worldwide, can aid admission decisions and predict COVID-19 severity in patients with cancer.

AB - PURPOSE: Patients with cancer are at increased risk of severe COVID-19 disease, but have heterogeneous presentations and outcomes. Decision-making tools for hospital admission, severity prediction, and increased monitoring for early intervention are critical. We sought to identify features of COVID-19 disease in patients with cancer predicting severe disease and build a decision support online tool, COVID-19 Risk in Oncology Evaluation Tool (CORONET).METHODS: Patients with active cancer (stage I-IV) and laboratory-confirmed COVID-19 disease presenting to hospitals worldwide were included. Discharge (within 24 hours), admission (≥ 24 hours inpatient), oxygen (O2) requirement, and death were combined in a 0-3 point severity scale. Association of features with outcomes were investigated using Lasso regression and Random Forest combined with Shapley Additive Explanations. The CORONET model was then examined in the entire cohort to build an online CORONET decision support tool. Admission and severe disease thresholds were established through pragmatically defined cost functions. Finally, the CORONET model was validated on an external cohort.RESULTS: The model development data set comprised 920 patients, with median age 70 (range 5-99) years, 56% males, 44% females, and 81% solid versus 19% hematologic cancers. In derivation, Random Forest demonstrated superior performance over Lasso with lower mean squared error (0.801 v 0.807) and was selected for development. During validation (n = 282 patients), the performance of CORONET varied depending on the country cohort. CORONET cutoffs for admission and mortality of 1.0 and 2.3 were established. The CORONET decision support tool recommended admission for 95% of patients eventually requiring oxygen and 97% of those who died (94% and 98% in validation, respectively). The specificity for mortality prediction was 92% and 83% in derivation and validation, respectively. Shapley Additive Explanations revealed that National Early Warning Score 2, C-reactive protein, and albumin were the most important features contributing to COVID-19 severity prediction in patients with cancer at time of hospital presentation.CONCLUSION: CORONET, a decision support tool validated in health care systems worldwide, can aid admission decisions and predict COVID-19 severity in patients with cancer.

KW - Adolescent

KW - Adult

KW - Aged

KW - Aged, 80 and over

KW - COVID-19/complications

KW - Child

KW - Child, Preschool

KW - Female

KW - Hospitals

KW - Humans

KW - Male

KW - Middle Aged

KW - Neoplasms/complications

KW - Oxygen

KW - SARS-CoV-2

KW - Young Adult

U2 - 10.1200/CCI.21.00177

DO - 10.1200/CCI.21.00177

M3 - Journal article

C2 - 35609228

SN - 2473-4276

VL - 6

JO - JCO Clinical Cancer Informatics

JF - JCO Clinical Cancer Informatics

M1 - e2100177

ER -

Establishment of CORONET, COVID-19 Risk in Oncology Evaluation Tool, to Identify Patients With Cancer at Low Versus High Risk of Severe Complications of COVID-19 Disease On Presentation to Hospital

Abstract

Bibliographical note

Keywords

Access to Document

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Cite this