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
Authors
Lee, Rebecca JWysocki, Oskar
Zhou, Cong
Shotton, Rohan
Tivey, Annngelos|Lever, L.
Woodcock, J.
Albiges, L.
Angelakas, A.
Arnold, D.
Aung, T.
Banfill, Kathryn
Baxter, M.
Barlesi, F.
Bayle, A.
Besse, B.
Bhogal, T.
Boyce, H.
Britton, Fiona
Calles, A.
Castelo-Branco, L.
Copson, E.
Croitoru, A. E.
Dani, S. S.
Dickens, E.
Eastlake, L.
Fitzpatrick, P.
Foulon, S.
Frederiksen, H.
Frost, Hannah
Ganatra, S.
Gennatas, S.
Glenthøj, A.
Gomes, Fabio
Graham, Donna
Hague, Christina|Harrington, K.
Harrison, M.
Horsley, Laura
Hoskins, R.
Huddar, P.
Hudson, Z.
Jakobsen, L. H.
Joharatnam-Hogan, N.
Khan, S.
Khan, U. T.
Khan, K.
Massard, C.
Maynard, A.
McKenzie, H.
Michielin, O.
Mosenthal, A. C.
Obispo, B.
Patel, R.
Pentheroudakis, G.
Peters, S.
Rieger-Christ, K.
Robinson, T.
Rogado, J.
Romano, E.
Rowe, M.
Sekacheva, M.
Sheehan, R.
Stevenson, Julie
Stockdale, A.
Thomas, A.
Turtle, L.
Viñal, D.
Weaver, Jamie M
Williams, S.
Wilson, C.
Palmieri, C.
Landers, Donal
Cooksley, Timothy J
Dive, Caroline
Freitas, Andre
Armstrong, Anne C
Affiliation
The Christie NHS Foundation Trust, ManchesterIssue Date
2022
Metadata
Show full item recordAbstract
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.Citation
Lee RJ, Wysocki O, Zhou C, Shotton R, Tivey A, Lever L, et al. 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. American Society of Clinical Oncology (ASCO); 2022.Journal
JCO Clinical Cancer InformaticsDOI
10.1200/cci.21.00177PubMed ID
35609228Additional Links
https://dx.doi.org/10.1200/cci.21.00177Type
ArticleLanguage
enae974a485f413a2113503eed53cd6c53
10.1200/cci.21.00177