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 J. , Wysocki Oskar , Zhou Cong , Shotton Rohan , Tivey Ann , Lever Louise , Woodcock Joshua , Albiges Laurence , Angelakas Angelos , Arnold Dirk , Aung Theingi , Banfill Kathryn , Baxter Mark , Barlesi Fabrice , Bayle Arnaud , Besse Benjamin , Bhogal Talvinder , Boyce Hayley , Britton Fiona , Calles Antonio , Castelo-Branco Luis , Copson Ellen , Croitoru Adina E. , Dani Sourbha S. , Dickens Elena , Eastlake Leonie , Fitzpatrick Paul , Foulon Stephanie , Frederiksen Henrik , Frost Hannah , Ganatra Sarju , Gennatas Spyridon , Glenthøj Andreas , Gomes Fabio , Graham Donna M. , Hague Christina , Harrington Kevin , Harrison Michelle , Horsley Laura , Hoskins Richard , Huddar Prerana , Hudson Zoe , Jakobsen Lasse H. , Joharatnam-Hogan Nalinie , Khan Sam , Khan Umair T. , Khan Khurum , Massard Christophe , Maynard Alec , McKenzie Hayley , Michielin Olivier , Mosenthal Anne C. , Obispo Berta , Patel Rushin , Pentheroudakis George , Peters Solange , Rieger-Christ Kimberly , Robinson Timothy , Rogado Jacobo , Romano Emanuela , Rowe Michael , Sekacheva Marina , Sheehan Roseleen , Stevenson Julie , Stockdale Alexander , Thomas Anne , Turtle Lance , Viñal David , Weaver Jamie , Williams Sophie , Wilson Caroline , Palmieri Carlo , Landers Donal , Cooksley Timothy ,   ESMO Co-Care, Dive Caroline , Freitas André , Armstrong Anne C. 
Published: May 2022

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.