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 (Lead / Corresponding author), 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 BrittonAntonio 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, Caroline Wilson

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)
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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 (O 2) 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 languageEnglish
Article numbere2100177
Number of pages18
JournalJCO Clinical Cancer Informatics
Publication statusPublished - 24 May 2022


  • 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

ASJC Scopus subject areas

  • Health Informatics
  • Oncology
  • Cancer Research


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