COVID-19 Risk in ONcology Evaluation Tool

CORONET is an online tool to support decisions regarding hospital admissions or discharge in cancer patients presenting with symptoms of COVID-19 and the likely severity of illness. It is based on real world patient data. It should not be used for patient management currently (pending peer review).

Key Features


CORONET asks for some details about the patient, their cancer and blood test results on presentation to hospital with symptoms of COVID-19. It then uses data about the admission, requirement for oxygen and survival of similar patients in the past to show likely outcome of the patient.


Data was obtained for consecutive patients with active cancer with laboratory confirmed COVID-19 presenting in 12 hospitals throughout the UK.


Univariable logistic regression was performed on pre-specified features to assess their association with admission (≥24 hours inpatient), oxygen requirement and death. Multivariable logistic regression and random forest models (RFM) were compared with patients randomly split into training and validation sets. Cost function determined cut-offs were defined for admission/death using RFM. Performance was assessed by sensitivity, specificity and Brier scores (BS). The CORONET model was then assessed in the entire cohort to build the online CORONET tool.

Link to Preprint