Establishment of CORONET; COVID-19 Risk in Oncology Evaluation Tool to identify cancer patients at low versus high risk of severe complications of COVID-19 infection upon presentation to hospital



Lee, RJ ORCID: 0000-0003-2540-2009, Zhou, C ORCID: 0000-0002-6938-4685, Wysocki, O ORCID: 0000-0002-7053-4919, Shotton, R ORCID: 0000-0003-4545-8296, Tivey, A ORCID: 0000-0002-5389-3741, Lever, L, Woodcock, J, Angelakas, A ORCID: 0000-0002-9769-9940, Aung, T, Banfill, K ORCID: 0000-0003-1832-548X
et al (show 36 more authors) Establishment of CORONET; COVID-19 Risk in Oncology Evaluation Tool to identify cancer patients at low versus high risk of severe complications of COVID-19 infection upon presentation to hospital.

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Abstract

<jats:title>Abstract</jats:title><jats:sec><jats:title>Background</jats:title><jats:p>Cancer patients are at increased risk of severe COVID-19. As COVID-19 presentation and outcomes are heterogeneous in cancer patients, decision-making tools for hospital admission, severity prediction and increased monitoring for early intervention are critical.</jats:p></jats:sec><jats:sec><jats:title>Objective</jats:title><jats:p>To identify features of COVID-19 in cancer patients predicting severe disease and build a decision-support online tool; COVID-19 Risk in Oncology Evaluation Tool (CORONET)</jats:p></jats:sec><jats:sec><jats:title>Method</jats:title><jats:p>Data was obtained for consecutive patients with active cancer with laboratory confirmed COVID-19 presenting in 12 hospitals throughout the United Kingdom (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.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>Training and validation sets comprised 234 and 66 patients respectively with median age 69 (range 19-93), 54% males, 46% females, 71% vs 29% had solid and haematological cancers. The RFM, selected for further development, demonstrated superior performance over logistic regression with AUROC predicting admission (0.85 vs. 0.78) and death (0.76 vs. 0.72). C-reactive protein was the most important feature predicting COVID-19 severity. CORONET cut-offs for admission and mortality of 1.05 and 1.8 were established. In the training set, admission prediction sensitivity and specificity were 94.5% and 44.3% with BS 0.118; mortality sensitivity and specificity were 78.5% and 57.2% with BS 0.364. In the validation set, admission sensitivity and specificity were 90.7% and 42.9% with BS 0.148; mortality sensitivity and specificity were 92.3% and 45.8% with BS 0.442. In the entire cohort, the CORONET decision support tool recommended admission of 99% of patients requiring oxygen and of 99% of patients who died.</jats:p></jats:sec><jats:sec><jats:title>Conclusions and Relevance</jats:title><jats:p>CORONET, a decision support tool validated in hospitals throughout the UK showed promise in aiding decisions regarding admission and predicting COVID-19 severity in patients with cancer presenting to hospital. Future work will validate and refine the tool in further datasets.</jats:p></jats:sec>

Item Type: Article
Divisions: Faculty of Health and Life Sciences
Faculty of Health and Life Sciences > Institute of Infection, Veterinary and Ecological Sciences
Depositing User: Symplectic Admin
Date Deposited: 28 Apr 2021 10:19
Last Modified: 18 Jun 2021 11:06
DOI: 10.1101/2020.11.30.20239095
Open Access URL: https://doi.org/10.1101/2020.11.30.20239095
URI: https://livrepository.liverpool.ac.uk/id/eprint/3120862