Lee, RJ, 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)
(2020)
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.
MedRxiV.
2020.11.30.20239095-.
Abstract
<h4>Background</h4> 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. <h4>Objective</h4> 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) <h4>Method</h4> 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. <h4>Results</h4> 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. <h4>Conclusions and Relevance</h4> 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.
| Item Type: | Article |
|---|---|
| Additional Information: | Source info: TLDIGITALHEALTH-D-21-00503 |
| Uncontrolled Keywords: | COVID-19, cancer, SARS-CoV-2, decision support tool; CORONET |
| 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: | 14 Mar 2024 22:07 |
| DOI: | 10.1101/2020.11.30.20239095 |
| Open Access URL: | https://doi.org/10.1101/2020.11.30.20239095 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3120862 |
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