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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 (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.

IntroductionManchester Cancer Research Centre (MCRC) has established an MTB to facilitate precision medicine decision-making within the TARGET trial (Tumour chAracterisation to Guide Experimental Targeted Therapy). The MTB meets monthly to review clinical data and next generation sequencing (NGS) results from tumour tissue and circulating DNA (ctDNA) for patients being considered for early phase clinical trials. Initially the MTB relied on multiple paper reports. Here we present eTARGET, a digital solution developed by the digital Experimental Cancer Medicine Team (digitalECMT), which integrates clinical and genomic NGS data to facilitate decision-making for matching patients with clinical trials.Material and methodsThe digitalECMT explored data sources and existing reports to define end-user and data requirements. Following a successful prototype, a beta version was developed. Created in Microsoft Azure, a secure cloud computing platform, components included a storage account for data upload from three different sources, and a database for storing and integrating the data. The solution enabled automated extraction of individual pseudonymised clinical and genomic data. In addition, a web application to view the data was developed with clinical input.Results and discussionsThe beta version of eTARGET went online in October 2017 and has been utilised at 5 MTB meetings for 55 patient cases. This portal interface presents patient characteristics, treatment history and genomic data. The portal can be viewed remotely, across multiple locations, where all attendees see the same view. eTARGET has enabled the MTB to review individual patient data in a single portal, capture meeting outcomes in real-time and upload to the electronic patient record. Decisions regarding significant variants, trial matching or requirements for further analytical or translational analyses are captured.Conclusion eTARGET has shown that a digital solution can be implemented to overcome the challenge of integrating data from disparate sources in different organisations to create a single view of patient clinical and genomic data. We have shown the utility of eTARGET in a hospital setting to support decision-making for an MTB. The eTARGET project opens the possibility of wider MTB participation including cross centre collaboration. Next steps are to enhance the software to visualise the global molecular dataset and serial changes in NGS profiles on treatment.

BackgroundCancer 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.ObjectiveTo 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)MethodData 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.ResultsTraining 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.Conclusions and RelevanceCORONET, 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.