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
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Abstract
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.
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SciScore for 10.1101/2020.11.30.20239095: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources In this study all statistical tests and modelling were carried out using R (ver 3.6.2) and Python (ver 3.7) (16,17) Pythonsuggested: (IPython, RRID:SCR_001658)Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Although NEWS2 has its own limitations and has been criticised especially in applicability to primary care (21), our validation of it as an important feature of severity in patients with cancer and COVID-19 suggests that it is helpful in the assessment of patients …
SciScore for 10.1101/2020.11.30.20239095: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources In this study all statistical tests and modelling were carried out using R (ver 3.6.2) and Python (ver 3.7) (16,17) Pythonsuggested: (IPython, RRID:SCR_001658)Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Although NEWS2 has its own limitations and has been criticised especially in applicability to primary care (21), our validation of it as an important feature of severity in patients with cancer and COVID-19 suggests that it is helpful in the assessment of patients at least in the hospital setting. We compared our model to the ISARIC 4C mortality risk score, created based on data from over 57,000 patients (13). Although a small cohort, it is important to note that our analysis of cancer patients using 4C showed that they were at higher risk of mortality with a lower 4C-score compared to the whole ISARIC population, which was mainly composed of patients without cancer. Thus, lower thresholds should be considered when using the 4C score in assessing patients with cancer. The 4C score had a better AUC for mortality compared to CORONET, which was likely due to our model training being focussed on admission of patients who were not only likely to die but also to require oxygen. In support of this, our model performed better in admission of patients requiring oxygen as a measure of COVID-19 severity. In addition, all those patients predicted by 4C to be at risk of mortality were admitted using our CORONET model, which is an important validation of its safety. Further comparisons in larger cohorts of patients are needed to better understand the benefits/limitations of each model in cancer patients. There were several limitations in our model development. Firstly, the cohort is relati...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
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