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

Abstract

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.

FIG 1.

CORONET modeling diagram. AUROC, area under the receiver operating characteristic curve; CORONET, COVID-19 Risk in Oncology Evaluation Tool; EPV, event per variable; max, maximum; min, minimum; MSE, mean squared error; RF, Random Forest; RFE, Recursive Feature Elimination; SHAP, Shapley Additive Explanation.

FIG 2.

Final CORONET model characteristics: (A) predicted CORONET score for all 920 patients in the data set using leave-one-out cross-validation and Random Forest model; (B) receiver operating characteristic curves and AUC metrics for CORONET score used as admission, requirement for O₂, and death determinants; (C) summary plot of feature contribution to CORONET prediction on the basis of SHAP explanation; and (D) metrics for requirement for O₂ and death depending on the CORONET score. The dotted line indicates admission threshold and severe disease threshold set at the maximum of the cost functions. AUC, area under the curve; CORONET, COVID-19 Risk in Oncology Evaluation Tool; CRP, C-reactive protein; ECOG PS, Eastern Cooperative Oncology Group performance status; NEWS2, National Early Warning Score-2; NPV, negative predictive value; PPV, positive predictive value; SHAP, Shapley Additive Explanations.

FIG 3.

Validation of the CORONET model on the external cohort of 282 patients: (A) predicted scores stratified by outcome and (B) receiver operating characteristic curves and AUC metrics for CORONET score used as admission, requirement for O₂, and death determinants. AUC, area under the curve; CORONET, COVID-19 Risk in Oncology Evaluation Tool.