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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.

Pembrolizumab is an anti-programmed cell death protein 1 immune checkpoint inhibitor with a dosing schedule of 200 mg 3 weekly (q3w). Dose of 400 mg 6 weekly (q6w) was approved based on simulation of dose/exposure relationships and predicted no difference in toxicity. We present real-world comparative toxicity data. Patients receiving pembrolizumab for any indication between March and December 2019 were included across 3 regional centers. Toxicity data were collected retrospectively using Common Terminology Criteria for Adverse Events, v5.0. Clinically significant immune-related adverse events (CSirAE) were defined as immune-related events and grade ≥ 3 rash. Data were analyzed using incidence (Poisson distribution) and incidence ratio. Overall, 63 patients started on q6w and 110 patients received q3w. There were 3 (q6w) and 8 (q3w) grade 3-5 CSirAE and 13 (q6w) and 31 (q3w) grade 1-2 CSirAE. The incidence of grade 3-5 CSirAE was 0.77 (95% confidence interval: 0.16-2.24) per 100 patientmonths in q6w and 0.68 (95% confidence interval: 0.29-1.34) per 100 patient-months in q3w (incidence ratio of 1.13; 95% confidence interval: 0.19-4.70). Low-grade toxicity was common (fatigue, pruritus, rash; q6w 46%, q3w 42%). Incidence of CSirAEs was low but lowgrade toxicity was common. Despite a limited number of events, there is the suggestion that the q6w schedule has a similar toxicity profile to q3w and therefore consideration should be given to the reduced burden to patients and health services when deciding treatment.