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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.

The Winter Society for Acute Medicine Benchmarking Audit (SAMBA) provides the first comparison of performance within acute medicine against clinical quality indicators during winter, a time of increased pressure and demand on acute services. 105 hospitals participated in Winter SAMBA, collecting data over 24-hours on 30th January 2020. 5626 patients were included. Participating units saw a median of 48 patients (range 13-131). Comparison between Winter SAMBA and SAMBA19 found less patients had an early warning score within 30 minutes during winter (74.3% vs 78.9%) and less were seen by a clinical decision maker within four hours (84.9% vs 87.9%). Unplanned admissions represented a higher proportion of workload (92.5% vs 90.1%). Patients were more likely to have a NEWS2 score of 3 or higher (30.1% vs 25.7%). Performance is poorer in winter, and patients are more unwell, needing prompt treatment. Services should ensure high quality care can be maintained through times of increased pressure, including winter.

Introduction: The eighth Society for Acute Medicine Benchmarking Audit (SAMBA19) took place on Thursday 27th June 2019. SAMBA gives a broad picture of acute medical care in the UK and allows individual units to compare their performance against their peers. Method: All UK hospitals were invited to participate. Unit and patient level were collected. Data were analysed against published Clinical Quality indicators (CQI) and standards. This was the biggest SAMBA to date, with data from 7170 patients across 142 units in 140 hospitals. Results: 84.5% of patients had an Early Warning Score measured within 30 minutes of arrival in hospital (SAMBA18 84.1%), 90.4% of patients were seen by a competent clinical decision maker within four hours of arrival in hospital (SAMBA18 91.4 %) and 68.6% of patients were seen by a consultant within the timeframe standard (SAMBA18 62.7%). Ambulatory Emergency Care is provided in 99.3% of hospitals. 61.8% of patients are initially seen in the Emergency Department (ED). Since SAMBA18 death rates and planned discharge rates, while the use of NEWS2 increased from 2.5% to 59.2% of hospitals. Conclusion: SAMBA19 highlighted the evolving complexity of acute medical pathways for patients. The challenge now is to increase sample frequency, assess the impact of SAMBA open a broader debate to define optimal CQIs.

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