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.

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.

An International Comparison of Presentation, Outcomes and CORONET Predictive Score Performance in Patients with Cancer Presenting with COVID-19 across Different Pandemic Waves.

Patients with cancer have been shown to have increased risk of COVID-19 severity. We previously built and validated the COVID-19 Risk in Oncology Evaluation Tool (CORONET) to predict the likely severity of COVID-19 in patients with active cancer who present to hospital. We assessed the differences in presentation and outcomes of patients with cancer and COVID-19, depending on the wave of the pandemic. We examined differences in features at presentation and outcomes in patients worldwide, depending on the waves of the pandemic: wave 1 D614G (n = 1430), wave 2 Alpha (n = 475), and wave 4 Omicron variant (n = 63, UK and Spain only). The performance of CORONET was evaluated on 258, 48, and 54 patients for each wave, respectively. We found that mortality rates were reduced in subsequent waves. The majority of patients were vaccinated in wave 4, and 94% were treated with steroids if they required oxygen. The stages of cancer and the median ages of patients significantly differed, but features associated with worse COVID-19 outcomes remained predictive and did not differ between waves. The CORONET tool performed well in all waves, with scores in an area under the curve (AUC) of >0.72. We concluded that patients with cancer who present to hospital with COVID-19 have similar features of severity, which remain discriminatory despite differences in variants and vaccination status. Survival improved following the first wave of the pandemic, which may be associated with vaccination and the increased steroid use in those patients requiring oxygen. The CORONET model demonstrated good performance, independent of the SARS-CoV-2 variants.

COVID-19 and Its Impact on Upper Gastrointestinal (GI) Cancer Management.

Coronavirus disease 2019 (COVID-19), caused by the novel, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, has left dramatic footprints on human health and economy. Cancer, whilst not an infective disease, is prevalent in epidemic proportions and cannot be pretermitted due to the impact of COVID-19. As we emanate from the second national lockdown in the UK with mixed feelings of hope and despair-due to vaccination and new COVID-19 variant, respectively-we reflect on the impact of the first wave on the provision on diagnosis and management of with upper gastrointestinal (UGI) cancers. This review provides a critical analysis of available literature on COVID-19 and its impact on cancer management in general and that of UGI cancers in particular.

COVID-19 in cancer patients on systemic anti-cancer therapies: outcomes from the CAPITOL (COVID-19 Cancer PatIenT Outcomes in North London) cohort study.

BACKGROUND: Patients with cancer are hypothesised to be at increased risk of contracting COVID-19, leading to changes in treatment pathways in those treated with systemic anti-cancer treatments (SACT). This study investigated the outcomes of patients receiving SACT to assess whether they were at greater risk of contracting COVID-19 or having more severe outcomes. METHODS: Data was collected from all patients receiving SACT in two cancer centres as part of CAPITOL (COVID-19 Cancer PatIenT Outcomes in North London). The primary outcome was the effect of clinical characteristics on the incidence and severity of COVID-19 infection in patients on SACT. We used univariable and multivariable models to analyse outcomes, adjusting for age, gender and comorbidities. RESULTS: A total of 2871 patients receiving SACT from 2 March to 31 May 2020 were analysed; 68 (2.4%) were diagnosed with COVID-19. Cancer patients receiving SACT were more likely to die if they contracted COVID-19 than those who did not [adjusted (adj.) odds ratio (OR) 9.84; 95% confidence interval (CI) 5.73-16.9]. Receiving chemotherapy increased the risk of developing COVID-19 (adj. OR 2.99; 95% CI = 1.72-5.21), with high dose chemotherapy significantly increasing risk (adj. OR 2.36, 95% CI 1.35-6.48), as did the presence of comorbidities (adj. OR 2.29; 95% CI 1.19-4.38), and having a respiratory or intrathoracic neoplasm (adj. OR 2.12; 95% CI 1.04-4.36). Receiving targeted treatment had a protective effect (adj. OR 0.53; 95% CI 0.30-0.95). Treatment intent (curative versus palliative), hormonal- or immunotherapy and solid versus haematological cancers had no significant effect on risk. CONCLUSION: Patients on SACT are more likely to die if they contract COVID-19. Those on chemotherapy, particularly high dose chemotherapy, are more likely to contract COVID-19, while targeted treatment appears to be protective.

Outcomes of the 2019 novel coronavirus in patients with or without a history of cancer: a multi-centre North London experience.

BACKGROUND: This study aims to compare the outcomes of COVID-19-positive disease in patients with a history of cancer to those without. METHODS: We retrospectively collected clinical data and outcomes of COVID-19 positive cancer patients treated consecutively in five North London hospitals (cohort A). Outcomes recorded included time interval between most recent anti-cancer treatment and admission, severe outcome [a composite endpoint of intensive care unit (ITU) admission, ventilation and/or death] and mortality. Outcomes were compared with consecutively admitted COVID-19 positive patients, without a history of cancer (cohort B), treated at the primary centre during the same time period (1 March-30 April 2020). Patients were matched for age, gender and comorbidity. RESULTS: The median age in both cohorts was 74 years, with 67% male, and comprised of 30 patients with cancer, and 90 without (1:3 ratio). For cohort B, 579 patients without a history of cancer and consecutively admitted were screened from the primary London hospital, 105 were COVID-19 positive and 90 were matched and included. Excluding cancer, both cohorts had a median of two comorbidities. The odds ratio (OR) for mortality, comparing patients with cancer to those without, was 1.05 [95% confidence interval (CI) 0.4-2.5], and severe outcome (OR 0.89, 95% CI 0.4-2.0) suggesting no increased risk of death or a severe outcome in patients with cancer. Cancer patients who received systemic treatment within 28 days had an OR for mortality of 4.05 (95% CI 0.68-23.95), p = 0.12. On presentation anaemia, hypokalaemia, hypoalbuminaemia and hypoproteinaemia were identified predominantly in cohort A. Median duration of admission was 8 days for cancer patients and 7 days for non-cancer. CONCLUSION: A diagnosis of cancer does not appear to increase the risk of death or a severe outcome in COVID-19 patients with cancer compared with those without cancer. If a second spike of virus strikes, rational decision making is required to ensure optimal cancer care.