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

Longitudinal characterisation of haematological and biochemical parameters in cancer patients prior to and during COVID-19 reveals features associated with outcome

Background Cancer patients are at increased risk of death from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Cancer and its treatment affect many haematological and biochemical parameters, therefore we analysed these prior to and during coronavirus disease 2019 (COVID-19) and correlated them with outcome. Patients and methods Consecutive patients with cancer testing positive for SARS-CoV-2 in centres throughout the United Kingdom were identified and entered into a database following local governance approval. Clinical and longitudinal laboratory data were extracted from patient records. Data were analysed using Mann–Whitney U test, Fisher's exact test, Wilcoxon signed rank test, logistic regression, or linear regression for outcomes. Hierarchical clustering of heatmaps was performed using Ward's method. Results In total, 302 patients were included in three cohorts: Manchester (n = 67), Liverpool (n = 62), and UK (n = 173). In the entire cohort (N = 302), median age was 69 (range 19-93 years), including 163 males and 139 females; of these, 216 were diagnosed with a solid tumour and 86 with a haematological cancer. Preinfection lymphopaenia, neutropaenia and lactate dehydrogenase (LDH) were not associated with oxygen requirement (O2) or death. Lymphocyte count (P < 0.001), platelet count (P = 0.03), LDH (P < 0.0001) and albumin (P < 0.0001) significantly changed from preinfection to during infection. High rather than low neutrophils at day 0 (P = 0.007), higher maximal neutrophils during COVID-19 (P = 0.026) and higher neutrophil-to-lymphocyte ratio (NLR; P = 0.01) were associated with death. In multivariable analysis, age (P = 0.002), haematological cancer (P = 0.034), C-reactive protein (P = 0.004), NLR (P = 0.036) and albumin (P = 0.02) at day 0 were significant predictors of death. In the Manchester/Liverpool cohort 30 patients have restarted therapy following COVID-19, with no additional complications requiring readmission. Conclusion Preinfection biochemical/haematological parameters were not associated with worse outcome in cancer patients. Restarting treatment following COVID-19 was not associated with additional complications. Neutropaenia due to cancer/treatment is not associated with COVID-19 mortality. Cancer therapy, particularly in patients with solid tumours, need not be delayed or omitted due to concerns that treatment itself increases COVID-19 severity

Mortality Among Adults With Cancer Undergoing Chemotherapy or Immunotherapy and Infected With COVID-19

Importance: Large cohorts of patients with active cancers and COVID-19 infection are needed to provide evidence of the association of recent cancer treatment and cancer type with COVID-19 mortality. // Objective: To evaluate whether systemic anticancer treatments (SACTs), tumor subtypes, patient demographic characteristics (age and sex), and comorbidities are associated with COVID-19 mortality. // Design, Setting, and Participants: The UK Coronavirus Cancer Monitoring Project (UKCCMP) is a prospective cohort study conducted at 69 UK cancer hospitals among adult patients (≥18 years) with an active cancer and a clinical diagnosis of COVID-19. Patients registered from March 18 to August 1, 2020, were included in this analysis. // Exposures: SACT, tumor subtype, patient demographic characteristics (eg, age, sex, body mass index, race and ethnicity, smoking history), and comorbidities were investigated. // Main Outcomes and Measures: The primary end point was all-cause mortality within the primary hospitalization. // Results: Overall, 2515 of 2786 patients registered during the study period were included; 1464 (58%) were men; and the median (IQR) age was 72 (62-80) years. The mortality rate was 38% (966 patients). The data suggest an association between higher mortality in patients with hematological malignant neoplasms irrespective of recent SACT, particularly in those with acute leukemias or myelodysplastic syndrome (OR, 2.16; 95% CI, 1.30-3.60) and myeloma or plasmacytoma (OR, 1.53; 95% CI, 1.04-2.26). Lung cancer was also significantly associated with higher COVID-19–related mortality (OR, 1.58; 95% CI, 1.11-2.25). No association between higher mortality and receiving chemotherapy in the 4 weeks before COVID-19 diagnosis was observed after correcting for the crucial confounders of age, sex, and comorbidities. An association between lower mortality and receiving immunotherapy in the 4 weeks before COVID-19 diagnosis was observed (immunotherapy vs no cancer therapy: OR, 0.52; 95% CI, 0.31-0.86). // Conclusions and Relevance: The findings of this study of patients with active cancer suggest that recent SACT is not associated with inferior outcomes from COVID-19 infection. This has relevance for the care of patients with cancer requiring treatment, particularly in countries experiencing an increase in COVID-19 case numbers. Important differences in outcomes among patients with hematological and lung cancers were observed

Establishment of CORONET; COVID-19 Risk in Oncology Evaluation Tool to identify cancer patients at low versus high risk of severe complications of COVID-19 infection upon presentation to hospital

AbstractBackgroundCancer patients are at increased risk of severe COVID-19. As COVID-19 presentation and outcomes are heterogeneous in cancer patients, decision-making tools for hospital admission, severity prediction and increased monitoring for early intervention are critical.ObjectiveTo identify features of COVID-19 in cancer patients predicting severe disease and build a decision-support online tool; COVID-19 Risk in Oncology Evaluation Tool (CORONET)MethodData was obtained for consecutive patients with active cancer with laboratory confirmed COVID-19 presenting in 12 hospitals throughout the United Kingdom (UK). Univariable logistic regression was performed on pre-specified features to assess their association with admission (≥24 hours inpatient), oxygen requirement and death. Multivariable logistic regression and random forest models (RFM) were compared with patients randomly split into training and validation sets. Cost function determined cut-offs were defined for admission/death using RFM. Performance was assessed by sensitivity, specificity and Brier scores (BS). The CORONET model was then assessed in the entire cohort to build the online CORONET tool.ResultsTraining and validation sets comprised 234 and 66 patients respectively with median age 69 (range 19-93), 54% males, 46% females, 71% vs 29% had solid and haematological cancers. The RFM, selected for further development, demonstrated superior performance over logistic regression with AUROC predicting admission (0.85 vs. 0.78) and death (0.76 vs. 0.72). C-reactive protein was the most important feature predicting COVID-19 severity. CORONET cut-offs for admission and mortality of 1.05 and 1.8 were established. In the training set, admission prediction sensitivity and specificity were 94.5% and 44.3% with BS 0.118; mortality sensitivity and specificity were 78.5% and 57.2% with BS 0.364. In the validation set, admission sensitivity and specificity were 90.7% and 42.9% with BS 0.148; mortality sensitivity and specificity were 92.3% and 45.8% with BS 0.442. In the entire cohort, the CORONET decision support tool recommended admission of 99% of patients requiring oxygen and of 99% of patients who died.Conclusions and RelevanceCORONET, a decision support tool validated in hospitals throughout the UK showed promise in aiding decisions regarding admission and predicting COVID-19 severity in patients with cancer presenting to hospital. Future work will validate and refine the tool in further datasets.</jats:sec

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

Establishment of CORONET: COVID-19 Risk in Oncology Evaluation Tool to Identify Cancer Patients at Low Versus High Risk of Severe Complications of COVID-19 Infection Upon Presentation to Hospital

Background: Patients with cancer are at increased risk of severe COVID-19, 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 in cancer patients 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 presenting to hospitals worldwide were included. Discharge (within 24hrs), admission (≥24hrs inpatient), oxygen requirement (O2) and death were combined in a 0-3 point severity scale. Association of features with outcome were investigated using Lasso regression and Random Forest (RF) combined with SHapley Additive exPlanations (SHAP). RF was further validated in 4 cohorts, split by geography. 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.Findings: The dataset comprised 920 patients; median age 70 (range 5-99), 56% males, 44% females, 81% solid vs. 19% haematological cancers. In derivation, RF demonstrated superior performance over Lasso with lower mean squared error (0.801 vs. 0.807) and was selected for development. During validation, RF achieved mean AUROC 0.77, 0.80 and 0.75 for prediction of admission, O 2 and death, respectively. Using the entire cohort, CORONET cut-offs 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. SHAP explanations revealed 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.Interpretation: CORONET, a decision-support tool validated in healthcare systems worldwide can aid admission decisions and predict COVID-19 severity in patients with cancer.Funding Information: R. Lee and T. Robinson and J. Weaver are supported by the National Institute for Health Research as Clinical Lecturers. T. Bhogal is supported by the National Institute for Health Research as an academic clinical fellow. U. Khan is an MRC Clinical Training Fellow based at the University of Liverpool supported by the North West England Medical Research Council Fellowship Scheme in Clinical Pharmacology and Therapeutics, which is funded by the Medical Research Council (Award Ref. MR/N025989/1). The Liverpool Experimental Cancer Medicine Centre for providing infrastructure support (Grant Reference: C18616/A25153) and The Clatterbridge Cancer charity (North West Cancer Research). C. Dive is funded by CRUK Core funding to Manchester Institute (C5757/A27412) and is supported by the CRUK Manchester Centre Award (C5759/A25254), and by the NIHR Manchester Biomedical Research Centre. C. Zhou is funded by the CRUK Manchester Centre Award (C5759/A25254), J. Stevenson and P. Fitzpatrick are funded by the CRUK Accelerator Award (29374). This research was funded in part, by the Wellcome Trust [205228/Z/16/Z]. LT is also supported by the National Institute for Health Research Health Protection Research Unit (HPRU) in Emerging and Zoonotic Infections (NIHR200907) at University of Liverpool in partnership with Public Health England (PHE), in collaboration with Liverpool School of Tropical Medicine and the University of Oxford. LT is based at University of Liverpool. MS is supported by a grant from the Ministry of Science and Higher Education of the Russian Federation for the state support for the creation and development of World Class Research Centers "Digital biodesign and personalized healthcare” N.075-15-2020-926.Declaration of Interests: R Lee research funding (institution) BMS and speaker fees Astrazeneca. A. Croitoru Consulting or Advisory Role: Lilly, Merck, Roche, Bayer, Novartis, Ipsen, Research Funding me and my hospital: Gilead Sciences, Pfizer, Canfite, NanoCarrier, Bristol-Myers Squibb, Merck, Amgen, Servier, Five Prime Therapeutics, Travel Accommodations: Pfizer, Genekor, and oz, Merck, Pfizer, Servier, Roche. O. Michielin reports personal fees from Bristol-Myers Squibb, personal fees from MSD, personal fees from Novartis, personal fees from Roche, personal fees from Amgen, personal fees from NeraCare GmbH, outside the submitted work. E. Romano institutional research grants from Amgen, Astra Zeneca, Bristol-Myers Squibb. G. Pentheroudakis advisory board for Amgen, Astra Zeneca, Bristol-Myers Squibb, Lilly, Merck, MSD, Roche, Abbvie, institutional research grants from Amgen, Astra Zeneca, Boehringer Ingelheim, Bristol Myers Squibb, Debbio, Enorasis, Genekor, Ipsen, Janssen, Lilly, Merck, MSD, Pfizer, Roche, Sanofi, Servier. Solange Peters reports consultation/advisory role: AbbVie, Amgen, AstraZeneca, Bayer, Beigene, Biocartis, Bio Invent, Blueprint Medicines, Boehringer Ingelheim, Bristol-Myers Squibb, Clovis, Daiichi Sankyo, Debiopharm, Eli Lilly, Elsevier, F. Hoffmann-La Roche/Genentech, Foundation Medicine, Illumina, Incyte, IQVIA, Janssen, Medscape, Merck Sharp and Dohme, Merck Serono, Merrimack, Mirati, Novartis, Pharma Mar, Phosplatin Therapeutics, Pfizer, Regeneron, Sanofi, Seattle Genetics, Takeda, Vaccibody, talk in a company’s organized public event: AstraZeneca, Boehringer Ingelheim, Bristol-Myers Squibb, e-cancer, Eli Lilly, F. Hoffmann-La Roche/Genentech, Illumina, Medscape, Merck Sharp and Dohme, Novartis, PER, Pfizer, Prime, RTP, Sanofi, Takeda, receipt of grants/research supports: (Sub)investigator in trials (institutional financial support for clinical trials) sponsored by Amgen, AstraZeneca, Biodesix, Boehringer Ingelheim, Bristol-Myers Squibb, Clovis, F. Hoffmann-La Roche/Genentech, GSK, Illumina, Lilly, Merck Sharp and Dohme, Merck Serono, Mirati, Novartis, and Pfizer, Phosplatin Therapeutics. M Rowe honoraria from Astellas Pharma, speaker fees MSD and Servier. C. Wilson consultancy and speaker fees Pfizer, Amgen, Novartis, A Armstrong conference fee Merck, spouse shares in Astrazeneca. T Robinson financial support to attend educational workshops from Amgen and Daiichi-Sankyo. C Dive, outside of this scope of work, has received research funding from AstraZeneca, Astex Pharmaceuticals, Bioven, Amgen, Carrick Therapeutics, Merck AG, Taiho Oncology, Clearbridge Biomedics, Angle PLC, Menarini Diagnostics, GSK, Bayer, Boehringer Ingelheim, Roche, BMS, Novartis, Celgene, Thermofisher. C Dive is on advisory boards for, and has received consultancy fees/honoraria from, AstraZeneca, Biocartis and Merck KGaA.No other authors have nothing to declare. Ethics Approval Statement: Approval (reference 20/WA/0269) was granted from the UK Research Ethics Committee for the study. Information regarding governance/regulatory approvals for each international cohort are available in the Supp. Methods

COVID-19 prevalence and mortality in patients with cancer and the effect of primary tumour subtype and patient demographics: a prospective cohort study

Background Patients with cancer are purported to have poor COVID-19 outcomes. However, cancer is a heterogeneous group of diseases, encompassing a spectrum of tumour subtypes. The aim of this study was to investigate COVID-19 risk according to tumour subtype and patient demographics in patients with cancer in the UK. Methods We compared adult patients with cancer enrolled in the UK Coronavirus Cancer Monitoring Project (UKCCMP) cohort between March 18 and May 8, 2020, with a parallel non-COVID-19 UK cancer control population from the UK Office for National Statistics (2017 data). The primary outcome of the study was the effect of primary tumour subtype, age, and sex and on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) prevalence and the case–fatality rate during hospital admission. We analysed the effect of tumour subtype and patient demographics (age and sex) on prevalence and mortality from COVID-19 using univariable and multivariable models. Findings 319 (30·6%) of 1044 patients in the UKCCMP cohort died, 295 (92·5%) of whom had a cause of death recorded as due to COVID-19. The all-cause case–fatality rate in patients with cancer after SARS-CoV-2 infection was significantly associated with increasing age, rising from 0·10 in patients aged 40–49 years to 0·48 in those aged 80 years and older. Patients with haematological malignancies (leukaemia, lymphoma, and myeloma) had a more severe COVID-19 trajectory compared with patients with solid organ tumours (odds ratio [OR] 1·57, 95% CI 1·15–2·15; p<0·0043). Compared with the rest of the UKCCMP cohort, patients with leukaemia showed a significantly increased case–fatality rate (2·25, 1·13–4·57; p=0·023). After correction for age and sex, patients with haematological malignancies who had recent chemotherapy had an increased risk of death during COVID-19-associated hospital admission (OR 2·09, 95% CI 1·09–4·08; p=0·028). Interpretation Patients with cancer with different tumour types have differing susceptibility to SARS-CoV-2 infection and COVID-19 phenotypes. We generated individualised risk tables for patients with cancer, considering age, sex, and tumour subtype. Our results could be useful to assist physicians in informed risk–benefit discussions to explain COVID-19 risk and enable an evidenced-based approach to national social isolation policies