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9 results on '"McKenzie, Hayley"'

1. A population-scale temporal case-control evaluation of COVID-19 disease phenotype and related outcome rates in patients with cancer in England (UKCCP).

Author

Starkey T, Ionescu MC, Tilby M, Little M, Burke E, Fittall MW, Khan S, Liu JKH, Platt JR, Mew R, Tripathy AR, Watts I, Williams ST, Appanna N, Al-Hajji Y, Barnard M, Benny L, Burnett A, Bytyci J, Cattell EL, Cheng V, Clark JJ, Eastlake L, Gerrand K, Ghafoor Q, Grumett S, Harper-Wynne C, Kahn R, Lee AJX, Lomas O, Lydon A, Mckenzie H, Panneerselvam H, Pascoe JS, Patel G, Patel V, Potter VA, Randle A, Rigg AS, Robinson TM, Roylance R, Roques TW, Rozmanowski S, Roux RL, Shah K, Sheehan R, Sintler M, Swarup S, Taylor H, Tillett T, Tuthill M, Williams S, Ying Y, Beggs A, Iveson T, Lee SM, Middleton G, Middleton M, Protheroe A, Fowler T, Johnson P, and Lee LYW

Subjects
Humans, Male, Female, Case-Control Studies, Treatment Outcome, England epidemiology, Adolescent, Young Adult, Adult, Middle Aged, Aged, Aged, 80 and over, Neoplasms complications, Neoplasms epidemiology, COVID-19 complications, COVID-19 epidemiology
Abstract

Patients with cancer are at increased risk of hospitalisation and mortality following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. However, the SARS-CoV-2 phenotype evolution in patients with cancer since 2020 has not previously been described. We therefore evaluated SARS-CoV-2 on a UK populationscale from 01/11/2020-31/08/2022, assessing case-outcome rates of hospital assessment(s), intensive care admission and mortality. We observed that the SARS-CoV-2 disease phenotype has become less severe in patients with cancer and the non-cancer population. Case-hospitalisation rates for patients with cancer dropped from 30.58% in early 2021 to 7.45% in 2022 while case-mortality rates decreased from 20.53% to 3.25%. However, the risk of hospitalisation and mortality remains 2.10x and 2.54x higher in patients with cancer, respectively. Overall, the SARS-CoV-2 disease phenotype is less severe in 2022 compared to 2020 but patients with cancer remain at higher risk than the non-cancer population. Patients with cancer must therefore be empowered to live more normal lives, to see loved ones and families, while also being safeguarded with expanded measures to reduce the risk of transmission., (© 2023. The Author(s).)

Published
2023
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2. The National COVID Cancer Antibody Survey: a hyper-accelerated study proof of principle for cancer research.

Author

Fittall M, Liu J, Platt J, Ionescu M, Sheehan R, Johal S, Mew R, Clark J, Watts I, Tripathy A, Little M, Patel G, Panneerselvam H, Appanna N, Burke E, McKenzie H, Tilby M, Khan S, and Lee LYW

Subjects
Humans, Pandemics, Immunoglobulins, Delivery of Health Care, COVID-19, Neoplasms
Abstract

The COVID-19 pandemic has led to a range of novel and adaptive research designs. In this perspective, we use our experience coordinating the National COVID Cancer Antibody Survey to demonstrate how a balance between speed and integrity can be achieved within a hyper-accelerated study design. Using the COVID-19 pandemic as an example, we show this approach is necessary in the face of uncertain and evolving situations wherein reliable information is needed in a timely fashion to guide policy. We identify streamlined participant involvement, healthcare systems integration, data architecture and real-world real-time analytics as key areas that differentiate this design from traditional cancer trials, and enable rapid results. Caution needs to be taken to avoid the exclusion of patient subgroups without digital access or literacy. We summarise the merits and defining features of hyper-accelerated cancer studies., (© 2023. The Author(s).)

Published
2023
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3. Association of SARS-CoV-2 Spike Protein Antibody Vaccine Response With Infection Severity in Patients With Cancer: A National COVID Cancer Cross-sectional Evaluation.

Author

Lee LYW, Tilby M, Starkey T, Ionescu MC, Burnett A, Hattersley R, Khan S, Little M, Liu JKH, Platt JR, Tripathy A, Watts I, Williams ST, Appanna N, Al-Hajji Y, Barnard M, Benny L, Buckley A, Cattell E, Cheng V, Clark J, Eastlake L, Gerrand K, Ghafoor Q, Grumett S, Harper-Wynne C, Kahn R, Lee AJX, Lydon A, McKenzie H, Panneerselvam H, Pascoe J, Patel G, Patel V, Potter V, Randle A, Rigg AS, Robinson T, Roylance R, Roques T, Rozmanowski S, Roux RL, Shah K, Sintler M, Taylor H, Tillett T, Tuthill M, Williams S, Beggs A, Iveson T, Lee SM, Middleton G, Middleton M, Protheroe AS, Fittall MW, Fowler T, and Johnson P

Subjects
Female, Adult, Male, Humans, Middle Aged, COVID-19 Vaccines, Spike Glycoprotein, Coronavirus, Cross-Sectional Studies, Antibody Formation, Quality of Life, SARS-CoV-2, Antibodies, Viral, Delivery of Health Care, COVID-19 epidemiology, COVID-19 prevention & control, Vaccines, Neoplasms epidemiology
Abstract

Importance: Accurate identification of patient groups with the lowest level of protection following COVID-19 vaccination is important to better target resources and interventions for the most vulnerable populations. It is not known whether SARS-CoV-2 antibody testing has clinical utility for high-risk groups, such as people with cancer., Objective: To evaluate whether spike protein antibody vaccine response (COV-S) following COVID-19 vaccination is associated with the risk of SARS-CoV-2 breakthrough infection or hospitalization among patients with cancer., Design, Setting, and Participants: This was a population-based cross-sectional study of patients with cancer from the UK as part of the National COVID Cancer Antibody Survey. Adults with a known or reported cancer diagnosis who had completed their primary SARS-CoV-2 vaccination schedule were included. This analysis ran from September 1, 2021, to March 4, 2022, a period covering the expansion of the UK's third-dose vaccination booster program., Interventions: Anti-SARS-CoV-2 COV-S antibody test (Elecsys; Roche)., Main Outcomes and Measures: Odds of SARS-CoV-2 breakthrough infection and COVID-19 hospitalization., Results: The evaluation comprised 4249 antibody test results from 3555 patients with cancer and 294 230 test results from 225 272 individuals in the noncancer population. The overall cohort of 228 827 individuals (patients with cancer and the noncancer population) comprised 298 479 antibody tests. The median age of the cohort was in the age band of 40 and 49 years and included 182 741 test results (61.22%) from women and 115 737 (38.78%) from men. There were 279 721 tests (93.72%) taken by individuals identifying as White or White British. Patients with cancer were more likely to have undetectable anti-S antibody responses than the general population (199 of 4249 test results [4.68%] vs 376 of 294 230 [0.13%]; P < .001). Patients with leukemia or lymphoma had the lowest antibody titers. In the cancer cohort, following multivariable correction, patients who had an undetectable antibody response were at much greater risk for SARS-CoV-2 breakthrough infection (odds ratio [OR], 3.05; 95% CI, 1.96-4.72; P < .001) and SARS-CoV-2-related hospitalization (OR, 6.48; 95% CI, 3.31-12.67; P < .001) than individuals who had a positive antibody response., Conclusions and Relevance: The findings of this cross-sectional study suggest that COV-S antibody testing allows the identification of patients with cancer who have the lowest level of antibody-derived protection from COVID-19. This study supports larger evaluations of SARS-CoV-2 antibody testing. Prevention of SARS-CoV-2 transmission to patients with cancer should be prioritized to minimize impact on cancer treatments and maximize quality of life for individuals with cancer during the ongoing pandemic.

Published
2023
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4. COVID-19: Third dose booster vaccine effectiveness against breakthrough coronavirus infection, hospitalisations and death in patients with cancer: A population-based study.

Author

Lee LYW, Ionescu MC, Starkey T, Little M, Tilby M, Tripathy AR, Mckenzie HS, Al-Hajji Y, Appanna N, Barnard M, Benny L, Burnett A, Cattell EL, Clark JJ, Khan S, Ghafoor Q, Panneerselvam H, Illsley G, Harper-Wynne C, Hattersley RJ, Lee AJ, Lomas O, Liu JK, McCauley A, Pang M, Pascoe JS, Platt JR, Patel G, Patel V, Potter VA, Randle A, Rigg AS, Robinson TM, Roques TW, Roux RL, Rozmanowski S, Taylor H, Tuthill MH, Watts I, Williams S, Beggs A, Iveson T, Lee SM, Middleton G, Middleton M, Protheroe A, Fittall MW, Fowler T, and Johnson P

Subjects
Hospitalization, Humans, Pandemics, Vaccination, Vaccine Efficacy, COVID-19 epidemiology, COVID-19 prevention & control, Neoplasms
Abstract

Purpose: People living with cancer and haematological malignancies are at an increased risk of hospitalisation and death following infection with acute respiratory syndrome coronavirus 2. Coronavirus third dose vaccine boosters are proposed to boost waning immune responses in immunocompromised individuals and increase coronavirus protection; however, their effectiveness has not yet been systematically evaluated., Methods: This study is a population-scale real-world evaluation of the United Kingdom's third dose vaccine booster programme for cancer patients from 8th December 2020 to 7th December 2021. The cancer cohort comprises individuals from Public Health England's national cancer dataset, excluding individuals less than 18 years. A test-negative case-control design was used to assess the third dose booster vaccine effectiveness. Multivariable logistic regression models were fitted to compare risk in the cancer cohort relative to the general population., Results: The cancer cohort comprised of 2,258,553 tests from 361,098 individuals. Third dose boosters were evaluated by reference to 87,039,743 polymerase chain reaction coronavirus tests. Vaccine effectiveness against breakthrough infections, symptomatic infections, coronavirus hospitalisation and death in cancer patients were 59.1%, 62.8%, 80.5% and 94.5%, respectively. Lower vaccine effectiveness was associated with a cancer diagnosis within 12 months, lymphoma, recent systemic anti-cancer therapy (SACT) or radiotherapy. Patients with lymphoma had low levels of protection from symptomatic disease. In spite of third dose boosters, following multivariable adjustment, individuals with cancer remain at an increased risk of coronavirus hospitalisation and death compared to the population control (OR 3.38, 3.01, respectively. p &lt; 0.001 for both)., Conclusions: Third dose boosters are effective for most individuals with cancer, increasing protection from coronavirus. However, their effectiveness is heterogenous and lower than the general population. Many patients with cancer will remain at the increased risk of coronavirus infections even after 3 doses. In the case of patients with lymphoma, there is a particularly strong disparity of vaccine effectiveness against breakthrough infection and severe disease. Breakthrough infections will disrupt cancer care and treatment with potentially adverse consequences on survival outcomes. The data support the role of vaccine boosters in preventing severe disease, and further pharmacological intervention to prevent transmission and aid viral clearance to limit the disruption of cancer care as the delivery of care continues to evolve during the coronavirus pandemic., Competing Interests: Conflict of interest statement The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Published
2022
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5. Vaccine effectiveness against COVID-19 breakthrough infections in patients with cancer (UKCCEP): a population-based test-negative case-control study.

Author

Lee LYW, Starkey T, Ionescu MC, Little M, Tilby M, Tripathy AR, Mckenzie HS, Al-Hajji Y, Barnard M, Benny L, Burnett A, Cattell EL, Charman J, Clark JJ, Khan S, Ghafoor Q, Illsley G, Harper-Wynne C, Hattersley RJ, Lee AJX, Leonard PC, Liu JKH, Pang M, Pascoe JS, Platt JR, Potter VA, Randle A, Rigg AS, Robinson TM, Roques TW, Roux RL, Rozmanowski S, Tuthill MH, Watts I, Williams S, Iveson T, Lee SM, Middleton G, Middleton M, Protheroe A, Fittall MW, Fowler T, and Johnson P

Subjects
Adolescent, Adult, COVID-19 Vaccines, Case-Control Studies, Humans, SARS-CoV-2, Vaccine Efficacy, COVID-19 epidemiology, COVID-19 prevention & control, Neoplasms epidemiology, Viral Vaccines
Abstract

Background: People with cancer are at increased risk of hospitalisation and death following infection with SARS-CoV-2. Therefore, we aimed to conduct one of the first evaluations of vaccine effectiveness against breakthrough SARS-CoV-2 infections in patients with cancer at a population level., Methods: In this population-based test-negative case-control study of the UK Coronavirus Cancer Evaluation Project (UKCCEP), we extracted data from the UKCCEP registry on all SARS-CoV-2 PCR test results (from the Second Generation Surveillance System), vaccination records (from the National Immunisation Management Service), patient demographics, and cancer records from England, UK, from Dec 8, 2020, to Oct 15, 2021. Adults (aged ≥18 years) with cancer in the UKCCEP registry were identified via Public Health England's Rapid Cancer Registration Dataset between Jan 1, 2018, and April 30, 2021, and comprised the cancer cohort. We constructed a control population cohort from adults with PCR tests in the UKCCEP registry who were not contained within the Rapid Cancer Registration Dataset. The coprimary endpoints were overall vaccine effectiveness against breakthrough infections after the second dose (positive PCR COVID-19 test) and vaccine effectiveness against breakthrough infections at 3-6 months after the second dose in the cancer cohort and control population., Findings: The cancer cohort comprised 377 194 individuals, of whom 42 882 had breakthrough SARS-CoV-2 infections. The control population consisted of 28 010 955 individuals, of whom 5 748 708 had SARS-CoV-2 breakthrough infections. Overall vaccine effectiveness was 69·8% (95% CI 69·8-69·9) in the control population and 65·5% (65·1-65·9) in the cancer cohort. Vaccine effectiveness at 3-6 months was lower in the cancer cohort (47·0%, 46·3-47·6) than in the control population (61·4%, 61·4-61·5)., Interpretation: COVID-19 vaccination is effective for individuals with cancer, conferring varying levels of protection against breakthrough infections. However, vaccine effectiveness is lower in patients with cancer than in the general population. COVID-19 vaccination for patients with cancer should be used in conjunction with non-pharmacological strategies and community-based antiviral treatment programmes to reduce the risk that COVID-19 poses to patients with cancer., Funding: University of Oxford, University of Southampton, University of Birmingham, Department of Health and Social Care, and Blood Cancer UK., Competing Interests: Declaration of interests We declare no competing interests., (Copyright © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Published
2022
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6. 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.

Author

Lee RJ, Wysocki O, Zhou C, Shotton R, Tivey A, Lever L, Woodcock J, Albiges L, Angelakas A, Arnold D, Aung T, Banfill K, Baxter M, Barlesi F, Bayle A, Besse B, Bhogal T, Boyce H, Britton F, Calles A, Castelo-Branco L, Copson E, Croitoru AE, Dani SS, Dickens E, Eastlake L, Fitzpatrick P, Foulon S, Frederiksen H, Frost H, Ganatra S, Gennatas S, Glenthøj A, Gomes F, Graham DM, Hague C, Harrington K, Harrison M, Horsley L, Hoskins R, Huddar P, Hudson Z, Jakobsen LH, Joharatnam-Hogan N, Khan S, Khan UT, Khan K, Massard C, Maynard A, McKenzie H, Michielin O, Mosenthal AC, Obispo B, Patel R, Pentheroudakis G, Peters S, Rieger-Christ K, Robinson T, Rogado J, Romano E, Rowe M, Sekacheva M, Sheehan R, Stevenson J, Stockdale A, Thomas A, Turtle L, Viñal D, Weaver J, Williams S, Wilson C, Palmieri C, Landers D, Cooksley T, Dive C, Freitas A, and Armstrong AC

Subjects
Adolescent, Adult, Aged, Aged, 80 and over, Child, Child, Preschool, Female, Hospitals, Humans, Male, Middle Aged, Oxygen, SARS-CoV-2, Young Adult, COVID-19 complications, COVID-19 diagnosis, Neoplasms complications, Neoplasms diagnosis, Neoplasms therapy
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 (O 2 ) 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., Competing Interests: Rebecca J. LeeSpeakers' Bureau: AstraZenecaResearch Funding: Bristol Myers Squibb (Inst), AstraZeneca/MedImmune (Inst) Rohan ShottonHonoraria: ServierTravel, Accommodations, Expenses: Servier Laurence AlbigesConsulting or Advisory Role: Bristol Myers Squibb (Inst), Ipsen (Inst), Roche (Inst), Novartis (Inst), Pfizer (Inst), Astellas Pharma (Inst), Merck (Inst), MSD (Inst), AstraZeneca (Inst), Janssen (Inst), Eisai (Inst), Corvus Pharmaceuticals (Inst), Bellerophon Therapeutics (Inst)Research Funding: Bristol Myers Squibb (Inst)Travel, Accommodations, Expenses: BMS, MSD Dirk ArnoldEmployment: Asklepios KlinikenHonoraria: Bayer, Merck Serono, Roche/Genentech, Servier, Bristol Myers Squibb, Merck Sharp and Dome, AstraZeneca, Amgen, Boston Scientific, Pierre Fabre, IpsenConsulting or Advisory Role: Bayer, Merck Serono, Biocompatibles, Terumo, Bristol Myers Squibb, MSD Oncology, AstraZenecaResearch Funding: Roche/Genentech (Inst), Sanofi (Inst), Oncolytics (Inst)Travel, Accommodations, Expenses: Boston ScientificUncompensated Relationships: ESMO Council, ESMO Journals (Ann Oncol, ESMO Open), German Society for Hematology and Medical Oncology, German Cancer Society, European Organisation for Research and Treatment of Cancer (EORTC) Kathryn BanfillStock and Other Ownership Interests: Roche (I)Honoraria: AstraZeneca Mark BaxterHonoraria: IpsenTravel, Accommodations, Expenses: Ipsen Fabrice BarlesiHonoraria: Genentech/Roche, Pfizer, Pierre Fabre, AstraZeneca, Bristol Myers Squibb, Boehringer Ingelheim, Lilly, Novartis, Merck Serono, MSD Oncology, Takeda, Bayer, Seattle Genetics, Mirati TherapeuticsConsulting or Advisory Role: Roche/Genentech, Pfizer, Novartis, Pierre Fabre, Bristol Myers Squibb, AstraZeneca/MedImmune, Boehringer Ingelheim, Lilly, Merck Serono, MSD Oncology, Takeda, Bayer, Mirati TherapeuticsResearch Funding: Roche/Genentech (Inst), AstraZeneca/MedImmune (Inst), Bristol Myers Squibb (Inst), Pierre Fabre (Inst), AbbVie (Inst), Amgen (Inst), Bayer (Inst), Boehringer Ingelheim (Inst), Eisai (Inst), Lilly (Inst), Ipsen (Inst), Innate Pharma (Inst), Novartis (Inst), Merck Serono (Inst), MSD Oncology (Inst), Pfizer (Inst), Sanofi/Aventis (Inst), Takeda (Inst)Travel, Accommodations, Expenses: Roche/Genentech, Bristol Myers Squibb, AstraZeneca/MedImmune, MSD Oncology Benjamin BesseResearch Funding: AstraZeneca (Inst), Pfizer (Inst), Lilly (Inst), Onxeo (Inst), Inivata (Inst), AbbVie (Inst), Amgen (Inst), Blueprint Medicines (Inst), Celgene (Inst), GlaxoSmithKline (Inst), Sanofi (Inst), Takeda (Inst), Cristal Therapeutics (Inst), Daiichi Sankyo (Inst), Janssen Oncology (Inst), OSE Immunotherapeutics (Inst), BeiGene (Inst), Boehringer Ingelheim (Inst), Roche/Genentech (Inst), Tolero Pharmaceuticals (Inst), 4D Pharma (Inst), Aptitude Health (Inst), Cergentis (Inst), Chugai Pharma (Inst), Genzyme (Inst), Ipsen (Inst), Turning Point Therapeutics (Inst), Eisai (Inst) Antonio CallesHonoraria: AstraZeneca, Boehringer Ingelheim, Pfizer, Roche/Genentech, Lilly, Novartis, Merck Sharp & Dohme, Bristol Myers Squibb, Amgen, Bayer, Takeda, Sanofi/RegeneronConsulting or Advisory Role: Boehringer Ingelheim, Roche/Genentech, Lilly, Merck Sharp & Dohme, Novartis, Pfizer, Bristol Myers Squibb, Takeda, Igen Biotech, Sanofi/RegeneronResearch Funding: MSD Oncology Ellen CopsonHonoraria: Roche, Pfizer, AstraZeneca, Lilly, NovartisConsulting or Advisory Role: Lilly, NanoString Technologies, Pfizer, Sanofi/Aventis, RocheSpeakers' Bureau: Roche, Pfizer, AstraZeneca, Sanofi/AventisResearch Funding: SECA, AstraZeneca (Inst)Travel, Accommodations, Expenses: Roche, AstraZeneca Adina E. CroitoruConsulting or Advisory Role: Ipsen, Pfizer, MSD OncologyResearch Funding: Bristol Myers Squibb (Inst), Amgen (Inst), Astellas Pharma (Inst), Exelixis (Inst), Merck (Inst), Merck KGaA (Inst)Travel, Accommodations, Expenses: Merck, ServierUncompensated Relationships: Lilly, Roche, Bayer, Pfizer, Sanofi Leonie EastlakeTravel, Accommodations, Expenses: Servier Paul FitzpatrickStock and Other Ownership Interests: AstraZeneca/MedImmuneResearch Funding: AstraZeneca/MedImmune (Inst)Other Relationship: Pistoia Alliance, EHDEN IMI project Henrik FrederiksenResearch Funding: AbbVie (Inst), Gilead Sciences (Inst), Sanofi (Inst) Hannah FrostResearch Funding: AstraZeneca (Inst) Sarju GanatraExpert Testimony: Haymarket Medical EducationTravel, Accommodations, Expenses: Haymarket Medical Education Andreas GlenthøjHonoraria: Novo NordiskConsulting or Advisory Role: Novartis, Celgene/Bristol Myers Squibb, Bluebird Bio, Sanofi, Novo Nordisk, AgiosResearch Funding: Sanofi, Saniona A/STravel, Accommodations, Expenses: Agios Fabio GomesHonoraria: AstraZeneca, Merck Serono, Roche Donna M. GrahamConsulting or Advisory Role: Clinigen GroupSpeakers' Bureau: Cancer Drug Development ForumResearch Funding: Pfizer (Inst) Kevin HarringtonHonoraria: Arch Oncology (Inst), AstraZeneca (Inst), BMS (Inst), Boehringer Ingelheim (Inst), Merck Serono (Inst), MSD (Inst), Oncolys BioPharma (Inst), Pfizer (Inst), Replimune (Inst), Inzen Therapeutics (Inst), Codiak Biosciences (Inst)Consulting or Advisory Role: Arch Oncology (Inst), AstraZeneca (Inst), BMS (Inst), Boehringer Ingelheim (Inst), Merck Serono (Inst), MSD (Inst), Oncolys BioPharma (Inst), Replimune (Inst), Inzen Therapeutics (Inst)Speakers' Bureau: BMS (Inst), Merck Serono (Inst), MSD (Inst)Research Funding: AstraZeneca (Inst), Merck Sharp & Dohme (Inst), Replimune (Inst), Boehringer Ingelheim (Inst) Lasse H. JakobsenHonoraria: Takeda, Roche Khurum KhanHonoraria: ServierConsulting or Advisory Role: Bayer Health Christophe MassardConsulting or Advisory Role: Amgen, Astellas Pharma, AstraZeneca, Bayer, BeiGene, BMS, Celgene, Debiopharm Group, Genentech/Roche, Ipsen, Janssen, Lilly, MSD, Novartis, Pfizer, Sanofi, ORION, Taiho Pharmaceutical, Blueprint Medicines, Innate Pharma, PharmaMar, Faron Pharmaceuticals Olivier MichielinConsulting or Advisory Role: Bristol Myers Squibb, MSD, Novartis, Roche, Amgen, Pierre FabreResearch Funding: MSD, Bristol Myers Squibb, NeraCare GmbHExpert Testimony: Bristol Myers SquibbTravel, Accommodations, Expenses: Bristol Myers Squibb, MSD Anne C. MosenthalHonoraria: Springer Nature Berta ObispoHonoraria: Sanofi, Lilly, Angelini, LEO Pharma, RoviConsulting or Advisory Role: Rovi George PentheroudakisHonoraria: Roche, Amgen, Bristol Myers Squibb, MSD, MerckConsulting or Advisory Role: Roche, AmgenResearch Funding: Roche (Inst), Amgen (Inst), Bristol Myers Squibb (Inst), Merck (Inst), AstraZeneca (Inst), Novartis (Inst), Pfizer (Inst)Travel, Accommodations, Expenses: Sanofi, MSD, Roche, Amgen, BMS Solange PetersHonoraria: Roche (Inst), Bristol Myers Squibb (Inst), Novartis (Inst), Pfizer (Inst), MSD (Inst), AstraZeneca (Inst), Takeda (Inst), Illumina (Inst), Medscape (Inst), Prime Oncology (Inst), RMEI Medical Education (Inst), Research to Practice (Inst), PER (Inst), Imedex (Inst), ecancer (Inst)Consulting or Advisory Role: Roche/Genentech (Inst), Novartis (Inst), Bristol Myers Squibb (Inst), Pfizer (Inst), MSD (Inst), Amgen (Inst), AstraZeneca (Inst), Janssen (Inst), Regeneron (Inst), Merck Serono (Inst), Boehringer Ingelheim (Inst), Takeda (Inst), Lilly (Inst), AbbVie (Inst), Bayer (Inst), Biocartis (Inst), Debiopharm Group (Inst), Illumina (Inst), PharmaMar (Inst), Sanofi (Inst), Seattle Genetics (Inst), Blueprint Medicines (Inst), Daiichi Sankyo (Inst), Incyte (Inst), Bioinvent (Inst), Clovis Oncology (Inst), Vaccibody (Inst), Phosplatin Therapeutics (Inst), Foundation Medicine (Inst)Research Funding: Roche (Inst), BMS (Inst), MSD (Inst), Amgen (Inst), Lilly (Inst), AstraZeneca (Inst), Pfizer (Inst), Illumina (Inst), Merck Serono (Inst), Novartis (Inst), Biodesix (Inst), Boehringer Ingelheim (Inst), Iovance Biotherapeutics (Inst), Phosplatin Therapeutics (Inst)Travel, Accommodations, Expenses: Roche, Bristol Myers Squibb, MSD, Sanofi, IncyteUncompensated Relationships: Journal of Thoracic Oncology, ESMO, European Thoracic Oncology Platform (ETOP), Annals of Oncology (I) Kimberly Rieger-ChristResearch Funding: Veracyte, Ravel, Grail, Exact Sciences, Nucleix Timothy RobinsonTravel, Accommodations, Expenses: Daiichi Sankyo/Lilly Emanuela RomanoConsulting or Advisory Role: AstraZeneca/MedImmune (Inst), Bristol Myers Squibb (Inst), Roche/Genentech (Inst)Research Funding: Bristol Myers Squibb, AmgenTravel, Accommodations, Expenses: AstraZeneca/MedImmune, Bristol Myers Squibb, Roche Michael RoweHonoraria: MSDSpeakers' Bureau: ServierTravel, Accommodations, Expenses: Astellas Pharma Anne ThomasConsulting or Advisory Role: BMSSpeakers' Bureau: Bristol Myers SquibbExpert Testimony: BMS Lance TurtleSpeakers' Bureau: Eisai (Inst) David ViñalSpeakers' Bureau: ServierTravel, Accommodations, Expenses: Merck Caroline WilsonConsulting or Advisory Role: Roche, Pfizer Carlo PalmieriHonoraria: PfizerConsulting or Advisory Role: Pfizer, Daiichi-Sankyo, Lilly, Novartis, Seattle GeneticsResearch Funding: Pfizer, Daiichi-SankyoTravel, Accommodations, Expenses: Roche Donal LandersEmployment: AstraZeneca, AthenexLeadership: DeLondra OncologyStock and Other Ownership Interests: DeLondra OncologyResearch Funding: AstraZeneca (Inst) Timothy CooksleyHonoraria: Bristol Myers Squibb Foundation Caroline DiveConsulting or Advisory Role: Biocartis, Merck, AstraZeneca, GRAIL, Boehringer IngelheimResearch Funding: AstraZeneca, Astex Pharmaceuticals, Bioven, Amgen, Carrick Therapeutics, Merck, Taiho Oncology, GlaxoSmithKline, Bayer, Boehringer Ingelheim, Roche, BMS, Novartis, Celgene, Epigene Therapeutics, Angle, Menarini, Clearbridge Biomedics, Thermo Fisher Scientific, NeoMed André FreitasResearch Funding: AstraZeneca (Inst) Anne C. ArmstrongStock and Other Ownership Interests: AstraZeneca (I)Consulting or Advisory Role: Gilead Sciences, MSDResearch Funding: AstraZeneca/MedImmune (Inst)Travel, Accommodations, Expenses: Gilead Sciences, MSD OncologyNo other potential conflicts of interest were reported.

Published
2022
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7. 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

Author

Lee, Rebecca J, Wysocki, Oskar, Zhou, Cong, Shotton, Rohan, Tivey, Ann, Lever, Louise, Woodcock, Joshua, Albiges, Laurence, Angelakas, Angelos, Arnold, Dirk, Aung, Theingi, Banfill, Kathryn, Baxter, Mark, Barlesi, Fabrice, Bayle, Arnaud, Besse, Benjamin, Bhogal, Talvinder, Boyce, Hayley, Britton, Fiona, Calles, Antonio, Castelo-Branco, Luis, Copson, Ellen, Croitoru, Adina E, Dani, Sourbha S, Dickens, Elena, Eastlake, Leonie, Fitzpatrick, Paul, Foulon, Stephanie, Frederiksen, Henrik, Frost, Hannah, Ganatra, Sarju, Gennatas, Spyridon, Glenthøj, Andreas, Gomes, Fabio, Graham, Donna M, Hague, Christina, Harrington, Kevin, Harrison, Michelle, Horsley, Laura, Hoskins, Richard, Huddar, Prerana, Hudson, Zoe, Jakobsen, Lasse H., Joharatnam-Hogan, Nalinie, Khan, Sam, Khan, Umair T, Khan, Khurum, Massard, Christophe, Maynard, Alec, McKenzie, Hayley, Michielin, Olivier, Mosenthal, Anne C, Obispo, Berta, Patel, Rushin, Pentheroudakis, George, Peters, Solange, Rieger-Christ, Kimberly, Robinson, Timothy, Rogado, Jacobo, Romano, Emanuela, Rowe, Michael, Sekacheva, Marina, Sheehan, Roseleen, Stevenson, Julie, Stockdale, Alexander, Thomas, Anne, Turtle, Lance, Viñal, David, Weaver, Jamie, Williams, Sophie, Wilson, Caroline, Palmieri, Carlo, Landers, Donal, Cooksley, Timothy, Dive, Caroline, Freitas, André, Armstrong, Anne C, Institut Gustave Roussy (IGR), Oncologie gynécologique, Département de médecine oncologique [Gustave Roussy], Institut Gustave Roussy (IGR)-Institut Gustave Roussy (IGR), Physikalisch-Technische Bundesanstalt [Braunschweig] (PTB), Centre de Recherche en Cancérologie de Marseille (CRCM), Aix Marseille Université (AMU)-Institut Paoli-Calmettes, Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Cancer Research and Personalized Medicine - CARPEM [Paris], Hôpital Européen Georges Pompidou [APHP] (HEGP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Hôpital Cochin [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Necker - Enfants Malades [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Genetics, University of Southampton, Centre de recherche en épidémiologie et santé des populations (CESP), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Paul Brousse-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Saclay, Hematology, Odense University Hospital, Department of Clinical Microbiology [Rigshospitalet], Rigshospitalet [Copenhagen], Copenhagen University Hospital-Copenhagen University Hospital, University of Manchester [Manchester], Lausanne University Hospital, Department of Medical Oncology, Ioannina University Hospital, Immunité et cancer (U932), Institut Curie [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), University of Liverpool, Clinical and Experimental Pharmacology Group, Paterson Institute for Cancer Research, University of Manchester, Universidade Estadual de Campinas = University of Campinas (UNICAMP), Cancer Research UK Department of Medical Oncology, and Christie Hospital NHS Foundation Trust

Subjects
Adult, Aged, 80 and over, Male, Adolescent, SARS-CoV-2, [SDV]Life Sciences [q-bio], COVID-19, General Medicine, Middle Aged, Hospitals, Oxygen, Young Adult, Neoplasms, Child, Preschool, COVID-19/complications, Humans, Female, Neoplasms/complications, Child, Aged
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.

Published
2022

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

Author

Lee, Lennard Y. W., Cazier, Jean-Baptiste, Starkey, Thomas, Briggs, Sarah E. W., Arnold, Roland, Bisht, Vartika, Booth, Stephen, Campton, Naomi A., Cheng, Vinton W. T., Collins, Graham, Curley, Helen M., Earwaker, Philip, Fittall, Matthew W., Gennatas, Spyridon, Goel, Anshita, Hartley, Simon, Hughes, Daniel J., Kerr, David, Lee, Alvin J. X., Lee, Rebecca J., Lee, Siow Ming, Mckenzie, Hayley, Middleton, Chris P., Murugaesu, Nirupa, Newsom-Davis, Tom, Olsson-Brown, Anna C., Palles, Claire, Powles, Thomas, Protheroe, Emily A., Purshouse, Karin, Sharma-Oates, Archana, Sivakumar, Shivan, Smith, Ashley J., Topping, Oliver, Turnbull, Chris D., Várnai, Csilla, Briggs, Adam D. M., Middleton, Gary, Kerr, Rachel, Gault, Abigail, Agnieszka, Michael, Bedair, Ahmed, Ghaus, Aisha, Akingboye, Akinfemi, Maynard, Alec, Pawsey, Alexander, Mohamed, Ali Abdulnabi, Okines, Alicia, Massey, Alison, Kwan, Amy, Ferreira, Ana, Angelakas, Angelos, Wu, Anjui, Tivey, Ann, Armstrong, Anne, Madhan, Annet, Pillai, Annet, Poon-King, Ashley, Kurec, Bartlomiej, Usborne, Caroline, Dobeson, Caroline, Thirlwell, Christina, Mitchell, Christian, Sng, Christopher, Scrase, Christopher, Jingree, Christopher, Brunner, Clair, Fuller, Claire, Griffin, Clare, Barrington, Craig, Muller, Daniel, Ottaviani, Diego, Gilbert, Duncan, Tacconi, Eliana, Copson, Ellen, Renninson, Emily, Cattell, Emma, Burke, Emma, Smith, Fiona, Holt, Francesca, Soosaipillai, Gehan, Boyce, Hayley, Shaw, Heather, Hollis, Helen, Bowyer, Helen, Anil, Iris, Illingworth, Jack, Gibson, Jack, Bhosle, Jaishree, Best, James, Barrett, Jane, Noble, Jillian, Sacco, Joseph, Chacko, Joseph, Chackathayil, Julia, Banfill, Kathryn, Feeney, Laura, Horsley, Laura, Cammaert, Lauren, Mukherjee, Leena, Eastlake, Leonie, Devereaux, Louise, Melcher, Lucinda, Cook, Lucy, Teng, Mabel, Hewish, Madeleine, Bhattacharyya, Madhumita, Choudhury, Mahbuba, Baxter, Mark, Scott-Brown, Martin, Fittall, Matthew, Tilby, Michael, Rowe, Michael, Alihilali, Mohammed, Galazi, Myria, Yousaf, Nadia, Chopra, Neha, Cox, Nicola, Chan, Olivia, Sheikh, Omar, Ramage, Paul, Greaves, Paul, Leonard, Pauline, Hall, Peter S., Naksukpaiboon, Piangfan, Corrie, Pippa, Peck, Rahul, Sharkey, Rachel, Bolton, Rachel, Sargent, Rebecca, Jyothirmayi, Rema, Goldstein, Robert, Oakes, Roderick, Shotton, Rohan, Kanani, Ruhi, Board, Ruth, Pettengell, Ruth, Claydon, Ryan, Moody, Sam, Massalha, Samah, Kathirgamakarthigeyan, Sangary, Dolly, Saoirse, Derby, Sarah, Lowndes, Sarah, Benafif, Sarah, Eeckelaers, Sarah, Kingdon, Sarah, Ayers, Sarah, Brown, Sean, Ellis, Shawn, Parikh, Shefali, Pugh, Sian, Shamas, Simon, Wyatt, Simon, Grumett, Simon, Lau, Sin, Wong, Yien Ning Sophia, McGrath, Sophie, Cornthwaite, Stephanie, Hibbs, Stephen, Tillet, Tania, Rabbi, Taslima, Robinson, Tim, Roques, Tom, Angelis, Vasileios, Woodcock, Victoria, Brown, Victoria, Peng, YingYing, Drew, Yvette, Hudson, Zoe, and HASH(0x5651ca095ff8)

Subjects
Adult, Male, 0301 basic medicine, medicine.medical_specialty, Pneumonia, Viral, Population, Risk Assessment, Betacoronavirus, 03 medical and health sciences, 0302 clinical medicine, Risk Factors, Neoplasms, Internal medicine, Correspondence, Epidemiology, medicine, Humans, Prospective Studies, Lung cancer, education, Prospective cohort study, Pandemics, Aged, Cause of death, Aged, 80 and over, education.field_of_study, Manchester Cancer Research Centre, SARS-CoV-2, business.industry, ResearchInstitutes_Networks_Beacons/mcrc, COVID-19, Cancer, Odds ratio, Middle Aged, medicine.disease, Hospitalization, 030104 developmental biology, Oncology, 030220 oncology & carcinogenesis, Cohort, Female, Coronavirus Infections, business, RA
Abstract

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

Published
2020

9. As COVID-19 cases surge despite mass vaccination, it's time to focus on the vulnerable.

Author

Little, Martin, Fittall, Matthew, McKenzie, Hayley, Tilby, Michael, Tripathy, Arvind, and Lee, Lennard Y. W.

Subjects
*COVID-19 pandemic, *VACCINE effectiveness, *COVID-19, *VACCINATION, *HERD immunity
Abstract

The COVID-19 pandemic is an era-defining, international emergency impacting the global economy, politics and countless individual lives. People living with cancer have increased risk of hospitalisation and mortality from COVID-19. There are limited data regarding vaccine efficacy in people with cancer, with lack of empirical evidence to guide vaccine strategy in cancer patients fostering uncertainty. Vulnerable groups, for whom vaccination protection may be attenuated, now carry the greatest burden of risk amongst the population. The cancer community needs to reconsider the potential on-going impact of COVID-19 and develop and plan new programs of work to mitigate it. Multiple potential future scenarios now exist, ranging from full protection from COVID-19 for cancer patients via herd immunity to viral evolution for vaccine resistance and increased virulence. Defining those most vulnerable to COVID-19 post-vaccination will require largescale data and evidence to comprehensively identify factors that reduce vaccine efficacy. Once identified, protecting these groups through transmission and mortality risk reduction will become paramount. As the pandemic progresses, "protecting the vulnerable" may enable a return to normal for the majority, whilst still protecting individuals living with and beyond cancer who already live with the challenges of having a cancer diagnosis. [ABSTRACT FROM AUTHOR]

Published
2021
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