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Article

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

by
Oskar Wysocki
1,2,
Cong Zhou
3,
Jacobo Rogado
4,
Prerana Huddar
5,
Rohan Shotton
5,6,
Ann Tivey
5,6,
Laurence Albiges
7,
Angelos Angelakas
5,
Dirk Arnold
8,
Theingi Aung
9,
Kathryn Banfill
5,6,
Mark Baxter
10,
Fabrice Barlesi
7,11,
Arnaud Bayle
12,13,
Benjamin Besse
12,
Talvinder Bhogal
14,
Hayley Boyce
9,
Fiona Britton
5,
Antonio Calles
15,
Luis Castelo-Branco
16,17,18,
Ellen Copson
19,
Adina Croitoru
20,
Sourbha S. Dani
21,
Elena Dickens
22,
Leonie Eastlake
23,
Paul Fitzpatrick
2,
Stephanie Foulon
13,24,
Henrik Frederiksen
25,
Sarju Ganatra
21,
Spyridon Gennatas
26,
Andreas Glenthøj
27,
Fabio Gomes
5,
Donna M. Graham
5,6,
Christina Hague
5,
Kevin Harrington
26,28,
Michelle Harrison
29,
Laura Horsley
5,
Richard Hoskins
30,
Zoe Hudson
31,
Lasse H. Jakobsen
32,
Nalinie Joharatnam-Hogan
33,34,
Sam Khan
22,
Umair T. Khan
14,35,
Khurum Khan
33,
Alexandra Lewis
5,
Christophe Massard
12,
Alec Maynard
9,
Hayley McKenzie
19,
Olivier Michielin
36,
Anne C. Mosenthal
21,
Berta Obispo
4,
Carlo Palmieri
14,35,
Rushin Patel
21,
George Pentheroudakis
16,
Solange Peters
16,37,
Kimberly Rieger-Christ
21,
Timothy Robinson
31,38,
Emanuela Romano
39,
Michael Rowe
40,
Marina Sekacheva
41,
Roseleen Sheehan
9,
Alexander Stockdale
42,
Anne Thomas
22,43,
Lance Turtle
42,
David Viñal
44,
Jamie Weaver
5,6,
Sophie Williams
9,
Caroline Wilson
9,
Caroline Dive
3,
Donal Landers
2,
Timothy Cooksley
5,
André Freitas
1,2,45,
Anne C. Armstrong
5,6,
Rebecca J. Lee
5,6,* and
on behalf of the ESMO Co-Care
†add Show full author list remove Hide full author list
1
Department of Computer Science, University of Manchester, Oxford Road, Manchester M13 9PL, UK
2
Digital Experimental Cancer Medicine Team, Cancer Biomarker Centre, Cancer Research UK Manchester Institute, University of Manchester, Alderley Park, Macclesfield SK10 4TG, UK
3
Cancer Research UK Manchester Institute, Cancer Biomarker Centre, The University of Manchester, Al-derley Park, Macclesfield SK10 4TG, UK
4
Department of Medical Oncology, Hospital Universitario Infanta Leonor, Av. Gran Vía del Este, 80, 28031 Madrid, Spain
5
Department of Medical Oncology, The Christie NHS Foundation Trust, Wilmslow Road, Manchester M20 4BX, UK
6
Faculty of Biology, Medicine and Health, The University of Manchester, Oxford Road, Manchester M13 9PL, UK
7
Department of Medical Oncology, Gustave Roussy, 94805 Villejuif, France
8
Department of Oncology, Haematology and Palliative Care, Asklepios Klinik Altona, Paul-Ehrlich-Str. 1, 22763 Hamburg, Germany
9
Weston Park Cancer Centre, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield S10 2JF, UK
10
Division of Molecular and Clinical Medicine, Ninewells School of Medicine, University of Dundee, Dundee DD2 1SY, UK
11
Multidisciplinary Oncology & Therapeutic Innovations Department, Aix Marseille University, CNRS, INSERM, CRCM, 13015 Marseille, France
12
Drug Development Department (DITEP), Gustave Roussy—Cancer Campus, 94805 Villejuif, France
13
Oncostat (CESP U1018 INSERM), Labeled Ligue Contre le Cancer, University Paris-Saclay, 94805 Villejuif, France
14
Department of Medical Oncology, The Clatterbridge Cancer Centre NHS Foundation Trust, 65 Pembroke Place, Liverpool L7 8YA, UK
15
Department of Medical Oncology, Hospital General Universitario Gregorio Marañón, Calle del Dr. Esquerdo, 46, 28007 Madrid, Spain
16
ESMO-CoCARE Steering Committee, European Society for Medical Oncology, Via Ginevra 4, 6900 Lugano, Switzerland
17
NOVA National School of Public Health, Av. Padre Cruz, 1600-560 Lisboa, Portugal
18
Department of Medical Oncology, University Hospital Center of Algarve, R. Leao Penedo, 8000-386 Faro, Portugal
19
Cancer Sciences Academic Unit, University Hospital Southampton NHS Foundation Trust, Tremona Road, Southampton SO16 6YD, UK
20
Medical Oncology Department, Fundeni Clinical Institute, 258, Fundeni Str., 022238 București, Romania
21
Department of Cardiology, Lahey Hospital and Medical Center, Burlington, MA 01805, USA
22
Oncology Department, University Hospitals of Leicester NHS Trust, Leicester LE1 5WW, UK
23
Department of Medical Oncology, University Hospitals Plymouth NHS Trust, Derriford Road, Plymouth PL6 8DH, Devon, UK
24
Biostatistics and Epidemiology Office, Gustave Roussy, University Paris-Saclay, 94805 Villejuif, France
25
Department of Haematology, Odense University Hospital, 5000 Odense, Denmark
26
Department of Medical Oncology, The Royal Marsden NHS Foundation Trust, Fulham Road, London SW3 6JJ, UK
27
Department of Haematology, Copenhagen University Hospital—Rigshospitalet, 2100 Copenhagen, Denmark
28
The Institute of Cancer Research NIHR Biomedical Research Centre, London SW3 6JB, UK
29
Ninewells Hospital and Medical School, Dundee DD2 1SG, UK
30
Research IT, University of Manchester, Oxford Road, Manchester M13 9PL, UK
31
Bristol Haematology and Oncology Centre, University Hospitals Bristol NHS Foundation Trust, Bristol BS2 8ED, UK
32
Department of Haematology, Clinical Cancer Research Center, Aalborg University Hospital, 9000 Aalborg, Denmark
33
Department of Medical Oncology, University College London Hospitals NHS Foundation Trust, 235 Euston Road, London NW1 2BU, UK
34
MRC Clinical Trials Unit, University College London, Gower St., London WC1E 6BT, UK
35
Institute of Systems, Molecular and Integrative Biology, The University of Liverpool, Liverpool L69 3BX, UK
36
Department of Oncology, Melanoma Clinic, Swiss Institute of Bioinformatics, Quartier Sorge—Batiment Amphipole, 1015 Lausanne, Switzerland
37
Medical Oncology, Centre Hospitalier Universitaire Vaudois, Rue du Bugnon 46, 1011 Lausanne, Switzerland
38
Population Health Sciences, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK
39
Department of Oncology, Institut Curie, PSL Research University, Office 2A-5, 26, Rue d’Ulm, 75005 Paris, France
40
Sunrise Centre, Royal Cornwall Hospitals NHS Trust, Truro TR1 3LJ, Cornwall, UK
41
World-Class Research Center “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University, 119991 Moscow, Russia
42
Tropical and Infectious Diseases Unit, Royal Liverpool University Hospital, 3Z Link, Prescot Street, Liverpool L7 8XP, UK
43
Leicester Cancer Research Centre, The University of Leicester, University Road, Leicester LE1 7RH, UK
44
Department of Medical Oncology, Hospital Universitario La Paz, Paseo de la Castellana, 261, 28046 Madrid, Spain
45
Idiap Research Institute, 1920 Martigny, Switzerland
 
add Show full affiliation list
 
remove Hide full affiliation list
*
Author to whom correspondence should be addressed.
†
See acknowledgements for members of the ESMO Co-Care.
Cancers 2022, 14(16), 3931; https://doi.org/10.3390/cancers14163931
Submission received: 12 June 2022 / Revised: 4 August 2022 / Accepted: 8 August 2022 / Published: 16 August 2022
(This article belongs to the Collection The Impact of COVID-19 Infection in Cancer)
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Abstract

:

Simple Summary

There have been huge improvements in both vaccination and the management of COVID-19 in patients with cancer. In addition, different variants may be associated with different presentations. Therefore, we examined whether indicators of the severity of COVID-19 in patients with cancer who present to hospital varied during different waves of the pandemic and we showed that these indicators remained predictive. We validated that the COVID-19 Risk in Oncology Evaluation Tool (CORONET), which predicts the severity of COVID-19 in cancer patients presenting to hospital, performed well in all waves. In addition, we examined patient outcomes and the factors that influence them and found that there was increased vaccination uptake and steroid use for patients requiring oxygen in later waves, which may be associated with improvements in outcome.

Abstract

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.

1. Introduction

The SARS-CoV-2 virus, which causes COVID-19 disease, has infected over half a billion people worldwide, resulting in at least 6 million deaths [1]. Patients with cancer have been particularly affected, in terms of both cancer treatment delays and risks of more severe COVID-19 disease. Early in the pandemic, studies reported mortality rates of between 10% and 30% [2,3,4,5,6,7]. Thereafter, vaccines have been rapidly developed and a number of disease-modifying treatments have been identified. Vaccinations have resulted in decreased numbers of patients experiencing severe COVID-19; however, vaccinations have been less effective in reducing infection rates, particularly with respect to the newer Omicron variants [8,9,10]. Upon presentation with COVID-19, depending on the variant and the severity of infection, patients may receive corticosteroids, antivirals such as remdesivir, or antibodies such as tocilizumab [11,12,13]. Together, these have improved outcomes, at least in the developed world [11,12,13].
Despite the rapid progress in the prevention and management of COVID-19, patients with cancer continue to present to hospital with severe COVID-19 disease. Studies have shown that the severity of COVID-19 is similar in patients who are infected with different SARS-CoV-2 variants; however, there are improved outcomes when patients with cancer are vaccinated, compared with patients who remain unvaccinated [14]. At the start of the pandemic, numerous studies examined the associations between different clinical features and the severity of COVID-19 [2,4,6,7,15,16]. However, it is not clear whether these associations remain discriminatory in patients who are infected with different SARS-CoV-2 variants and in patients who are vaccinated. Therefore, we examined whether clinical features at presentation to hospital changed over time, based on the increased uptake of vaccinations and on infection by different coronavirus variants. In addition, we examined whether patient outcomes changed following presentation to hospital with COVID-19 and cancer. Previously, we developed the COVID-19 Risk in Oncology Evaluation Tool (CORONET) to guide healthcare professionals and systems in decision-making regarding the need for admission and to provide information regarding the likely severity of COVID-19 illness [17]. This tool allowed us to examine whether there were differences in the severity of infection, determined by CORONET scores during different waves of the pandemic. Having previously shown that the CORONET tool performed well for patients with D614G SARS-CoV-2 and Alpha variants, we validated its usage for patients with Omicron infections.

2. Materials and Methods

Approval (reference 20/WA/0269) was granted from the UK Research Ethics Committee for this study. Information regarding governance/regulatory approvals for each international cohort are available in previously published papers [17]. Patients with active cancer were included in the study, We defined active cancer as solid (stage I–IV) or haematological cancer diagnosed in the last 6 months or cancer or recurrent or metastatic or haematological cancer, not in complete remission, that was treated for 6 months or more. The selected patients had a laboratory-confirmed SARS-CoV-2 infection. They were defined as vaccinated if they had more than one dose of any available vaccination against SARS-CoV-2. No patients in the dataset were infected more than one time; thus, each patient presented with a unique case of COVID-19. Asymptomatic patients who were screened as part of routine testing for surgical procedures and found to be positive were not included, as their data were not routinely captured. Details regarding data collection and outcome definitions are provided in Lee et al. [17].
Patient data were collected worldwide from the United Kingdom, the United States, Spain, Denmark, and France, and from medical centres contributing to ESMO-CoCARE. We included a range of hospitals that manage cancer patients in different settings, from local district general/local community hospitals, in which general acute physicians manage acute oncology admissions, to tertiary cancer centres, within which there are highly specialised oncology services [17].
We defined the different dominant variants responsible for the SARS-CoV-2 waves as follows: the wave 1 D614G SARS-CoV-2 variant, including patients who presented from March 2020 until the end of August 2020; the wave 2 Alpha variant, comprising patients who presented from September 2020 until the end of April 2021; and wave 4 Omicron variant from December 2021 until the end of February 2022 (the date of cut-off for data collection) [1]. Of note, we did not include the Delta wave 3, due to the insufficiency of available data (n = 15) to support meaningful conclusions.
We previously developed CORONET to help determine the need to admit patients to hospital on the bases of their likelihood of needing oxygen (as generally oxygen is only given in hospital) and their severity of COVID-19, as indicated by predictions for required oxygen and/or death [17]. As a result, we established four key outcomes, arranged in a 0–3 point ordinal scale: discharged, admitted (≥24 h inpatient), admitted+O2 requirement (including ventilator support), and admitted+O2+died (with the death directly attributable to COVID-19 disease, not to cancer). These four outcomes were used as measures of disease severity [17]. Contrary to an analysis of binary outcomes (e.g., need for oxygen vs. no need for oxygen), this strategy improved the ability to provide a complete clinical picture that was important for overall decision-making regarding hospital admission. A threshold of ≥1.0 determined admissions and a threshold of ≥2.3 determined severe outcome (additional details are available in [17]).
Differences between waves and in outcomes were assessed using the Pearson’s Chi-square (Χ2) test and the Mann–Whitney U test. We evaluated the differences in both the CORONET scores and in the performances of the model during different waves. Patients who missed more than one variable from the five features that provided the greatest contribution to the score (i.e., National Early Warning Score 2 (NEWS2), C-reactive protein (CRP), albumin, age, and platelets) were excluded from the CORONET score evaluation. The significance level was set at p = 0.05, and p values were adjusted for multiple testing using the Benjamini–Hochberg method. CORONET’s performance on clinical decision-making for patients presenting with different SARS-CoV-2 variants was examined using the receiver operating characteristic (ROC). CORONET scores were calculated for patients that were not in the model’s derivation cohort (see [17]).

3. Results

Patient data were collected for wave 1 D614G (n = 1430) and wave 2 Alpha (n = 475) from 12 participating hospitals in the UK, two hospitals in Spain, four hospitals in the USA, and (via the ESMO-CoCARE registry) hospitals throughout the world, excluding the USA, Canada, and Latin America (see [17]). Patients in wave 4 Omicron (n = 63) were treated in the UK and Spain (details regarding number of patients are provided in Supplementary Table S1). Clinical features of the patients are shown in Table 1. Intriguingly, patients who presented to hospital in wave 2 had a significantly lower median age, compared with first wave patients (61 vs. 68 p < 0.001, Table 1). Almost all patients (i.e., 95.8%) in the UK/Spain cohort who presented to hospital with available data (48 of the 63, with 15 having missing data) were vaccinated with at least one dose by the time the Omicron wave developed.
We observed that there were significantly more patients discharged within 24 h in waves 2 and 4, compared with patients discharged within 24 h in wave 1 (186, or 39.2%, 26, or 41.3%, and 336. or 23.5%, respectively; see Table 2). The mortality rate for patients admitted with COVID-19 and cancer was 26.4% in wave 1 vs. 13.1% in wave 2 and 0% in wave 4 (wave 2 vs. wave 1; 1 p < 0.001, with no deaths observed in the UK/Spain Omicron cohort). We observed no significant difference in the requirements for oxygen between the waves (wave 1 vs. wave 2, p = 0.265; wave 1 vs. wave 4, p = 0.368; wave 2 vs. wave 4, p = 0.265). Steroids were the mainstay of treatment following hospital admission, particularly in waves 2 and 4. Antivirals and antibodies were rarely used (Table 3). Of note, 40% (32/81) of patients requiring oxygen in wave 1, 88% (78/89) of patients requiring oxygen in wave 2, and 94% (15/16) of patients requiring oxygen in wave 4 were treated with steroids, showing the impact of the RECOVERY trial data (the use of steroids only for those patients requiring oxygen) in the management of COVID-19 [12].
Next, we examined whether there were differences in patients’ clinical features during different waves of the pandemic (Table 4). In particular, patients with COVID-19 were stratified by treatment outcomes to explore differences that were specific to COVID-19 severity. Patients in wave 1 were older than patients in wave 2, independent of COVID-19 severity. Similarly, wave 1 patients presented with significantly lower cancer stages than patients in waves 2 or 4, independent of COVID-19 severity. Performance status differed only among patients who died due to COVID-19 in waves 1 and 2 (higher in wave 1, p = 0.0075). There were no significant differences during the three waves in the laboratory values of patients who presented with severe COVID-19 (patients who required oxygen or who were dying), apart from albumin, which was observed to be lower in patients requiring oxygen in wave 2 than in patients requiring oxygen in wave 1 (p = 0.0029).
We investigated whether the features remained significant in predicting the severity of COVID-19 during each wave (Figure 1, Supplementary Table S2). Older patients were significantly more likely to have severe outcomes in both waves 1 and wave 2, but there was no such correlation in wave 4. Similarly, lower lymphocytes and platelets were associated with increased severity in waves 1 and 2, but these associations were not observed in wave 4 (Figure 1; for p-values see Supplementary Table S2). Patients with higher NEWS2, CRP, and neutrophils, and lower albumin, had more severe outcomes in all waves.
We previously developed the CORONET score to predict the severity of COVID-19 upon patients’ presentation to hospital and to aid in deciding whether to admit or discharge patients with cancer and COVID-19 [17]. In this analysis, CORONET scores were calculated for 258, 48, and 54 patients who were included from waves 1, 2, and 4, respectively, following exclusions due to missing values (Supplementary Table S3). Intriguingly, the CORONET scores during the three waves were not significantly different (p > 0.05, Supplementary Table S4, Figure 2), indicating consistent patient severity at the times of presentation and admission to hospital for all variants (Supplementary Table S4). Treatments for COVID-19 for each cohort are provided in Supplementary Table S5; steroid use was the treatment that differed most significantly. With consistent area under the curve (AUC) during the different waves, CORONET demonstrated its continuing capability to predict COVID-19 severity (Figure 3). The AUC for admission was 0.82 for wave 1 vs. 0.72 for wave 2 vs. 0.80 for wave 4; the AUC for death was 0.77 for wave 1 vs. 0.78 for wave 2 vs. 0.88 for wave 4. Sensitivity, specificity, positive predictive value, and negative predictive value for the CORONET scores in the different waves, based on the cutoffs determined in [17], are presented in Supplementary Tables S6–S8. CORONET scores indicated that admission was recommended for 94% of patients who required oxygen or died in wave 1, for 89% of patients who required oxygen or died in wave 2 and for 89% of patients who required oxygen or died in wave 4 (Supplementary Tables S6–S8 and Figure 2).

4. Discussion

Multiple studies have shown that patients with cancer have more severe outcomes due to COVID-19 than do patients without cancer, due to a combination of immune dysregulation and the effects of certain treatments, particularly those that deplete B cells in haematological cancers [18,19,20]. Over time, mortality in the general population versus mortality in members of the population who are infected has decreased during the different waves of the pandemic [21], which is likely due to multiple factors, including country-dependent public health measures, vaccination, and improved management of severe acute COVID-19 disease. However, patients with cancer, in general, have been shown to have reduced immune responses to vaccination and can present with severe COVID-19 [22,23,24]. It is important to note that following the first wave of the pandemic, the volume of cancer-directed therapies offered to patients returned to normal levels. Despite this, the mortality rate in cancer patients also decreased in the different waves, which is reassuring for patients who are undergoing cancer treatment.
Our study showed that features that were previously associated with COVID-19 severity in patients with cancer [2,6,25,26], such as low albumin, higher CRP, and neutrophils, remain discriminatory in patients who present with different variants. This is important in terms of assessing the likely severity of SARS-CoV-2 infections for individual patients. Indeed, we validated the CORONET score’s ability to identify patients with severe COVID-19 due to different variants and in patients with different vaccination status. The AUC was 0.80 for admission and 0.87 for the requirement of oxygen in patients presenting with the Omicron variant. Given the rapidly changing nature of the COVID-19 pandemic, it is particularly important to assess the temporal stability of risk prediction models. Here, we showed that in a small validation cohort, CORONET has a similar performance during the different pandemic waves and in different countries.
Our study revealed that at time of presentation to hospital, markers of COVID-19 severity were similar during the different waves, despite the fact that the majority of the patients were vaccinated during the Omicron wave. This is intriguing, as it suggests that management of patients with COVID-19 and cancer, following their presentation with COVID-19 to hospital, might affect outcomes. Vaccination may also affect the likelihood of mortality in severe COVID-19 patients [14]; however, we note that there was an increased use of steroids (40% in wave 1 88% in wave 2, and 94% in wave 4) in patients requiring oxygen, which may have had a significant impact, as other treatments were rarely used in our cohorts. The improvement in mortality (26.5% in wave 1, 13.1% in wave 2), particularly in wave 2 where no patients were vaccinated, could be associated with steroid treatment. Therefore, studies evaluating the differences in COVID-19 outcomes over time should consider not only vaccination, but also post-infection management. However, there were significant amounts of missing data in our analysis, and to test these findings further, we propose to examine steroid use and mortality in vaccinated and non-vaccinated cancer patients, in larger cohorts. Based on the RECOVERY Phase III trial data in the general population and the interesting observations in our cohorts, we emphasise that it is essential that all patients with cancer and COVID-19, requiring oxygen, are managed with steroids unless contraindicated [12]. Of note, we observed that the disease stage of cancer in patients who present to hospital with COVID-19 was higher in waves 2 and 4 than in wave 1. This difference suggests that cancer severity may have an effect on the necessity for admission; however, the patients’ performance status was similar, which indicates that the situation is more complex. Larger cohorts are needed to consider this further. We also observed age differences in patients who presented during the different waves, with wave 2 patients having a lower median age. This may be due to the vaccine rollout, which targeted older patients first, or it may be that younger patients were more likely to become infected due to social factors, as observed in other studies [27].
The main limitation of this study was the small size of cohorts, particularly the cohort size of the Omicron wave. This factor may have affected the levels of significance for some of the statistical tests, particularly in the comparisons between wave 4 and wave 1. Significant data were missing, particularly in terms of the treatments used to manage COVID-19. In addition, due to this study’s retrospective nature, there was no centrally determined COVID-19 management plan; therefore, treatment of COVID-19 varied among the various treatment centres. Nonetheless, this study provides important provisional insights, associated with different pandemic waves, into the features of patients with cancer and COVID-19. These insights may be further examined in larger studies in the future.

5. Conclusions

Patients with cancer who present and are admitted to hospital with COVID-19 have similar features of severity, which remain discriminatory despite differences in SARS-CoV-2 variants and vaccination status. Survival improved following the first wave of the pandemic, which, at time of presentation to hospital, may be related to both vaccination and the increased steroid use in patients who require oxygen. The CORONET model demonstrated good performance, independent of the SARS-CoV-2 variants.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/cancers14163931/s1, Table S1: Number of patients in the dataset obtained from 12 participating hospitals in the UK, 2 hospitals in Spain, 4 hospitals in the USA, and as part of the ESMO-CoCARE registry, hospitals throughout the world, excluding the USA, Canada and Latin America. Table S2: Known features associated with COVID-19 severity compared between different waves, stratified by outcome. Table S3: Number of patients selected for CORONET validation before and after exclusion based on the criteria for missing values. Table S4: Predicted CORONET score for patients from wave D614G, Alpha and Omicron waves. Table S5: Treatments for COVID-19 amongst patients used to evaluate CORONET. Table S6: Sensitivity, specificity, Positive Predictive Value and Negative Predictive Value for admission, requirement for oxygen in relation to the threshold defined by the CORONET score for 258 patients from D614G wave. Row highlighted in grey signifies the admission threshold. Table S7: Sensitivity, specificity, Positive Predictive Value and Negative Predictive Value for admission, requirement for oxygen in relation to the threshold defined by the CORONET score for 48 patients from Alpha wave. Row highlighted in grey signifies the admission threshold. Table S8: Sensitivity, specificity, Positive Predictive Value and Negative Predictive Value for admission, requirement for oxygen in relation to the threshold defined by the CORONET score for 54 patients from Omicron wave. Row highlighted in grey signifies the admission threshold.

Author Contributions

Conception and design: R.J.L., O.W., C.Z., A.F., and A.C.A.; development of methodology: R.J.L., O.W., and C.Z.; acquisition of data (providing animals, acquiring and managing patients, providing facilities, etc.): R.J.L., R.S. (Rohan Shotton), A.T. (Ann Tivey), L.A., F.B. (Fabrice Barlesi), A.B., B.B., S.F., H.F., A.G., C.M., L.H.J., A.A., D.A., T.A., K.B., M.B., T.B., H.B., F.B. (Fiona Britton), A.C. (Antonio Calles), L.C.-B., E.C., A.C. (Adina Croitoru), S.S.D., E.D., L.E., S.G. (Sarju Ganatra), S.G. (Spyridon Gennatas), F.G., C.H., K.H., M.H., L.H., P.H., Z.H., N.J.-H., S.K., U.T.K., K.K., A.M., H.M., O.M., A.C.M., A.L., B.O., R.P., G.P., S.P., K.R.-C., T.R., J.R., E.R., M.R., M.S., R.S. (Roseleen Sheehan), A.S., A.T. (Anne Thomas), L.T., D.V., J.W., S.W., C.W., C.P., T.C., and ESMO Co-Care; analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): R.J.L., O.W., and C.Z.; writing, review, and/or revision of the manuscript: R.J.L., O.W., C.Z., A.T. (Ann Tivey), D.A., A.C. (Antonio Calles), L.C.-B., E.C., H.F., S.G. (Sarju Ganatra), K.H., O.M., G.P., T.R., J.R., D.L., A.F., A.C.A., D.M.G., and C.D.; administrative, technical, or material support (i.e., reporting or organizing data and constructing databases): R.J.L., O.W., R.H., P.F., D.L. Study supervision: R.J.L., A.C.A., A.F., and C.D. All authors have read and agreed to the published version of the manuscript.

Funding

R.J.L., T.R., and J.W. were supported by the National Institute for Health Research as Clinical Lecturers. T.B. was supported by the National Institute for Health Research as an academic clinical fellow. U.K. was supported by 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 provided infrastructure support (Grant Reference: C18616/A25153) and The Clatterbridge Cancer charity (North West Cancer Research) provided funding to C.P. C.D. was funded by CRUK Core funding to Manchester Institute (C5757/A27412) and was supported by the CRUK Manchester Centre Award (C5759/A25254) and by the NIHR Manchester Biomedical Research Centre. C.Z. was funded by the CRUK Manchester Centre Award (C5759/A25254). P.F. was funded by the CRUK Accelerator Award (29374). This research was funded, in part, by the Wellcome Trust [205228/Z/16/Z]. LT was supported by the National Institute for Health Research Health Protection Research Unit (HPRU) in Emerging and Zoonotic Infections (NIHR200907) at the University of Liverpool in partnership with Public Health England (PHE), in collaboration with the Liverpool School of Tropical Medicine and the University of Oxford. MS was supported by the Ministry of Science and Higher Education of the Russian Federation within the framework of state support for the creation and development of World-Class Research Centers’ “Digital biodesign and personalized healthcare” № 075-15-2020-926. The views expressed in this article are those of the author(s) and not necessarily those of the NHS, the NIHR, the Department of Health, or Public Health England. Funding for COVID-19 work was provided by The Christie Charitable Fund (1049751).

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the UK Research Ethics Committee (reference 20/WA/0269).

Informed Consent Statement

Patient consent was waived, as non-identifiable patient data and pseudo-anonymised registry data were used. In addition, the study was performed under the common law legal basis to process confidential patient information (CPI) without consent for COVID-19 public health, surveillance, and research purposes in the UK.

Data Availability Statement

Within the constraints of the data agreements between the University of Manchester and other institutions, data are available upon request.

Acknowledgments

The authors thank Clare Griffin, Joanne Lewis, Sinead Ward, Anthony Murphy, Amy Kavanagh, and Alison Backen for their support in the project, the Digital ECMT team for insights into data analysis, and the Research Information technology team at the University of Manchester. The authors also thank Manchester and Liverpool ECMC. The ESMO CoCARE Steering Committee acknowledges the contributions of Klizia Marinoni and Delanie Young in developing and managing the Registry and the participating centres, and Sylvain Pradervand for support in the database management. These are affiliated with ESMO CO-Care. Abigail Temple: The Royal Marsden NHS Foundation Trust, London, SW3 6JJ, UK; Natasha Duperray: The Royal Marsden NHS Foundation Trust, London, SW3 6JJ, UK; Aasfa Nadeem: The Royal Marsden NHS Foundation Trust, London, SW3 6JJ, UK; Olivia Chudy: The Royal Marsden NHS Foundation Trust, London, SW3 6JJ, UK; Vlad Croitoru: Medical Oncology Department, Fundeni Clinical Institute Bucharest, Romania; Diana Bogdan: Medical Oncology Department, Fundeni Clinical Institute Bucharest, Romania; Irina Sandra: Medical Oncology Department, Fundeni Clinical Institute Bucharest, Romania; Madalina Soanca: Medical Oncology Department, Fundeni Clinical Institute Bucharest, Romania; Ana Maria Tirdea: Medical Oncology Department, Fundeni Clinical Institute Bucharest, Romania; Irina Cazacu: Medical Oncology Department, Fundeni Clinical Institute Bucharest, Romania; Vitorino: Hospital Professor Doutor Fernando Fonseca, Lisbon.; Salah Khallaf: South Egypt Miron Monica: Medical Oncology Department, Fundeni Clinical Institute Bucharest, Romania; Marina Cancer Institute; Snežana Šušnjar: Department of Medical Oncology, Institute for Oncology and Radiology of Serbia, Belgrade, Serbia.; Widyanti Soewoto: Department of Surgery, Oncology Division, Sebelas Maret University, Surakarta, Indonesia; Ana Cardeña: Hospital Universitario Fundación Alcorcón.; Mohamed Djerouni: Department of Oncology, Dr Saadane hospital, Algeria; Vittorio Fusco: Oncology Unit, Azienda Ospedaliera “SS Antonio e Biagio e C. Arrigo”; Teresa Alonso Gordoa: Hospital Universitario Ramón y Cajal; corazon ngelangel: Director of the Asian Cancer Institute, Asian Hospital and Medical Center, Alabang, Muntinlupa, Philip-pines; Filipa Mar-garida Ferreira Pereira: ULSAM, Viana do Castelo; Gabor Liposits: Regional Hospital West Jutland, Herning, Denmark; Nikolaos: Tsoukalas: 401 General Military Hospital of Athens, Greece; Syed Hussain: Syed Md Akram Hussain, Senior Consultant, Oncology, Square Oncology and Radiotherapy Centre, Square Hospitals; Jay Naik: Department of Oncology, Harrogate & District Foundation Trust; Michele Ghidini: Medical Oncology Unit, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy; Matthew Wheater: University Hospital Southampton, Southampton, UK; Bahaaeldin Baraka: Speciality oncology doctor, Southend University Hospitals, Mid and South Essex NHS Foundation Trust UK; Nur Fadh-lina Abdul Satar: Department of Clinical Oncology, University of Malaya Medical Centre, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia; Marniza Saad: Department of Clinical Oncology, University of Malaya Medical Centre, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia; Mohammad Eslami-jouybari: Imam Hospital (gastrointestinal cancer reaserch center); Edmond Ang: Regional Cancer and Blood Services, Auckland City Hospital.; Laura Mazilu: Ovidius University of Constanta, Clinical Emergency Hospital of Constanta, Romania; Evangelia Razis: Hygeia Hospital, 3rd Oncology Department; muhammad rafiqul islam: National Institute of Cancer Research and Hospital, Bangladesh; Yeon Hee: Park: Samsung Medical Center. Sungkyunkwan University School of Medicine, Seoul, Korea; Ibrahim Sallam: Ibrahim sallam FEBS. MSC. FACS. PhD. MISR Cancer Center. Al Jizah. Egypt; Ziad Emarah: Oncology Center, Faculty of Medicine, Mansoura University, Mansoura, Egypt.

Conflicts of Interest

R. Lee research funding (institution) BMS, Pierre Fabre, Astrazeneca 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, Sandoz, Merck, Pfizer, Servier, Roche. H. Fredericksen Research funding from: Alexion, Gilead, Abbvie, Janssen Pharmaceuticals, and Novartis; 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. Arnaud Bayle As part of the Drug Development Department (DITEP) = Principal/sub-Investigator of Clinical Trials for Abbvie, Adaptimmune, Adlai Nortye USA Inc, Aduro Biotech, Agios Pharmaceuticals, Amgen, Argen-X Bvba, Astex Pharmaceuticals, Astra Zeneca Ab, Aveo, Basilea Pharmaceutica International Ltd., Bayer Healthcare Ag, Bbb Technologies Bv, Beigene, BicycleTx Ltd., Blueprint Medicines, Boehringer Ingelheim, Boston Pharmaceuticals, Bristol Myers Squibb, Ca, Celgene Corporation, Chugai Pharmaceutical Co, Clovis Oncology, Cullinan-Apollo, Curevac, Daiichi Sankyo, Debiopharm, Eisai, Eisai Limited, Eli Lilly, Exelixis, Faron Pharmaceuticals Ltd., Forma Tharapeutics, Gamamabs, Genentech, Glaxosmithkline, H3 Biomedicine, Hoffmann La Roche Ag, Imcheck Therapeutics, Innate Pharma, Institut De Recherche Pierre Fabre, Iris Servier, Iteos Belgium SA, Janssen Cilag, Janssen Research Foundation, Kura Oncology, Kyowa Kirin Pharm. Dev, Lilly France, Loxo Oncology, Lytix Biopharma As, Medimmune, Menarini Ricerche, Merck Sharp & Dohme Chibret, Merrimack Pharmaceuticals, Merus, Millennium Pharmaceuticals, Molecular Partners Ag, Nanobiotix, Nektar Therapeutics, Novartis Pharma, Octimet Oncology Nv, Oncoethix, Oncopeptides, Orion Pharma, Ose Pharma, Pfizer, Pharma Mar, Pierre Fabre, Medicament, Roche, Sanofi Aventis, Seattle Genetics, Sotio A.S, Syros Pharmaceuticals, Taiho Pharma, Tesaro, Turning Point Therapeutics, Xencor Research Grants from Astrazeneca, BMS, Boehringer Ingelheim, GSK, INCA, Janssen Cilag, Merck, Novartis, Pfizer, Roche, Sanofi Non-financial support (drug supplied) from Astrazeneca, Bayer, BMS, Boringher Ingelheim, GSK, Medimmune, Merck, NH TherAGuiX, Pfizer. Laurence Albiges: Type of Relationship Honoraria (Institution) Advisory/Consultancy Astellas-Astrazeneca—Bellerophon—BMS—Corvus Pharmaceuticals—Ipsen—Janssen—Merck & Co—MSD—Novartis—Pfizer—Springer Healthcare Research Grant/Funding (Institution) BMS. B.B. Sponsored Research at Gustave Roussy Cancer Center; 4D Pharma, Abbvie, Amgen, Aptitude Health, AstraZeneca, BeiGene, Blueprint Medicines, BMS, Boehringer Ingelheim, Celgene, Cergentis, Cristal Therapeutics, Daiichi-Sankyo, Eli Lilly, GSK, Inivata, Janssen, Onxeo, OSE immunotherapeutics, Pfizer, Roche-Genentech, Sanofi, Takeda, Tolero Pharmaceuticals. Christophe Massard: Consultant/Advisory fees from Amgen, Astellas, Astra Zeneca, Bayer, BeiGene, BMS, Celgene, Debiopharm, Genentech, Ipsen, Janssen, Lilly, MedImmune, MSD, Novartis, Pfizer, Roche, Sanofi, Orion. Pr. Barlesi reports personal fees from Astra-Zeneca, Bayer, Bristol-Myers Squibb, Boehringer–Ingelheim, Eli Lilly Oncology, ß. Hoffmann–La Roche Ltd., Novartis, Merck, MSD, Pierre Fabre, Pfizer and Takeda, outside the submitted work.

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Cancers 14 03931 g001 550
Figure 1. Known features associated with COVID-19 severity, compared during different waves and stratified by outcome. Statistically significant differences between features within the waves were marked by: ****—p < 0.0001, ***—p < 0.001, **—p < 0.01, *—p < 0.05; Mann–Whitney U and Chi2 tests were used for numeric and categorical (solid cancer stage and performance status) features, accordingly; p values were adjusted using the Benjamini–Hochberg method.
Figure 1. Known features associated with COVID-19 severity, compared during different waves and stratified by outcome. Statistically significant differences between features within the waves were marked by: ****—p < 0.0001, ***—p < 0.001, **—p < 0.01, *—p < 0.05; Mann–Whitney U and Chi2 tests were used for numeric and categorical (solid cancer stage and performance status) features, accordingly; p values were adjusted using the Benjamini–Hochberg method.
Cancers 14 03931 g001
Cancers 14 03931 g002 550
Figure 2. CORONET scores predicted for individual patients from wave 1. D614G, 2. Alpha, and wave 4. Omicron.
Figure 2. CORONET scores predicted for individual patients from wave 1. D614G, 2. Alpha, and wave 4. Omicron.
Cancers 14 03931 g002
Cancers 14 03931 g003 550
Figure 3. Performance of the CORONET model in different waves. CORONET scores were calculated for 258, 48, and 54 patients who were included from wave 1. D614G, 2. Alpha, and wave 4. Omicron. AUC = area under the curve calculated for admission and requirement for oxygen (O2).
Figure 3. Performance of the CORONET model in different waves. CORONET scores were calculated for 258, 48, and 54 patients who were included from wave 1. D614G, 2. Alpha, and wave 4. Omicron. AUC = area under the curve calculated for admission and requirement for oxygen (O2).
Cancers 14 03931 g003
Table
Table 1. Patient characteristics during three SARS-CoV-2 waves.
Table 1. Patient characteristics during three SARS-CoV-2 waves.
UnknownOverall1
D614G		2
Alpha		4
Omicron
n 1968143047563
Age *, median [Q1, Q3] 067.0 [57.0, 76.0]68.0 [59.0, 77.0] 61.0 [50.0, 72.0] 66.0 [54.0, 73.0]D614G vs. Alpha: p < 0.001, CLES ** = 0.621
Cancer type, n (%)Breast141325 (17.8)223 (17.3)93 (19.6)9 (14.3)
Colorectal161 (8.8)108 (8.4)47 (9.9)6 (9.5)
Lung248 (13.6)170 (13.2)67 (14.1)11 (17.5)
Other solid cancer801 (43.9)576 (44.7)197 (41.6)28 (44.4)
Haematological malignancy291 (15.9)212 (16.4)70 (14.8)9 (14.3)
Solid cancer stage, n (%)1539197 (17.3)189 (24.5)7 (2.2)1 (1.9)
2135 (11.9)103 (13.4)25 (7.9)7 (13.5)
3328 (28.8)193 (25.1)123 (38.9)12 (23.1)
4478 (42.0)285 (37.0)161 (50.9)32 (61.5)
Chemotherapy, n (%) 314653 (33.2)377 (26.4)249 (52.4)27 (42.9)
Immunotherapy, n (%) 31492 (4.7)54 (3.8)33 (6.9)5 (7.9)
Targeted Therapy, n (%) 314197 (10.0)121 (8.5)64 (13.5)12 (19.0)
Radiotherapy, n (%) 486106 (5.4)50 (3.5)45 (9.5)11 (17.5)
Vaccination, n (%)Vaccinated
1 dose
2 doses
3 doses		15-0046 (95.8)
1 (2.2)
8 (17.4)
37 (80.4)
Unvaccinated-1430 (100.0)475 (100.0)2 (4.2)
* Statistical differences were calculated only for age because there were too many missing values for other categorical features; ** CLES = common language effect size, the probability that a score selected randomly from one distribution will be greater than a score selected randomly from another.
Table
Table 2. Patient outcomes during three SARS-CoV-2 waves.
Table 2. Patient outcomes during three SARS-CoV-2 waves.
Wave
Overall1
D614G		2
Alpha		4
Omicron		Significant Differences between Waves *
n 1968143047563
Outcome, n (%)Discharged548 (27.8)336 (23.5) 186 (39.2) 26 (41.3) 1 vs. 2: p < 0.001; 1 vs. 4: p = 0.002
Admitted527 (26.8)379 (26.5)129 (27.2)19 (30.2)
Admitted+O2454 (23.1)338 (23.6)98 (20.6)18 (28.6)
Admitted+O2+
died		439 (22.3)377 (26.4) 62 (13.1) 01 vs. 2: p < 0.001
* Chi-squared tests; p values adjusted for multiple comparisons using the Benjamini–Hochberg method.
Table
Table 3. Treatments for COVID-19.
Table 3. Treatments for COVID-19.
1
D614G		2
Alpha		4
Omicron
No. Patients TreatedNo. Patients Not TreatedMissingNo. Patients TreatedNo. Patients Not TreatedMissingNo. Patients TreatedNo. Patients Not TreatedMissing
steroids36 (32.4%)75 (67.6%)131988 (51.2%)84 (48.8%)30319 (41.3%)27 (58.7%)17
remdesivir1 (0.9%)110 (99.1%)131914 (8.3%)155 (91.7%)3060 (0%)45 (100%)18
lipinovir16 (14.4%)95 (85.6%)13190 (0%)167 (100%)3080 (0%)46 (100%)17
interferon0 (0%)108 (100%)13220 (0%)167 (100%)3080 (0%)46 (100%)17
interferon beta4 (3.6%)106 (96.4%)13200 (0%)167 (100%)3080 (0%)46 (100%)17
anticoagulation prophylaxis70 (64.2%)39 (35.8%)132179 (46.7%)90 (53.3%)30622 (47.8%)24 (52.2%)17
anticoagulation treatment dose6 (5.5%)103 (94.5%)132120 (11.9%)148 (88.1%)3070 (0%)46 (100%)17
antibiotic79 (71.8%)31 (28.2%)1320100 (58.8%)70 (41.2%)30517 (37%)29 (63%)17
plasma2 (1.8%)108 (98.2%)13205 (3%)162 (97%)3080 (0%)46 (100%)17
tocilizumab5 (4.5%)105 (95.5%)13208 (4.8%)159 (95.2%)3082 (4.3%)44 (95.7%)17
nebulised interferonb1 (0.9%)106 (99.1%)13230 (0%)167 (100%)3080 (0%)46 (100%)17
hydroxychloroquine51 (45.9%)60 (54.1%)13190 (0%)166 (100%)3090 (0%)46 (100%)17
aspirin1 (1%)102 (99%)13272 (1.2%)165 (98.8%)3080 (0%)46 (100%)17
baricitinib0 (0%)15 (100%)14150 (0%)49 (100%)4260 (0%)46 (100%)17
molnupiravir--1430--4750 (0%)42 (100%)21
sotrovimab--1430--4750 (0%)42 (100%)21
other drug--1430-----63
Table
Table 4. Comparison of patients’ characteristics associated with severity of COVID-19 during waves, stratified by outcome.
Table 4. Comparison of patients’ characteristics associated with severity of COVID-19 during waves, stratified by outcome.
Wave, Median [Q1, Q3]
VariableOutcome1
D614G		2
Alpha		4
Omicron		Significant Differences between Waves *
AgeDischarged62.0 [53.0, 71.0]58.0 [44.0, 67.8]66.0 [60.5, 72.0]1 vs. 2 p = 0.0017; 2 vs. 4 p = 0.0473
Admitted65.0 [56.0, 74.0]61.0 [51.0, 71.0]53.0 [43.5, 73.0]1 vs. 2 p = 0.0273
Admitted+O271.0 [63.0, 78.0]68.0 [55.0, 76.0]67.5 [64.2, 74.5]1 vs. 2 p = 0.0125
Admitted+O2+died73.0 [65.0, 81.0]70.5 [58.2, 78.0] 1 vs. 2 p = 0.0034
Solid cancer stageDischarged2.5 [1.0, 3.0]3.0 [3.0, 4.0]4.0 [3.0, 4.0]1 vs. 2 p = 0.0000, 1 vs. 4 p = 0.0041
Admitted3.0 [2.0, 4.0]4.0 [3.0, 4.0]4.0 [3.0, 4.0]1 vs. 2 p = 0.0001
Admitted+O23.0 [1.0, 4.0]4.0 [3.0, 4.0]4.0 [3.0, 4.0]1 vs. 2 p = 0.0000, 1 vs. 4 p = 0.0146
Admitted+O2+died4.0 [2.0, 4.0]4.0 [3.0, 4.0] 1 vs. 2 p = 0.0125
Performance statusDischarged1.0 [0.0, 1.0]1.0 [1.0, 1.0]1.0 [0.2, 1.0]
Admitted1.0 [1.0, 2.0]1.0 [1.0, 2.0]1.0 [0.0, 1.0]
Admitted+O21.0 [1.0, 2.0]1.0 [1.0, 2.0]2.0 [1.0, 2.0]
Admitted+O2+died2.0 [1.0, 3.0]1.0 [1.0, 2.0] 1 vs. 2 p = 0.0075
Total no.
comorbidities		Discharged1.0 [1.0, 2.0]1.0 [1.0, 2.0]1.0 [1.0, 2.0]
Admitted1.0 [1.0, 3.0]1.0 [0.0, 2.0]1.0 [0.0, 2.0]
Admitted+O22.0 [1.0, 3.0]1.0 [1.0, 2.0]1.0 [1.0, 2.8]1 vs. 2 p = 0.0014
Admitted+O2+died2.0 [1.0, 4.0]2.0 [1.0, 3.0]
NEWS2Discharged1.0 [0.0, 2.0]1.0 [0.0, 3.0]1.0 [0.0, 1.0]
Admitted2.0 [1.0, 3.0]2.0 [1.0, 4.0]1.0 [0.0, 2.0]
Admitted+O24.0 [2.0, 6.0]3.0 [1.0, 4.0]3.5 [2.0, 5.0]1 vs. 2 p = 0.0063
Admitted+O2+died4.0 [2.0, 7.0]5.5 [3.0, 8.0]
AlbuminDischarged40.0 [36.0, 43.0]41.0 [36.0, 44.0]40.0 [36.0, 43.2]
Admitted37.0 [32.0, 41.0]39.0 [34.0, 42.0]37.0 [31.5, 43.0]
Admitted+O233.0 [28.0, 37.0]30.0 [23.5, 35.5]30.5 [25.0, 36.0]1 vs. 2 p = 0.0029
Admitted+O2+died32.0 [26.0, 38.0]31.0 [28.0, 38.0]
C-reactive proteinDischarged12.0 [4.6, 47.0]9.5 [3.0, 39.8]4.3 [3.1, 17.0]
Admitted36.0 [12.0, 90.4]22.0 [6.4, 65.2]43.6 [7.8, 108.9]
Admitted+O270.0 [36.3, 127.0]57.1 [18.5, 122.4]62.4 [25.7, 128.1]
Admitted+O2+died91.0 [40.0, 168.0]118.8 [52.8, 201.7]
LymphocytesDischarged1.1 [0.7, 1.9]1.3 [0.9, 1.8]1.1 [0.6, 1.6]
Admitted0.9 [0.5, 1.4]0.9 [0.6, 1.6]0.8 [0.5, 1.6]
Admitted+O20.9 [0.6, 1.4]0.8 [0.6, 1.2]1.1 [0.6, 1.4]
Admitted+O2+died0.7 [0.4, 1.2]0.6 [0.4, 1.2]
NLRDischarged2.9 [1.9, 5.1]2.4 [1.6, 4.2]3.3 [1.7, 5.9]
Admitted3.7 [1.9, 8.6]3.9 [2.1, 7.5]4.3 [2.6, 11.1]
Admitted+O24.5 [2.7, 9.3]4.6 [2.1, 7.6]5.4 [2.9, 6.8]
Admitted+O2+died5.9 [3.1, 13.0]6.6 [3.1, 12.3]
NeutrophilsDischarged3.5 [2.2, 4.9]3.2 [2.2, 4.5]3.5 [2.0, 4.2]
Admitted3.9 [2.2, 6.3]3.7 [2.2, 5.8]3.9 [3.0, 9.2]
Admitted+O24.4 [2.9, 6.8]4.0 [2.1, 5.4]5.3 [3.6, 8.3]
Admitted+O2+died5.0 [3.0, 7.9]5.3 [2.6, 9.6]
PlateletsDischarged217.0 [170.0, 271.0]229.0 [188.0, 275.0]193.0 [130.5, 285.5]
Admitted216.0 [149.2, 296.0]234.0 [151.8, 311.5]243.0 [196.5, 389.0]
Admitted+O2192.0 [143.0, 264.0]204.0 [143.0, 275.2]202.5 [157.5, 312.8]
Admitted+O2+died183.5 [110.0, 270.0]181.0 [127.0, 287.0]
* Mann–Whitney U and Chi2 tests were used for numeric and categorical (solid cancer stage and performance status) features, accordingly; p-values were corrected for multiple testing, using one-step Sidak correction.
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Wysocki, O.; Zhou, C.; Rogado, J.; Huddar, P.; Shotton, R.; Tivey, A.; Albiges, L.; Angelakas, A.; Arnold, D.; Aung, T.; et al. An International Comparison of Presentation, Outcomes and CORONET Predictive Score Performance in Patients with Cancer Presenting with COVID-19 across Different Pandemic Waves. Cancers 2022, 14, 3931. https://doi.org/10.3390/cancers14163931

AMA Style

Wysocki O, Zhou C, Rogado J, Huddar P, Shotton R, Tivey A, Albiges L, Angelakas A, Arnold D, Aung T, et al. An International Comparison of Presentation, Outcomes and CORONET Predictive Score Performance in Patients with Cancer Presenting with COVID-19 across Different Pandemic Waves. Cancers. 2022; 14(16):3931. https://doi.org/10.3390/cancers14163931

Chicago/Turabian Style

Wysocki, Oskar, Cong Zhou, Jacobo Rogado, Prerana Huddar, Rohan Shotton, Ann Tivey, Laurence Albiges, Angelos Angelakas, Dirk Arnold, Theingi Aung, and et al. 2022. "An International Comparison of Presentation, Outcomes and CORONET Predictive Score Performance in Patients with Cancer Presenting with COVID-19 across Different Pandemic Waves" Cancers 14, no. 16: 3931. https://doi.org/10.3390/cancers14163931

APA Style

Wysocki, O., Zhou, C., Rogado, J., Huddar, P., Shotton, R., Tivey, A., 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., ... on behalf of the ESMO Co-Care. (2022). An International Comparison of Presentation, Outcomes and CORONET Predictive Score Performance in Patients with Cancer Presenting with COVID-19 across Different Pandemic Waves. Cancers, 14(16), 3931. https://doi.org/10.3390/cancers14163931

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