Prevalence and risks of severe events for cancer patients with COVID-19
infection: a systematic review and meta-analysis
Qiang Su a (MD), Jie-xuan Hua(MD), Hai-shan Lina(MD), Zheng Zhangb(MD),
Emily C. Zhuc(PhD), Chen-guang Zhangd*(PhD), Di-ya Wange*(PhD), Zu-hua
Gaof*(MD), and Bang-wei Caoa*(MD) a Department of Oncology, Beijing Friendship Hospital, Capital Medical University,
Beijing, 100050, China. b Department of Gastroenterology, Beijing Friendship Hospital, Capital Medical
University, Beijing, 100050, China. c McCoy College of Business, Texas State University, 601 University Dr., San
Marcos, Texas 78666, USA. d Department of Biochemistry and Molecular Biology, School of Basic Medical
Sciences, Capital Medical University, Beijing, 100069, China. e Department of Radiation Oncology, University of California, San Francisco, CA,
USA. f Department of Pathology, Research Institute of McGill University Health Center,
Montreal, QC, Canada
* Correspondence authors
E-mail address:
[email protected] for Dr Chenguang Zhang;
[email protected] for Dr Di-ya Wang;
[email protected] for Dr Zu-hua Gao;
[email protected] for Dr Bang-wei Cao.
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NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.
Summary
Background
The corona virus disease 2019 (COVID-19) pandemic poses a severe challenge
to public health, especially to those patients with underlying diseases. In this
meta-analysis, we studied the prevalence of cancer among patients with COVID-19
infection and their risks of severe events.
Methods
We searched the Pubmed, Embase and MedRxiv databases for studies between
December 2019 and May 3, 2020 using the following key words and terms:
sars-cov-2, covid-19, 2019-ncov, 2019 novel coronavirus, corona virus disease-2019,
clinical, clinical characteristics, clinical course, epidemiologic features, epidemiology,
and epidemiological characteristics. We extracted data following PICO (patient,
intervention, comparison and outcome) chart. Statistical analyses were performed
with R Studio (version 3.5.1) on the group-level data. We assessed the studies’ risk of
bias in accordance to the adjusted Joanna Briggs Institute. We estimated the
prevalence or risks for severe events including admission into intensive care unit or
death using meta-analysis with random effects.
Findings
Out of the 2,551 studies identified, 32 studies comprising 21,248 participants
have confirmed COVID-19. The total prevalence of cancer in COVID-19 patients
was 3.97% (95% CI, 3.08% to 5.12%), higher than that of the total cancer rate
(0.29%) in China. Stratification analysis showed that the overall cancer prevalence of
COVID-19 patients in China was 2.59% (95% CI, 1.72% to 3.90%), and the
prevalence reached 3.79% in Wuhan (95% CI, 2.51% to 5.70%) and 2.31% (95% CI,
1.16% to 4.57%) in other areas outside Wuhan in China. The incidence of ICU
admission in cancer patients with COVID-19 was 26.80% (95% CI, 21.65% to
32.67%) and the mortality was 24.32% (95% CI, 13.95% to 38.91%), much higher
than the overall rates of COVID-19 patients in China. The fatality in COVID patients
with cancer was lower than those with cardiovascular disease (OR 0.49; 95% CI,
0.34 to 0.71; p=0.39), but comparable with other comorbidities such as diabetes (OR
1.32; 95% CI, 0.42 to 4.11; p=0.19), hypertension (OR 1.27; 95% CI, 0.35 to 4.62;
p=0.13), and respiratory diseases (OR 0.79; 95% CI, 0.47 to 1.33; p=0.45).
Interpretation
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This comprehensive meta-analysis on the largest number of patients to date
provides solid evidence that COVID-19 infection significantly and negatively
affected the disease course and prognosis of cancer patients. Awareness of this could
help guide clinicians and health policy makers in combating cancer in the context of
COVID-19 pandemic.
Funding
Beijing Natural Science Foundation Program and Scientific Research Key
Program of Beijing Municipal Commission of Education (KZ202010025047).
Key words:COVID-19; Cancer; Prevalence; Fatality; Meta-analysis
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Introduction
The pandemic of severe acute respiratory syndrome corona virus 2
(SARS-CoV-2) has caused 4,794,932 confirmed cases and 316,420 deaths globally
by May 7, 2020, affecting almost all the countries in the world.1 WHO announced
COVID-19 as a public health emergency of international concern on January 30,
2020. Most COVID-19 cases experience mild to moderate respiratory symptoms or
signs such as fever and cough, and will recover without special treatment.
Unfortunately, the elder people, and those suffering underlying medical conditions
such as cardiovascular disease, diabetes, chronic respiratory disease, and cancer are
more likely to develop into more serious conditions, including multiple organ
dysfunction syndrome or even death.2-4
Cancer is a major public health problem worldwide and is the second leading
cause of death in the United States. Recent research had shown that there were about
24.5 million cancer cases worldwide and 9.6 million cancer deaths in 2017. In 2020,
the number of new cancer cases and cancer related death is estimated to
reach1,806,590 and 606,520 in the US, respectively.5Cancer patients are more
susceptible to infections than those without cancer due to the systemic
immunosuppression caused by the malignancy itself or anticancer treatments, such as
chemo-radiation. 6-9 Therefore, it is conceivable that cancer patients are at higher risk
of COVID-19 infection. Moreover, unplanned interruptions of regular anti-tumor
therapies, due to the overloaded healthcare systems in the pandemic battle, expose
cancer patients’ adverse clinical risks of uncontrolled primary disease. Furthermore,
some tumor markers such as CEA, CA-125 can be elevated and positively correlated
with the pathological progression of COVID-19 infection, which may increase the
cancer detection rate. 10
The relationship between cancer and COVID-19 has drawn great attention from
the beginning of the pandemic. An early study by Liang et al reported a cancer
prevalence of 1.13% (95% CI, 0.61% to 1.65%) among 1,590 cases of COVID-19 in
China (only 18 patients with cancer), which was higher compared with the overall
cancer incidence of 0.29% in the Chinese population.11 More importantly, cancer
patients with COVID-19 had a higher risk of severe events (a composite end point
defined as the percentage of patients who were admitted to the intensive care unit
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(ICU) and required invasive ventilation or who died) compared with those
COVID-19 patients without cancer (39% v 8%, HR: 5.34; 95%CI, 1.80 to 16.18; P =
0.0026). A pooled meta-analysis by Aakash Desai indicated that prevalence of cancer
in COVID-19 patients was 2.0% (95% CI, 2.0% to 3.0%; I2 = 83.2%), while the
prevalence was 3.0% (95% CI, 1.0% to 6.0%) in studies with a sample size smaller
than 100, and 2.0% (95% CI, 1.0% to 3.0%) in studies with a sample size not smaller
than 100.12 To guide clinical management and appropriate medical resources
allocation, it is necessary to validate the relationship of cancer and COVID-19 in a
much larger international scale. In the current study, we performed a meta-analysis of
21,248 participants in 32 clinical studies across six countries (China, Japan, Korea,
Italy, France, USA)11,13-43to exploit the prevalence of cancer in COVID-19 patients as
well as risks of severe events including ICU admission and death of these patients.
Methods
Search Methods and Study Selection
We searched Pubmed, Embase and MedRxiv databases for studies according to
the following key words and terms: sars-cov-2, covid-19, 2019-ncov, 2019 novel
coronavirus, Corona Virus Disease-2019, clinical, clinical characteristics, clinical
course, epidemiologic features, epidemiology, epidemiological characteristics. The
following data from all relevant studies between December 2019 and May, 2020
were included in our meta-analysis: (1) the study subjects were patients with
confirmed diagnosis of SARS-CoV-2 viral infection, based on the detection of the
viral nucleic acid by reverse transcription quantitative PCR (RT-qPCR) assay; (2) the
results included age, sex, epidemiology and cancer information; (3) study types
include cohort studies and clinical trials; (4) restriction of language to English only.
Animal studies, systematic reviews, case reports, and) small studies of fewer than 10
patients were excluded. This study was performed according to the Preferred
Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guideline.44
(eTable 1)
Data extraction
The two authors (Q.S and JX.H) independently extracted the data by checking
the titles, abstracts and full text. We then assessed the eligibility of the extracted
studies using the PICO (patient, intervention, comparison and outcome) chart.
Disagreements in the assessment were resolved via discussion with the third reviewer
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(CG.Z).45 Finally, they extracted the following information from all eligible studies:
the first author, study period, district, gender, age, diagnosis methods, the number of
total patients, the number of cancer patients, serious events, death toll, the number of
comorbidities, and death toll of comorbidities.
Data analysis
Statistical analyses were performed using R’s META package in R Studio
(version 3.5.1). We focused on the following parameters in COVID-19 patients: the
cancer prevalence, the ICU admission rate, and the cancer mortality. Different
subgroups of patient population with cancer were compared. The prevalence for
cancer in COVID-19 patients was expressed as percentage with 95% confidence
interval (95% Cis). Odds ratio (OR) was used to estimate the -risks of ICU admission
and death of cancer patients with COVID-19 compared with those suffering other
comorbidities including cardiovascular diseases, diabetes, hypertension or non-cancer
patients with COVID-19. Heterogeneity was evaluated using the Q test and Higgin’s
and Thompson’s I2 (I2) statistics. Defined as the percentage of variations in the
effect sizes that is not the cause of sampling errors, I2 statistics is robust and not
sensitive to the number of studies under analysis. We consider I2 value of over 50%
as moderate to high heterogeneity. P<0.05 was considered statistically significant.
We used random-effects model if the I2 value was more than 50%. We also
performed a multi-group subgroup analysis to explore the source of heterogeneity.
Specifically, subgroup design was based on the sample size (smaller or greater than
100), and the area of the patients (China/Outside China;
Japan/Korea/Italy/France/USA). The quality of the included studies was assessed by
the Joanna Briggs Institute (JBI),46and we slightly modified the table to make it adapt
with selected studies (eFigure 1). We also conducted sensitivity analysis by
sequentially removing individual studies one by one, and evaluating the stability of
the results. Begger test and Egger test were used to evaluate publication bias, caused
by possibly missing unpublished studies of low effect sizes. Finally, we developed a
funnel plot to describe the results of publication bias.
Role of the funding source
The funder of the study had no role in study design, data collection, data analysis,
data interpretation, or writing of the report. The corresponding author had full access
to all the data in the study and had final responsibility for the decision to submit for
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publication.
Results
Eligible Studies and Characteristics
Using our searching principles, we retrieved 2,551 studies from three databases
between Dec 1st, 2019 and May 3rd, 2020, among which, 32 studies comprising
21,248 patients met our selection criteria (eTable 2). 22 of the 32 studies were
mutually exclusive cohorts of patients in China (Wuhan n=13; outside of Wuhan n=5;
unclear n=4), 2 studies based on cohorts of other Asian countries (Korean and Japan),
and 8 studies were based in Europe and USA (Italy n=1; France n=1; USA n=6). The
detailed research flow chart is shown in Figure 5, characteristics of the included
studies are shown in Table 1, and data on severe events in patients with COVID-19 is
shown in eTable3. Among the 21,248 patients in those 32 studies, valid data
including basic information, epidemiological characteristics, clinical characteristics,
prevalence and mortality of cancer or other complications were extracted from
COVID-19 patients. All patients presented exhibited mild to severe clinical
manifestations and were confirmed of COVID-19 infection RT-qPCR.
Result of meta-analyses
Prevalence of cancer in COVID-19 patients
We first analyzed the cancer prevalence rates in COVID-19 patients. As shown
in Figure 1, the total prevalence of cancer in COVID-19 patients was 3.97% (95% CI,
3.08% to 5.12%), which is higher than the total cancer rate (0.29%) in China.
When analyzing in subgroups of different sample size, the prevalence showed
some variation. The prevalence of cancer was 4.16% (95% CI, 2.80% to 6.14%) in
the subgroup whose sample size was ≤100, and 3.99% (95% CI, 2.96% to 5.37%) in
the subgroup whose sample size was >100 (Figure 2). This observation is consistent
and comparable with the analysis by Aakash Desai.12
We then analyzed the cancer prevalence based on patients’ geographic location.
The overall cancer prevalence of COVID-19 patients was 2.59% (95% CI, 1.72% to
3.90%) in China, 3.85% (95% CI, 1.45% to 9.80%) in Japan, and 3.57% (95% CI,
0.50% to 21.42%) in Korea (Figure 1). The prevalence was higher in Italy and USA,
which reached 11.35% (95% CI, 10.19% to 12.61%) and 6.09% (95% CI, 5.13% to
7.21%), respectively (Figure 1). Moreover, the cancer prevalence of COVID-19
patients in Wuhan was 3.79% (95% CI, 2.51% to 5.70%), higher than the average
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level of China (Figure 3).
Our analyses clearly showed that the cancer prevalence of COVID-19 patients
was higher than that of the general population. In USA and European countries, the
prevalence was higher than in China and other Asian countries. In China, the
prevalence in Wuhan was higher than in other areas of China (Figure1 and 3).
Risk of severe events of cancer in COVID-19 patients
In China, the incidence of ICU admission in COVID19 patients with cancer was
38.77% (95% CI, 26.04% to 53.24%) (Figure 4). In Wuhan, the incidence of ICU
admission and death in COVID19 patients with cancer were 42.80 (95% CI, 23.38 %
to 64.72%) and 59.61% (95% CI, 28.86% to 84.30%) respectively. The incidence of
ICU admission in COVID19 patients with cancer were 23.71% (95% CI, 18.41% to
29.99%) outside China, and the fatality reached 17.45% (95% CI, 11.92% to 24.83%)
in Europe. The COVID19 patients in Wuhan apparently had a worse outcome than
the rest of the China. Moreover, COVID-19 patients with cancer had a significantly
higher ICU admission rate than those without cancer in China (OR 2.77; 95% CI,
1.28 to 6.00; p=0.46), and had overall higher fatality irrelative with areas (OR 4.87;
95% CI, 1.52 to 15.59; p=0.74/OR 3.97; 95% CI, 1.10 to 14.32; p=0.88) (eFigure 4).
This may imply the existence of complex interrelationship between COVID-19 and
cancer, where cancer was able to exaggerate COVID-19 infection and vice versa.
We further compared the rate of ICU admission rate and death of cancer with
other underlying diseases in COVID-19 patients. As shown in eFigure 2, there was
no significantly difference in the ICU admission rate of COVID 19 patients with
cancer from COVID 19 patients with other comorbidities, including cardiovascular
diseases (OR 0.89; 95% CI, 0.52 to 1.53; p=0.91), diabetes (OR 0.81; 95% CI, 0.48
to 1.37; p=0.85), hypertension (OR 0.88; 95% CI, 0.53 to 1.44; p=0.64), and
respiratory disease (OR 0.65;95% CI, 0.37 to 1.15; p=0.45). Similarly, mortality rate
of COVID 19 patients with cancer is close to COVID 19 patients with diabetes (OR
1.32; 95% CI, 0.42 to 4.11; p=0.39), hypertension (OR 1.27; 95% CI, 0.35 to 4.62;
p=0.13), and respiratory diseases (OR 0.79; 95% CI, 0.47 to 1.33; p=0.45).
Nevertheless, it is worth noting that COVID19 patients with cancer showed higher
mortality rate than cardiovascular diseases (OR 0.49; 95% CI, 0.34 to 0.71; p=0.39)
(eFigure 3).
Quality assessment and publication bias
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The quality of the included literature was satisfactory. Insufficient number of
patients and incomplete coverage of the population were the main cause of low
quality literature (eFigure 1), which we excluded. We performed sensitivity analysis
of the included studies to assess their stability. After each study was excluded in turn,
we found no significant difference in the results of the analysis (eTable 4,5). The
funnel plot and Egger test results showed the risk of publication bias is low (p-value
= 0.35) (eFigure 5).
The heterogeneity among our study subgroups was high. In the subgroup
mortality analysis, the heterogeneity parameter I2 were >50% in all subgroups except
group IA (sample size ≤100). So, the random-effects model was deployed.
Discussion
A comprehensive analysis of cancer prevalence and severe events among
patients with COVID-19 can serve as an important reference for oncology clinicians
and healthcare policy makers. A few existing studies focused mainly on the
prevalence analysis of cancer patients with COVID-19, lacking a comparison of the
prevalence or risk for severe events among different areas. 11,12We performed a
systematic review on the prevalence of cancer and risks of severe events among
patients with COVID-19 using a collection of sparse binomial data from published
studies. To our knowledge, this is the largest and most comprehensive meta-analysis
of the prevalence and fatality analysis of cancer among patients with COVID-19 to
date.
In this study, we found that the total prevalence of cancer in COVID-19 patients
was 3.97% (95% CI, 3.08% to 5.12%), which was significantly higher than that of
the total cancer rate (0.29%) in China. 47In other words, approximately 3 in 100
patients with COVID-19 had suffered from superimposed malignancy. The higher
prevalence of cancer in COVID19 patients could be due to multiple factors. Firstly,
caner is more common among senior patients, who are also susceptible to COVID19.
Secondly, due to primary disease and the anticancer treatment, cancer patients are
immune compromised, hence they are more vulnerable to viral infection. Thirdly,
COVID19 infection has been shown to elevate the serum level of some cancer
biomarkers such as CEA and CA19-9 etc., which may increase the cancer detection
rate in this population.10
The cancer prevalence in COVID19 patients in the city of Wuhan was
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significantly higher than the rest of China. Meanwhile, it was significantly higher in
European and American countries than in Asian countries. The geographical
differences cannot be explained by factors like race or climate. It seems to be
associated with overall incidence of COVID19 infection in those affected areas.
Therefore, effective control of COVID19 infection, especially non-pharmaceutical
interventions, could potentially reduce the cancer prevalence.48
In addition to the high prevalence, we found that nearly a third of cancer patients
with COVID-19 experienced aggravated conditions and were admitted to ICU, which
was apparently higher than total COVID-19 patients. Moreover, fatality in cancer
patients with COVID-19 was about 24.32% (95% CI, 13.95% to 38.91%), also
significantly higher than the rate of total patients with COVID-19 (about 3-7%).49
Factors that affected cancer prevalence mentioned above might also explain the
worse prognosis of cancer patients with COVID19. Firstly, individuals with cancer
are generally older,50 which has been shown to express higher levels of
angiotensin-converting enzyme II (ACE2), a receptor for SARS-CoV-2 entry into the
cell.51 Secondly, cancer patients often suffer from immunosuppression, like
lymphopenia, which often results in the impairment of the required immune response
and may lead to viral propagation, tissue destruction, and progression to severe stages
of the disease in ACE2-rich tissues .52 Meanwhile, the overload in the hospitals may
prevent timely and sufficient medical care for COVID19 patients with cancer, also
leading to high fatality. Severe events are not only a major public health issue, but
also a serious economic concern. Although health care personnel and resources were
heavily allocated to ICU and treating severe cases, the outcomes were far from
satisfactory. Thus, prevention of CoVID19 infection using strict quarantine and
social distance measures is essential to protect cancer patients from worsening their
health conditions.
The outcome of cancer patients with COVID19 was also compared with other
morbidities, especially chronic diseases, which were thought as risk factors for severe
event in patients with COVID-19. A meta-analysis by Yang et al showed that
COVID-19 patients with hypertension, cardiovascular diseases and respiratory
disease have higher risk for developing severe complications.53 Du et al found that
61.9% patients with hypertension and 57.1% patients with cardiovascular or
cerebrovascular diseases developed severe events.54 Kang et al found that
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superimposed heart disease is the highest cause of death among all complications for
COVID-19 patients. 55From our analyses, the ICU admission rate for cancer
patients with COVID19 was comparable with diabetes, hypertension, cardiovascular
diseases and respiratory diseases. Meanwhile, the fatality of cancer patients with
COVID19 was also similar with other chronic diseases above expect cardiovascular
diseases. COVID19 patients with cardiovascular diseases showed higher mortality
rate than cancer. Thus, cancer should become one of the notable superimposed
comorbidities together with diabetes, cardiovascular and chronic respiratory diseases,
contributing to COVID-19 infection and severe events.
Our findings are of values to both cancer patients and oncology clinicians in
COVID-19 outbreak countries and territories. Future studies should rely on more
detailed analyses. For example, the prevalence and risks of severe events can vary
with different cancer types. A recent cohort study by Dai et al. indicated that
COVID-19 patients in Wuhan with hematologic cancer had the highest frequency of
severe events, which was followed by lung cancer, and patients with metastatic
cancer hade much higher incidence of severe events than those with non-metastatic
cancers. Moreover, cancer patients who had received surgery tended to have higher
risks of severe events than other treatments except immunotherapy. 56 We anticipate
further investigations on these issues in broader geographic and clinical settings.
Limitations
Our meta-analysis studied the cancer prevalence and risks of severe events in
COVID-19 patients by accommodating fully reported and censored data from diverse
literature. But this meta-analysis has limitations. Small-study effects were observed
when studies with smaller sample sizes had different incidences and wider CIs for
fatality in patients with COVID-19 and cancer. Although the forest plots showed no
asymmetry favoring cancer fatality studies, this meta-analysis is subject to
publication bias given that all of our analyses were based on publications. In addition,
this study is subject to any biases or errors of the original investigators, and the
results are generalizable only to patient groups with COVID-19. Meanwhile, it
currently cannot summarize the prevalence or risk of severe events of different types
of cancer in COVID-19 patients due to the lack of enough data.
Conclusions:
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In summary, using the largest dataset to date, our meta-analysis has clearly
demonstrated that (1) there is higher prevalence of COVID19 infection in cancer
patients, (2) when COVID19 infection occur in a patient with cancer, the patient is
more likely to develop severe clinical events. Patients with chronic health conditions
require more stringent preventive measures and more delicate therapeutic strategies
during the COVID19 pandemic.
Author contributions
QS, JXH and EC. Z had access to all the data included in the study and are
responsible for the completeness of the data and the accuracy of our analysis. JXH,
HSL and ZZ helped to design the study. QS, DYW, and BWC contributed to the
statistical analysis and the revision of this manuscript. ZHG and CGZ approved the
final manuscript.
Declaration of interests
All other authors declare no competing interests.
Acknowledgments
This work was supported by Beijing Natural Science Foundation Program and
Scientific Research Key Program of Beijing Municipal Commission of Education
(KZ202010025047).
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Table 1. List of the 32 Studies Included in This Meta-Analysis
Study Year District Diagnosis methods
Total patients
Cancer patients
Cancer- related Death
Death toll
Huang C13 12-6,19 1-2,20 Wuhan, CHN RT-PCR and
NGS 41 1
NA 6
Chen N14 1-1,20 1-20,20
Wuhan, CHN RT-PCR 99 1
NA 11
Wang D15 1-1,20 1-28,20
Wuhan, CHN
bron-
choalveolar
lavage fluid
138 10
NA 6
Huang Y16 12-21,2019
1-28,2020
Wuhan, CHN unclear 34 3
NA 0
Zhang J17 1-16,2020
1-3,2020
Wuhan, CHN RT-PCR 140 6
NA NA
Yang X18 12,2019
1-26,2020
Wuhan, CHN RT-PCR 52 2
NA 32
Shi H19 12-20,2019
1-23,2020
Wuhan, CHN RT-PCR and
NGS 81 4
NA 3
. C
C-B
Y-N
C-N
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Zhou F20 12-29,2019
1-31,2020
Wuhan, CHN unclear 191 2
NA 54
Chen L21 12,2019 2-3,2020
Wuhan, CHN RT-PCR 29 2 1 2
Wu C22 12-30,2019
1-15,2020
Wuhan, CHN RT-PCR 201 1
NA 44
Liu W23 12-30,2019
1-15,2020
Wuhan, CHN RT-PCR 78 4
NA 2
Zhang L24 1-13,2020
2-26,2020
Wuhan, CHN RT-PCR 28 28 8 8
Chen T25 1-13,2020
2-12,2020
Wuhan, CHN RT-PCR 274 7 5 113
Liu K26 12-30,2019
1-24,2020 NA, CHN RT-PCR 137 2 NA 16
Wang J27 12-17,2019
3-18,2020 NA, CHN RT-PCR 283 283 50 50
. C
C-B
Y-N
C-N
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Zhu W28 1-24,2020
2-20,2020
outside of
WH,CHN RT-PCR 32 2 NA NA
Yang W29 1-17,2020
2-10,2020
outside of
WH,CHN unclear 149 2
NA 0
Wu J30 1-22,2020
2-14,2020
outside of
WH,CHN RT-PCR 80 1
NA 0
Xu X31 1-23,2020
2-4,2020
outside of
WH,CHN RT-PCR 90 2
NA NA
Leung KS-S32 1-26,2020
2-28,2020
outside of
WH,CHN RT-PCR 50 1
NA NA
Guan W33 12,2019 1
29,2020 NA, CHN RT-PCR 1099 10
NA 15
Liang W11 12,2019 1
31,2020 NA, CHN unclear 1590 18
NA NA
Kim ES34 12,2019 2,2020 Korea unclear 28 1
NA 0
. C
C-B
Y-N
C-N
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Tabata S35 2-11,2020
2-25,2020 Japan RT-PCR 104 4
NA 0
Rossi PG36 2-27,2020
4-2,2020 Italy PCR 2653 301 44 217
Basse C37 3,2020 5,2020 France RT-PCR 141 141 30 30
McMichael TM38 2-28,2020
3-18,2020
King County,
Washington, US RT-PCR 167 15
NA 34
Petrilli CM39 3-1,2020 4-2,2020 New York, US RT-PCR 4103 185
NA 176
Cummings MJ40 3-2,2020 4-1,2020 New York, US RT-PCR 257 18
NA 86
Paranjpe I41 2-27,2020
4-2,2020 New York, US RT-PCR 2199 151
NA 310
Argenziano MG42 3-1,2020
4-15,2020 New York, US RT-PCR 1000 66
NA 172
. C
C-B
Y-N
C-N
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Richardson S43 3-1,2020 4-4,2020 New York, US RT-PCR 5700 320
NA 553
Note:RT-PCR and NGS:Real-time RT-PCR and next-generation sequencing; NA: Not available; NA, CHN: The patients were from China, but
was not sure whether from Wuhan or outside of Wuhan
. C
C-B
Y-N
C-N
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Figure legends Figure 1. Prevalence of cancer in COVID-19 patients in China and outside China.
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Figure 2. Prevalence of cancer in COVID-19 patients in subgroups based on sample size over or not over 100.
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Figure 3. Prevalence of cancer in COVID-19 patients based on areas.
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Figure 4. The incidence of ICU admission and fatality of cancer patients with COVID-19 in different areas.
A:ICU admission incidence of cancer patients with COVID-19 in Wuhan, B:ICU
admission incidence of cancer patients with COVID-19 outside Wuhan, C:ICU
admission incidence of cancer patients with COVID-19 outside China, A+B:ICU
admission incidence of cancer patients with COVID-19 in China, A+B+C:ICU
admission incidence of cancer patients with COVID-19 in all areas(all available
statistics), D:Fatality of cancer patients with COVID-19 in Wuhan; E:Fatality of
cancer patients with COVID-19 in Europe; D+E: Fatality of cancer patients with
COVID-19 in all areas(all available statistics)
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Figure 5. Flowchart depicting the studies selection process.
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eTable 1: Checklist of items included when reporting a systematic review and meta- analysis Section/topic Item No Checklist item Reported on page No Title Title 1 Identify the report as a systematic review, meta-analysis, or both 1 Abstract
Structured summary 2 Provide a structured summary including, as applicable, background, objectives, data
sources, study eligibility criteria, participants, interventions, study appraisal and synthesis
methods, results, limitations, conclusions and implications of key findings, systematic
review registration number
1-2
Introduction Rationale 3 Describe the rationale for the review in the context of what is already known 2-4
Objectives 4 Provide an explicit statement of questions being addressed with reference to participants,
interventions, comparisons, outcomes, and study design (PICOS) 3-4
Methods
Protocol and registration 5 Indicate if a review protocol exists, if and where it can be accessed (such as web address),
and, if available, provide registration information including registration number 4
Eligibility criteria 6 Specify study characteristics (such as PICOS, length of follow-up) and report
characteristics (such as years considered, language, publication status) used as criteria for
eligibility, giving rationale
4
Information sources 7 Describe all information sources (such as databases with dates of coverage, contact with
study authors to identify additional studies) in the search and date last searched 4
Search 8 Present full electronic search strategy for at least one database, including any limits used,
such that it could be repeated 4
Study selection 9 State the process for selecting studies (that is, screening, eligibility, included in systematic
review, and, if applicable, included in the meta-analysis) 4
Data collection process 10 Describe method of data extraction from reports (such as piloted forms, independently, in
duplicate) and any processes for obtaining and confirming data from investigators 4
Data items 11 List and define all variables for which data were sought (such as PICOS, funding sources)
and any assumptions and simplifications made 4
Risk of bias in individual studies 12 Describe methods used for assessing risk of bias of individual studies
(including specification of whether this was done at the study or outcome level), and how
this information is to be used in any data synthesis
5
Summary measures 13 State the principal summary measures (such as risk ratio, difference in means). 4-5
Synthesis of results 14 Describe the methods of handling data and combining results of studies, if done, including
measures of consistency (such as I2 statistic) for each meta-analysis 4-5
Risk of bias across studies 15 Specify any assessment of risk of bias that may affect the cumulative evidence (such as
publication bias, selective reporting within studies) 5
Additional analyses 16 Describe methods of additional analyses (such as sensitivity or subgroup analyses,
meta-regression), if done, indicating which were pre-specified 5
Results
Study selection 17 Give numbers of studies screened, assessed for eligibility, and included in the review, with
reasons for exclusions at each stage, ideally with a flow diagram 5
Study characteristics 18 For each study, present characteristics for which data were extracted (such as study size,
PICOS, follow-up period) and provide the citations 5
Risk of bias within studies 19 Present data on risk of bias of each study and, if available, any outcome-level assessment
(see item 12). 7
Results of individual studies 20 For all outcomes considered (benefits or harms), present for each study (a) simple
summary data for each intervention group and (b) effect estimates and confidence intervals,
ideally with a forest plot
5-7
Synthesis of results 21 Present results of each meta-analysis done, including confidence intervals and measures of
consistency 5
Risk of bias across studies 22 Present results of any assessment of risk of bias across studies (see item 15) 7
Additional analysis 23 Give results of additional analyses, if done (such as sensitivity or subgroup analyses,
meta-regression) (see item 16) 6-7
Discussion
Summary of evidence 24 Summarize the main findings including the strength of evidence for each main outcome;
consider their relevance to key groups (such as health care providers, users, and policy
makers)
7-10
Limitations 25 Discuss limitations at study and outcome level (such as risk of bias), and at review level
(such as incomplete retrieval of identified research, reporting bias) 10
Conclusions 26 Provide a general interpretation of the results in the context of other evidence, and
implications for future research 10
Funding
Funding 27 Describe sources of funding for the systematic review and other support (such as supply of
data) and role of funders for the systematic review 11
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eTable 2. Search performed in Pubmed and Embase
Recent queries in EMBASE No. Query Results #3 #1 AND #2 1061
#2 'clinical characteristics':ab,ti OR clinical: ab,ti OR 'clinical course': ab,ti OR 'epidemiologic features': ab,ti OR epidemiology: ab,ti OR 'epidemiological characteristics': ab,ti
4990590
#1 'sars cov 2':ti,ab OR 'covid 19':ab,ti OR '2019 ncov':ab,ti OR '2019 novel coronavirus': ab,ti OR 'corona virus disease 2019':ab,ti
5612
Recent queries in medrxiv
#1
covid-19;sars-cov-2;2019 novel coronavirus;2019-ncov;Corona Virus Disease-2019;clinical;clinical characteristics; clinical course; epidemiologic features;epidemiology;epidemiological characteristics
173
Recent queries in pubmed
Search Query Items found
#3 #1 AND #2 1317
#2 Search (((((sars-cov-2) OR covid-19) OR 2019 novel coronavirus) OR 2019-ncov) OR Corona Virus Disease-2019) Filters: Humans
1677
#1 Search ((((((clinical) OR clinical characteristics) OR clinical course) OR epidemiologic features) OR epidemiology) OR epidemiological characteristics) Filters: Humans
257113
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eTable 3. List of studies that reported at least one comorbidity-related severe events
Death
Study Basse C37 Chen T25 Rossi PG36 Wang J27 Zhang L24 Chen L21
Total patients 141 274 2653 283 28 29
Death toll 30 113 217 50 8 2
Single Comorbidities
Cancer NA 7 301 283 28 2
Cancer-related death
NA 5 44 50 8 1
Hypertension NA 93 430 NA NA 8
. C
C-B
Y-N
C-N
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Hypertension-related death
NA 54 87 NA NA 0
Diabetes NA 47 284 NA NA 5
Diabetes-related death
NA 24 51 NA NA 0
Cardiac disease NA 23 490 NA NA NA
Cardiac disease-related death NA 16 130 NA NA NA
Respiratory disease NA 18 128 NA NA NA
Respiratory disease -related death
NA 11 24 NA NA NA
ICU Admission
. C
C-B
Y-N
C-N
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Chen L21 Liang W11 Wang D15 Guan W33 Huang C13 Zhang J17 Argenziano MG42 Basse C37
Total patients 29 1590 138 1099 41 140 1000 141
Patients in ICU 5 NA 36 67 13 58 231 11
Single Comorbidities
Cancer 2 18 10 10 1 6 66 NA
Cancer patients in ICU
1 7 4 3 0 3 14 NA
Hypertension 8 NA 43 165 6 42 598 NA
Hypertension patients in ICU NA NA 21 24 2 22 157 NA
. C
C-B
Y-N
C-N
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Diabetes 5 NA 14 81 8 17 372 NA
Diabetes patients in ICU
NA NA 8 18 1 8 100 NA
Cardiac disease NA NA 20 27 6 12 233 NA
Cardiac disease patients in ICU
NA NA 9 6 3 8 53 NA
Respiratory disease NA NA 4 12 1 4 438 NA
Respiratory disease patients in ICU
NA NA 3 7 1 4 106 NA
Note: Single Comorbidities:COVID-19 patients with a single comorbidity; NA: Not available; Cancer-related
death: Deaths of COVID-19 patients with cancer; Patients in ICU: Covid-19 patients admitted to the ICU in the studies; Cancer patients
in ICU:Covid-19 patients with cancer admitted to the ICU . C
C-B
Y-N
C-N
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eTable4. Results of sensitivity analysis of the included studies Proportion p-value 95%CI I2
Omitting Chen L 0.0392 [0.0303; 0.0507] 90.3% Omitting Liang W 0.0437 [0.0342; 0.0555] 87.8% Omitting Wang D 0.0385 [0.0296; 0.0500]] 90.3% Omitting Guan W 0.0433 [0.0338; 0.0554] 88.7% Omitting Liu K 0.0407 [0.0315; 0.0525] 90.2% Omitting Yang X 0.0398 [0.0307; 0.0514] 90.3% Omitting Zhou F 0.0411 [0.0318; 0.0529] 90.1% Omitting Huang C 0.0400 [0.0309; 0.0516] 90.3% Omitting Chen N 0.0405 [0.0314; 0.0523] 90.2% Omitting Zhu W 0.0393 [0.0303; 0.0508] 90.3% Omitting Shi H 0.0394 [0.0304; 0.0510] 90.3% Omitting Wu C 0.0410 [0.0317; 0.0528] 90.1% Omitting Yang W 0.0408 [0.0315; 0.0526] 90.2% Omitting Zhang J 0.0396 [0.0305; 0.0513] 90.3% Omitting Wu J 0.0404 [0.0312; 0.0521] 90.2% Omitting Huang Y 0.0388 [0.0299; 0.0502] 90.3% Omitting Xu X 0.0403 [0.0311; 0.0520] 90.3% Omitting Chen T 0.0406 [0.0313; 0.0525] 90.1% Omitting Liu W 0.0394 [0.0303; 0.0510] 90.3% Omitting Kim ES 0.0398 [0.0307; 0.0514] 90.3% Omitting McMichael TM 0.0380 [0.0292; 0.0493] 90.3% Omitting Tabata S 0.0398 [0.0306; 0.0515] 90.3% Omitting Leung KS-S 0.0401 [0.0310; 0.0518] 90.3% Omitting Petrilli CM 0.0389 [0.0296; 0.0512] 89.3% Omitting Rossi PG 0.0385 [0.0307; 0.0481] 80.3% Omitting Cummings MJ 0.0384 [0.0295; 0.0500] 90.3% Omitting Paranjpe I 0.0377 [0.0284; 0.0498] 90.3% Omitting Argenziano MG 0.0382 [0.0291; 0.0500] 90.3% Omitting Richardson S 0.0377 [0.0280; 0.0505] 90.1% Pooled estimate 0.0397 [0.0308; 0.0512] 90.0% eTable5. Results of sensitivity analysis of the included studies(subgroup: prognosis of cancer)
Proportion p-value 95%CI I2 Omitting Chen T 0.1616 [0.1141; 0.2240] 69.50% Omitting Rossi PG 0.2060 [0.1142; 0.3429] 80.60% Omitting Argenziano MG 0.1818 [0.1102; 0.2849] 81.20% Omitting Wang J 0.1981 [0.1067; 0.3382] 81.80%
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Omitting Zhang L 0.1704 [0.1081; 0.2581] 79.40%
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eFigure 1. Quality assessment for studies
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eFigure 2. The incidence of ICU admission of Cancer patients VS Other Comorbidities with COVID-19
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eFigure 3. Fatality of Cancer patients VS Other Comorbidities with COVID-19
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eFigure 4. The incidence of ICU admission and fatality of cancer patients with COVID-19 VS Non-cancer patients
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eFigure 5. Results of publication bias
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Dear Editor,
We have enclosed a manuscript titled “Prevalence and risks of severe events for cancer patients with COVID-19 infection: a systematic review and meta-analysis” by Su et al. In this study, a meta-analysis was carried out to evaluate the prevalence of cancer patients in COVID19 infection and their risks of severe events. We searched related studies from diverse literatures, extracted data following PICO chart, and carried out statistical analyses using R Studio (version 3.5.1) on the group-level data. We estimated the overall prevalence and risks for severe events including admission into intensive care unit or death in cancer patients with COVID19 worldwide and also made preliminary comparisons among different countries and territories. Meanwhile, comparisons between cancer and other comorbidities including diabetes, hypertension, cardiovascular and respiratory diseases were also made concerning the prevalence, risks for ICU admission and death during COVID19 infection.
The major findings from this study can be described as, 1) The total prevalence of cancer in COVID-19 patients was 3.97% (95% CI, 3.08% to 5.12%) worldwide, while in China was 2.59% (95% CI, 1.72% to 3.90%), 3.79% in Wuhan (95% CI, 2.51% to 5.70%). 2) The prevalence of cancer in COVID19 was higher in USA and European countries than in Asian countries.
3) The incidence of ICU admission in cancer patients with COVID-19 was 26.80% (95% CI, 21.65% to 32.67%) and the mortality was 24.32% (95% CI, 13.95% to 38.91%).
4) The fatality in COVID patients with cancer was lower than those with cardiovascular disease, but comparable with other comorbidities such as diabetes, hypertension, and respiratory diseases.
The listed authors have participated and have made significant contribution to the study, and will take the responsibility for the appropriateness regarding the experimental design, acquired methodology, the collection, analysis and summary of the data. As the corresponding author, I have reviewed the final version of the manuscript and approve it for submission.
We also certify that this manuscript has not been published in whole or as part for publication elsewhere. I have read and abided by the statement of ethical standards for manuscripts submitted to your journal. The attached are the required submission items. We believe that the results from this manuscript fit the field readers’ interests and warrant for a publication in “medRxiv” as a full length article. Please consider our manuscript seriously and feedback us with valuable scientific critiques. Thank you very much.
Sincerely yours,
Chen-guang Zhang, Ph.D.
School of Basic Medical Sciences
Capital Medical University, Beijing, China.
E-mail: [email protected]
Di-ya Wang, Ph.D.
Department of Radiation Oncology, University of California, San Francisco, CA, United States.
E-mail: [email protected].
Zu-hua Gao, MD.
Department of Pathology, Research Institute of McGill University Health Center, Montreal, QC,
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Canada.
E-mail: [email protected]
Bang-wei Cao, MD.
Department of Oncology, Beijing Friendship Hospital, Capital Medical University, Beijing,
100050, China.
E-mail: [email protected]
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Research in context
Evidence before this study
We searched Pubmed, Embase and MedRxiv databases for cohort
studies and clinical trials of evaluating prevalence of cancer patients with
COVID-19 as well as risks of severe clinical events including ICU
admission and death in those patients between Dec 1st, 2019 and May 3rd,
2020. Meanwhile, preliminary comparisons concerning cancer prevalence
and risks for severe events among different countries were made.
Furthermore, risks for ICU admission and death were also made between
cancer patients with patients suffering other comorbidities including
diabetes, hypertension, cardiovascular and respiratory diseases.
Conclusions of previous similar meta-analyses were limited due to small
sample sizes, which also did not make comparisons among different
countries or territories. Meanwhile, the relative risks of severe events for
cancer over other underlying diseases for COVID19 patients were largely
unknown. Our search did not retrieve any systematic review of
information on prevalence and risks of severe events of cancer patients in
COVID19 infection.
Added value of this study
We did a systematic review of 32 studies comprising 21,248 cases
with confirmed COVID-19. To our knowledge, this is the largest and
most comprehensive meta-analysis to date on the prevalence of cancer
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patients with COVID19 infection and their risks of ICU admission or
death. According to our analyses, the total prevalence of cancer in
COVID-19 patients was 3.97% worldwide. In China, the overall cancer
prevalence of COVID-19 patients was 2.59%, and in Wuhan, the city
where COVID-19 was first reported, the prevalence reached 3.79% while
2.31%in other areas outside Wuhan in China. The prevalence was higher
in Italy and USA, reaching 11.35% and 6.09%, respectively. The
incidence of ICU admission in cancer patients with COVID-19 was
26.80% worldwide, and the mortality was 24.32% (95% CI, 13.95% to
38.91%) according to the data available from Wuhan and European
countries. The fatality in COVID patients with cancer was lower than
those with cardiovascular disease (OR 0.49; 95% CI, 0.34 to 0.71;
p=0.39), but comparable with other comorbidities such as diabetes,
hypertension, and respiratory diseases. The ICU admission rate was close
with each other in cancer patients and patients with other comorbidities
mentioned above in COVID19 infection.
Implications of all the available evidence
Our findings based on larger population provide some clarification in
multiple aspects and have implications for cancer patients, oncology
clinicians as well as policy makers all around the world, especially in
COVID19 outbreak countries. The prevalence of cancer in COVID19 is
higher than cancer rates in total population. Special attention should paid
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on cancer patients during the pandemic, and more strict quarantine
measures, longer social distances are applicable to cancer patients, and
more appropriate personal protective equipment (eg, face masks) should
be provided for them, especially in circumstances of high risks. Besides
high prevalence, high rates of ICU admission and death were also
observed in cancer patients with COVID19, possibly resulting from
infection itself or a delay of timely medication for cancer. Thus, the
balance between protection from COVID19 infection and cancer
progression due to inadequate or delayed medication should be well
handled for oncologists and health policy makers. The ICU admission
rate of cancer patients is similar to common chronic diseases like diabetes,
hypertension, cardiovascular and respiratory diseases. However, the
mortality of cardiovascular disease patients with COVID19 is higher than
patients with cancer, diabetes, hypertension and respiratory disease. Thus,
the policies of medical resource allocation were supposed to include
cancer as one priority, at least equal to other common chronic diseases.
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