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BMJ open · 2022

Impact of cancer diagnoses on the outcomes of patients with COVID-19: a systematic review and meta-analysis.

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Han S, Zhuang Q, Chiang J, Tan SH, Chua GWY, Xie C, Chua MLK, Soon YY, Yang VS

Аннотация

BackgroundThe COVID-19 has caused significant mortality and morbidity across the globe. Patients with cancer are especially vulnerable given their immunocompromised state. We aimed to determine the proportion of COVID-19 patients with cancer, their severity and mortality outcomes through a systematic review and meta-analysis (MA).MethodsSystematic review was performed through online databases, PubMed, Medline and Google Scholar, with keywords listed in the Methods section (1 November 2019-31 December 2020). Studies with clinical outcomes of at least 10 COVID-19 patients and at least one with a diagnosis of cancer were included. The studies for MA were assessed with PRISMA guidelines and appraised with Newcastle-Ottawa Scale. The data were pooled using a random-effects model using STATA software. The main outcomes were planned before data collection, including proportion of patients with cancer among COVID-19 populations, relative risk (RR) of severe outcomes and death of patients with cancer compared with general COVID-19 patients.ResultsWe identified 57 case series (63 413 patients), with 230 patients with cancer with individual patient data (IPD). We found that the pooled proportion of cancer among COVID-19 patients was 0.04 (95% CI 0.03 to 0.05, I2=97.69%, pConclusionPatients with cancer were at a higher risk of severe and death outcomes from COVID-19 infection as compared with general COVID-19 populations. Limitations of this study include publication bias. A collaborative effort is required for a more complete database.

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https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ .

Strengths and limitations of this study

Largest meta-analysis on cancer and COVID-19 to date.

Specific objectives looking at proportion of patients with cancer among COVID-19 cases reported, with severity and mortality outcomes presented as relative risk against general population with COVID-19.

Detailed cancer cohorts presented with breakdown of tumour types, recent systemic anticancer therapy and other characteristics, with analysis of patients with lung cancer versus non-lung cancer and stage IV versus non-stage IV patients.

Limitations include publication biases and incomplete reporting of mortality outcomes by selected papers.

Lack of stratification for age and comorbidities for specific analysis due to limited clinical data.

Background

The COVID-19 pandemic, since its emergence in December 2019, has caused significant mortality and morbidity across the globe. In early 2021, more than 100 million people had been infected with more than 2 000 000 deaths. It has disrupted many aspects of healthcare delivery and overwhelmed many hospitals and their intensive care resources.

Patients with cancer are an at-risk population as they are generally older, immunocompromised and have multiple comorbidities. However, the outcomes of patients with cancer with COVID-19 remains conflicted despite multiple cohort studies and meta-analyses. A multicentre study of 105 patients with cancer in China suggested that patients with cancer with COVID-19 infection had higher risk of severe disease outcomes as compared with 536 age-matched non-cancer patients. 1 Another study from the Chinese Centre for Disease Control and Prevention Group reported a case fatality rate (CFR) of 2.3% (1023/44672) among confirmed COVID-19 cases and a more than double CFR of 5.6% (6/107) among the patients with cancer with COVID-19. 2 In contrast, a database report of 5688 COVID-19 positive patients from a tertiary hospital in New York found no significant difference in risk of death between cancer and non-cancer patients (RR 1.15, 95% CI 0.84 to 1.57) although there was an increased risk of intubation among patients with cancer (RR 1.89, 95% CI 1.37 to 2.61), 3 and other cross-sectional studies also showed similar risk of morbidity and mortality as the general population 4 5 though with a small number of patients. A few meta-analyses had recently addressed this as well and found that there was increased risk of severe COVID-19 infection among patients with cancer on active oncological treatment, 6 especially chemotherapy. 7

Hence, we aimed to review the current literature and to determine first the proportion of patients with cancer among the reported COVID-19 cases, second whether patients with cancer with COVID-19 had more severe outcomes and higher mortality compared with the general population and lastly, if there were any specific characteristics of patients with cancer that were associated with an increased risk of severe COVID-19 infection.

Methods

Search methods and study selection

A systematic search of the literature was conducted to identify studies that include patients with cancer with COVID-19 infection. The investigators searched biomedical databases including PubMed, Medline and Google Scholar using the terms COVID-19, coronavirus, nCoV, SARS-CoV-2, characteristics, comorbidity, mortality, death, risk, oncology, cancer, malignancy, neoplasm, tumor, tumour, and carcinoma ( online supplemental appendix 1 ). The search was conducted on 29 January 2021 for a search period from 1 November 2019 to 31 December 2020. The references of relevant meta-analysis (MA) and published reviewers were searched for additional eligible studies. The articles included met the following criteria: (1) COVID-19 diagnosis should be confirmed by real time PCR, or based on 2019 Novel COVID-19 Diagnostic criteria, if applicable ( online supplemental appendix 1 ), (2) studies should contain clinical data of at least ten COVID-19 patients with at least one patient with a diagnosis of cancer (including haematological malignancies), (3) severity outcomes (defined as hypoxaemia, admission to intensive care unit (ICU), and/or mechanical ventilation) and/or death outcomes should be presented and (4) studies should be available in English and full text. Exclusion criteria included studies that reported only specific patient populations, that is, intensive care setting, paediatric population or single tumour type cohorts. Studies published online ahead of print and selected preprint papers were included at the discretion of the authors. For the pooled analysis on severity and death outcomes of cancer and general COVID-19 patients, we mandated that the studies included at least 10 patients with cancer.

The literature search and study selection were performed independently by three of the authors (SH, QZ and VSY), and discrepancies were resolved by consensus. The study followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines ( online supplemental appendix 1 ).

Data extraction

Two investigators (SH and VSY) independently graded the studies based on Newcastle-Ottawa Scale (NOS) for assessing the quality of non-randomised studies and discrepancies were resolved by consensus ( online supplemental appendix 2 ). Data extraction was performed by SH, and the parameters extracted included the location (hospital and city) of the study, type of study, number of patients, demographics including median or mean age, gender, proportion of patient with comorbidities including cancer, and the clinical outcomes of severe disease and death (of patients with cancer and overall COVID-19 population) were collected for the available studies.

Statistical analysis

All statistical analyses were performed using the STATA V.15 software. MA using random-effects models was performed to analyse the pooled proportion of cancer among COVID-19 patients. The included studies were stratified into subgroups by region (within Hubei Province, China vs outside Hubei Province, including other countries), median/mean age (above or equal to vs below 60 years old) and gender (proportion of male more than or equal to 60% vs less than 60% in population). We further analysed the pooled relative risks (RR) of severe COVID-19 infection outcomes and related deaths for patients with cancer with COVID-19 versus the general population with COVID-19. The subgroup of lung cancer and stage IV patients were also evaluated for RR of severe COVID-19 infection outcomes. The results were presented as proportions (decimal) with 95% CI. Heterogeneity between studies was analysed by the I 2 statistics, where I 2 >50% was considered moderate heterogeneity and χ 2 p<0.05 was considered statistically significant. χ 2 tests were performed to look for associations between patient with cancer characteristics (age, gender, lung cancers, stage IV cancers, comorbidities and recent systemic therapy) and severe COVID-19 outcomes in the individual patient data (IPD).

Patient and public involvement

The research question was formed based on the urgent need to understand the effect of COVID-19 on patients with cancer and to address the clinical decisions that follows this understanding. No patient was directly involved in this systematic review and MA. We would like to thank the authors and also the patients that were involved in the publications that we had included in our MA.

Results

Eligible studies and characteristics

We identified 3083 relevant publications after literature search and review. Additional studies were identified from the references of published meta-analyses. After screening and eligibility assessment, we included in our MA a total of 57 case series involving 63 413 patients ( figure 1 ; online supplemental appendix 2; table 1 ). Studies that did not meet the MA criteria were summarised in online supplemental appendix 2; table 2 .

Figure 1 Flow chart of study identification and exclusion for data analysis.
Figure 1 Flow chart of study identification and exclusion for data analysis.

Among the 57 studies, 37 studies were from public hospitals in China (24 from Hubei province) with the rest from worldwide (USA, Canada and Spain in CCC19 study) (1), Europe (1), Korea (1), Iran (1), France (1), Spain (1), Italy (2), UK (3) and USA (9) ( table 1 ). Most studies were retrospective cohort studies and a few observational cross-sectional studies. The quality of all the included studies ranged from 6 to 8 (out of 9) using the NOS ( online supplemental table 3 ). Among the 57 case series, 13 contained detailed descriptions of COVID-19 patients with cancer ( table 2 ). Only five studies provided detailed IPD ( online supplemental appendix 3; table 1 ). Forty-seven studies were used for proportion analyses for cancer diagnoses among COVID-19 patients. For the pooled analysis on RR of severe outcomes among cancer COVID-19 patients, we identified 10 case series that contained severity data, and 7 studies that contained mortality data for analysis (studies with at least 10 patients with cancer). Five studies contained severity outcomes of lung cancer versus other patients with cancer with COVID-19 infection, and four studies contained severity outcomes of patients with stage IV vs non-stage IV cancer.

Table 1
Baseline characteristics, comorbidities and severity outcomes of 57 COVID-19 case series
All figures presented as n (%)—number (percentage) unless otherwise stated. Age presented as median with IQR: age (range) or as mean: age (±SD). *Composite endpoint: severe disease composite endpoints (ICU, mechanical ventilation and/or death). †See below: severity as defined by Chinese National Health Commission Definition of Severity (refer to online supplemental appendix 1 ). ‡IDSA/ATS: Infectious Diseases Society of America/American Thoracic Society Criteria for Defining Severe Community-acquired Pneumonia 2007 ( online supplemental appendix 1 ). §Article in press (accepted, peer-reviewed). ¶Article not peer-reviewed ARDS, acute respiratory distress syndrome; CKD, chronic kidney disease; CLD, chronic liver disease; CVS, cardiovascular disease; DM, diabetes mellitus; FiO2, supplemental oxygen; HTN, hypertension; ICU, intensive care unit; IDSA/ATS, Infectious Disease Society of America/American Thoracic Society Guidelines; N.A, not applicable; NIV, non-invasive ventilation.

NoAuthorsLocationDefinition of severityTotalDeath n (%)Severe n (%)Gender —male n (%)AgeDMHTNPulnomary diseaseSmokingCKDCVSCLDCancer total n (%)Cancer —severe n (% of cancer total)Cancer —death (% of cancer total)
1Argenziano et al 31New York, USAICU, death1000211 (21.1)236 (23.6)596 (59.6)63 (50–75372 (37.2)601 (60.1)66 (6.6)49 (4.9)137 (13.7)131 (13.1)15 (1.5)67 (6.7)17 (25.4)N.A
2Assaad et al 32Lyon, FranceDeathN.AN.AN.AN.AN.AN.AN.AN.AN.AN.AN.AN.A302N.A30 (9.9)
3Benelli et al 33Crema, ItalyICU +NIV, death41172 (17.5)140 (34.1)359 (66.6)70.5 (1–99)67 (16.3)193 (47.0)48 (11.7)N.A22 (5.3)93 (22.6)N.A33 (8.0)10 (30.3)9 (27.3)
4Cai et al 34Shenzhen, ChinaICU, death2983 (1.0)58 (19.5)145 (48.7)47.5 (33–61)18 (6.0)47 (15.8)N.AN.AN.A25 (8.4)28 (9.4)4 (1.3)2 (50.0)N.A
5Cao et al 35Shanghai, ChinaICU, death1981 (0.5)19 (9.6)101 (51.0)50.1 (±16.3)15 (7.6)42 (21.2)N.A11 (5.6)N.A12 (6.0)6 (3.0)4 (2.0)0N.A
6Cao et al 35Hubei, ChinaICU, death10217 (16.7)18 (17.6)53 (52.0)54 (37–67)11 (10.8)28 (27.5)10 (9.8)N.A4 (3.9)11 (10.8)2 (2.0)4 (3.9)N.A1 (25.0)
7Chen et al 36Shanghai, ChinaICU, death2492 (0.8)22 (8.8)126 (50.6)51 (31–64)25 (10.0)N.A5 (2.0)N.AN.A55 (22.1)N.A1 (0.4)N.AN.A
8Chen et al 37Hubei, ChinaICU, death9911 (0.11)23 (23.2)67 (67.7)55.5 (±13.1)12 (12.1)01 (1.0)N.AN.A40 (40.4)N.A1 (1.0)N.AN.A
9Colaneri et al 38Pavia, ItalyRequire high FiO2442 (4.5)17 (38.6)28 (63.6)67.5 (10–94)7 (15.9)15 (34.1)2 (4.5)N.AN.A11 (25.0)N.A6 (13.6)2 (4.5)N.A
10Dai et al 8Hubei, ChinaNIV and ventilation, death641N.A84 (13.1)57 (8.9)N.AN.AN.AN.AN.AN.AN.AN.A105 (16.4)20 (19.1)12 (11.4)
11Docherty et al 39UKHD/ICU, death20 1335165 (25.7)3001 (14.9)12 068 (59.9)73 (58–82)3650 (18.1)N.A3128 (15.5)852 (4.2)2830 (14.1)5469 (27.2)281 (1.4)1743 (8.7)N.AN.A
12Du et al 40Hubei, ChinaDeath17921 (11.7)N.A97 (54.1)57.6 (±13.7)33 (18.4)58 (32.4)N.AN.A4 (2.2)29 (16.2)N.A4 (2.2)N.A1 (25.0)
13Duanmu et al 41California, USADeath1001 (1.0)N.A56 (56.0)45 (32–65)10 (10.0)19 (19.0)1 (1.0)2 (2.0)6 (6.0)N.AN.A3 (3.0)N.AN.A
14Feng et al 42Hubei, ChinaSee below47638 (8.0)124 (26.1)271 (56.9)53 (40–64)17 (3.6)40 (8.4)14 (2.9)44 (9.2)2 (0.4)26 (5.5)N.A12 (2.5)7 (58.3)N.A
15Guan et al 43ChinaComposite endpoint*109915 (1.4)67 (6.1)640 (58.2)47 (35–58)81 (7.4)165 (15.0)12 (1.1)158 (14.4)8 (0.7)42 (3.8)23 (2.1)10 (0.9)1 (10.0)N.A
16Guo et al 44Hubei, ChinaMechanical ventilation18743 (23.0)45 (24.1)91 (48.7)58.5 (±14.7)28 (15.0)61 (32.6)4 (2.1)18 (9.6)6 (3.2)29 (15.5)N.A13 (7.0)N.AN.A
17Huang et al 45Hubei, ChinaICU416 (14.6)13 (31.7)30 (73.2)49 (41–58)8 (19.5)6 (14.6)1 (2.4)3 (7.3)N.A6 (14.6)1 (2.4)1 (2.4)0N.A
18Huang et al 46Hubei, ChinaICU34N.A8 (23.5)14 (41.2)56 (26–88)4 (11.8)8 (23.5)3 (8.8)N.AN.A6 (17.6)1 (2.9)3 (8.8)N.AN.A
19Kim et al 47KoreaRequire high FiO22806 (24.1)15 (53.6)40 (20–73)2 (7.1)005 (17.9)001 (3.6)1 (3.6)N.AN.A
20Kuderer et al 48WorldwideComposite endpointN.AN.AN.AN.AN.AN.AN.AN.AN.AN.AN.AN.A928242 (26.0)121 (13.0)
21Lee et al 49UKICU, deathN.AN.AN.AN.AN.AN.AN.AN.AN.AN.AN.AN.A80053 (7.0)226 (28.3)
22Li et al 50Hubei, ChinaIDSA/ATS†54890 (16.4)269 (49.1)279 (50.9)60 (48–69)83 (15.1)166 (30.3)17 (3.1)N.A10 (1.8)34 (6.2)524 (0.9)14N.A
23Lian et al 51Zhejiang, ChinaNot defined788N.A18 (2.3)407 (51.6)N.A57 (7.2)126 (16.0)354 (6.9)7 (0.9)11 (1.4)31 (3.9)6 (0.8)N.AN.A
24Liang et al 10ChinaComposite endpoint1590N.A254 (16.0)N.AN.AN.AN.AN.AN.AN.AN.AN.A18 (1.1)9 (50.0)9 (50.0)
25Liu K et al 52Hubei, ChinaNIV13716 (11.7)34 (24.8)61 (44.5)57 (20–83)14 (10.2)13 (9.5)2 (1.5)N.AN.A10N.A2 (1.5)N.AN.A
26Ma et al 53Hubei, ChinaSee below‡1380N.AN.AN.AN.AN.AN.AN.AN.AN.AN.AN.A37 (2.7)20 (54.1)5 (13.5)
27Mehta et al 17New York, USADeath1308210 (16.1)N.AN.AN.AN.AN.AN.AN.AN.AN.AN.A218 (16.7)N.A61 (28.0)
28Myers et al 54New York, USAIntubation, death5688555 (9.8)351 (6.2)N.AN.AN.AN.AN.AN.AN.AN.AN.A334 (5.9)N.A37 (11.1)
29Myers et al 54California, USAICU377100 (26.5)113 (30.0)212 (56.2)61 (50–73)118 (31.3)164 (43.5)28 (7.4)N.A48 (12.7)22 (5.8)21 (5.6)18 (4.8)6 (33.3)N.A
30Nikpouraghdam et al 55IranDeath2964239 (8.1)N.A1955 (66.0)56 (46–65)113 (3.8)59 (2.0)60 (2.0)N.A18 (0.6)37 (1.2)N.A17 (0.6)N.A1 (5.9%)
31Pan et al 56Hubei, ChinaICU, death20436 (17.6)16 (7.8)107 (52.5)52.9 (±16.0)44 (21.6)N.A9 (4.4)N.AN.A44 (21.6)N.A13 (6.4)N.AN.A
32Paranjpe et al 57New York, USADeath1078310 (28.8)N.AN.AN.AN.AN.AN.AN.AN.AN.AN.A64 (5.9)N.A24 (37.5)
33Petrilli et al 58New York, USAICU, death5279665 (12.6)990 (18.8)2615 (49.5)54 (38–66)1195 (22.6)2256 (42.7)786 (14.9)288 (5.5)647 (12.3)704 (13.3)N.A403 (7.6)138 (34.2)N.A
34Pinato et al 59EuropeHD/ICU, deathN.AN.AN.AN.AN.AN.AN.AN.AN.AN.AN.AN.A890110 (12.4)299 (33.6)
35Robilotti et al 60New York, USAMechanical ventilation /high flow, deathN.AN.AN.AN.AN.AN.AN.AN.AN.AN.AN.AN.A42387 (20.6)51 (12.1)
36Richardson et al 61New York, USAICU, death5700553 (9.7)320 (5.6)3437 (60.3)63 (52–75)1808 (31.7)3026 (53.1)287 (5.0)2691 (47.2)454 (8.0)966 (16.9)30 (0.5)320 (5.6)N.AN.A
37Shi et al 62Hubei. ChinaNot defined81N.AN.A42 (51.9)49.5 (±11.0)19 (23.5)12 (14.8)9 (11.1)N.A3 (3.7)14 (17.3)N.A4 (4.9)N.AN.A
38Shi et al 63Zhejiang, ChinaNot defined487049 (10.1)259 (53.2)46 (±19.0)29 (6.0)99 (20.3)040 (8.2)7 (1.4)11 (2.3)22 (4.5)5 (1.0)2 (40.0)N.A
39Tomlins et al 64UKNIV +intubation, death9520 (21.1)16 (16.8)60 (63.2)75 (59–82)N.AN.AN.AN.AN.AN.AN.A20 (21.1)N.A3 (15.0)
40Wan et al 65Chongqing, ChinaNot defined1351 (0.7)40 (29.6)72 (53.3)47 (36–55)12 (8.9)13 (9.6)09 (6.7)N.A7 (5.2)2 (1.5)4 (3.0)3 (75.0)N.A
41Wang et al 66Hubei, ChinaICU1386 (4.3)36 (26.1)75 (54.3)56 (42–68)14 (10.1)43 (31.2)4 (2.9)N.A2 (1.5)27 (19.6)010 (7.2)4 (40.0)N.A
42Wang et al 67Anhui. ChinaICU125019 (15.2)71 (56.8)38.8 (±13.8)10 (8.0)N.A2 (1.6)16 (12.8)N.A18 (14.4)N.A1 (0.8)N.AN.A
43Wu et al 21Hubei, ChinaARDS, death20144 (21.9)84 (41.8)N.A51 (43–60)22 (10.9)39 (19.4)5 (2.5)N.A2 (1.0)8 (4.0)7 (3.5)1 (0.5)N.AN.A
44Wu et al 68Jiangsu, ChinaSee below8003 (37.5)39 (48.8)46.1 (±15.4)5 (6.3)N.A0N.A1 (1.3)25 (31.3)1 (1.3)1 (1.3)N.AN.A
45Xu et al 69Guangzhou, ChinaNot defined90N.AN.A39 (43.3)50 (18–86)5 (55.6)17 (18.9)1 (1.1)N.AN.A3 (3.3)N.A2 (2.2)N.AN.A
46Yang et al 70Zhejiang, ChinaNIV14902 (1.3)81 (54.4)45.1 (±13.4)9 (6.0)01 (0.7)N.AN.A28 (18.8)N.A2 (1.3)N.AN.A
47Yang et al 71Hubei, ChinaICU, mechanical ventilation, deathN.AN.AN.AN.AN.AN.AN.AN.AN.AN.AN.AN.A20530 (14.6)40 (19.5)
48Yarza et al 72SpainRespi failureN.AN.AN.AN.AN.AN.AN.AN.AN.AN.AN.AN.A6334 (54.0)16 (25.4)
49Yu et al 19Hubei, ChinaARDS, death1524N.AN.AN.AN.AN.AN.AN.AN.AN.AN.AN.A12 (0.8)3 (25.0)3 (25.0)
50Zhang et al 13Hubei, ChinaComposite endpoint1276N.AN.AN.AN.AN.AN.AN.AN.AN.AN.AN.A28 (2.2)15 (53.6)8 (28.6)
51Zhang et al 73Hubei, ChinaIDSA/ATS22112 (5.4)55 (24.9)108 (48.9)55 (39–66.5)22 (10.0)54 (24.4)6 (2.7)N.A6 (2.7)37 (16.7)7 (3.2)9 (4.1)4 (44.4)N.A
52Zhang et al 74Hubei, ChinaSee below66325 (3.8)409 (61.7)321 (48.4)55.6 (44–69)67 (10.1)N.A51 (7.7)N.AN.A164 (24.7)31 (4.7)14 (2.1)11 (78.6)N.A
53Zhang et al .§Hubei, ChinaSee below1548N.AN.AN.AN.AN.AN.AN.AN.AN.AN.AN.A67 (4.3)32 (47.8)18 (26.9)
54Zhang et al ¶Hubei, ChinaDeath31547 (14.9)178 (56.5)175 (55.6)57 (44–66)41 (13.0)78 (24.8)3 (1.0)N.A2 (0.6)42 (13.3)9 (2.9)12 (3.8)N.A4 (33.3)
55Zhao et al 75Hubei, ChinaNot defined912 (2.2)30 (33.0)49 (53.8)46 (Not stated)3 (3.3)18 (19.8)1 (1.1)N.A1 (1.1)N.AN.A3 (3.3)2 (2.2)N.A
56Zhou et al 76Hubei, ChinaNot defined19154 (28.3)119 (62.3)119 (62.3)56 (46–67)36 (18.8)58 (30.4)6 (3.1)11 (5.8)2 (1.0)15 (7.9)02 (1.0)N.AN.A
57Zhu et al 77Hefei, ChinaNot defined32N.AN.A15 (46.9)46 (35–52)4 (12.5)7 (21.9)2 (6.3)6 (18.8)1 (3.1)10 (31.3)2 (6.3)2 (6.3)N.AN.A

Table 2
Baseline characteristics of patients with cancer with COVID-19 infections from 13 case series
ARDS, acute respiratory distress syndrome; BSC, best supportive care; Ca, carcinoma; CNS, central nervous system; CRC, colorectal cancer; GI, gastrointestinal; ICU, intensive care unit; ICU, intensive care unit; MDS, myelodysplastic syndrome; MSKCC, Memorial Sloan Kettering Cancer Center; N.A, not applicable; NPC, nasopharyngeal cancer; RCC, renal cell carcinoma; RT, radiotherapy; UKCCMP, Coronavirus Cancer Monitoring Project UK.

CharacteristicsZhang et al 13 2020 (n=28) n (%) or median (range)Yu et al 19 2020 (n=12) n (%) or median (range)Liang et al 10 2020 (n=18) n (%) or median (range)Ma et al 53 2020 (n=37) n (%) or median (range)Dai et al 8 2020 (n=105) n (%) or median (range)Zhang et al 73 2020 (n=67) n (%) or median (range)
Age65.0 (56.0–70.0)66 (48–78)63.1 (51–75)62 (59–70)64 (50–78)66 (37–98)
Gender
Male17 (60.7)10 (83.3)12 (66.7)20 (54.1)57 (54.7)41 (61.2)
Female11 (39.3)2 (16.7)6 (33.3)17 (45.9)48 (45.3)26 (38.8)
Stage
I-III18 (64.3)5 (41.7)13 (72.2)N.A88 (83.8)N.A
IV10 (35.7)6 (50.0)4 (22.2)N.A17 (16.2)N.A
UnknownN.A1 (8.3)1 (5.6)N.AN.AN.A
Smoking history
YesN.AN.A4 (22.2)N.A36 (34.3)9 (13.4)
NoN.AN.AN.AN.A69 (65.7)N.A
UnknownN.AN.AN.AN.AN.AN, A
Lung ca7 (25.0)Lung ca7 (58.3)Lung ca5 (27.8)CRC11 (29.7)Lung ca22 (20.1)Lung ca15 (22.4)
Oesophageal ca4 (14.3)Rectal ca1 (8.3)CRC4 (22.2)Lung ca8 (21.6)GI ca13 (12.4)CRC11 (16.4)
Breast ca3 (10.7)Colon ca1 (8.3)Breast ca3 (16.7)Breast ca7 (18.9)Breast ca11 (10.5)Thyroid ca8 (11.9)
Laryngoca2 (7.1)Pancreatic ca1 (8.3)Bladder ca2 (11.1)Gynae ca5 (13.5)Thyroid ca11 (10.5)Urinary system8 (11.9)
Liver ca2 (7.1)Breast ca1 (8.3)Lymphoma1 (0.56)Other ca6 (16.2)Blood ca9 (8.6)Breast & gynae7 (10.4)
Prostatic ca2 (7.1)Urothelial ca1 (8.3)Thyroid ca1 (0.56)Cervix ca6 (5.7)Head and Neck5 (7.5)
Cervical ca1 (3.6)Adrenal ca1 (0.56)Oesophageal ca6 (5.7)CNS tumour4 (5.9)
Gastric ca1 (3.6)RCC1 (0.56)Haematological3 (4.5)
Colon ca1 (3.6)Gastric ca3 (4.5)
Rectum ca1 (3.6)Oesophageal ca2 (3.0)
NPC1 (3.6)HCC1 (1.15)
Endometrial ca1 (3.6)
Ovarian ca1 (3.6)
Ca of testis1 (3.6)
Baseline treatmentChemoRT25 (89.3)BSC4 (33.3)Surveillance12 (75.0)Antitumour therapy13 (35.1)Chemotherapy17 (16.2)Follow-up44 (65.7)
Operation21 (75.0)Chemo-immuno Therapy2 (16.7)Chemo and surgery within 1 month4 (25.0)RT13 (12.4)On treatment23 (34.3)
Targeted therapy/ immunotherapy6 (21.4)Surveillance2 (16.7)Surgery8 (7.6)
Chemo (<14 d)3 (10.7)Targeted Therapy/RT1 (8.3)Immunotherapy6 (5.7)
Targeted Therapy2 (7.1)Chemo/RT1 (8.3)Targeted Therapy4 (3.8)
RT (<14 d)1 (3.6)Adjuvant RT1 (8.3)
Immunotherapy (<14 d)1 (3.6)Not started1 (8.3)
Severe diseaseSevere 15 (53.6) 8/15 Non-invasive ventilation 2/15 Invasive ventilationSevere 3 (25.0)Severe 9 (50.0)Severe 20 (54.1)Severe 40 (38.0) ICU 20 (19.1)Severe 32 (47.8) Mechanical ventilation 28 (41.8)
ARDS8 (28.6)N.AN.AN.AN.A14 (20.9)
Death8 (28.6)3 (25.0)9 (50.0)5 (13.5)12 (11.4)18 (26.9)
CharacteristicsKuderer et al 48 (CCC19) (n=928) n (%) or median (range)Lee LY et al 49 (UKCCMP) (n=800) n (%) or median (range)Robilotti et al 60 (MSKCC) (n=423) n (%) or median (range)Pinato et al 59 (n=890) n (%) or median (range)Assaad et al 32 (n=302) n (%) or median (range)Yang et al 70 (n=205) n (%) or median (range)Yarza et al 72 (n=63) n (%) or median (range)
Age66 (57–76)69 (59–76)N.AMean: 68.0±12.8Mean: 58.2±1.163 (56–70)66 (63–69)
Gender
Male468 (50)449 (56)212 (50.1)503 (56.5)144 (47.7)96 (46.8)34 (54)
Female459 (49)349 (44)211 (49.9)387 (43.5)158 (52.3)109 (53.2)29 (46)
Stage
I-IIIN.A227 (29)N.A539 (60.6)N.AN.A52 (82.5)
IVN.A347 (43)238 (56.3)351 (39.4)161 (53.3)N.A11 (17.5)
UnknownN.A205 (25)N.A0N.AN.AN.A
Smoking history
Yes369 (40)N.A167 (39.5)380 (42.7)N.AN.A34 (54.0)
No469 (51)N.A249 (58.9)343 (38.5)N.AN.A29 (46.0)
Unknown90 (10)N.A7 (1.7)167 (18.8)N.AN.A0
Haematological ca204 (22.0)Digestive organs150 (18.8)Others137 (32.4)Breast162 (18.2)Solid tumour234 (77.5)Solid tumour183 (89.3)Lung17 (27.0)
Breast ca191 (20.6)Other haematological109 (13.6)Breast86 (20.3)Haematological137 (15.4)Haematological68 (22.5)Breast40 (19.5)
Prostate ca152 (16.4)Breast102 (12.8)Lymphoma48 (11.3)Genito-Urinary132 (14.8)Lung42 (13.9)Colorectal28 (13.6)
Gastrointestinal ca108 (11.6)Respiratory and intrathoracic organs90 (11.3)Colorectal ca37 (8.7)Lung119 (13.4)Lung24 (11.7)
Lung ca91 (9.8)Male genital organs78 (9.8)Lung35 (8.3)Gastrointestinal105 (11.8)Haematological22 (10.7)
Gynaecological ca49 (5.3)Lymphoma60 (7.5)Leukaemia32 (7.6)Gynaecological41 (4.6)Thyroid16 (7.8)
RCC45 (4.8)Female genital organs45 (5.6)Prostate26 (6.1)Gastro-oesophageal40 (4.5)Stomach12 (5.9)
Endocrine ca39 (4.2)Urinary tract50 (6.3)Melanoma22 (5.2)Hepatobiliary45 (5.1)Cervical9 (4.4)
Melanoma38 (4.1)Other or unspecified47 (5.9)Head and Neck29 (3.3)Lymphoma7 (3.4)
Head and neck ca30 (3.2)Lip, oral cavity, and pharynx27 (3.4)Skin28 (3.1)Acute lymphoblastic leukaemia5 (2.4)
Sarcoma24 (2.6)Melanoma (skin)27 (3.4)Other52 (5.8)Chronic lymphoblastic leukaemia4 (2.0)
CNS12 (1.3)CNS15 (1.9)Multiple myeloma3 (1.5)
Solid tumour, not otherwise specified43 (4.6)Acute myelogenous leukaemia2 (1.0)
MDS1 (<1)
Baseline treatmentNone553 (59.6)Chemotherapy281 (35.1)Chemo in last 30 days191 (45.2)Chemotherapy164 (69.8)Any cancer treatment194 (64.2)All54/182 (29.7)Chemotherapy36 (57.1)
Chemotherapy160 (17.2)None272 (34.0)Surgery in last 30 days31 (7.3)Ongoing anticancer therapy at COVID-19 diagnosis479 (53.8)Cytotoxics137 (45.4)Chemotherapy31/182 (17.0)Endocrine therapy10 (15.9)
Endocrine85 (9.2)RT76 (9.5)ICI31 (7.3)Surgery417 (46.8)No cancer treatment108 (35.8)Targeted therapy12/182 (6.6)Immunotherapy8 (12.7)
Targeted therapy75 (8.1)Targeted treatment72 (9)Adjuvant /Neoadjuvant chemotherapy210 (23.6)Immunotherapy26 (8.6)Others10/182 (5.5)Targeted therapy7 (11.1)
Immunotherapy38 (4.1)Hormone therapy64 (8.0)Endocrine Therapy48 (20.4)Antiangiogenic18 (6.0)RT9/182 (5.0)No active tx2 (3.2)
RT12 (1.3)Other60 (7.5)Target Therapy41 (17.4)Anti-CD2014 (4.6)Surgery4/182 (2.2)
Immunotherapy44 (5.5)Immunotherapy38 (16.2)Anti-HER212 (4.0)Immunotherapy4/182 (2.2)
Surgery29 (3.6)Antiproteasomes8 (2.6)
No information10 (1.3)Everolimus (mTORi)4 (1.3)
Severe diseaseSevere (composite) 242 (26.1) ICU 132 (14.2) Mechanical ventilation 116 (12.5)Severe/critical 360 (45) ICU 53 (6.6)Mechanical ventilation 40 (9.5) High flow 47 (11.1)Admission to ICU /sub-intensive 110/760 (14.5)N.AICU 30 (14.6) Mechanical ventilation 21 (10.2)Respi failure 34 (54.0)
ARDSNANANA127 (22.5)N.A23 (11.2)23 (36.5)
Death121 (13.0)226 (28.3)51 (12.1)299 (33.6)30 (9.9)40 (19.5)16 (25.4)

Overall, there were more males than females affected, with a median age above 60 in all case series. This median age was notably higher than the non-cancer populations reported in previous studies. 8 Severe COVID-19 disease among patients with cancer ranged from 9% to 54% in 13 eligible studies ( table 2 ). Overall, 230 patients from five case series were identified with oncological history, stage, recent treatment history and clinical outcomes ( online supplemental appendix 3; table 1 ). However, among these patients, there was inconsistent reporting in those on active systemic chemotherapy and some clinical data on stage of cancer were missing, limiting the usefulness of the dataset in predicting the outcomes of COVID-19 in patients with active cancer.

Proportion of cancer diagnoses

The overall pooled proportion of cancer diagnoses among COVID-19 patients from 47 studies was 0.04 (95% CI 0.03 to 0.05) with significant heterogeneity among the included studies, I 2 =97.69%, χ2 p≤0.001 ( figure 2 ). The proportion of patients with cancer was significantly lower in the studies with median or mean age less than 60 years, at 0.03 (95% CI 0.02 to 0.03) compared with 0.07 (95% CI 0.05 to 0.09) for studies with median or mean age above or equal 60 years ( figure 3 ). The proportion of cancer was higher in the studies with male predominance (more than or at least 60% population being male), at 0.05 (95% CI 0.02 to 0.07) than studies with less than 60% population being male, at 0.03 (95% CI 0.02 to 0.05; figure 4 ). The proportion of cancer in Hubei province was 0.03 (95% CI 0.02 to 0.05) and outside Hubei province 0.04 (95% CI 0.02 to 0.04; online supplemental appendix 3; figure 1 ).

Figure 2 Random effects pooled proportion of patients with cancer among patients diagnosed with COVID-19. 10 papers were excluded in the proportion calculations (did not represent…
Figure 2 Random effects pooled proportion of patients with cancer among patients diagnosed with COVID-19. 10 papers were excluded in the proportion calculations (did not represent overall COVID-19 populations).
Figure 3 Random effects pooled proportion of patients with cancer among total patients diagnosed with COVID-19 (subgroup by median/mean age ≥60 vs <60 years).
Figure 3 Random effects pooled proportion of patients with cancer among total patients diagnosed with COVID-19 (subgroup by median/mean age ≥60 vs <60 years).
Figure 4 Random effects pooled proportion of patients with cancer among total patients diagnosed with COVID-19 (subgroup by male ≥60% or <60%).
Figure 4 Random effects pooled proportion of patients with cancer among total patients diagnosed with COVID-19 (subgroup by male ≥60% or <60%).

Mortality outcomes of patients with cancer with COVID-19

Overall, the mortality rates of patients with cancer with COVID-19 infection ranged widely from 5.9% to 50% across 20 studies ( table 3 ). The pooled RR of death was significantly higher in cancer patients with COVID-19 infection than in the general population with COVID-19 infection based on seven studies as shown in table 3 (RR 1.41; 95% CI 1.15 to 1.73), I 2 =26.2%, χ 2 p=0.229 ( figure 5A ). The pooled mortality rate was estimated at 0.22 (95% CI 0.21 to 0.33) among patients with cancer with COVID-19, which was significantly higher than that of the overall COVID-19 patients, at 0.09 (95% CI 0.07 to 0.11; online supplemental appendix 3; figure 2A–C ).

Table 3
Mortality rate among patients with cancer diagnosed with COVID-19 infection*
*Only papers with at least 10 patients with cancer were included. N.A, not available.

Case seriesDeath/cancer populationMortality rate in cancer cohorts (%)Death/total COVID-19 populationMortality rate in general COVID-19 cohorts (%)Relative risk of death (95% CI)
Liang et al 109/1850.0N.AN.A.N.A
Zhang et al 138/2828.6N.AN.AN.A
Yu et al 193/1225N.AN.AN.A
Ma et al 535/3713.5N.AN.A.N.A
Zhang et al 7318/6726.9N.AN.AN.A
Dai et al 812/10511.4N.AN.AN.A
Kuderer et al 11121/92813.0N.AN.AN.A
Lee et al 16226/80028.3N.AN.AN.A
Robilotti et al 6051/42312.1N.AN.AN.A
Assaad et al 3230/3029.9N.AN.AN.A
Pinato et al 59299/89033.6N.AN.AN.A
Yang et al 7040/20519.5N.AN.AN.A
Yarza et al 7216/6325.4N.AN.AN.A
Benelli et al 339/3327.372/41117.51.56 (0.86 to 2.82)
Mehta et al 1761/21828.0210/130816.11.74 (1.36 to 2.23)
Miyashita et al 337/33411.1555/56889.81.14 (0.83 to 1.55)
Nikpouraghdam et al 551/175.9239/29648.10.73 (0.11 to 4.90)
Paranjpe et al 5724/6437.5310/107828.81.30 (0.94 to 1.81)
Tomlins et al 643/2015.020/9521.10.71 (0.23 to 2.17)
Zhang et al 744/1233.347/31514.92.23 (0.96 to 5.19)
Figure 5 (A) Random effects pooled relative risk (RR) of death for cancer COVID-19 patients versus total population of COVID-19 patients. (B) Random effects pooled RR for severe o…
Figure 5 (A) Random effects pooled relative risk (RR) of death for cancer COVID-19 patients versus total population of COVID-19 patients. (B) Random effects pooled RR for severe outcome of cancer COVID-19 patients versus total population of COVID-19 patients.

Severity outcomes of patients with cancer with COVID-19

Further analyses were conducted for the 10 case series with available severity outcomes for both cancer and general COVID-19 patient cohorts. We found that patients with cancer had higher risk of severe COVID-19 outcomes compared with the general COVID-19 population, with a pooled RR of 1.49 (95% CI 1.18 to 1.87) and substantial heterogeneity, I 2 =66.7%, χ 2 p=0.001 ( figure 5B ). Also, we noted that the pooled proportion of severe outcomes in patients with cancer was 0.42 (95% CI 0.30 to 0.54), which was higher than the proportion of severe cases of all COVID-19 patients at 0.27 (95% CI 0.21 to 0.33; online supplemental appendix 3; figure 3A–C ). There was no statistically significant difference in RR of severe outcomes between those patients (available data from five studies identified) with lung cancers vs non lung cancers (RR at 1.46; 95% CI 1.84 to 2.52, I 2 =48.1%, χ 2 p=0.7; figure 6A ). There was no statistically significant difference in RR of severe outcomes between stage IV vs non stage IV cancers from four studies identified (RR 1.48; 95% CI 0.89 to 2.47; figure 6B , online supplemental figure 4 ).

Figure 6 (A) Random effects pooled relative risk (RR) for severe outcome of lung cancer versus other patients with cancer infected with COVID-19. (B) Random effects pooled RR for …
Figure 6 (A) Random effects pooled relative risk (RR) for severe outcome of lung cancer versus other patients with cancer infected with COVID-19. (B) Random effects pooled RR for severe outcome of patients with stage IV cancer vs non-stage IV cancer infected with COVID-19.

The severe outcomes of COVID-19 in patients with cancer was also analysed from the available 230 IPD and summarised in online supplemental table 1 . Only patient age and gender appeared to be significantly associated with severity outcomes by χ 2 test ( online supplemental table 2 ).

Discussion

We performed a systematic review and MA to elucidate the proportion of cancer diagnoses among COVID-19 patients, and whether patients with cancer had more severe outcomes compared with the general population. We also looked at the patient characteristics that may be associated with severe clinical outcomes among these patients. To our knowledge, this is the largest MA that directly addresses the clinical outcomes of COVID-19 patients with cancer, including the RR of severity and death as compared with general COVID-19 patients.

First, we found that the pooled proportion of patients with cancer among COVID-19 cases was 4%. This result was similar to the proportion rate from an earlier MA 9 of 11 studies with a proportion of 2%, but higher than the 0.2% described by the Chinese Centre for Disease Control and Prevention Group. 2 We found that the cancer proportion was higher among older patients and in male patients with COVID-19. In the subsequent IPD analysis, it was also advanced age and male gender that were significantly associated with severity of COVID-19 infection. We also noted that the cancer proportion among the COVID-19 case series from Hubei province was 3% ( online supplemental appendix 3; figure 1 ), which was higher than the estimated cancer prevalence of 0.45% in the region. 10 However, cancer proportion can vary across geographical locations and specialist medical centres.

We have shown that COVID-19 patients with cancer had significantly more severe outcomes compared with the general COVID-19 patients, with a pooled RR of 1.49 (95% CI 1.18 to 1.87, p=0.001). The pooled estimated proportion of severe COVID-19 outcomes was higher among patients with cancer in our MA compared with the largest cancer cohort data (CCC19). 11 Overall, the proportion of patients with cancer with severe COVID-19 outcomes varied across studies but remained high, highlighting the vulnerability of patients with cancer to SARS-CoV-2 infection.

We also observed that lung cancer was the most common cancer among those affected by COVID-19 in Chinese studies ( table 2 ) and this may be due to the fact that lung cancer is the most common cancer in China, rather than a direct relation to the risk of COVID-19 infection. 12 A few case series noted that patients with lung cancers had significantly worse outcomes compared with non-lung cancer cohorts, 8 13 which was also supported by data from TERAVOLT, 14 an international database of thoracic cancers (not included in our MA as it is a single tumour registry). TERAVOLT demonstrated 33% mortality and high morbidity among patients with thoracic malignancies, but with low ICU admission rates, presumably partly due to resource allocations and patient prognosis. However, in our MA, we did not find the RR of severity to be significant for lung cancers (vs other tumour types) or stage IV cancers (vs stage I–III) across four studies. This was also echoed by our IPD analysis, where lung cancer and the stage of cancer were not significantly associated with severe outcomes. This could reflect that lung cancer or stage IV cancer did not necessarily confer a higher risk of severe COVID-19 infection, but given the small number of included studies for this part of the analysis, this conclusion should be cautiously interpreted.

Separately, haematological cancers also showed severe outcomes with high mortality rates in multiple cohort studies. 8 15 The available individual data in our study were not large enough to analyse the influence of modality of systemic treatment (targeted therapy, immunotherapy or chemotherapy), surgery or radiotherapy on the outcome of COVID-19 infections. Recent studies did not consistently find active systemic therapy to be associated with risk of severe COVID-19 infection 16 though some studies suggest that patients on recent chemotherapy 10 or immunotherapy 8 were at higher risk. These questions remain unanswered and require further collaborative efforts to identify risk factors of severe COVID-19 infection among patients with cancer. One suggestion is to have standardised reporting format for cancer COVID-19 cohorts including specific tumour type, stage, date of last chemotherapy or systemic treatment, comorbidities, active versus past cancer diagnosis and also the severity and mortality data for each subgroup.

Overall, the pooled RR of death among the patients with cancer was 1.41 (95% CI 1.15 to 1.73), compared with general COVID-19 patients. However, pooled mortality risk of patients with cancer with COVID-19 should be interpreted with caution due to the limited sample size. Some case series also had patients who remained hospitalised or with incomplete patient outcome at the time of reporting. CFR were lower at 5.6% among cancer COVID-19 patients in a large Chinese report 2 and 11.1% from a tertiary centre in New York, USA. 3 Notably, among selected COVID-19 mortality reports from Italy and USA, around 20%–30% of deaths had a history of cancer. 17 18 We would encourage mortality data to be taken from database reports instead of small case series from single centres.

In light of the severity of COVID-19 in patients with cancerpatients with cancer, efforts should be undertaken to reduce nosocomial exposure in patients with cancer, as nosocomial infections may contribute to incidence of COVID-19 in oncology patients. 19 The decision to attend clinics and continue non-urgent systemic therapy should be made judiciously, especially in regions with high burden of disease transmission. This has been echoed by various oncology bodies, including American Society of Clinical Oncology and European Society of Medical Oncology. 20 Given the higher risk of severe outcomes and mortality among patients with cancer, it is important that patients with cancer with COVID-19 infection should be monitored closely and decisions for ICU support discussed early. ICU outcomes should also be an important question for future retrospective studies in view of the ethical and clinical considerations of putting patients with advanced cancer on mechanical ventilation, especially in countries with significant outbreak and limited ICU resources. In light of the recent advances in COVID-19 vaccine efforts, another area of active discussion is that of the risk and benefit of vaccination for patients with cancer, especially those who are deemed to be significantly immunocompromised. Our data shows that there is risk of severe COVID-19 infection and risk of death among patients with cancer compared with general population. Hence, this factor should be strongly considered in the individualised decision for vaccination.

The limitations of this MA include the heterogeneity and retrospective nature of the case studies, including reporting and selection biases. A few studies were reported from the same hospitals and hence there may be overlapping patients although efforts were taken to reduce this. During screening of papers by the authors, we have ensured that the analysed studies were not reported from the same hospital during the same period of admission ( online supplemental appendix 2; table 1 ). The small numbers and incomplete death outcomes at the time of reporting also restricted our mortality analysis. Due to the limited clinical data, we could not adjust for co-morbidities and other characteristics that may contribute to severe disease. It should also be noted that severity outcomes were defined inconsistently across some studies but efforts were taken to indicate the different definitions in table 1 . Of note, some earlier studies also included clinically diagnosed COVID-19 cases.

While we acknowledge other risk factors such as age, 21–23 gender, 24 cardiovascular risk factors 23 25 26 including diabetes mellitus 27 28 and hypertension 29 remain important in COVID-19 infection, this is the largest MA to show that malignancy is also a significant risk factor for severe disease and mortality. However, we did not identify other tumour or treatment-related factors associated with severe outcomes among patients with cancer with COVID-19. We also did not stratify for gender, age and comorbidities in our analysis due to limited available data for pooled analysis on severity and mortality. As more data becomes available in the literature, further analyses may be performed to discern the clinical outcomes of patients with cancer with COVID-19 infection, with specific attention to underlying lung cancers, haematological cancers and the effects of various systemic therapies or other treatment modalities. CCC19, UKCCMP (Coronavirus Cancer Monitoring Project UK) and TERAVOLT are some examples of collaborative efforts on the outcomes of patients with cancer with COVID-19 infection, though a limitation is that there is a lack of comparison against general population. Information on COVID-19 patients with cancer should also be collected in more detail and with consistency across studies, to allow further meta-analyses to ascertain the outcomes of patients with cancer with COVID-19 infection.

Conclusion

Given that COVID-19 may persist in the foreseeable future, it is imperative to continue active research and data collection on the effect of SARS-CoV-2 on oncology patients. Due to the clinical burden of their disease, oncology patients will continue to require systemic chemotherapy and urgent care at medical centres. 30 As such, treating physicians need to be able to make more discerning decisions on the choice of systemic treatment and escalation of care.

Overall, we found through MA that the severity and mortality rates of COVID-19 in patients with cancer were higher than that in the general population. However, the dataset remains small and should be interpreted with caution. We hope that with collaborative efforts, comprehensive patient outcomes of COVID-19 patients, and specifically, of patients with cancer, can be collated to better understand the disease trajectory and outcomes of COVID-19.

Supplementary Material

Reviewer comments

Author's manuscript

Twitter: @DrMLChua

Contributors: SH and VSY conceived the idea of the study. SH, VSY and QZ contributed to data collection. SHT, SH and YYS contributed to data analysis. SH, QZ, JC, GWYC and VSY contributed to the manuscript writing. MLKC and CX contributed to clinical data. MLKC, YYS and VSY provided guidance. YYS and VSY are joint last authors. SH is responsible for the overall content as the guarantor.

Funding: The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests: None declared.

Provenance and peer review: Not commissioned; externally peer reviewed.

Supplemental material: This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

Data availability statement

All data relevant to the study are included in the article or uploaded as online supplemental information. Data are available in a public, open access repository.

Ethics statements

Patient consent for publication

Not applicable.

References

1 Dai M , Liu D , Liu M , et al . Patients with cancer appear more vulnerable to SARS-CoV-2: a multicenter study during the COVID-19 outbreak . Cancer Discov 2020 ; 10 : 783 – 91 . 10.1158/2159-8290.CD-20-0422 32345594 PMC7309152

2 Wu Z , McGoogan JM . Characteristics of and Important Lessons from the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72 314 Cases from the Chinese Center for Disease Control and Prevention . JAMA 2020 ; 323 : 1239 – 42 . 10.1001/jama.2020.2648 32091533

3 Miyashita H , Mikami T , Chopra N , et al . Do patients with cancer have a poorer prognosis of COVID-19? an experience in New York City . Ann Oncol 2020 ; 31 : 1088 – 9 . 10.1016/j.annonc.2020.04.006 32330541 PMC7172785

4 Joharatnam-Hogan N , Hochhauser D , Shiu K-K , et al . Outcomes of the 2019 novel coronavirus in patients with or without a history of cancer: a multi-centre North London experience . Ther Adv Med Oncol 2020 ; 12 : 1758835920956803 . 10.1177/1758835920956803 32968429 PMC7493246

5 Jee J , Foote MB , Lumish M , et al . Chemotherapy and COVID-19 outcomes in patients with cancer . J Clin Oncol 2020 ; 38 : 3538 – 46 . 10.1200/JCO.20.01307 32795225 PMC7571792

6 Park R , Lee SA , Kim SY , et al . Association of active oncologic treatment and risk of death in cancer patients with COVID-19: a systematic review and meta-analysis of patient data . Acta Oncol 2021 ; 60 : 13 – 19 . 10.1080/0284186X.2020.1837946 33131376

7 Yekedüz E , Utkan G , Ürün Y . A systematic review and meta-analysis: the effect of active cancer treatment on severity of COVID-19 . Eur J Cancer 2020 ; 141 : 92 – 104 . 10.1016/j.ejca.2020.09.028 33130550 PMC7538140

8 Dai M , Liu D , Liu M , et al . Patients with cancer appear more vulnerable to SARS-CoV-2: a multicenter study during the COVID-19 outbreak . SSRN Electron J 2020 10.2139/ssrn.3558017 PMC7309152 32345594

9 Desai A , Sachdeva S , Parekh T , et al . Covid-19 and cancer: lessons from a pooled meta-analysis . JCO Glob Oncol 2020 ; 6 : 557 – 9 . 10.1200/GO.20.00097 32250659 PMC7193801

10 Liang W , Guan W , Chen R , et al . Cancer patients in SARS-CoV-2 infection: a nationwide analysis in China . Lancet Oncol 2020 ; 21 : 335 – 7 . 10.1016/S1470-2045(20)30096-6 32066541 PMC7159000

11 Kuderer NM , Choueiri TK , Shah DP , et al . Clinical impact of COVID-19 on patients with cancer (CCC19): a cohort study . Lancet 2020 ; 6736 : 1 – 13 . 10.1016/S0140-6736(20)31187-9 PMC7255743 32473681

12 Cao M , Chen W . Epidemiology of lung cancer in China . Thorac Cancer 2019 ; 10 : 3 – 7 . 10.1111/1759-7714.12916 30485694 PMC6312841

13 Zhang L , Zhu F , Xie L , et al . Clinical characteristics of COVID-19-infected cancer patients: a retrospective case study in three hospitals within Wuhan, China . Ann Oncol 2020 ; 31 : 894 – 901 . 10.1016/j.annonc.2020.03.296 32224151 PMC7270947

14 Garassino MC , Whisenant JG , Huang L-C , et al . COVID-19 in patients with thoracic malignancies (TERAVOLT): first results of an international, registry-based, cohort study . Lancet Oncol 2020 ; 21 : 914 – 22 . 10.1016/S1470-2045(20)30314-4 32539942 PMC7292610

15 He W , Chen L , Chen L , et al . COVID-19 in persons with haematological cancers . Leukemia 2020 ; 34 : 1637 – 45 . 10.1038/s41375-020-0836-7 32332856 PMC7180672

16 Lee LYW , Cazier JB , Starkey T . COVID-19 mortality in patients with cancer on chemotherapy or other anticancer treatments : a prospective cohort study . Lancet 2020 ; 395 : P1919 – 26 . 10.1016/S0140-6736(20)31173-9 PMC7255715 32473682

17 Mehta V , Goel S , Kabarriti R , et al . Case fatality rate of cancer patients with COVID-19 in a new York hospital system . Cancer Discov 2020 ; 10 : 935 – 41 . 10.1158/2159-8290.CD-20-0516 32357994 PMC7334098

18 Onder G , Rezza G , Brusaferro S . Case-Fatality rate and characteristics of patients dying in relation to COVID-19 in Italy . JAMA 2020 ; 323 : 1775 – 6 . 10.1001/jama.2020.4683 32203977

19 Yu J , Ouyang W , Chua MLK , et al . SARS-CoV-2 transmission in patients with cancer at a tertiary care hospital in Wuhan, China . JAMA Oncol 2020 ; 6 : 1108 . 10.1001/jamaoncol.2020.0980 32211820 PMC7097836

20 Banna G , Curioni-Fontecedro A , Friedlaender A , et al . How we treat patients with lung cancer during the SARS-CoV-2 pandemic: primum non nocere . ESMO Open 2020 ; 5 : e000765 . 10.1136/esmoopen-2020-000765 32245904 PMC7211064

21 Wu C , Chen X , Cai Y , et al . Risk factors associated with acute respiratory distress syndrome and death in patients with coronavirus disease 2019 pneumonia in Wuhan, China . JAMA Intern Med 2020 ; 180 : 934 – 43 . 10.1001/jamainternmed.2020.0994 32167524 PMC7070509

22 Centers for Disease Control and Prevention . People who are at higher risk for severe illness | CDC . Centers Dis Control Prev 2020 .

23 Yang J , Zheng Y , Gou X . Prevalence of comorbidities and its effects in coronavirus disease 2019 patients: a systematic review and meta-analysis . Int J Infect Dis 2020 ; 94 : 91 – 5 . 10.1016/j.ijid.2020.03.017 32173574 PMC7194638

24 Cai H . Sex difference and smoking predisposition in patients with COVID-19 . Lancet Respir Med 2020 ; 8 : e20 . 10.1016/S2213-2600(20)30117-X 32171067 PMC7103991

25 CDC COVID-19 Response Team . Preliminary estimates of the prevalence of selected underlying health conditions among patients with coronavirus disease 2019 - United States, February 12-March 28, 2020 . MMWR Morb Mortal Wkly Rep 2020 ; 69 : 382 – 6 . 10.15585/mmwr.mm6913e2 32240123 PMC7119513

26 Li B , Yang J , Zhao F , et al . Prevalence and impact of cardiovascular metabolic diseases on COVID-19 in China . Clin Res Cardiol 2020 ; 109 : 531 – 8 . 10.1007/s00392-020-01626-9 32161990 PMC7087935

27 Huang I , Lim MA , Pranata R . Diabetes mellitus is associated with increased mortality and severity of disease in COVID-19 pneumonia – a systematic review, meta-analysis, and meta-regression . Diabetes Metab Syndr 2020 ; 14 : 395 – 403 . 10.1016/j.dsx.2020.04.018 32334395 PMC7162793

28 Hu Y , Sun J , Dai Z , et al . Prevalence and severity of corona virus disease 2019 (COVID-19): a systematic review and meta-analysis . J Clin Virol 2020 ; 127 : 104371 . 10.1016/j.jcv.2020.104371 32315817 PMC7195434

29 Li L-Q , Huang T , Wang Y-Q , et al . COVID-19 patients' clinical characteristics, discharge rate, and fatality rate of meta-analysis . J Med Virol 2020 ; 92 : 577 – 83 . 10.1002/jmv.25757 32162702 PMC7228329

30 Xu Y , Liu H , Hu K , et al . [Clinical management of lung cancer patients during the outbreak of 2019 novel coronavirus disease (Covid-19) . Zhongguo Fei Ai Za Zhi 2020 ; 23 : 136 – 41 . 10.3779/j.issn.1009-3419.2020.03.02 32077441 PMC7118333

31 Argenziano MG , Bruce SL , Slater CL , et al . Characterization and clinical course of 1000 patients with coronavirus disease 2019 in New York: retrospective case series . BMJ 2020 ; 34 : m1996 . 10.1136/bmj.m1996 PMC7256651 32471884

32 Assaad S , Avrillon V , Fournier M-L , et al . High mortality rate in cancer patients with symptoms of COVID-19 with or without detectable SARS-COV-2 on RT-PCR . Eur J Cancer 2020 ; 135 : 251 – 9 . 10.1016/j.ejca.2020.05.028 32540204 PMC7275994

33 Benelli G , Buscarini E , Canetta C . SARS-COV-2 comorbidity network and outcome in hospitalized patients in Crema, Italy . PLoS One 2020 ; 16 : e0248498 . 10.1371/journal.pone.0248498 PMC7993836 33765013

34 Cai Q , Huang D , Ou P , et al . COVID‐19 in a designated infectious diseases hospital outside Hubei Province, China . Allergy 2020 ; 75 : 1742 – 52 . 10.1111/all.14309 32239761

35 Cao J , Tu W-J , Cheng W , et al . Clinical features and short-term outcomes of 102 patients with coronavirus disease 2019 in Wuhan, China . Clin Infect Dis 2020 ; 71 : 748 – 55 . 10.1093/cid/ciaa243 32239127 PMC7184479

36 Chen J , Qi T , Liu L , et al . Clinical progression of patients with COVID-19 in Shanghai, China . J Infect 2020 ; 80 : e1 – 6 . 10.1016/j.jinf.2020.03.004 PMC7102530 32171869

37 Chen N , Zhou M , Dong X , et al . Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study . Lancet 2020 ; 395 : 507 – 13 . 10.1016/S0140-6736(20)30211-7 32007143 PMC7135076

38 Colaneri M , Sacchi P , Zuccaro V , et al . Clinical characteristics of coronavirus disease (COVID-19) early findings from a teaching hospital in Pavia, North Italy, 21 to 28 February 2020 . Euro Surveill 2020 ; 25 . 10.2807/1560-7917.ES.2020.25.16.2000460 PMC7189652 32347201

39 Docherty AB , Harrison EM , Green CA , et al . Features of 20 133 UK patients in hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: prospective observational cohort study . BMJ 2020 ; 369 : m1985 . 10.1136/bmj.m1985 32444460 PMC7243036

40 Du R-H , Liang L-R , Yang C-Q , et al . Predictors of mortality for patients with COVID-19 pneumonia caused by SARS-CoV-2: a prospective cohort study . Eur Respir J 2020 ; 55 : 2000524 . 10.1183/13993003.00524-2020 32269088 PMC7144257

41 Duanmu Y , Brown IP , Gibb WR , et al . Characteristics of emergency department patients with COVID-19 at a single site in northern California: clinical observations and public health implications . Acad Emerg Med 2020 ; 27 : 505 – 9 . 10.1111/acem.14003 32344458 PMC7267565

42 Feng Y , Ling Y , Bai T , et al . COVID-19 with different severities: a multicenter study of clinical features . Am J Respir Crit Care Med 2020 ; 201 : 1380 – 8 . 10.1164/rccm.202002-0445OC 32275452 PMC7258639

43 Guan W-J , Ni Z-Y , Hu Y , et al . Clinical characteristics of coronavirus disease 2019 in China . N Engl J Med 2020 ; 382 : 1708 – 20 . 10.1056/NEJMoa2002032 32109013 PMC7092819

44 Guo T , Fan Y , Chen M , et al . Cardiovascular implications of fatal outcomes of patients with coronavirus disease 2019 (COVID-19) . JAMA Cardiol 2020 ; 5 : 811 . 10.1001/jamacardio.2020.1017 32219356 PMC7101506

45 Huang C , Wang Y , Li X , et al . Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China . Lancet 2020 ; 395 : 497 – 506 . 10.1016/S0140-6736(20)30183-5 31986264 PMC7159299

46 Huang Y , Tu M , Wang S , et al . Clinical characteristics of laboratory confirmed positive cases of SARS-CoV-2 infection in Wuhan, China: a retrospective single center analysis . Travel Med Infect Dis 2020 ; 36 : 101606 . 10.1016/j.tmaid.2020.101606 32114074 PMC7102650

47 Kim ES , Chin BS , Kang CK , et al . Clinical course and outcomes of patients with severe acute respiratory syndrome coronavirus 2 infection: a preliminary report of the first 28 patients from the Korean cohort study on COVID-19 . J Korean Med Sci 2020 ; 35 : e142 . 10.3346/jkms.2020.35.e142 32242348 PMC7131901

48 Kuderer NM , Choueiri TK , Shah DP , et al . Clinical impact of COVID-19 on patients with cancer (CCC19): a cohort study . Lancet 2020 ; 395 : 1907 – 18 . 10.1016/S0140-6736(20)31187-9 32473681 PMC7255743

49 Lee LYW , Cazier J-B , Starkey T , et al . COVID-19 prevalence and mortality in patients with cancer and the effect of primary tumour subtype and patient demographics: a prospective cohort study . Lancet Oncol 2020 ; 21 : 1309 – 16 . 10.1016/S1470-2045(20)30442-3 32853557 PMC7444972

50 Li X , Xu S , Yu M , et al . Risk factors for severity and mortality in adult COVID-19 inpatients in Wuhan . J Allergy Clin Immunol 2020 ; 146 : 110 – 8 . 10.1016/j.jaci.2020.04.006 32294485 PMC7152876

51 Lian J , Jin X , Hao S , et al . Analysis of epidemiological and clinical features in older patients with coronavirus disease 2019 (COVID-19) outside Wuhan . Clin Infect Dis 2020 ; 71 : 740 – 7 . 10.1093/cid/ciaa242 32211844 PMC7184356

52 Liu K , Fang Y-Y , Deng Y , et al . Clinical characteristics of novel coronavirus cases in tertiary hospitals in Hubei Province . Chin Med J 2020 ; 133 : 1025 – 31 . 10.1097/CM9.0000000000000744 32044814 PMC7147277

53 Ma J , Yin J , Qian Y , et al . Clinical characteristics and prognosis in cancer patients with COVID-19: a single center's retrospective study . J Infect 2020 ; 81 : 318 – 56 . 10.1016/j.jinf.2020.04.006 PMC7194544 32298677

54 Myers LC , Parodi SM , Escobar GJ , et al . Characteristics of hospitalized adults with COVID-19 in an integrated health care system in California . JAMA 2020 ; 323 : 2195 . 10.1001/jama.2020.7202 32329797 PMC7182961

55 Nikpouraghdam M , Jalali Farahani A , Alishiri G , et al . Epidemiological characteristics of coronavirus disease 2019 (COVID-19) patients in Iran: a single center study . J Clin Virol 2020 ; 127 : 104378 . 10.1016/j.jcv.2020.104378 32353762 PMC7172806

56 Pan L , Mu M , Yang P , et al . Clinical characteristics of COVID-19 patients with digestive symptoms in Hubei, China: a descriptive, cross-sectional, multicenter study . Am J Gastroenterol 2020 ; 115 : 766 – 73 . 10.14309/ajg.0000000000000620 32287140 PMC7172492

57 Paranjpe I , Russak A , De FJK . Clinical characteristics of hospitalized Covid-19 patients in New York City . medRxiv 2020 . 10.1101/2020.04.19.20062117

58 Petrilli CM , Jones SA , Yang J , et al . Factors associated with hospital admission and critical illness among 5279 people with coronavirus disease 2019 in New York City: prospective cohort study . BMJ 2020 ; 369 : m1966 . 10.1136/bmj.m1966 32444366 PMC7243801

59 Pinato DJ , Zambelli A , Aguilar-Company J , et al . Clinical portrait of the SARS-CoV-2 epidemic in European patients with cancer . Cancer Discov 2020 ; 10 : 1465 – 74 . 10.1158/2159-8290.CD-20-0773 PMC7668225 32737082

60 Robilotti EV , Babady NE , Mead PA , et al . Determinants of COVID-19 disease severity in patients with cancer . Nat Med 2020 ; 26 : 1218 – 23 . 10.1038/s41591-020-0979-0 32581323 PMC7785283

61 Richardson S , Hirsch JS , Narasimhan M , et al . Presenting characteristics, comorbidities, and outcomes among 5700 patients hospitalized with COVID-19 in the new York City area . JAMA 2020 ; 323 : 2052 – 9 . 10.1001/jama.2020.6775 32320003 PMC7177629

62 Shi H , Han X , Jiang N , et al . Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: a descriptive study . Lancet Infect Dis 2020 ; 20 : 425 – 34 . 10.1016/S1473-3099(20)30086-4 32105637 PMC7159053

63 Shi Y , Yu X , Zhao H , et al . Host susceptibility to severe COVID-19 and establishment of a host risk score: findings of 487 cases outside Wuhan . Crit Care 2020 ; 24 : 108 . 10.1186/s13054-020-2833-7 32188484 PMC7081524

64 Tomlins J , Hamilton F , Gunning S , et al . Clinical features of 95 sequential hospitalised patients with novel coronavirus 2019 disease (COVID-19), the first UK cohort . J Infect 2020 ; 81 : e59 – 61 . 10.1016/j.jinf.2020.04.020 PMC7184992 32353384

65 Wan S , Xiang Y , Fang W , et al . Clinical features and treatment of COVID-19 patients in northeast Chongqing . J Med Virol 2020 ; 92 : 797 – 806 . 10.1002/jmv.25783 32198776 PMC7228368

66 Wang D , Hu B , Hu C , et al . Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China . JAMA 2020 ; 323 : 1061 – 9 . 10.1001/jama.2020.1585 32031570 PMC7042881

67 Wang R , Pan M , Zhang X , et al . Epidemiological and clinical features of 125 hospitalized patients with COVID-19 in Fuyang, Anhui, China . Int J Infect Dis 2020 ; 95 : 421 – 8 . 10.1016/j.ijid.2020.03.070 32289565 PMC7151431

68 Wu J , Liu J , Zhao X , et al . Clinical characteristics of imported cases of coronavirus disease 2019 (COVID-19) in Jiangsu Province: a multicenter descriptive study . Clin Infect Dis 2020 ; 71 : 706 – 12 . 10.1093/cid/ciaa199 32109279 PMC7108195

69 Xu X , Yu C , Qu J , et al . Imaging and clinical features of patients with 2019 novel coronavirus SARS-CoV-2 . Eur J Nucl Med Mol Imaging 2020 ; 47 : 1275 – 80 . 10.1007/s00259-020-04735-9 32107577 PMC7080117

70 Yang W , Cao Q , Qin L , et al . Clinical characteristics and imaging manifestations of the 2019 novel coronavirus disease (COVID-19):A multi-center study in Wenzhou city, Zhejiang, China . J Infect 2020 ; 80 : 388 – 93 . 10.1016/j.jinf.2020.02.016 32112884 PMC7102539

71 Yang K , Sheng Y , Huang C , et al . Clinical characteristics, outcomes, and risk factors for mortality in patients with cancer and COVID-19 in Hubei, China: a multicentre, retrospective, cohort study . Lancet Oncol 2020 ; 21 : 904 – 13 . 10.1016/S1470-2045(20)30310-7 32479787 PMC7259917

72 Yarza R , Bover M , Paredes D , et al . SARS-CoV-2 infection in cancer patients undergoing active treatment: analysis of clinical features and predictive factors for severe respiratory failure and death . Eur J Cancer 2020 ; 135 : 242 – 50 . 10.1016/j.ejca.2020.06.001 32586724 PMC7275164

73 Zhang G-qin , Hu C , Luo L-jie , et al . Clinical features and treatment of 221 patients with COVID-19 in Wuhan, China . SSRN Journal 2020 . 10.2139/ssrn.3546095 PMC7194884 32311650

74 Zhang J , Wang X , Jia X , et al . Risk factors for disease severity, unimprovement, and mortality in COVID-19 patients in Wuhan, China . Clin Microbiol Infect 2020 ; 26 : 767 – 72 . 10.1016/j.cmi.2020.04.012 32304745 PMC7159868

75 Zhao X-Y , Xu X-X , Yin H-S , et al . Clinical characteristics of patients with 2019 coronavirus disease in a non-Wuhan area of Hubei Province, China: a retrospective study . BMC Infect Dis 2020 ; 20 . 10.1186/s12879-020-05010-w PMC7188494 32345226

76 Zhou F , Yu T , Du R , et al . Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study . Lancet 2020 ; 395 : 1054 – 62 . 10.1016/S0140-6736(20)30566-3 32171076 PMC7270627

77 Zhu W , Xie K , Lu H , et al . Initial clinical features of suspected coronavirus disease 2019 in two emergency departments outside of Hubei, China . J Med Virol 2020 ; 92 : 1525 – 32 . 10.1002/jmv.25763 32167181 PMC7228360

ИП Урсолов Владлен Сергеевич

ИНН 500118482352 · ОГРНИП 314500117700049

Реквизиты и контакты