Literature DB >> 32776581

The outcome of COVID-19 in patients with hematological malignancy.

Tugce N Yigenoglu1, Naim Ata2, Fevzi Altuntas1, Semih Bascı1, Mehmet Sinan Dal1, Serdal Korkmaz3, Sinem Namdaroglu4, Abdulkadir Basturk5, Tuba Hacıbekiroglu6, Mehmet H Dogu7, İlhami Berber8, Kursat Dal9, Mehmet A Erkurt8, Burhan Turgut10, Mustafa Mahir Ulgu11, Osman Celik12, Ersan Imrat11, Suayip Birinci13.   

Abstract

In this study, we aim to report the outcomes for COVID-19 in patients with hematological malignancy in Turkey. Data from laboratory-confirmed 188 897 COVID-19 patients diagnosed between 11 March 2020 and 22 June 2020 included in the Republic of Turkey, Ministry of Health database were analyzed retrospectively. All COVID-19 patients with hematological malignancy (n = 740) were included in the study and an age, sex, and comorbidity-matched cohort of COVID-19 patients without cancer (n = 740) at a 1:1 ratio was used for comparison. Non-Hodgkin lymphoma (30.1%), myelodysplastic syndrome (19.7%), myeloproliferative neoplasm (15.7%) were the most common hematological malignancies. The rates of severe and critical disease were significantly higher in patients with hematological malignancy compared with patients without cancer (P = .001). The rates of hospital and intensive care unit (ICU) admission were higher in patients with hematological malignancy compared with the patients without cancer (P = .023, P = .001, respectively). The length of hospital stay and ICU stay was similar between groups (P = .7, P = .3, retrospectively). The rate of mechanical ventilation (MV) support was higher in patients with hematological malignancy compared with the control group (P = .001). The case fatality rate was 13.8% in patients with hematological malignancy, and it was 6.8% in the control group (P = .001). This study reveals that there is an increased risk of COVID-19-related serious events (ICU admission, MV support, or death) in patients with hematological malignancy compared with COVID-19 patients without cancer and confirms the high vulnerability of patients with hematological malignancy in the current pandemic.
© 2020 Wiley Periodicals LLC.

Entities:  

Keywords:  COVID-19; SARS-CoV-2; hematological malignancy

Mesh:

Year:  2020        PMID: 32776581      PMCID: PMC7436524          DOI: 10.1002/jmv.26404

Source DB:  PubMed          Journal:  J Med Virol        ISSN: 0146-6615            Impact factor:   20.693


acute lymphoblastic leukemia acute myeloid leukemia coronary artery disease chronic obstructive pulmonary disease chronic lymphocytic leukemia chronic myeloid leukemia diabetes mellitus hairy cell leukemia Hodgkin lymphoma hypertension intensive care unit myelodysplastic syndrome multiple myeloma myeloproliferative neoplasm mechanical ventilation non‐Hodgkin lymphoma

INTRODUCTION

Most of the coronaviruses (CoVs) are pathogenic to humans, but they rarely cause severe infections. However, in the last two decades, two CoVs have caused severe infections in humans: the severe acute respiratory syndrome coronavirus (SARS‐CoV) and the Middle East respiratory syndrome coronavirus (MERS‐CoV). , , At the end of 2019, a cluster of pneumonia patients of an unidentified cause was observed in Wuhan, China. After the genetic analysis of the virus, it was understood that these pneumonia cases were caused by the 2019 novel coronavirus (2019‐nCoV), which was later named SARS‐CoV‐2. , The new disease presented with similar clinical findings to SARS‐CoV and MERS‐CoV, such as fever, dyspnea, and multilobed lesions in the computed tomography of the thorax. The disease caused by SARS‐CoV‐2 was named as COVID‐19, by the World Health Organization (WHO). It was declared a pandemic, on 11 March 2020. , Older age and comorbidities such as diabetes, hypertension, or cardiac disease are risk factors for a more aggressive clinical course in patients with COVID‐19. In addition, in a previous report, it was reported that 39% of COVID‐19 patients with cancer had severe events such as intensive care unit (ICU) admission, need of mechanical ventilation (MV) and death during the COVID‐19 course, whereas only 8% of COVID‐19 patients without cancer had those severe events. The more aggressive clinical course of cancer patients with COVID‐19 may be attributed to immunosuppression due to the chemotherapies, radiotherapy, or immunosuppressive drugs they are receiving or increased coexisting medical conditions or lung invasion by the primary tumor itself or metastasis. Patients with hematological malignancies may be more vulnerable than patients with solid tumors because of the immune system dysfunction that they have. , However, there are only a limited number of studies about COVID‐19 in patients with hematological malignancy, and most of these data are based on case series. Therefore, in this study, we aim to report the outcome of COVID‐19 in patients with hematological malignancies treated in Turkey.

MATERIALS AND METHODS

Ethics committee approval was obtained from the Republic of Turkey, Ministry of Health.

Patients

The data of laboratory‐confirmed 188 897 COVID‐19 patients diagnosed between 11 March 2020 and 22 June 2020 included in the Republic of Turkey, Ministry of Health database were analyzed retrospectively. All COVID‐19 patients with hematological malignancy (n = 740) were included in the study and age, sex, and comorbidity‐matched COVID‐19 patients without cancer (n = 740) at 1:1 ratio was used for comparison.

Laboratory analysis

Real‐time reverse‐transcriptase polymerase chain reaction (RT‐PCR) tests for SARS‐CoV‐2 RNA were performed using nasopharyngeal swabs. Total nucleic acid extraction of nasopharyngeal swabs of viral isolates was performed using a Biospeedy and Coyote extraction system (Bioeksen Ltd and Coyote Bioscience Ltd). RT‐PCR assays for SARS‐CoV‐2 RNA detection were performed using a Biospeedy COVID‐19 RT‐qPCR Detection Kit (Bioeksen, Istanbul, Turkey), a Direct Detect SARS‐Cov2 Detection Kit (Coyote Bioscience Co Ltd, China), a Probe RT‐PCR Kit in a LightCycler 960 real‐time PCR system (Roche, Basel, Switzerland), a CFX96 Touch RT‐PCR Detection System (Bio‐Rad, CA), and a Rotor‐Gene Q (Qiagen, Hilden, Germany).

Disease severity

Severe COVID‐19 was defined as the existence of dyspnea, blood oxygen saturation less than or equal to 93%, PaO2/FiO2 < 300 and greater than 50% progression in lung infiltrates within 24 to 48 hours. Critical COVID‐19 was defined as the existence of respiratory failure, septic shock, and/or multiple organ dysfunctions.

Statistical analysis

Data analysis was performed using IBM SPSS v26 software. Variables were assessed for normal distribution with the Kolmogorov‐Smirnov test. Categorical data were presented as number‐percentages, and numerical data were presented as the median, minimum, and maximum. Differences between categorical variables were analyzed with the χ 2 test, and numeric variables were compared with the Mann‐Whitney U test.

RESULTS

In total, there were 1480 laboratory‐confirmed COVID‐19 patients; 740 of them had hematological malignancy and the other 740 comprised the age, sex, and comorbidity‐matched cohort. The demographic and clinical characteristics of all patients in the study are given in Table 1.
Table 1

Demographic and clinical characteristics of the patients

Patients with hematological malignancy (n = 740)Patients without cancer (n = 740) P value
Sex
Male, n (%)397 (53.6)400 (54.1).9
Female, n (%)343 (46.4)340 (45.9)
Age, y56 (18‐94)56 (18‐87)
Comorbidity, n (%)
Hypertension379 (51.2)378 (51.1)1
Diabetes mellitus198 (26.8)198 (26.8)1
Cardiovascular diseases156 (21.1)135 (18.2).2
Respiratory system diseases175 (23.6)164 (22.2).5
Additional treatment, n (%)
Favipiravir189 (27.4)193 (26.1).6
Oseltamivir309 (44.8)349 (47.2).4
Lopinavir/ritonavir35 (5.1)19 (2.6).013*
Hydroxychloroquine508 (73.6)541 (73.1).8
High‐dose vitamin C118 (17.1)109 (14.7).2
Not available500

P ≤ .05, statistically significant.

Demographic and clinical characteristics of the patients P ≤ .05, statistically significant. The number and percentages of the hematological malignancies in COVID‐19 patients were as follows: 223 (30.1%) non‐Hodgkin lymphoma (NHL), 116 (15.7%) myeloproliferative neoplasm (MPN), 146 (19.7%) myelodysplastic syndrome (MDS), 77 (10.4%) multiple myeloma (MM), 54 (7.3%) chronic lymphocytic leukemia (CLL), 40 (5.4%) acute myeloid leukemia (AML), 30 (4.1%) chronic myeloid leukemia (CML), 27 (3.6%) Hodgkin's lymphoma (HL), 18 (2.4%) acute lymphoblastic leukemia (ALL), and 9 (1.2%) hairy cell leukemia (HCL). Hypertension was the most common comorbid disease in COVID‐19 patients with hematological malignancy and was observed in 51.2% of the patients. The rate of lopinavir/ritonavir use was higher in patients with hematological malignancy compared with the control group (P = .013) (Table 1).

Outcome

A severe course of COVID‐19 was observed in 15.5% of patients with hematological malignancy whereas it was observed in 13% of patients without cancer. In addition, the rate of critically ill COVID‐19 patients was 13.2% among patients with hematological malignancy whereas it was 6.6% among patients without cancer. The rates of severe and critical diseases were significantly higher in patients with hematological malignancy compared with patients without cancer (P = .001). The rates of hospital and ICU admission were higher in patients with hematological malignancy compared with the patients without cancer (P = .023, P = .001, respectively). The length of hospital stay and ICU stay was similar between groups (P = .7, P = .3, retrospectively). The rate of MV support was higher in patients with hematological malignancy compared with the control group (P = .001). The case fatality rate (CFR) was 13.8% in patients with hematological malignancy, and it was 6.8% in the control group (P = .001) (Table 2). The highest CFR among COVID‐19 patients with hematological malignancy was observed in HCL (44%) followed by AML (20%) and MM (19.5%). When hematological malignancies were classified into two groups according to their origins as lymphoid malignancies (NHL, HL, ALL, CLL, HCL, MM) and myeloid malignancies (AML, MDS, MPN, CML), no significant difference was observed regarding CFR (P = .6). The distribution of the deceased patients according to their hematological malignancies is given in Table 3. No significant difference was observed between deceased patients with hematological malignancy and deceased patients without cancer regarding sex, age, number of comorbidities, and COVID‐19 treatment they received (Table 4).
Table 2

The outcome of COVID‐19 patients with hematological malignancy and control group

Patients with hematological malignancyPatients without cancer P value
COVID‐19 severity, n (%)
Severe115 (15.5)96 (13).001*
Critical98 (13.2)49 (6.6)
Hospital admission, n (%)452 (61.1)409 (55.3).023*
ICU admission, n (%)140 (18.9)85 (11.5).001*
MV, n (%)102 (13.8)53 (7.2).001*
Duration in hospital, d10 (2‐57)10 (2‐61).7
Duration in ICU, d6 (1‐37)8 (1‐57).3
CFR, n (%)102 (13.8)50 (6.8).001*

Abbreviations: CFR, case fatality rate; COVID‐19, coronavirus disease 2019; ICU, intensive care unit; MV, mechanical ventilation.

P ≤ .05, statistically significant.

Table 3

The distribution of deceased patients according to their hematological malignancies

DiseaseAll (n)Deceased (n)CFR (%)
HL27414.8
CLL54916.6
MM771519.5
ALL18316.6
MPN116108.6
CML30310
NHL2232410.8
MDS1462215
AML40820
HCL9444
Total74010213.8

Abbreviations: ALL, acute lymphoblastic leukemia; AML, acute myeloid leukemia; CFR, case fatality rate; CLL, chronic lymphocytic leukemia; CML, chronic myeloid leukemia; HCL, hairy cell leukemia; HL, Hodgkin lymphoma; MDS, myelodysplastic syndrome; MM, multiple myeloma; MPN, myeloproliferative neoplasm; NHL, non‐Hodgkin lymphoma.

Table 4

Clinical and demographic features of the deceased patients

Patients with hematological malignancy (n = 102)Patients without cancer (n = 50) P value
Sex, n (%)
Male65 (63.7)38 (76).13
Female37 (36.3)12 (24)
Age, median (y)69 (24‐92)71.5 (48‐87).44
Comorbidity, n (%)
≥259 (57.8)26 (52).8
128 (27.5)16 (32)
015 (14.7)8 (16)
Treatment, n (%)
Favipiravir66 (65.3)36 (72).4
Lopinavir/ritonavir10 (9.9)7 (14).5
Hydroxychloroquine77 (76.2)41 (82).4
Azithromycin51 (50.5)34 (68).4
Not available10
The outcome of COVID‐19 patients with hematological malignancy and control group Abbreviations: CFR, case fatality rate; COVID‐19, coronavirus disease 2019; ICU, intensive care unit; MV, mechanical ventilation. P ≤ .05, statistically significant. The distribution of deceased patients according to their hematological malignancies Abbreviations: ALL, acute lymphoblastic leukemia; AML, acute myeloid leukemia; CFR, case fatality rate; CLL, chronic lymphocytic leukemia; CML, chronic myeloid leukemia; HCL, hairy cell leukemia; HL, Hodgkin lymphoma; MDS, myelodysplastic syndrome; MM, multiple myeloma; MPN, myeloproliferative neoplasm; NHL, non‐Hodgkin lymphoma. Clinical and demographic features of the deceased patients

DISCUSSION

The prevalence of cancer in patients with COVID‐19 is uncertain. Previous studies from China reported that 1% to 2% of COVID‐19 patients had cancer, and a study from the United States reported that 6% of hospitalized patients with COVID‐19 had cancer. In Lombardy, Italy, they observed that 8% of the patients admitted to the ICU for COVID‐19 had cancer. In a meta‐analysis, the prevalence of cancer was 2% among COVID‐19 patients. , Although there are reports about the prevalence of cancer among COVID‐19 patients, the data about the prevalence of hematological malignancies among COVID‐19 patients are very limited. In our study, we found that 0.39% of the laboratory‐confirmed COVID‐19 patients had hematological malignancy. The most common hematological malignancies in COVID‐19 patients were NHL (30.1%) followed by MDS (19.7%). There is also less knowledge existing in the literature about the disease course in COVID‐19 patients with hematological malignancies. In a previous study, researchers analyzed the data of 105 patients with cancer hospitalized for COVID‐19 and compared their results to patients without cancer. Among 105 COVID‐19 patients with cancer, nine had hematological malignancy. When compared with patients without cancer, they found that patients with cancer had higher death rates, higher rates of ICU admission, and a more severe COVID‐19 course and had a higher rate of MV support. In addition, they observed that patients with hematologic malignancies, lung cancer, and metastatic cancer had the highest frequency of severe events. In a study conducted by Mehta et al, the ICU admission rate and MV support rate were higher in patients with hematological malignancy (26%) compared with patients with solid tumors (19%); however, this did not achieve statistical significance. In our study, a severe course of COVID‐19 was observed in 15.5% of patients with hematological malignancy whereas it was observed in 13% of patients without cancer. In addition, the rate of critically ill COVID‐19 patients was 13.2% among patients with hematological malignancy whereas it was 6.6% among patients without cancer. We found that the rates of severe and critical diseases were significantly higher in patients with hematological malignancy compared with patients without cancer. The rates of ICU and hospital admission and MV support were higher in COVID‐19 patients with hematological malignancy compared with the control group. This finding supports the hypothesis of the high probability of immunopathogenic damage due to a cytokine storm because of the increased risk of an immunological hyperactivation induced by SARS‐CoV‐2 in hematological malignancies involving T lymphocytes, natural killer cells, histiocytes and antigen‐presenting cells. In a previous study, the mortality rate in myeloid malignancies (MDS/AML/MPN) was higher than that of the lymphoid neoplasms (NHL/CLL/ALL/MM/HL) (43% vs 35%).  In contrast to their results, we did not find a significant difference between lymphoid malignancies (NHL, HL, ALL, CLL, HCL, MM) and myeloid malignancies (AML, MDS, MPN, CML) regarding CFRs. In their study, there were 14 patients with myeloid malignancies and 40 patients with lymphoid malignancies; however, in our study, there were 332 patients with myeloid malignancies and 408 patients with lymhoid malignancies, therefore, this contrast between the two studies may be attributed to the different sizes of the studies. The CFR in COVID‐19 patients with hematological malignancy also differs in the limited published studies. In a study conducted by Mehta et al, CFR in COVID‐19 patients with hematological malignancy was 37%. He et al, in their study, compared the outcome of hospitalized COVID‐19 patients with hematological malignancy to the healthcare providers with COVID‐19. They found that hospitalized COVID‐19 patients with hematological malignancy had more severe disease and a higher CFR compared with hospitalized healthcare providers. More complications including acute respiratory distress syndrome, acute renal failure, and sepsis were observed in COVID‐19 patients with hematological malignancy compared with healthcare providers with COVID‐19; none of the healthcare providers and eight patients with hematologic malignancy died at the end of observation (P =.001) In a study from Spain, Martin‐Moro et al investigated 34 hospitalized COVID‐19 patients with hematological malignancy and observed that the CFR was 32%. They concluded that hematologic malignancy status at the time of COVID‐19 is related to mortality; patients with no active cancer presented better outcomes. Additionally, Aries et al also reported CFR as 40% in hemato‐oncology patients in their small cohort study including 35 patients. In the study conducted by Yang et al, among 52 COVID‐19 patients with solid tumors or hematological malignancies, the rate of severe/critical disease was 36.5% and CFR of severe/critical patients was 57.8%. In our study, CFR was 13.8% in COVID‐19 patients with hematological malignancy. The lower CFR in our study compared with the other studies may be attributed to a high number of MPN patients in our study who were thought to be less immunocompromised compared with leukemia, MM, or lymphoma patients. Also, our study included both hospitalized and nonhospitalized patients. To the best of our knowledge, this is the first large‐scale population‐based study investigating the COVID‐19 patients with hematological malignancies and comparing their results to an age, sex, and comorbidity‐matched cohort of COVID‐19 patients without cancer. The main findings of the current study were that (a) hypertension was the most common comorbid disease in both of the groups; (b) the rates of severe and critical diseases, hospital and ICU admission, and MV support were higher in patients with hematological malignancy compared with the COVID‐19 patients without cancer; (c) length of hospital stay and ICU stay was similar between groups; (d) CFR was higher in patients with hematological malignancy compared with the control group; (e) no significant difference was observed between lymphoid malignancies and myeloid malignancies regarding CFRs. A retrospective design and lack of information about anticancer treatments and the hematological disease status are the limitations of our study. The merits of our study are that the control group was composed of age, sex, and comorbidity‐matched patients, however, in most studies, control groups are not comorbidity‐matched. In conclusion, it is important to consider that patients with hematological malignancy are immunocompromised, and our study reveals that there is an increased risk of COVID‐19‐related serious events (ICU admission, requirement for MV, or death) in patients with hematological malignancy compared with COVID‐19 patients without cancer and supports the high vulnerability of patients with hematological malignancy in the current pandemic. Therefore, physicians should pay great attention to the management of COVID‐19 patients with hematological malignancy. A triage performed by telephone or other online technologies should be used to verify the need for treatment or follow‐up in inpatient or outpatient clinics. In non–life‐threatening diseases, hospitalization should be postponed. Patients who have a fever or any other symptoms that may be related to COVID‐19 should be tested to detect SARS‐CoV‐2 RNA and they should not be accepted into the hematology ward before ruling out the possibility of COVID‐19.

CONFLICT OF INTERESTS

The authors declare that there are no conflict of interests.

AUTHOR CONTRIBUTIONS

Concept and design: TNY, FA; acquisition, analysis, or interpretation of data: MMU, EI; drafting of the manuscript: TNY, FA; statistical analysis: SB; critical revision of the manuscript for important intellectual content: all authors.
  20 in total

1.  Presenting Characteristics, Comorbidities, and Outcomes Among 5700 Patients Hospitalized With COVID-19 in the New York City Area.

Authors:  Safiya Richardson; Jamie S Hirsch; Mangala Narasimhan; James M Crawford; Thomas McGinn; Karina W Davidson; Douglas P Barnaby; Lance B Becker; John D Chelico; Stuart L Cohen; Jennifer Cookingham; Kevin Coppa; Michael A Diefenbach; Andrew J Dominello; Joan Duer-Hefele; Louise Falzon; Jordan Gitlin; Negin Hajizadeh; Tiffany G Harvin; David A Hirschwerk; Eun Ji Kim; Zachary M Kozel; Lyndonna M Marrast; Jazmin N Mogavero; Gabrielle A Osorio; Michael Qiu; Theodoros P Zanos
Journal:  JAMA       Date:  2020-05-26       Impact factor: 56.272

2.  [Report of cancer epidemiology in China, 2015].

Authors:  R S Zheng; K X Sun; S W Zhang; H M Zeng; X N Zou; R Chen; X Y Gu; W W Wei; J He
Journal:  Zhonghua Zhong Liu Za Zhi       Date:  2019-01-23

3.  COVID-19 and Cancer: Lessons From a Pooled Meta-Analysis.

Authors:  Aakash Desai; Sonali Sachdeva; Tarang Parekh; Rupak Desai
Journal:  JCO Glob Oncol       Date:  2020-04

4.  Isolation of a novel coronavirus from a man with pneumonia in Saudi Arabia.

Authors:  Ali M Zaki; Sander van Boheemen; Theo M Bestebroer; Albert D M E Osterhaus; Ron A M Fouchier
Journal:  N Engl J Med       Date:  2012-10-17       Impact factor: 91.245

5.  Patients with Cancer Appear More Vulnerable to SARS-CoV-2: A Multicenter Study during the COVID-19 Outbreak.

Authors:  Mengyuan Dai; Dianbo Liu; Miao Liu; Fuxiang Zhou; Guiling Li; Zhen Chen; Zhian Zhang; Hua You; Meng Wu; Qichao Zheng; Yong Xiong; Huihua Xiong; Chun Wang; Changchun Chen; Fei Xiong; Yan Zhang; Yaqin Peng; Siping Ge; Bo Zhen; Tingting Yu; Ling Wang; Hua Wang; Yu Liu; Yeshan Chen; Junhua Mei; Xiaojia Gao; Zhuyan Li; Lijuan Gan; Can He; Zhen Li; Yuying Shi; Yuwen Qi; Jing Yang; Daniel G Tenen; Li Chai; Lorelei A Mucci; Mauricio Santillana; Hongbing Cai
Journal:  Cancer Discov       Date:  2020-04-28       Impact factor: 39.397

Review 6.  Epidemiology, Genetic Recombination, and Pathogenesis of Coronaviruses.

Authors:  Shuo Su; Gary Wong; Weifeng Shi; Jun Liu; Alexander C K Lai; Jiyong Zhou; Wenjun Liu; Yuhai Bi; George F Gao
Journal:  Trends Microbiol       Date:  2016-03-21       Impact factor: 17.079

7.  Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus-Infected Pneumonia.

Authors:  Qun Li; Xuhua Guan; Peng Wu; Xiaoye Wang; Lei Zhou; Yeqing Tong; Ruiqi Ren; Kathy S M Leung; Eric H Y Lau; Jessica Y Wong; Xuesen Xing; Nijuan Xiang; Yang Wu; Chao Li; Qi Chen; Dan Li; Tian Liu; Jing Zhao; Man Liu; Wenxiao Tu; Chuding Chen; Lianmei Jin; Rui Yang; Qi Wang; Suhua Zhou; Rui Wang; Hui Liu; Yinbo Luo; Yuan Liu; Ge Shao; Huan Li; Zhongfa Tao; Yang Yang; Zhiqiang Deng; Boxi Liu; Zhitao Ma; Yanping Zhang; Guoqing Shi; Tommy T Y Lam; Joseph T Wu; George F Gao; Benjamin J Cowling; Bo Yang; Gabriel M Leung; Zijian Feng
Journal:  N Engl J Med       Date:  2020-01-29       Impact factor: 176.079

8.  Clinical Characteristics of Coronavirus Disease 2019 in China.

Authors:  Wei-Jie Guan; Zheng-Yi Ni; Yu Hu; Wen-Hua Liang; Chun-Quan Ou; Jian-Xing He; Lei Liu; Hong Shan; Chun-Liang Lei; David S C Hui; Bin Du; Lan-Juan Li; Guang Zeng; Kwok-Yung Yuen; Ru-Chong Chen; Chun-Li Tang; Tao Wang; Ping-Yan Chen; Jie Xiang; Shi-Yue Li; Jin-Lin Wang; Zi-Jing Liang; Yi-Xiang Peng; Li Wei; Yong Liu; Ya-Hua Hu; Peng Peng; Jian-Ming Wang; Ji-Yang Liu; Zhong Chen; Gang Li; Zhi-Jian Zheng; Shao-Qin Qiu; Jie Luo; Chang-Jiang Ye; Shao-Yong Zhu; Nan-Shan Zhong
Journal:  N Engl J Med       Date:  2020-02-28       Impact factor: 91.245

9.  Case Fatality Rate of Cancer Patients with COVID-19 in a New York Hospital System.

Authors:  Vikas Mehta; Sanjay Goel; Rafi Kabarriti; Balazs Halmos; Amit Verma; Daniel Cole; Mendel Goldfinger; Ana Acuna-Villaorduna; Kith Pradhan; Raja Thota; Stan Reissman; Joseph A Sparano; Benjamin A Gartrell; Richard V Smith; Nitin Ohri; Madhur Garg; Andrew D Racine; Shalom Kalnicki; Roman Perez-Soler
Journal:  Cancer Discov       Date:  2020-05-01       Impact factor: 38.272

10.  Clinical outcome of coronavirus disease 2019 in haemato-oncology patients.

Authors:  James A Aries; Jeffrey K Davies; Rebecca L Auer; Simon L Hallam; Silvia Montoto; Matthew Smith; Belen Sevillano; Vanessa Foggo; Bela Wrench; Krzysztof Zegocki; Samir Agrawal; Rifca Le Dieu; Edward Truelove; Thomas Erblich; Shamzah Araf; Jessica Okosun; Heather Oakervee; Jamie D Cavenagh; John G Gribben; John C Riches
Journal:  Br J Haematol       Date:  2020-06-10       Impact factor: 8.615

View more
  34 in total

1.  Patient and health practitioner views and experiences of a cancer trial before and during COVID-19: qualitative study.

Authors:  Frances C Sherratt; Peter Fisher; Amy Mathieson; Mary G Cherry; Andrew R Pettitt; Bridget Young
Journal:  Trials       Date:  2022-06-18       Impact factor: 2.728

Review 2.  Prevention of HBV Reactivation in Hemato-Oncologic Setting during COVID-19.

Authors:  Caterina Sagnelli; Antonello Sica; Massimiliano Creta; Alessandra Borsetti; Massimo Ciccozzi; Evangelista Sagnelli
Journal:  Pathogens       Date:  2022-05-11

3.  Differences in Outcomes and Factors Associated With Mortality Among Patients With SARS-CoV-2 Infection and Cancer Compared With Those Without Cancer: A Systematic Review and Meta-analysis.

Authors:  Emma Khoury; Sarah Nevitt; William Rohde Madsen; Lance Turtle; Gerry Davies; Carlo Palmieri
Journal:  JAMA Netw Open       Date:  2022-05-02

4.  Convalescent plasma therapy in patients with COVID-19.

Authors:  Fevzi Altuntas; Tugce Nur Yigenoglu; Semih Bascı; Mehmet Sinan Dal; Serdal Korkmaz; Burhan Turgut; Mehmet Ali Erkurt
Journal:  Transfus Apher Sci       Date:  2020-11-19       Impact factor: 1.764

Review 5.  Impact of COVID-19 in patients with lymphoid malignancies.

Authors:  John Charles Riches
Journal:  World J Virol       Date:  2021-05-25

6.  Characterization of Metal-Bound Benzimidazole Derivatives, Effects on Tumor Cells of Lung Cancer.

Authors:  Anita Raducka; Agnieszka Czylkowska; Katarzyna Gobis; Kamila Czarnecka; Paweł Szymański; Marcin Świątkowski
Journal:  Materials (Basel)       Date:  2021-05-30       Impact factor: 3.623

7.  Patients with hematologic cancers are more vulnerable to COVID-19 compared to patients with solid cancers.

Authors:  Semih Başcı; Naim Ata; Fevzi Altuntaş; Tuğçe Nur Yiğenoğlu; Mehmet Sinan Dal; Serdal Korkmaz; Sinem Namdaroğlu; Abdülkadir Baştürk; Tuba Hacıbekiroğlu; Mehmet Hilmi Doğu; İlhami Berber; Kürşat Dal; Mehmet Ali Erkurt; Burhan Turgut; Osman Çelik; Mustafa Mahir Ülgü; Şuayip Birinci
Journal:  Intern Emerg Med       Date:  2021-06-10       Impact factor: 5.472

8.  The outcome of COVID-19 in patients with hematological malignancy.

Authors:  Banu Cakir
Journal:  J Med Virol       Date:  2020-10-10       Impact factor: 20.693

9.  The impact of COVID-19 on patients with hematological malignancies: the mixed-method analysis of an Israeli national survey.

Authors:  Ilana Levy; Giora Sharf; Shlomit Norman; Tamar Tadmor
Journal:  Support Care Cancer       Date:  2021-06-14       Impact factor: 3.603

10.  The outcome of COVID-19 in patients with hematological malignancy.

Authors:  Tugce N Yigenoglu; Naim Ata; Fevzi Altuntas; Semih Bascı; Mehmet Sinan Dal; Serdal Korkmaz; Sinem Namdaroglu; Abdulkadir Basturk; Tuba Hacıbekiroglu; Mehmet H Dogu; İlhami Berber; Kursat Dal; Mehmet A Erkurt; Burhan Turgut; Mustafa Mahir Ulgu; Osman Celik; Ersan Imrat; Suayip Birinci
Journal:  J Med Virol       Date:  2020-08-26       Impact factor: 20.693

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.