Literature DB >> 33113551

Outcomes of patients with hematologic malignancies and COVID-19: a systematic review and meta-analysis of 3377 patients.

Abi Vijenthira1, Inna Y Gong2, Thomas A Fox3, Stephen Booth4, Gordon Cook5, Bruno Fattizzo6,7, Fernando Martín-Moro8, Jerome Razanamahery9, John C Riches10, Jeff Zwicker11, Rushad Patell11, Marie Christiane Vekemans12, Lydia Scarfò13, Thomas Chatzikonstantinou14, Halil Yildiz15, Raphaël Lattenist15, Ioannis Mantzaris16, William A Wood17, Lisa K Hicks2,18.   

Abstract

Outcomes for patients with hematologic malignancy infected with COVID-19 have not been aggregated. The objective of this study was to perform a systematic review and meta-analysis to estimate the risk of death and other important outcomes for these patients. We searched PubMed and EMBASE up to 20 August 2020 to identify reports of patients with hematologic malignancy and COVID-19. The primary outcome was a pooled mortality estimate, considering all patients and only hospitalized patients. Secondary outcomes included risk of intensive care unit admission and ventilation in hospitalized patients. Subgroup analyses included mortality stratified by age, treatment status, and malignancy subtype. Pooled prevalence, risk ratios (RRs), and 95% confidence intervals (CIs) were calculated using a random-effects model. Thirty-four adult and 5 pediatric studies (3377 patients) from Asia, Europe, and North America were included (14 of 34 adult studies included only hospitalized patients). Risk of death among adult patients was 34% (95% CI, 28-39; N = 3240) in this sample of predominantly hospitalized patients. Patients aged ≥60 years had a significantly higher risk of death than patients <60 years (RR, 1.82; 95% CI, 1.45-2.27; N = 1169). The risk of death in pediatric patients was 4% (95% CI, 1-9; N = 102). RR of death comparing patients with recent systemic anticancer therapy to no treatment was 1.17 (95% CI, 0.83-1.64; N = 736). Adult patients with hematologic malignancy and COVID-19, especially hospitalized patients, have a high risk of dying. Patients ≥60 years have significantly higher mortality; pediatric patients appear to be relatively spared. Recent cancer treatment does not appear to significantly increase the risk of death.
© 2020 by The American Society of Hematology.

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Year:  2020        PMID: 33113551      PMCID: PMC7746126          DOI: 10.1182/blood.2020008824

Source DB:  PubMed          Journal:  Blood        ISSN: 0006-4971            Impact factor:   22.113


Introduction

A substantial number of guidance documents and review articles have been published regarding the management of patients with cancer and the novel severe acute respiratory syndrome coronavirus 2 (COVID-19).[1-10] However, there are no systematic reviews or meta-analyses specific to patients with hematologic malignancies. These patients are recognized to be highly immunocompromised due to their underlying disease as well as the treatments they receive, causing significant concern about a risk of heightened morbidity and mortality from COVID-19 in this population. On the other hand, some authors have suggested that some patients with hematologic malignancies might be “protected” from severe COVID-19 morbidity due to an attenuated inflammatory response.[11-13] Cohort and registry studies have emerged to answer these and other questions, including the COVID-19 and Cancer Consortium (CCC19), the UK Coronavirus Cancer Monitoring Project (UKCCMP), and the American Society of Hematology (ASH) Research Collaborative. Given the rapidly evolving literature and overall limited data in patients with hematologic malignancy, aggregating data to obtain more precise estimates of the risks related to COVID-19 is essential to inform clinical decision-making. The objective of this study was to perform a systematic review and meta-analysis to quantify the outcomes (deaths, hospitalizations, and complications) of patients with hematologic malignancy and COVID-19.

Methods

This study is reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses.

Eligibility criteria

All studies published since 1 January 2019 on outcomes of patients with cancer and COVID-19 were considered for inclusion. Only studies providing data on patients with hematologic malignancy (bone marrow failure syndromes such as myelodysplastic syndromes [MDS], acute leukemias, lymphomas, plasma cell dyscrasias, and/or myeloproliferative neoplasms [MPNs]) were included. Both adult (age ≥18 years) and pediatric (age <18 years) studies were included. Case reports, case series, or cohort studies with <10 patients were excluded. Only English and Chinese language reports were included. Full inclusion criteria are available in supplemental Table 1 (available on the Blood Web site).

Information sources and search strategy

PubMed and EMBASE databases were searched up to the week of 17 August 2020. The full search strategy is available in supplemental Table 2. Two authors (A.V. and I.Y.G.) independently conducted the search strategy, and results were compared to ensure concordance. Differences in opinion were discussed and resolved, with a third author (L.K.H.) available for resolution of disagreements. Titles and abstracts of articles were reviewed and any that were clearly irrelevant were excluded. Full texts of remaining articles were reviewed to find studies that met the inclusion criteria. Additionally, systematic reviews related to cancer and COVID-19 were screened to identify additional references.

Data collection

A data-extraction form was used to extract relevant information from the articles. Information extracted was specific to patients with hematologic malignancy, and included geographic location of study, total number of patients, median age, distribution by sex, and total study duration, as well as whether follow-up was complete, death rate, death rate in inpatients, intensive care unit (ICU) admission rate, mechanical ventilation rate, noninvasive ventilation rate (continuous positive airway pressure, bilevel positive airway pressure, high-flow oxygen by nasal cannula), and death rate stratified by treatment, age, and hematologic malignancy subtype. For treatment subgroups, “systemic anticancer therapy” (SACT) was defined as patients on active anticancer therapy (ie, cytotoxic chemotherapy, immunotherapy, targeted agents; single-agent hydroxyurea for MPNs and steroids were excluded from this definition) within 28 days to 6 months of COVID-19 diagnosis (depending on varying definitions used in each study). A subgroup of SACT was defined as “cytotoxic SACT” and included patients on cytotoxic therapy only (eg, multiagent systemic chemotherapy or antimyeloma therapy; excluding single-agent immunotherapy, single-agent targeted therapy, single-agent hydroxyurea for MPNs, or steroids). “Not on treatment” was defined as patients on observation or those for whom it had been >28 days to 6 months since their last active treatment. “Best supportive care” (BSC) was defined as patients on supportive care only, such as hydroxyurea alone for acute leukemia, erythropoietin-stimulating agents, or patients who were on BSC as indicated in studies. Hematologic malignancy subtypes were divided as follows: acquired bone marrow failure syndromes (eg, MDS, aplastic anemia); acute leukemias (myeloid and lymphoid); lymphomas (non-Hodgkin and Hodgkin); plasma cell dyscrasias (multiple myeloma, amyloidosis, smoldering myeloma, monoclonal gammopathy of undetermined significance); and MPNs (chronic myeloid leukemia, polycythemia vera, essential thrombocytosis, myelofibrosis). In select cases for which key data were not included, authors of studies were e-mailed for clarification of the published data.

Risk of bias in individual studies

As the majority of studies included were descriptive cohort studies with no comparator arm, the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Studies Reporting Prevalence Data was used (see supplemental Table 3 for checklist).[14] Sample-size adequacy was assessed using previously described methods,[15] using an estimated risk of death of 0.35 and precision of 0.05 indicating a confidence interval (CI) width of 10%. Studies scoring at least 6 of 9 were considered low risk, as previously reported.[16] Assessment was conducted by 2 authors (A.V. and I.Y.G); a third author (L.K.H.) was available to resolve differences of opinion.

Outcomes

The primary outcome of the meta-analysis was the pooled risk of death among patients with hematologic malignancies and COVID-19, subdivided into adult and pediatric patients. Risks of death in all patients as well as within hospitalized patients are reported. Secondary outcomes included the proportion of hospitalized patients requiring ICU admission and ventilation support (mechanical and noninvasive). Prespecified subgroup analyses were conducted for pooled risk of death stratified by age, race (non-White vs White), treatment status, hematologic malignancy subtype, and geographic location (Asia vs Europe vs North America). Prespecified sensitivity analyses were conducted on the primary outcomes, limiting to studies with low risk of bias, to studies with complete follow-up of all patients, to studies diagnosing COVID-19 based solely on real-time polymerase chain reaction (RT-PCR), and to studies that included a combination of outpatients and hospitalized patients. Due to data limitations, secondary outcomes, subgroup analyses, and sensitivity analyses were not completed for the studies reporting on pediatric patients.

Synthesis of results

The principal summary measures used were pooled prevalence and risk ratios (RR) with 95% CIs. Heterogeneity between estimates was assessed using the I2 statistic, and interpreted per the Cochrane Handbook recommendations: I2 of 0% to 40%, heterogeneity likely not substantial; 30% to 60%, moderate heterogeneity; 50% to 90%, substantial heterogeneity; 75% to 100%, considerable heterogeneity.[17] For the primary outcome, secondary outcomes, and subgroup analyses, estimates were transformed using the Freeman-Tukey double arcsine method,[18] and the final pooled results were back-transformed with 95% CI for ease of interpretation. For secondary outcomes involving RR, pooled dichotomous-effect measures were expressed as RR with 95% CI. Meta-analysis was performed using a random-effects model (DerSimonian and Laird) using the MetaXL (www.epigear.com) add-in for Microsoft Excel, as well as Review Manager 5.4 (Cochrane Collaboration 2020). Publication bias was assessed using the Doi plot and the Luis Furuya-Kanamori asymmetry index (LFK index).[19] The closer the value of the LFK index to 0, the more symmetrical the Doi plot (ie, low risk of publication bias). LFK index values outside of the interval between −1 and +1 are consistent with asymmetry (ie, publication bias). Sensitivity testing was performed to assess the main source of asymmetry.

Results

Figure 1 shows the flow diagram for study selection. A total of 34 adult studies (32 peer-reviewed, 1 preprint, 1 open online registry) and 5 pediatric studies (4 peer-reviewed, 1 open online registry) comprising 3377 patients from Asia, Europe, and North America were included.[20-57] Total duration of studies ranged from 3 weeks to 15 weeks. COVID-19 was diagnosed based solely on RT-PCR in the majority of studies (27 of 38 sources); others did not specify (4 of 38) or used clinical suspicion, imaging, or external reporting in some patients (7 of 38). The majority of data are regarding patients followed at hospitals or cancer centers; 1 study used a countrywide Ministry of Health database.[52] Table 1 lists the summary characteristics of the included studies. Table 2 lists the results of the risk-of-bias assessment for individual studies, and supplemental Table 4 includes details of the scoring. When assessing for publication bias, major asymmetry was noted (LFK index, 2.18) (supplemental Figure 1); this was largely driven by the only study that used population-based data.[52] The LFK index, when excluding this study due to the differences in methodology, was 0.94, indicating no asymmetry and a low risk of publication bias.
Figure 1.

Flow diagram of studies assessed for inclusion.[

Table 1.

Characteristics of included studies

First author/yearLocationType of malignancy includedDuration of studyTotal no. of pts with HMTotal no. of pts with HM hospitalizedMedian age of pts with HM, yFemale pts with HM, N (%)Died, N (%)No. of pts who had not yet recovered/ unknown status at end of study
Adult studies
 Aries[20]/2020*EuropeAll2 mo35246912 (34)14 (40)0
 Biernat[21]/2020EuropeAll1 mo1010588 (80)7 (70)0
 Booth[22]/2020*EuropeAll2 mo66667325 (38)34 (52)4
 Cook[23]/2020*EuropeMyeloma15 wk75727330 (40)41 (55)NR
 Dufour[24]/2020*EuropeMyelomaNR2018688 (40)7 (35)NR
 Engelhardt[25]/2020EuropeMyeloma3 mo2117594 (19)0 (0)NR
 Fattizzo[26]/2020*EuropeAll6 wk1613776 (38)5 (31)NR
 Ferrara[27]/2020EuropeAML1 mo1010605 (50)5 (50)1
 Fox[28]/2020*EuropeAll1 mo54516318 (33)19 (35)1
 He[29]/2020AsiaAll3 wk1313356 (46)8 (62)0
 Hultcrantz[30]/2020North AmericaMyeloma7 wk100746842 (42)18 (18)NR
 Infante[31]/2020EuropeAll1 mo41297619 (47)15 (37)NR
 Kuderer[32]/2020MulticenterAll1 mo204104NRNR24 (14)NR
 Lattenist[33]/2020*EuropeAll2 mo1212743 (25)6 (50)NR
 Lee[34]/2020EuropeAll6 wk169NRNRNR60 (36)0
 Malard[35]/2020EuropeAll1 mo2525728 (32)10 (40)NR
 Martín-Moro[36]/2020*EuropeAll5 wk34347315 (44)11 (32)5
 Mato[37]/2020MulticenterCLL2.5 mo1981787173 (37)66 (33)49
 Mehta[38]/2020*North AmericaAll3 wk5454NRNR20 (37)NR
 Mei[39]/2020AsiaAllUnclear1313NRNR6 (46)NR
 Passamonti[40]/2020EuropeAll12 wk53645168196 (37)198 (37)11
 Patell[41]/2020*North AmericaAll2 mo1919NRNR13 (68)4
 Razanamahery[42]/2020*EuropeAll8 wk2020697 (35)6 (30)NR
 Rugge[43]/2020EuropeAll5 wk8148NRNR13 (16)NR
 Russell[44]/2020EuropeAll10 wk28NRNRNR7 (25)NR
 Sanchez-Pina[45]/2020EuropeAll1 mo39346516 (41)14 (40)19
 Scarfò[46]/2020*EuropeCLL10 wk1901697264 (34)56 (29)38
 Shah[47]/2020EuropeAll8 wk80807328 (35)28 (35)5
 Tian[48]/2020AsiaAll9 wk1212NR5 (42)5 (42)NR
 Varma[49]/2020North AmericaAllNR34255712 (35)7 (21)NR
 Wang[50]/2020North AmericaMyeloma2 mo58366728 (48)14 (24)0
 Yang[51]/2020AsiaAll2 mo2222557 (32)9 (41)0
 Yigenoglu[52]/2020AsiaAll15 wk74045256343 (46)102 (14)NR
 ASH registry[53]/2020*MulticenterAllOngoing (3 mo at time of data extraction)264176NR106 (40)74 (30)16
Pediatric studies
 Bisogno[54]/2020EuropeAll2 mo20NRNRNR0 (0)0
 de Rojas[55]/2020EuropeAllNR11NR111 (9)0 (0)NR
 Faura[56]/2020EuropeAll3 mo41NRNRNR2 (5)NR
 Ferrari[57]/2020EuropeAll8 wk12NRNRNR0 (0)NR
 ASH registry[53]/2020*MulticenterAllOngoing (3 mo at time of data extraction)18NRNRNR2 (11)2

AML, acute myeloid leukemia; ASH, American Society of Hematology; CLL, chronic lymphocytic leukemia; HM, hematologic malignancy; NR, not reported; pts, patients; UK, United Kingdom.

Authors who provided extra information via e-mail communication.

Outcome data only available on 167 patients.

Outcome data only available on 248 patients.

Table 2.

Risk-of-bias assessments

Study: first author/yearTotal scoreLow risk of bias
Aries[20]/20207
Biernat[21]/20207
Booth[22]/20206
Cook[23]/20207
Dufour[24]/20205
Engelhardt[25]/20206
Fattizzo[26]/20206
Ferrara[27]/20206
Fox[28]/20206
He[29]/20206
Hultcrantz[30]/20207
Infante[31]/20206
Kuderer[32]/20206
Lattenist[33]/20205
Lee[34]/20208
Malard[35]/20207
Martín-Moro[36]/20205
Mato[37]/20206
Mehta[38]/20207
Mei[39]/20201
Passamonti[40]/20207
Patell[41]/20208
Razanamahery[42]/20205
Rugge[43]/20206
Russell[44]/20206
Sanchez-Pina[45]/20207
Scarfò[46]/20206
Shah[47]/20208
Tian[48]/20206
Varma[49]/20206
Wang[50]/20207
Yang[51]/20208
Yigenoglu[52]/20207
ASH registry[53]/20202
Flow diagram of studies assessed for inclusion.[ Characteristics of included studies AML, acute myeloid leukemia; ASH, American Society of Hematology; CLL, chronic lymphocytic leukemia; HM, hematologic malignancy; NR, not reported; pts, patients; UK, United Kingdom. Authors who provided extra information via e-mail communication. Outcome data only available on 167 patients. Outcome data only available on 248 patients. Risk-of-bias assessments

COVID-19–associated mortality: adult studies

A total of 34 studies (3240 patients) had data available regarding mortality associated with a diagnosis of COVID-19 in adult patients with hematologic malignancy. The pooled risk of death was 34% (95% CI, 28-39) (Figure 2A). Substantial heterogeneity was detected (I2, 87%). When limiting the analysis to mortality among inpatients only, a total of 28 studies with 2361 hospitalized patients showed a pooled risk of 39% (95% CI, 34-44), with decreased heterogeneity (I2, 66%) (Figure 2B).
Figure 2.

Pooled risk of mortality. (A) In all studies. (B) In hospitalized patients only. (C) Pooled risk of mortality in pediatric patients.

Pooled risk of mortality. (A) In all studies. (B) In hospitalized patients only. (C) Pooled risk of mortality in pediatric patients.

COVID-19–associated mortality: pediatric studies

The pooled risk of death was 4% (95% CI, 1-9) in pediatric studies (5 studies, 102 patients) (Figure 2C). No statistically significant heterogeneity was detected (I2, 0%).

ICU admission and ventilation

Twenty-four studies (2192 patients) provided data regarding need for ICU admission among hospitalized patients, 21 studies (1320 patients) provided data regarding need for mechanical ventilation in hospitalized patients, and 12 studies (373 patients) provided data regarding need for noninvasive ventilation in hospitalized patients. For ICU admission, the pooled risk was 21% (95% CI, 16-27; I2, 87%); for mechanical ventilation, the pooled risk was 17% (95% CI, 13-21; I2, 63%); for noninvasive ventilation, the pooled risk was 16% (95% CI, 9-26; I2, 79%) (supplemental Figure 2).

Subgroup and sensitivity analyses

Prespecified age-stratified (<60 years and ≥60 years) subgroup analyses were conducted. Data with age were available from 16 studies (1169 patients). Patients under 60 years of age had a lower pooled risk of mortality (25%; 95% CI, 19-33, I2, 24%) compared with patients aged 60 years and older (47%; 95% CI, 41-54; I2, 58%). The pooled RR for death for patients under 60 years vs patients aged 60 and older was 0.55 (95% CI, 0.44-0.69; P < .00001), with no statistically significant heterogeneity (I2, 0%) (Figure 3).
Figure 3.

RR of death in patients aged under 60 years vs aged 60 years and older.

RR of death in patients aged under 60 years vs aged 60 years and older. Subgroup analysis based on race using data from 5 studies that reported race-based outcomes (162 patients) showed that non-White patients had a significantly higher risk of death compared with White patients (RR, 2.2 [95% CI, 1.3-3.8; P = .003; I2, 0%]) (supplemental Figure 3). Subgroup analyses were also conducted for patients with recent SACT (19 studies, 736 patients), the subgroup of cytotoxic SACT (13 studies, 614 patients), patients not on treatment (14 studies, 356 patients), and patients on BSC (5 studies, 21 patients). The pooled risk of mortality was 39% (95% CI, 32-46; I2, 67%), 40% (95% CI, 32-47; I2, 68%), 25% (95% CI, 17-34; I2, 53%), and 85% (95% CI, 67-97; I2, 0%) for recent SACT, the subgroup cytotoxic SACT, no treatment, and BSC, respectively. The pooled RR for death for patients with recent SACT compared with no treatment was 1.17 (95% CI, 0.83-1.64; P = .37), with no statistically significant heterogeneity (I2, 36%; P = .09) (Figure 4A). When restricting the analysis to the subgroup of patients with recent cytotoxic SACT vs no treatment, the pooled RR of death was 1.29 (95% CI, 0.78-2.15; P = .32), with no statistically significant heterogeneity (I2, 36%; P = .15) (Figure 4B).
Figure 4.

RR of death in patients. (A) On systemic anticancer therapy vs on no treatment. (B) On cytotoxic systemic anticancer therapy vs on no treatment.

RR of death in patients. (A) On systemic anticancer therapy vs on no treatment. (B) On cytotoxic systemic anticancer therapy vs on no treatment. Pooled risk of death was also calculated by hematologic malignancy subtype. The following list outlines the risk of death for:Subgroup analysis based on geographic location (Asia, Europe, or North America) was performed using data from 31 studies (2627 patients). The overall risk of death was similar between regions: 38% (95% CI, 16-63; I2, 88%) in Asia, 35% (95% CI, 30-40; I2, 72%) in Europe, and 31% (95% CI, 18-45; I2, 81%) in North America. acquired bone marrow failure syndromes (14 studies, 231 patients), 53% (95% CI, 34-72; I2, 77%); acute leukemias (18 studies, 289 patients), 41% (95% CI, 30-52; I2, 57%); plasma cell dyscrasias (23 studies, 412 patients), 33% (95% CI, 25-41; I2, 58%); lymphomas, including CLL (20 studies, 1324 patients), 32% (95% CI, 24-40; I2, 43%); lymphomas, excluding CLL (14 studies, 485 patients), 32% (95% CI, 18-48; I2, 65%); CLL specifically (15 studies, 517 patients), 31% (95% CI, 23-40; I2, 52%); and MPNs (12 studies, 293 patients), 34% (95% CI, 19-51; I2, 73%) (supplemental Figure 4). Sensitivity analysis including only studies with a lower risk of bias showed a similar estimate for risk of death among all patients (33% [95% CI, 27-39; I2, 89%], 28 studies with 2893 patients) compared with all studies. Sensitivity analysis including only studies with complete follow-up for all patients showed a similar risk of death compared with all studies (40% [95% CI, 30-51; I2, 58%], 6 studies with 307 patients). Sensitivity analysis including only studies that diagnosed COVID-19 using RT-PCR showed a similar estimate for risk of death among all patients compared with all studies (33% [95% CI, 26-39; I2, 90%]; 24 studies with 2674 patients). Sensitivity analysis including only studies that reported on a combination of outpatients and hospitalized patients showed a similar estimate for risk of death among all patients (31% [95% CI, 24-39; I2, 92%]; 18 studies with 2407 patients) compared with all studies.

Discussion

We report the first meta-analysis to date of the risk of death in patients with hematologic malignancies and COVID-19, incorporating data from 3377 patients from 3 continents. The estimates of mortality are most applicable for hospitalized patients as the majority of patients included in this analysis were hospitalized (77%). The pooled risk of mortality in all adult patients was 34% (95% CI, 28-39), whereas the pooled risk of mortality limited to hospitalized patients alone was 39% (95% CI, 34-44). Furthermore, patients aged 60 years and older had a significantly higher risk of death than patients under 60 years (47% vs 25%; RR, 1.82; 95% CI, 1.45-2.27; P < .00001), though the risk in both age groups was substantial. On the other hand, the pooled risk of death in pediatric patients was significantly lower than adult patients at 4% (95% CI, 0-8), confirming previous reports that increasing age is highly correlated with risk of death from COVID-19.[58,59] Why age is such a powerful correlate of COVID mortality has not been determined. Theories include the possibility that children are less prone to a hyperinflammatory immune response compared with adults, as well as differences in their angiotensin-converting enzyme 2 distribution that may limit viral entry and subsequent inflammation, hypoxia, and tissue injury.[60] The adult mortality rate reported in this meta-analysis appears substantially higher than in patients with solid tumors, or in the general population; however, the context of these data are important. The majority (77%) of the patients in our analysis were hospitalized and 14 of 34 adult studies included only hospitalized patients. In cohort studies exclusively of hospitalized patients with cancer, the mortality rate ranges from 19% to 42% in patients with solid tumor.[38,42] The risk of death in hospitalized patients without cancer was 21% to 22% in large studies from New York and Germany,[61,62] including 36% in patients aged ≥60 years.[62] Thus, the risk of death in hospitalized patients with hematologic malignancy of 39% found in our analysis is comparable to hospitalized patients with solid tumor, but remains substantially higher than in the general population. The comparable risk of death to patients with solid tumor supports the notion that patients with hematologic malignancy should not be excluded from more intensive supportive care for COVID-19 solely on the basis of their hematologic diagnosis. To ascertain the true risk of mortality among all patients with hematologic malignancy and COVID-19 (including all outpatients), it will be important for studies to collect data on an unselected population of patients. The largest study included in this meta-analysis, by Yigenoglu and colleagues from Turkey,[52] likely has the best estimate for the true population mortality risk for patients with hematologic malignancy infected with COVID-19 (14%), as they used population-based data from a countrywide Ministry of Health database. This estimate remains higher than the risk of death for a control population in their study (7%),[52] and the risk reported in a previous meta-analysis including noncancer inpatients and outpatients with COVID-19 (8%).[5] The risk estimate of 14% reported by Yigenoglu is also comparable to the estimated risk of death of 13% in patients with all cancers.[5] There is concern that recent SACT may result in inferior outcomes in patients with COVID-19. However, our analysis did not show evidence that recent SACT conferred a statistically significant excess risk of death compared with no treatment (RR, 1.22; 95% CI, 0.84-1.78; P = .29). This finding persisted even when limiting the analysis to a subgroup of patients on recent cytotoxic SACT (RR, 1.29; 95% CI, 0.78-2.15; P = .32). This is consistent with reports from other large studies of patients with cancer.[32,34,63] This finding may be related to recent observations that patients with therapy-induced anergy of the immune system might have a milder form of COVID-19. In fact, some therapies tested in treating COVID-19 are hematologic/immunosuppressive drugs.[64] Although it is sensible to withhold or delay SACT where disease kinetics permit, these data suggest that in patients who require urgent therapy for their hematologic malignancy, treatment can be delivered despite the risks of COVID-19. However, the analysis should be considered with caution given the heterogeneity of definitions of “recent treatment” among included studies. Clinicians should make decisions on a case-by-case basis with their patients, considering the community prevalence of COVID-19 in their region and the availability of health care resources. Finally, we did find that race was an important contributor to mortality, with non-White patients having a significantly higher risk of mortality than White patients, consistent with previous reports.[65,66] We do not know whether the differences in mortality reflect an inherent biologic risk of poor outcome, impact of comorbidities, impact of social determinants of health, vs implicit bias in the provision of health care. Following the outbreak of COVID-19, many hospitals, particularly in Europe, opened clinical areas where high-level care interventions such as noninvasive ventilation could be delivered to mitigate shortages of ICU beds. The establishment of such high-dependency areas outside of a traditional ICU setting made the risk of ICU admission difficult to quantify and introduced substantial heterogeneity in our analysis. A previous meta-analysis showed a risk of ICU admission of 38% among all patients with cancer, utilizing a modified definition of ICU admission to include these high dependency clinical areas.[5] This study has several important limitations. First, there is the possibility of duplicate patients within studies. We are aware of 2 studies with overlap of 3 patients,[24,33] and 3 studies from centers[20,22,28] that report data to the UKCCMP; thus duplicate patients may potentially have been reported by Lee et al.[34] Although it is not known which centers contributed to the ASH registry, the registry was not accepting batch data until recently, making overlap between other large aggregate data efforts unlikely (L.K.H., written personal communication, 19 September 2020). Additionally, the majority of studies included were from different centers, different regions, or described differing diagnoses, thus we feel that duplicate reporting is unlikely to be a major factor in our meta-analysis. A more important limitation of our work is the significant heterogeneity that was observed in many of the reported pooled estimates of mortality. In particular, the pooled overall mortality estimate had substantial heterogeneity (I2, 87%). This likely reflects the diverse nature of included patients including inpatients vs outpatients, wide age ranges, diverse hematologic diagnoses, and varied treatment practices across geographic areas. We sought to explore the observed heterogeneity through subgroup analyses. Our findings suggest that age is an important contributor to heterogeneity. When patients <60 years vs ≥60 years were analyzed separately, or pediatric patients were analyzed, heterogeneity substantially decreased. It is also likely that the primary hematologic diagnosis contributed to heterogeneity, as stratified analyses by diagnosis also decreased heterogeneity. Thus, our pooled estimates of overall mortality should be interpreted with caution pending the publication of additional primary data. An additional limitation of this report is the possibility that mortality may be overestimated due to the included cohort studies being enriched with hospitalized patients and patients with frequent medical visits. Fourteen of the 34 adult reports in this meta-analysis included exclusively hospitalized patients. Moreover, even in those studies that included ambulatory patients, case ascertainment was usually dependent on the patients intersecting with the medical system: healthier, asymptomatic, or pauci-symptomatic patients may thus be underrepresented in the included sample. This bias may result in an overestimate of the risk of dying from COVID-19 among patients with hematologic malignancy. Additionally, many of the included studies report outcomes from the earliest phases of the pandemic; it is possible that mortality rates will improve due to increasing experience, expanding therapeutic options, and improved capacity of health systems to manage an influx of patients. On the other hand, several studies included in this sample had insufficient follow-up to determine the final vital status of all patients in their sample (Table 1), introducing potential bias in the other direction. A final limitation of our study relates to the fact that mortality reported in the included studies was assumed to be related to the diagnosis of COVID-19 given the short interval follow-up and highest risk of death from COVID-19 within weeks of the diagnosis; however, we acknowledge that certain hematologic malignancies (eg, acute leukemia) are also immediately life-threatening. However, a previous study found that the risk of mortality in inpatients with hematologic malignancy increases by 50% if they are infected with COVID-19.[21] During the COVID-19 pandemic, gathering, analyzing, and reporting outcome data are more important than ever for specific at-risk patient populations. The rate at which data on clinical outcomes of COVID-19 in cancer patients is being collected and published is remarkable; within a 10-week period between our initial and final search strategy execution, over 1600 new studies were published. This pace of publication presents a challenge for clinicians, researchers, and guideline committees to assimilate the latest findings. Meta-analyses such as this are critical in order to analyze outcomes in larger cohorts of patients and to assess trends across specific at-risk groups. We report a systematic review and meta-analysis of the literature regarding the risk of mortality in patients with hematologic malignancy and COVID-19, current to 20 August 2020. We report a high risk of death in this population (34%), partially owing to a large percentage of hospitalized patients in studies published to date. Nonetheless, our findings highlight the importance of preventing COVID-19 among patients with hematologic malignancy. Evidence-based prevention strategies such as infection-control measures, physical distancing, and appropriate shielding advice should be emphasized for hematology patients and the units in which they receive their care.[67] Importantly, despite a concerning risk of death, a majority of patients with hematologic malignancy and COVID-19 recover, even following recent SACT. As a result, we recommend that hematology patients with COVID-19 should be considered for intensive supportive interventions where appropriate and if consistent with patient preference. Finally, our data suggest that among patients who require urgent treatment of a hematologic malignancy, treatment should not be routinely withheld due to a fear of excess mortality from COVID-19.

Take-home points for clinical practice regarding patients with hematologic malignancy and COVID

Mortality appears to be high, estimated at 34%; however, the estimate may be biased by a high number of hospitalized patients in published studies Age is strongly associated with mortality: among those >60 years, mortality is estimated at 47% (95% CI, 41% to 54%); among those <18 years, mortality is estimated at 4% (95% CI, 1% to 9%) Non-White patients appear to experience higher mortality than White patients Recent systemic anticancer therapy may not impact mortality Most patients with hematologic malignancy and COVID survive

Supplementary Material

The online version of this article contains a data supplement. Click here for additional data file.
  65 in total

1.  Determinants of COVID-19 disease severity in patients with cancer.

Authors:  Ying Taur; Mini Kamboj; Elizabeth V Robilotti; N Esther Babady; Peter A Mead; Thierry Rolling; Rocio Perez-Johnston; Marilia Bernardes; Yael Bogler; Mario Caldararo; Cesar J Figueroa; Michael S Glickman; Alexa Joanow; Anna Kaltsas; Yeon Joo Lee; Anabella Lucca; Amanda Mariano; Sejal Morjaria; Tamara Nawar; Genovefa A Papanicolaou; Jacqueline Predmore; Gil Redelman-Sidi; Elizabeth Schmidt; Susan K Seo; Kent Sepkowitz; Monika K Shah; Jedd D Wolchok; Tobias M Hohl
Journal:  Nat Med       Date:  2020-06-24       Impact factor: 53.440

2.  Incidence and prevalence of falls in adults with intellectual disability living in the community: a systematic review.

Authors:  Portia Ho; Max Bulsara; Jenny Downs; Shane Patman; Caroline Bulsara; Anne-Marie Hill
Journal:  JBI Database System Rev Implement Rep       Date:  2019-03

3.  ILROG emergency guidelines for radiation therapy of hematological malignancies during the COVID-19 pandemic.

Authors:  Joachim Yahalom; Bouthaina Shbib Dabaja; Umberto Ricardi; Andrea Ng; N George Mikhaeel; Ivan R Vogelius; Tim Illidge; Shunan Qi; Andrew Wirth; Lena Specht
Journal:  Blood       Date:  2020-05-21       Impact factor: 22.113

4.  Managing patients with cancer during the COVID-19 pandemic: frontline experience from Wuhan.

Authors:  Heng Mei; Xiaorong Dong; Yadan Wang; Liang Tang; Yu Hu
Journal:  Lancet Oncol       Date:  2020-05       Impact factor: 41.316

Review 5.  SARS-CoV-2 in children: spectrum of disease, transmission and immunopathological underpinnings.

Authors:  Phoebe C M Williams; Annaleise R Howard-Jones; Peter Hsu; Pamela Palasanthiran; Paul E Gray; Brendan J McMullan; Philip N Britton; Adam W Bartlett
Journal:  Pathology       Date:  2020-08-19       Impact factor: 5.306

6.  Clinical characteristics and outcome of multiple myeloma patients with concomitant COVID-19 at Comprehensive Cancer Centers in Germany.

Authors: 
Journal:  Haematologica       Date:  2020-07-30       Impact factor: 9.941

7.  Survival study of hospitalised patients with concurrent COVID-19 and haematological malignancies.

Authors:  Fernando Martín-Moro; Juan Marquet; Miguel Piris; Berta M Michael; Adolfo J Sáez; Magdalena Corona; Carlos Jiménez; Beatriz Astibia; Irene García; Eulalia Rodríguez; Carlota García-Hoz; Jesús Fortún-Abete; Pilar Herrera; Javier López-Jiménez
Journal:  Br J Haematol       Date:  2020-05-27       Impact factor: 6.998

8.  Outcomes of COVID-19 in patients with CLL: a multicenter international experience.

Authors:  Anthony R Mato; Lindsey E Roeker; Nicole Lamanna; John N Allan; Lori Leslie; John M Pagel; Krish Patel; Anders Osterborg; Daniel Wojenski; Manali Kamdar; Scott F Huntington; Matthew S Davids; Jennifer R Brown; Darko Antic; Ryan Jacobs; Inhye E Ahn; Jeffrey Pu; Krista M Isaac; Paul M Barr; Chaitra S Ujjani; Mark B Geyer; Ellin Berman; Andrew D Zelenetz; Nikita Malakhov; Richard R Furman; Michael Koropsak; Neil Bailey; Lotta Hanson; Guilherme F Perini; Shuo Ma; Christine E Ryan; Adrian Wiestner; Craig A Portell; Mazyar Shadman; Elise A Chong; Danielle M Brander; Suchitra Sundaram; Amanda N Seddon; Erlene Seymour; Meera Patel; Nicolas Martinez-Calle; Talha Munir; Renata Walewska; Angus Broom; Harriet Walter; Dima El-Sharkawi; Helen Parry; Matthew R Wilson; Piers E M Patten; José-Ángel Hernández-Rivas; Fatima Miras; Noemi Fernández Escalada; Paola Ghione; Chadi Nabhan; Sonia Lebowitz; Erica Bhavsar; Javier López-Jiménez; Daniel Naya; Jose Antonio Garcia-Marco; Sigrid S Skånland; Raul Cordoba; Toby A Eyre
Journal:  Blood       Date:  2020-09-03       Impact factor: 25.476

9.  Initial report on Spanish pediatric oncologic, hematologic, and post stem cell transplantation patients during SARS-CoV-2 pandemic.

Authors:  Anna Faura; Susana Rives; Álvaro Lassaletta; Elena Sebastián; Luis Madero; Jorge Huerta; Marina García-Morín; Antonio Pérez Martínez; Luisa Sisinni; Itziar Astigarraga; Pablo Velasco; Luis Gros; Lucas Moreno; Ana Carboné; Carmen Rodríguez-Vigil; Susana Riesco; María Del Carmen Mendoza; Elena García Macias; Maria Trabazo; Montse Torrent; Isabel Badell; José Luis Fuster; Nerea Dominguez-Pinilla; Antonio Juan Ribelles; Vanesa Pérez-Alonso; Manuel Fernández Sanmartín; Marta Baragaño; Maite Gorostegui; Sara Perez-Jaume; Ana Fernández-Teijeiro; Andrés Morales La Madrid; José Luis Dapena
Journal:  Pediatr Blood Cancer       Date:  2020-07-16       Impact factor: 3.167

10.  Characterisation of clinical, laboratory and imaging factors related to mild vs. severe covid-19 infection: a systematic review and meta-analysis.

Authors:  Xiaomei Wu; Lei Liu; Jinghua Jiao; Lina Yang; Bo Zhu; Xin Li
Journal:  Ann Med       Date:  2020-08-11       Impact factor: 4.709

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  175 in total

1.  COVID-19 in pediatric cancer: Where are the brain tumors?

Authors:  Rebecca Ronsley; Eric Bouffet
Journal:  Neuro Oncol       Date:  2021-11-02       Impact factor: 13.029

2.  Real-World Third COVID-19 Vaccine Dosing and Antibody Response in Patients With Hematologic Malignancies.

Authors:  Michael A Thompson; Sigrun Hallmeyer; Veronica E Fitzpatrick; Yunqi Liao; Michael P Mullane; Stephen C Medlin; Kenneth Copeland; James L Weese
Journal:  J Patient Cent Res Rev       Date:  2022-07-18

Review 3.  Facts and Hopes in Multiple Myeloma Immunotherapy.

Authors:  Adam S Sperling; Kenneth C Anderson
Journal:  Clin Cancer Res       Date:  2021-03-26       Impact factor: 12.531

Review 4.  Hematopoietic stem cell transplantation for autoimmune diseases in the time of COVID-19: EBMT guidelines and recommendations.

Authors:  Raffaella Greco; Tobias Alexander; Joachim Burman; Nicoletta Del Papa; Jeska de Vries-Bouwstra; Dominique Farge; Jörg Henes; Majid Kazmi; Kirill Kirgizov; Paolo A Muraro; Elena Ricart; Montserrat Rovira; Riccardo Saccardi; Basil Sharrack; Emilian Snarski; Barbara Withers; Helen Jessop; Claudia Boglione; Ellen Kramer; Manuela Badoglio; Myriam Labopin; Kim Orchard; Selim Corbacioglu; Per Ljungman; Malgorzata Mikulska; Rafael De la Camara; John A Snowden
Journal:  Bone Marrow Transplant       Date:  2021-05-24       Impact factor: 5.483

5.  [COVID-19 severity and outcomes in patients with cancer: a matched cohort study].

Authors:  Claudius Söhn; Alexander Bott
Journal:  Strahlenther Onkol       Date:  2021-06-09       Impact factor: 3.621

6.  CD8+ T cells contribute to survival in patients with COVID-19 and hematologic cancer.

Authors:  Erin M Bange; Nicholas A Han; Paul Wileyto; Justin Y Kim; Sigrid Gouma; James Robinson; Allison R Greenplate; Madeline A Hwee; Florence Porterfield; Olutosin Owoyemi; Karan Naik; Cathy Zheng; Michael Galantino; Ariel R Weisman; Caroline A G Ittner; Emily M Kugler; Amy E Baxter; Olutwatosin Oniyide; Roseline S Agyekum; Thomas G Dunn; Tiffanie K Jones; Heather M Giannini; Madison E Weirick; Christopher M McAllister; N Esther Babady; Anita Kumar; Adam J Widman; Susan DeWolf; Sawsan R Boutemine; Charlotte Roberts; Krista R Budzik; Susan Tollett; Carla Wright; Tara Perloff; Lova Sun; Divij Mathew; Josephine R Giles; Derek A Oldridge; Jennifer E Wu; Cécile Alanio; Sharon Adamski; Alfred L Garfall; Laura A Vella; Samuel J Kerr; Justine V Cohen; Randall A Oyer; Ryan Massa; Ivan P Maillard; Kara N Maxwell; John P Reilly; Peter G Maslak; Robert H Vonderheide; Jedd D Wolchok; Scott E Hensley; E John Wherry; Nuala J Meyer; Angela M DeMichele; Santosha A Vardhana; Ronac Mamtani; Alexander C Huang
Journal:  Nat Med       Date:  2021-05-20       Impact factor: 87.241

Review 7.  Impact of COVID-19 in patients with lymphoid malignancies.

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

8.  Blunted humoral response after mRNA vaccine in patients with haematological malignancies.

Authors:  Mini Kamboj
Journal:  Lancet Haematol       Date:  2021-07-02       Impact factor: 18.959

Review 9.  COVID-19 in immunocompromised populations: implications for prognosis and repurposing of immunotherapies.

Authors:  Jason D Goldman; Philip C Robinson; Thomas S Uldrick; Per Ljungman
Journal:  J Immunother Cancer       Date:  2021-06       Impact factor: 13.751

10.  Detection of SARS-CoV-2 RNA in serum is associated with increased mortality risk in hospitalized COVID-19 patients.

Authors:  Diego A Rodríguez-Serrano; Emilia Roy-Vallejo; Isidoro González-Álvaro; Laura Cardeñoso; Nelly D Zurita Cruz; Alexandra Martín Ramírez; Sebastián C Rodríguez-García; Nuria Arevalillo-Fernández; José María Galván-Román; Leticia Fontán García-Rodrigo; Lorena Vega-Piris; Marta Chicot Llano; David Arribas Méndez; Begoña González de Marcos; Julia Hernando Santos; Ana Sánchez Azofra; Elena Ávalos Pérez-Urria; Pablo Rodriguez-Cortes; Laura Esparcia; Ana Marcos-Jimenez; Santiago Sánchez-Alonso; Irene Llorente; Joan Soriano; Carmen Suárez Fernández; Rosario García-Vicuña; Julio Ancochea; Jesús Sanz; Cecilia Muñoz-Calleja; Rafael de la Cámara; Alfonso Canabal Berlanga
Journal:  Sci Rep       Date:  2021-06-23       Impact factor: 4.379

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