EPICOVIDEHA registry: Epidemiology and outcomes in patients with hematologic malignancies infected with COVID-19

Patients with hematologic malignancies (HMs) may have intensified immune dysregulation resulting from both therapeutic agents and malignancy burden. As a result, these patients are at increased risk for COVID-19 infection complications and mortality.

Much work has been done to explore the effect of COVID-19 on HMs since the beginning of the pandemic, including assessment of patient outcomes and stratification of subgroups by mortality risk. One example is the EPICOVIDEHA platform, which is a collaborative project including all hematology department members of the European Hematology Association who recorded the incidence and outcomes of patients with HMs infected with COVID-19. At the European Hematology Association (EHA)2021 Virtual Congress, Livio Pagano of Università Cattolica del Sacro Cuore presented key results from this platform, summarized below.¹

Study design

The EPICOVIDEHA survey is a multicenter, retrospective project which began in February 2020 and is ongoing. The first phase, summarized here, focused on assessing outcomes in patients with HMs infected with COVID-19. The second stage will analyze further sub-groups identified by steering committee members who are experts in HMs.²

The main information assessed in the EPICOVIDEHA survey is described in Table 1.

Table 1. Information assessed in EPICOVIDEHA survey*

Allo-HSCT, allogeneic HSCT; auto-HSCT, autologous HSCT; BMI, body mass index; CAR-T, chimeric antigen receptor T; COPD, chronic pulmonary obstructive disease; COVID-19, coronavirus disease 2019; HSCT, hematopoietic stem-cell transplantation; ICU, intensive care unit; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2. *Table from Jon, et al.2
Category	Subcategory
Identification	Institution, city, country, inclusion in other registries, already published
Demographics	Sex, age, date of birth, ethnic origin, date of COVID-19 diagnosis, strain of SARS-CoV-2, previous vaccination, and site of state during the COVID-19
Underlying diseases	Chronic cardiopathy (atrial fibrillation, hypertension, obstructive arteriopathy, etc.), chronic pulmonary disease (asthma, COPD, cystic fibrosis, fibrosis, etc.), diabetes (treated with insulin or antidiabetic oral drugs), liver disease, obesity (BMI >30) or underweight (BMI <18.5), renal impairment (creatinine >2 mg/dl), smoking history, other risk factors, no risk factor identified, absolute leukocyte, neutrophil, and lymphocyte number
Hematologic malignancy	Type of malignancy, details on the diagnosis, state of the malignancy at COVID-19 diagnosis day, time span between malignancy and COVID-19 diagnosis, type of treatment (chemotherapy, radiotherapy, allo-HSCT, auto-HSCT, CAR-T, others, no treatment)
COVID-19	Identification method, reason for COVID-19 test, ICU stay during COVID-19 (invasive/non-invasive mechanical ventilation)
Outcome	Survival status at last contact, last day of follow up, date of death, overall and per ward hospital stay, reason for death

Currently, over 120 institutions from 29 countries have registered >3,500 cases in EPICOVIDEHA. 

Inclusion and exclusion criteria

Inclusion criteria:

• Patients ≥18 years old.
• Active HMs, including acute leukemias, myelodysplastic syndromes (MDS) , myeloproliferative neoplasm, lymphomas, myeloma, and chronic myeloproliferative disorders, at any age/status.
• SARS-CoV-2 positive test determined using real-time reverse transcriptase polymerase chain reaction testing on nasopharyngeal, bronchioalveolar lavage, or fecal samples.

Exclusion criteria:

• Patients with any hematological disease other than HMs,
• patients who had not tested positive for COVID-19; and,
• patients off therapy for >5 years.

The primary objective was to assess the epidemiology and outcomes of patients with HMs infected with COVID-19.

The secondary objectives were to:

• estimate the prevalence and type of COVID-19 disease (symptomatic, asymptomatic, severe disease)

• evaluate the prevalence of severe COVID-19 infection requiring intensive care unit (ICU) admission
• estimate the frequency of pre-existing comorbidities
• evaluate the acute mortality rate (within 30 days of COVID-19 diagnosis)
• estimate the overall case-fatality rate
• assess the geographical patterns of infection
• stratify patients based on whether they are on therapy and by therapy type

Results

Overall, 4,117 patients were enrolled and 3,801 valid cases were included for analysis.

Patient characteristics are summarized in Table 2.

Table 2. Patient characteristics*

*Adapted from Pagano1
Characteristic, % (unless otherwise stated)	N = 3,801
Median age (range), years	65 (18–95)
<25	2.6
26–50	17.5
51–69	41.1
≥70	38.8
Female	41.5
Ethnicity, white	86.3
Comorbidities	60.7
Type of comorbidity (n = 2,307)
Chronic cardiopathy	30.1
Chronic pulmonary disease	16.2
Diabetes	16.3
Liver disease	4.4
Obesity	9.1
Renal impairment	8.7
Smokers or ex-smokers	12.5

Symptoms and association with comorbidities 

In total, 49% of patients had pulmonary symptoms (cough, dyspnea, imaging signs etc.) while 33% had extra-pulmonary symptoms, including anosmia, fever, abdominal disturbances, and skin signs. Overall, >80% of patients were not neutropenic and >70% of patients were not lymphopenic, suggesting that these symptoms associated with HMs and their treatments had no effect on the onset of COVID-19 infection. The proportion of severe COVID cases increased as the number of pre-existing comorbidities increased (Table 3).

Table 3. Number of comorbidities and infection severity*

*Adapted from Pagano1
Number of comorbidities	Infection severity
	Asymptomatic	Mild	Severe
0, n	349	327	800 (54.2%)
1, n	209	200	696 (63%)
2, n	102	130	448 (65.9)
≥3, n	67	85	341 (69.2%)

Treatment type and disease status

In total, >70% of patients received chemotherapy in the 3 months prior to COVID-19 infection (Table 4). There was an even distribution of patients who were in a disease state (stable disease, relapsed/refractory, or onset) compared with those who were in remission (partial or complete remission), 49% vs 47%, respectively (Table 4).

 Table 4. Chemotherapy and disease status prior to COVID-19 infection*

*Data from Pagano1
Chemotherapy and disease status, %	N = 3,801
Chemotherapy ended >3 months before COVID-19 infection	29
Chemotherapy received in the 3 months prior to COVID-19 infection	15
Chemotherapy received in the month prior to COVID-19 infection	56
Disease status
Stable disease	14
Unknown	4
Relapsed/refractory	12
Onset	23
Partial remission	16
Complete remission	31

Immunochemotherapy was the most common treatment type received by participants.  

Regarding transplants, 261 patients had a history of allo-HSCT, though only 173 underwent this procedure as the last therapy prior to COVID-19 infection. A total of 293 patients received auto-HSCT, though only 74 patients underwent the procedure as their last therapy prior to COVID-19 infection. When compared with the total number of patients who were transplanted in 2020, analysis showed a 2% incidence of COVID-19 infection in auto-HSCT recipients and a 6.4% incidence in auto-HSCT recipients, compared with a 4.6% incidence among patients receiving CAR T-cell infusion. The incidence of COVID-19 infection was 6.7% in haploidentical transplants, which was similar to matched sibling (7.6%) and matched unrelated donor (5.2%) transplants.

lCU admission

Of the patients who required hospitalization, 25% required admission to ICU following COVID-19 infection, and 65% required ventilation. The median duration of hospital stay was 15 days; however, it was highlighted that there was evaluation bias resulting from the inclusion of patients who died early.

Mortality

The all-cause mortality rate was 31%, with 26% attributed to COVID-19 infection. When stratified by age, mortality rate was higher in patients >70 years old. When stratified by hematologic malignancy, patients with acute myeloid leukemia (AML) and MDS had higher mortality rates (near 40%) and patients with high-risk MDS had an even higher rate of 46%, indicating an at-risk population. The severity of COVID-19 infection was also unsurprisingly associated with mortality.

Impact of treatment on mortality

Demethylating agents were associated with the highest mortality rates from COVID-19 infection (58.8%), while palliative care (53.7%) and CAR T-cell infusion (47.6%) were also associated with significant mortality rates. The impact of all treatments on mortality are summarized in Table 5.

Table 5. The mortality rate of patients with COVID-19 infections classified by therapy regimen*

Allo-HSCT; allogeneic hematopoietic stem cell transplant; Auto-HSCT; autologous hematopoietic stem cell transplant; CAR-T, chimeric antigen receptor T; HU, hydroxyurea; IMiDs, immunomodulatory drugs. *Adapted from Pagano1
Therapy	Total number of patients	Mortality rate, %
Anagrelide/HU	145	26.8
Conventional Chemotherapy	572	29.8
Demethylating agents	141	58.8
Immunotherapy only	125	28.8
Immunochemotherapy	857	30.6
IMiDs	218	36.2
Targeted therapies	607	25.3
Palliative	151	53.7
Maintenance	25	12.0
Allo-HSCT	173	24.8
Auto-HSCT	74	27.0
CAR-T	21	47.6
No treatment	538	29.0
Unknown	41	31.7
Supportive	75	26.6
Other	38	31.5

The mortality rates in patients receiving auto-HSCT and allo-HSCT were 24.8% and 27% respectively, nearly half the rate seen in patients receiving CAR T-cell therapy. There was no difference in mortality rate in patients receiving chemotherapy or auto-HSCT. Mortality in the first wave of COVID-19 was near 40.7%, while in the second wave, despite an increase in number of cases, it was significantly lower (24%), perhaps owing to more knowledge on how to manage HMs. When looking at influences on mortality rates, using Hodgkin lymphoma as a control, factors with higher risk of mortality included AML, MDS, active disease, older age, cardiovascular disease comorbidities, and smoking.

Conclusion

The EPICOVIDEHA survey has provided extensive insight into the incidence and impact of COVID-19 infection on patients with HMs. AML and MDS, active disease, older age, demethylating agents, and smoking were identified as factors influencing greater risk of COVID-19 infection-related complications. The investigators also demonstrated an association of the number of baseline comorbidities with severity of infection. 

Limitations of this study included that survey was created in April/May of 2020, therefore other risk factors that have been subsequently identified as important to the symptomology of COVID-19, such as the role of platelets and increased thrombotic risk, were not included in the survey. Also, data was not requested regarding treatment of COVID-19, and common denominators were unknown for HM treatment types (with the exception of HSCT procedures).