The infection caused by the novel coronavirus-19 (COVID-19) has emerged in December 2019 and was declared pandemic in March 2020.[
To date, few data exist regarding COVID-19 infection in patients with cancer in South America and the associations between outcomes and risk factors should be described. This is a retrospective and multicenter analysis of cancer patients, treated in nine private oncology centers in Brazil, describing association between demographic characteristics, risk factors, and clinical outcomes.
We conducted a multicenter, retrospective study, based on systematic review of medical records. The study population consisted of cancer patients treated in nine private centers belonging to Americas Oncologia, located in five different Brazilian cities, who were diagnosed with COVID-19 between March 15th, 2020 and August 13th, 2020. These patients could be diagnosed on an outpatient or inpatient basis. However, outpatients have not been identified systematically due to the low availability of RT-PCR testing and barriers to screening. Inpatients were identified by database from the Department of Epidemiology and Infection Control of the included institutions. All patients diagnosed with cancer, who were undergoing an active treatment or follow-up and, who had sufficient data for collection could be included. We enrolled patients with 18 years of age or older, with COVID-19 infection confirmed by reverse-transcription polymerase chain reaction (RT-PCR) on nasopharyngeal swab. Exclusion criteria were patients without cancer diagnosis, RTPCR negative for SARS-CoV-2, patients with clinical and radiological suspicion but RT-PCR negative, and patients without consistent data in medical records. The study was approved by regional ethical committee and was conducted according to the declaration of Helsinki.
An electronic form was prepared to collect patient's information including demographic data, cancer diagnosis, oncological treatment, and clinical conditions related to COVID-19 infection, preexistent comorbidities, medicine use, and outcomes. Patients were divided into subgroups according to their primary tumor site, such as hematological, gastrointestinal, breast, chest, and urological tumors. Staging was divided into groups I to IV according to AJCC 8th edition. Regarding cancer treatment, patients could be on active treatment (chemotherapy, targeted therapy, immunotherapy or hormonal treatment) or follow-up, with or without evidence of active disease. Data on the last treatment performed, history of radiation therapy, surgery, and bone marrow transplantation were collected and described. During the course of COVID-19 infection, all procedures performed, including laboratory tests and concomitant medications, were analyzed.
Patients' demographics and clinical characteristics were reported as frequencies (proportions) for categorical variables and median for continuous variables. Data were described using absolute and percentage frequencies (qualitative variables) and through measures such as mean, standard deviation, median, quartiles, minimum and maximum (quantitative variables). We described baseline epidemiological data divided into two groups: the group of inpatients and the total population. For the analysis of risk factor for mortality, we evaluated only the population that was hospitalized, to reduce selection bias. Comorbidities known as risk factors and treatments that could potentially affect outcomes were included. For smoking status, never smoker was defined as an individual who smoked less than 100 cigarettes in lifetime. Former smoker included patients who had quit smoking for at least 12 months before inclusion.
Poisson regression models were then used to test for an association between outcome measure (death due to COVID-19) and clinical characteristics. We chose this method because it provides a risk ratio (RR) - which is easier to interpret -, and robust error estimation that ensure accurate inference.[
The analysis of the relationship between the variables of interest and the length of stay was performed using a multiple linear regression model. In order to correlate risk factors with mortality, we excluded outpatients, as the better prognosis of this population could lead to bias.
The primary endpoint of the study was the mortality rate of inpatients with cancer and COVID-19. Secondary endpoints included the association between tumor subtype, recent chemotherapy, comorbidities, and location of metastasis with allcause mortality and the association between clinical findings and length of hospital stay.
At the data of cutoff we had 130 oncologic patients eligible for analysis. Five patients who were transferred to hospices and 17 patients without available outcomes were also excluded from the final analysis. Six patients with negative RT-PCR for SARS-CoV-2 were not included. Finally, association between risk factor and mortality was performed in 85 patients, which corresponded to hospitalized patients (
Figure 1 Flow diagram for study selection.
One hundred and two patients were included, and of these, 85 were hospitalized and had their data collected for analysis of risk factors. Baseline characteristics are summarized in
| Variable | All patients (N=102) | Inpatients (N=85) |
|---|---|---|
| Demographic factors Median age | 65,8 (53,6-75,4) N (%) | 68,1 (55,3-76,7) N (%) |
| Gender - Male | - 39 (38.2%) | - 35 (41.2%) |
| - Female | - 63 (61.8%) | - 50 (58.8%) |
| Ethnicity - White | - 75 (73.5%) | - 62 (72.9%) |
| - Afrodescendant | - 7 (6.8%) | - 5 (5.9%) |
| - Unknown | - 20 (19.6%) | - 18 (21.2%) |
| Clinical factors Performance status (ECOG) - 0 | - 55 (53.9%) | - 43 (50.6%) |
| - 1 | - 25 (24.5%) | - 21 (24.7%) |
| - 2 | - 9 (8.8%) | - 9 (10.6%) |
| - 3 | - 6 (5.9%) | - 5 (5.9%) |
| - Unknown | - 7 (6.9%) | - 7 (8.2%) |
| Body mass index (BMI) - <18,5 | - 5 (4.9%) | - 4 (4.7%) |
| - 18,6 - 24,9 | - 28 (27.4%) | - 26 (30.6%) |
| - 25 - 29,9 | - 40 (39.2%) | - 31 (36.5%) |
| - 30 - 34,9 | - 16 (15.7%) | - 12 (14.1%) |
| - 35 - 39,9 | - 6 (5.9%) | - 5 (5.9%) |
| - =40 | - 3 (2.9%) | - 3 (3.5%) |
| - Unknown | - 4 (3.9%) | - 4 (4.7%) |
| Smoking status - Current smoker | - 8 (7.8%) | - 8 (9.4%) |
| - Former smoker | - 31 (30.4%) | - 26 (30.6%) |
| - Never smoker | - 44 (43.1%) | - 32 (37.6%) |
| - Unknown | - 19 (18.6%) | - 19 (22.3%) |
| Comorbidities - COPD | - 13 (12.7%) | - 12 (14.1%) |
| - Hypertension | - 46 (45.0%) | - 41 (48.2%) |
| - Diabetes | - 32 (31.4%) | - 31 (36.5%) |
| - Obesity | - 14 (13.7%) | - 11 (12.9%) |
| - Coronary disease | - 9 (8.8%) | - 7 (8.2%) |
| Variable | All patients (N=102) | Inpatients (N=85) | ||
|---|---|---|---|---|
| Type of cancer - Solid | - 88 (86.3%) | - 73 (85.9%) | ||
| - Hematologic | - 14 (13.7%) | - 12 (14.1%) | ||
| Subtype - Head and neck | - 5 (4.9%) | - 5 (5.9%) | ||
| - Urologic | - 10 (9.8%) | - 9 (10.6%) | ||
| - Gynecologic | - 5 (4.9%) | - 4 (4.7%) | ||
| - Hematologic | - 14 (13.7%) | - 12 (14.1%) | ||
| - Breast | - 23 (22.5%) | - 16 (18.8%) | ||
| - Occult primary | - 2 (2.0%) | - 2 (2.3%) | ||
| - Central nervous system | - 1 (1.0%) | - 0 (0) | ||
| - Sarcoma | - 1 (1.0%) | - 1 (1.2%) | ||
| - Gastrointestinal | - 31 (30.4%) | - 27 (31.8%) | ||
| - Thoracic | - 10 (9.8%) | - 9 (10.6%) | ||
| Staging - I | - 15 (14.7%) | - 8 (9.4%) | ||
| - II | - 9 (8.8%) | - 9 (10.6%) | ||
| - III | - 22 (21.6%) | - 19 (22.3%) | ||
| - IV | - 40 (39.2%) | - 35 (41.2%) | ||
| - Unknown/not applicable | - 16 (15.7%) | - 14 (16.5%) | ||
| Variable | All Patients (N=102) | Inpatients (N=85) | ||
|---|---|---|---|---|
| Systemic treatment - Chemotherapy | - 33 (32.3%) | - 25 (29.4%) | ||
| - Immunotherapy | - 5 (4.9%) | - 3 (3.5%) | ||
| - Target therapy | - 10 (9.8%) | - 10 (1.2%) | ||
| - Endocrine therapy | - 14 (13.7%) | - 3 (3.5%) | ||
| - Other | - 2 (2.0%) | - 1 (1.2%) | ||
| - None | - 45 (44.1%) | - 39 (45.9%) | ||
| Treatment goal - Curative | - 62 (60.8%) | - 49 (57.6%) | ||
| - Palliative | - 40 (39.2%) | - 36 (42.3%) | ||
| Treatment performed in the last 12 months - Radiotherapy | - 19 (18.6%) | - 14 (16.5%) | ||
| - Thoracic radiotherapy | - 12 (11.8%) | - 7 (8.2%) | ||
| - Surgery | - 25 (24.5%) | - 18 (21.2%) | ||
| - Bone marrow transplant | - 4 (3.9%) | - 4 (4.7%) | ||
Among tumor characteristics, patients with solid and hematological tumors comprised 88.3% and 13.7% of the population, respectively. The most common types of cancer were gastrointestinal (30.4%), breast (22.5%), and hematological (13.7%). Patients with urological and thoracic tumors comprised 9.8% of the population each. Almost 40% of the population had stage IV disease (
Regarding cancer treatment, about half of patients were undergoing some type of systemic treatment. Of these, a high proportion of patients undergoing treatment with palliative intent was found in the cohort of hospitalized patients (84.7%). Among patients who were on endocrine therapy, most were not hospitalized. Less than a third of the patients had undergone any local treatment, such as surgery or radiation therapy. And finally, only 4 patients underwent bone marrow transplantation, all of whom were hospitalized during the course of COVID-19 (
Of the cohort patients who were hospitalized, 38 (44.7%) were referred to the ICU. The median length of hospital stay was 12 days in both cases. For those patients admitted at ICU, the median length under intensive care was also 12 days. Five patients (5.9%) were not referred to the ICU because they were in palliative/end-of-life care, as part of the medical and patient or family decision. Regarding the intensive supportive care performed, 29 (34.1%) required invasive mechanical ventilation, 8 (9.4%) tracheostomy, 11 (12.9%) dialysis, and 15 (17.6%) prone. Only one patient was enrolled in a clinical trial for COVID-19, which involved convalescent plasma infusion. All of these results are summarized in
| Variables | N | % | Median (days) |
|---|---|---|---|
| Length of stay | 85 | 100 | 12 (7-22) |
| Length of stay in the ICU | 38 | 44.7 | 12 (6-29) |
| Patient who did not go to the ICU for being in palliative care | 5 | 5.9 | - |
| Mechanical ventilation | 29 | 34.1 | - |
| Tracheostomy | 8 | 9.4 | - |
| Dialysis | 11 | 12.9 | - |
| Prone | 15 | 17.6 | - |
| Clinical protocol for COVID-19 treatment | 1 | 1.2 | - |
The primary objective of the study was to assess the mortality rate of inpatients with cancer and COVID-19. The results in the total hospitalized population and in those who were referred to the ICU are described in
| Variable | N | Number of deaths (%) |
|---|---|---|
| Inpatients | 85 | 31 (36.5%) |
| ICU patients | 38 | 26 (68.4%) |
| Palliative/end-of-life care | 5 | 5 (100%) |
We also aimed to assess variables that could increase the risk of death due to COVID-19 in cancer patients. In
Figure 2 Forest plot diagram describing association between the main clinical variables and all-cause mortality rate during COVID-19 infection. *Variables with significance at univariate analysis; ¥: Variables with significance at multivariate analysis.
| Clinical characteristics | Univariate analysis | Multivariate analysis | |||
|---|---|---|---|---|---|
| RR (IC 95%) | p-value | RR (IC 95%) | p-value | ||
| Age at the onset of symptoms | 1.04 (1.01 - 1.05) | <0,01** | 1.02 (1.01 - 1.04) | <0,01** | |
| Male gender | 1.04 (0.64 - 1.68) | 0,88 | 0.90 (0.55 - 1.48) | 0,68 | |
| Lung cancer | 1.28 (0.68 - 2.42) | 0,45 | 2.61 (1.40 - 4.87) | <0,01** | |
| Lung metastasis | 1.95 (1.17 - 3.26) | 0,01** | 2.86 (1.73 - 4.73) | <0,01** | |
| Lymphocytes <1,000 | 2.40 (1.14 - 5.03) | 0,02** | 0.98 (0.58 - 1.67) | 0,94 | |
| NLR=4* | 2.25 (1.18 - 4.27) | 0,01** | 1.35 (0.77 - 2.38) | 0,3 | |
| PLR=126,7* | 1.31 (0.71 - 2.41) | 0,4 | - | - | |
| G-CSF* use (last 14 days) | 1.11 (0.40 - 3.05) | 0,84 | - | - | |
| Comorbidities | 1.62 (0.75 - 3.53) | 0,22 | - | - | |
| - Hypertension | 1.72 (1.04 - 2.86) | 0,04** | 0.98 (0.55 - 1.75) | 0,94 | |
| - Diabetes | 1.45 (0.91 - 2.33) | 0,12 | 1.10 (0.55 - 2.20) | 0,78 | |
| - Obesity | 1.04 (0.52 - 2.08) | 0,92 | - | - | |
| - Coronary disease | 1.33 (0.67 - 2.66) | 0,41 | 3.76 (1.56 - 9.07) | <0,01** | |
| - COPD* | 1.40 (0.81 - 2.43) | 0,23 | 0.52 (0.26 - 1.02) | 0,06 | |
| ECOG = 2* | 1.83 (1.16 - 2.87) | <0,01** | 1.33 (0.85 - 2.06) | 0,21 | |
| Systemic treatment | 0.66 (0.40 - 1.09) | 0,11 | 0.81 (0.43 - 1.51) | 0,5 | |
| Current/former smoker | 0.88 (0.53 - 1.47) | 0,63 | - | - | |
| Length of stay (every 3 days) | 1.05 (1.00 - 1.10) | 0,04** | - | - | |
| ICU admission* | 3.43 (1.91 - 6.15) | <0,01** | 5.77 (2.41 - 13.85) | <0,01** | |
| End-of-life care | 2.35 (1.64 - 3.35) | <0,01** | 6.41 (2.65 - 15.47) | <0,01** | |
| Mechanical ventilation | 3.85 (2.35 - 6.30) | <0,01** | - | - | |
| Tracheostomy | 1.78 (1.10 - 2.88) | 0,02** | - | - | |
| Dialysis | 2.74 (2.03 - 3.70) | <0,01** | 1.24 (0.60 - 2.57) | 0,56 | |
| Prone | 2.60 (1.75 - 3.86) | <0,01** | - | - | |
As a secondary endpoint, we aimed to evaluate the association between clinical characteristics and length of hospital stay. However, the only variables that significantly increased length of stay were ICU admission and tracheostomy. Comorbidities, metastatic disease, number of lymphocytes, ECOG=2 or systemic treatment were not responsible for longer hospitalization (
| Variables | Estimated mean difference | IC 95% | p-value |
|---|---|---|---|
| Presence of metastasis | 0.33 | -0.11 - 0.77 | 0,77 |
| Lung cancer | -0.20 | -0.90 - 0.49 | 0,56 |
| Lymphocytes =1,000 | -0.05 | -0.60 - 0.50 | 0,86 |
| NLR=4 | -0.06 | -0.61 - 0.48 | 0,81 |
| Comorbidities | 0.42 | -0.10 - 0.94 | 0,11 |
| ECOG=2 | -0.26 | -0.85 - 0.33 | 0,38 |
| Systemic treatment | 0.26 | -0.17 - 0.69 | 0,22 |
| ICU admission | 0.91 | 0.04 - 1.79 | 0,04 |
| Mechanical ventilation | -0.96 | -2.02 - 0.10 | 0,07 |
| Tracheostomy | 1.37 | 0.54 - 2.20 | <0,01 |
| Dialysis | 0.45 | -0.34 - 1.25 | 0,26 |
NLR = Neutrophil-to-lymphocyte ratio; ECOG = Eastern cooperative oncology group performance status; ICU = Intensive care unit;
p<0,05 were considered statistically significant.
In this multicenter retrospective study involving cancer patients with COVID-19 infection, we found higher mortality among patients in ICU compared with those treated on the ward. The median admission time in ICU was 12 days, which was the same length time of all admission period for less serious infection. Patients in end-of-life care at the palliative unit, ICU admission, and mechanical ventilation were the most important hospitalization variables to increase mortality. The presence of coronary disease was the most important patient comorbidity to increase the risk of death. Regarding cancer characteristics, although primary lung cancer increased the risk of death, patients with metastatic disease in the lungs had an even worse outcome. The mortality rate of cancer inpatients was our primary endpoint, and it was slightly higher than other international reports, but it was similar to that observed in Brazilian cohorts.
A trial developed by the Brazilian National Cancer Institute, which included 181 hospitalized cancer patients, reported a mortality rate of 33.1%. Patients with older age, lung or bone metastasis, and two or more metastatic sites had higher risk.[
Early Chinese data, in an unselected population, showed a mortality rate by 30% for patients with severe disease and more than 80% for those admitted to the ICU. Median duration of hospitalization was 12 days, also similar to our data.[
There is limited data on the incidence of COVID-19 in cancer patients. The Department of Radiation and Medical Oncology of Zhongnan Hospital of Wuhan University have published a cohort of 1,524 cancer patients, of which 12 (0.79%) had COVID-19, versus 0.37% of the general population of Wuhan during the same period of time (OR 2.31, 95% CI: 1.89-3.02).[
During the course of the pandemic, retrospective trials were also developed by many countries. A consort of more than 120 centers from US, Canada and Spain have published data on cancer patients and COVID-19. Of 928 patients, the most common types of cancer were breast (21%) and prostate (16%), however, it also included outpatients; therefore, it had a mortality rate of 13%, lower than that observed in our study. Among the patients who required hospitalization, the mortality rate within 30 days of inclusion was 23%. This rate may have been lower due to distinct population or shorter followup. Among risk factors for mortality, male gender, smoking status, ECOG, and number of comorbidities were responsible for worse outcomes.[
A multicenter, prospective study involving data collection from patients with thoracic malignancies included 200 patients, of whom 66 (33%) died, whether hospitalized or at home. The risk factors were similar, including age and smoking status.[
By using a multiple linear regression model, it was possible to analyze the relationship between length of stay and clinical variables. However, only variables related to the severity of COVID-19, such as ICU admission and tracheostomy, were associated with prolonged hospitalization.
The main strengths of our study is that it is a multicenter trial, which explores different levels of hospital care. The main limitations are the retrospective design, leading to missing data, and the absence of a comparator group of patients without cancer. In addition, there is a selection bias, represented by the low rate of outpatients, not reflecting the real incidence of COVID-19 in patients treated in our cancer centers.
Several studies involving the development of vaccines against COVID-19 infection are underway. As cancer patients are generally not included in these trials, many questions may arise regarding the risks and effectiveness of this type of prevention for cancer patients. Whereas more than 17 million patients are diagnosed with cancer every year worldwide, we must always consider cancer as pandemic and assess the possibility of including such patients in prospective trials. Currently, we also know that COVID-19 pneumonia can lead to chronic symptoms, many of which coincide with symptoms of cancer or its treatment. Longer follow-up may be necessary to better clarify the real impact of the pandemic on this population.
Despite the high mortality of patients hospitalized with COVID-19, our data are compatible with other Brazilian cohorts and with other risk groups. Cancer patients must be carefully monitored in pandemic periods of infectious diseases.
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Journal: Brazilian Journal of Oncology
DOI: 10.1055/s-00059887
e-issn: 2526-8732
Publisher: Thieme Revinter Publicações Ltda.
Publisher address: Rua do Matoso 170, Rio de Janeiro, RJ, CEP 20270-135, Brazil
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