Abstract
Objective
We evaluated survival outcomes for patients with cancer and COVID-19 in this population-based study.
Methods
A total of 631 patients who tested positive for severe acute respiratory syndrome coronavirus 2 and were seen at BC Cancer between 03/03/2020 and 01/21/2021 were included, of whom 506 had a diagnosis of cancer and PCR-confirmed positive test for coronavirus disease 2019. Patient clinical characteristics were retrospectively reviewed and the influence of demographic data, cancer diagnosis, comorbidities, and anticancer treatment(s) on survival following severe acute respiratory syndrome coronavirus 2 infection were analyzed.
Results
Age ≥65 years (Hazard Ratio [HR] 4.77, 95% Confidence Interval [CI] 2.72–8.35, P < 0.0001), those with Eastern Cooperative Oncology Group Performance Status ≥2 (HR 8.36, 95% CI 2.89–24.16, P < 0.0001), hypertension (HR 3.17, 95% CI 1.77–5.66, P < 0.0001), and metastatic/advanced stage (HR 3.70, 95% CI 1.77–7.73, P < 0.0001) were associated with worse coronavirus disease 2019 specific survival outcomes following severe acute respiratory syndrome coronavirus 2 infection. Patients with lung cancer had the highest 30-day COVID-19 specific mortality (25.0%), followed by genitourinary (18.1%), gastrointestinal (16.0%), and other cancer types (<10.0%). Patients with the highest 30-day coronavirus disease 2019 specific mortality according to treatment type were those on chemotherapy (23.0%), rituximab (22.2%), and immunotherapy (16.7%) while patients on hormonal treatments (2.2%) had better survival outcomes (P = 0.041) compared to those on other anticancer treatments.
Conclusion
This study provides further evidence that patients with cancer are at increased risk of mortality from coronavirus disease 2019 and emphasizes the need for vaccination.
Keywords: Cancer, COVID-19, SARS-CoV-2, Comorbidities, Chemotherapy, Treatment
Cancer; COVID-19; SARS-CoV-2; Comorbidities; Chemotherapy; Treatment.
1. Introduction
Worldwide spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the coronavirus disease 2019 (COVID-19) pandemic, and has resulted in over 348 million total cases of COVID-19 and over 5.5 million deaths globally as of January 2022 (COVID-19 dashboard by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU), 2021). Patients with cancer may be at increased risk of severe complications and mortality from COVID-19 due to immunosuppression caused by anticancer therapies and/or the underlying cancer (Williamson et al., 2020). The most recent update from the COVID-19 and Cancer Consortium (CCC19) reported 30-day all-cause mortality between 13% and 33% (Garassino et al., 2020; Kuderer et al., 2020; Reboot: COVID-Cancer Project, 2021) in patients with cancer and confirmed SARS-CoV-2 infection, compared with 0.5%–2% in the general population (COVID-19 dashboard by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU), 2021; Lau et al., 2020).
Although the effects of COVID-19 on patients with cancer have been studied extensively, the larger studies thus far have been based on multi-institutional efforts and were not population-based. There may be bias in outcomes based on the populations included and differences in access to care between countries and health systems, and thus these studies may not be representative of all patients with cancer within a single institution. The purpose of our study is to systematically examine the clinical effects of SARS-CoV-2 on patients with cancer in British Columbia (BC), Canada, providing a clear picture on the state of COVID-19 disease management associated with cancer at a broad population level in our publicly funded health care system. Almost all cancer care in the province is centralized within one network of oncology clinics, allowing a robust assessment of diagnoses, treatments, and outcomes in the province's 5.1 million people.
2. Methods
Patients who had been seen at BC Cancer for care after March 1, 2020 with SARS-CoV-2 confirmed by PCR results between 03/03/2020 and 01/21/2021 from across the province of British Columbia were included in this retrospective cohort study (Figure 1). Individual patient data were abstracted from BC Cancer's Electronic Health Record (EHR). Within the EHR, patients with a COVID-19 diagnosis were flagged by provincial testing centers, allowing robust capture of all confirmed cases. Patients were assumed to have died from COVID-19 if there was evidence of this in the EHR through chart review (i.e., COVID-19, virus identified) or if the death was within 30 days of their positive test. Patients were classified as having died due to non-COVID-19-related causes if their date of death was over 30 days from the date of their positive test, and if they died due to other health concerns related to progression of their cancer or other comorbidities (e.g., cardiac arrest, unknown causes, medical assistance in dying due to reasons unrelated to COVID-19, etc.). This study was conducted in accordance with the Declaration of Helsinki and approved by the Research Ethics Board of The University of British Columbia (H20-00892–approved 4/1/2020) for research involving human subjects. A waiver of consent was obtained as this was a retrospective research study.
Figure 1.
Flow diagram of patient disposition in study.
Variables concerning demographic data, cancer diagnosis, comorbidities, anti-cancer treatment(s), smoking status, date of COVID-19 diagnosis and follow up survival data were collected. A cancer diagnosis was assigned if patients had active or prior cancer with scheduled follow ups at BC Cancer, and further classified by cancer stage (early: stages I-III, metastatic: stage IV) for applicable cancers. Patients who were seen for hereditary cancer screening or assessment/treatment of benign tumours but who did not have active or prior cancer diagnoses were classified as having no cancer. Patients with liquid cancers (leukemia and lymphoma) were excluded from analyses involving cancer stage as they differ significantly from conventional classification of cancer stage used by solid tumors. Patients without a malignant cancer diagnosis were seen for hereditary screening or management of benign tumours and were used as a control in survival curves; these patients were classified as having no cancer (Figures 2 and 3). Cancer types were categorized as follows: breast, gastrointestinal (GI), lung, lymphoma, genitourinary (GU), gynecologic, leukemia, central nervous system (CNS) and other (including thyroid cancer, skin cancer, liposarcoma, head and neck cancer, carcinoma of unknown primary, etc.). Patients with CLL were classified as leukemia. Patients were classified as on active anticancer therapy if the last dose was received within 12 months prior to a COVID-19 diagnosis, and untreated if over 12 months had passed. Therapy types were assigned to the following categories: chemotherapy, hormone therapy, immunotherapy, radiotherapy, no therapy, other treatment, surgery, and CD20 directed (i.e., rituximab). The list of all anticancer treatments classified under each treatment category is detailed in Table 1.
Figure 2.
COVID-specific survival for patients seen at a BC Cancer facility stratified by (A) presence of cancer, (B) type of cancer in those with an active cancer and (C) type of anti-cancer treatment.
Figure 3.
Overaell survival for patients seen at a BC Cancer facility stratified by (A) presence of cancer, (B) type of cancer in those with an active cancer and (C) type of anti-cancer treatment.
Table 1.
Classification of anticancer treatments.
| Chemotherapy | Hormone therapy | Immunotherapy | Radiotherapy | Rituximab | Other |
|---|---|---|---|---|---|
| 5-FU and oxaliplatin | Anastrozole | Ipilimumab | Adjuvant radiation therapy | R–CHOP/CHOP-R (rituximab, cyclophosphamide, doxorubicin, vincristine, prednisone) | Brentuximab vedotin |
| 5-FU, irinotecan and bevacizumab | Apalutamide | Nivolumab | Adjuvant radiotherapy | Rituximab | Dasatinib |
| Abraxane | Bicalutamide | Pembrolizumab | Brachytherapy | Dexamethasone | |
| ABVD/LYABVD (doxorubicin, bleomycin, vinblastine, dacarbazine) | Bicalutamide | Definitive radiotherapy | Gefitinib | ||
| AC (Adriamycin, cyclophosphamide) | BRAJTAM | External beam radiotherapy | Herceptin | ||
| ACT/ACTW (Doxorubicin, Cyclophosphamide, followed by Paclitaxel or Docetaxel) | Degarelix | I-131 therapy | Ibrutinib | ||
| Bendamustine | Enzalutamide | Radioactive iodine ablation | Imatinib | ||
| BEP (bleomycin, etoposide, platinum) | Exemestane | SABR | Inotuzumab | ||
| Bleomycin | Exemestane | Salvage radiation | Lenalidomide | ||
| Capecitabine | Faslodex and Ibrance | SBRT | Olaparib | ||
| Carboplatin | Goserelin | Stereotactic radiotherapy | Osimertinib | ||
| Cisplatin | Letrozole | Palbociclib | |||
| Cisplatin and etoposide | Lupron | Panitumumab | |||
| Cisplatin and pemetrexed | Provera | Prednisone | |||
| CyBorD (cyclophosphamide, bortezomib, dexamethasone) | Tamoxifen | Regorafenib | |||
| Cyclophosphamide | Sorafenib | ||||
| Docetaxel | Trastuzumab | ||||
| Doxorubicin | Vismodegib | ||||
| Doxorubicin, cyclophosphamide, paclitaxel and trastuzumab | |||||
| Epirubicin | |||||
| Etoposide | |||||
| FLAG (fludarabine + high-dose cytarabine + G-CSF) | |||||
| Fludarabine | |||||
| Fluorouracil | |||||
| FOLFIRI (folinic acid/leucovorin, fluorouracil, irinotecan) | |||||
| FOLFIRINOX (folinic acid/leucovorin, fluorouracil, irinotecan, oxaliplatin) | |||||
| Gemcitabine | |||||
| Gemcitabine and cisplatin | |||||
| Gemcitabine and nab-paclitaxel | |||||
| GOENDCAT protocol (carboplatin and paclitaxel) | |||||
| Hydroxyurea | |||||
| Ifosfamide, gemcitabine, and vinorelbine | |||||
| Irinotecan | |||||
| Leukocorin with fluorouracil, docetaxel, oxaliplatin | |||||
| Long-acting octreotide and lutetium | |||||
| methotrexate and mercaptopurine | |||||
| Oxaliplatin | |||||
| Paclitaxel | |||||
| PCV (procarbazine, lomustine (CCNU) and vincristine) | |||||
| Pegylated liposomal doxorubicin and carboplatin | |||||
| Pemetrexed | |||||
| Temozolomide | |||||
| vincristine, prednisone, L-asparagine, daunomycin, cyclophosphamide, cytarabine, 6 thioguanine, 6 MP, methotrexate, dexamethasone | |||||
| Vinorelbine |
Descriptive statistics were used to summarize demographic characteristics and outcomes. Kaplan-Meier curves were generated using Graphpad Prism 8.4.3. After satisfying the proportional hazards assumption, multivariate models were created using a forward likelihood ratio selection with variables incorporated into the model if P < 0.05 and removed from the model when P > 0.1. Patients with missing data for any variable were excluded. Multivariate analysis was performed in SPSS version 17.0. Overall survival was defined as the time between COVID-19 diagnosis and death. Patients alive at the time of last contact were censored. Patients who died due to non-COVID-19-related causes were censored at their time of death when COVID-19-specific survival was assessed and counted as an event when generating overall survival analyses. Three patients were considered clinical diagnoses and did not include a PCR-confirmed test for COVID-19. These patients were included in Table 2 (n = 631) but excluded from all subsequent analyses (n = 628) so as not to introduce bias in time to event analyses. See Figure 1 for a flow diagram detailing eligibility criteria and methods of selection of patients included in the study and numbers of censored patients.
Table 2.
Patient demographical and clinical characteristics.
| Metric | Category | Patients who tested positive for COVID-19 (n = 631) |
Patients with cancer who tested positive for COVID-19 (n = 509) |
||
|---|---|---|---|---|---|
| Count | Percentage (%) | Count | Percentage (%) | ||
| Sex | Male | 298 | 47.2 | 248 | 48.7 |
| Female | 333 | 52.8 | 261 | 51.3 | |
| Age, years | Median age = 62, IQR1: 49–73 | Median age = 66, IQR: 53–76 | |||
| Comorbidities (Data available in 539 patients total and 496 patients with cancer) | Asthma | 29 | 5.4 | 28 | 5.6 |
| Chronic Obstructive Pulmonary Disease | 34 | 6.3 | 34 | 6.9 | |
| Congestive Heart Failure | 19 | 3.5 | 19 | 3.8 | |
| Diabetes | 103 | 19.1 | 100 | 20.2 | |
| Hypertension | 204 | 37.9 | 196 | 39.5 | |
| Rheumatologic Disease | 19 | 3.5 | 18 | 3.6 | |
| Obstructive Sleep Apnea | 25 | 4.6 | 24 | 4.8 | |
| Chronic Kidney Disease | 25 | 4.6 | 25 | 5.0 | |
| History of Solid Organ Transplant | 1 | 0.2 | 1 | 0.2 | |
| Cirrhosis | 5 | 0.9 | 5 | 1.0 | |
| Cancer Type | Breast | 96 | 15.2 | 96 | 18.9 |
| GI2 | 58 | 9.2 | 58 | 11.4 | |
| Lung | 50 | 7.9 | 50 | 9.8 | |
| Lymphoma | 60 | 9.5 | 60 | 11.8 | |
| GU3 | 67 | 10.6 | 67 | 13.2 | |
| Gynecologic | 32 | 5.1 | 32 | 6.3 | |
| Leukemia | 25 | 4.0 | 25 | 4.9 | |
| CNS4 | 20 | 3.2 | 20 | 3.9 | |
| Other | 101 | 16.0 | 101 | 19.8 | |
| No cancer | 122 | 19.3 | NA | NA | |
| Last cancer treatment prior to COVID diagnosis (within 12 months) | Chemotherapy | 64 | 10.1 | 64 | 12.6 |
| Hormone therapy | 55 | 8.7 | 55 | 10.8 | |
| Immunotherapy | 6 | 1.0 | 6 | 1.2 | |
| Radiotherapy | 46 | 7.3 | 46 | 9.0 | |
| No therapy | 410 | 65.0 | 292 | 57.4 | |
| Other | 28 | 4.4 | 26 | 5.1 | |
| Surgery | 11 | 1.7 | 10 | 2.0 | |
| Rituximab | 11 | 1.7 | 10 | 2.0 | |
| Smoking status (Data available in 474 patients and 458 patients with cancer) | 184 | 38.8 | 173 | 37.8 | |
| Number hospitalized (Data available in 626 patients and 504 with cancer) | 96 | 15.3 | 95 | 18.8 | |
1 Interquartile Range. 2 Gastrointestinal. 3 Genitourinary. 4 Central Nervous System.
Hazard ratios and 95% confidence intervals (CI) were computed for survival curves and a multivariate analysis comparing each term in the model. Odds ratios (OR), 95% confidence intervals and P values were calculated from two-sided chi-squared tests and estimated from contingency tables looking at comorbidity distributions for each cancer type. Thirty-day COVID-19-specific mortality was estimated from survival curves for each variable being compared. ECOG performance status was retrieved from BC Cancer's EHR through chart review prior to a patient's PCR-confirmed positive test for COVID-19, although this data was not always consistently reported. Data on laboratory values at the time of COVID-19 diagnosis, presentation of COVID-19 symptoms, and hospitalization information was collected but excluded from analysis due to inconsistency in timing and availability of this data.
3. Results
Patient demographic and clinical characteristics are outlined in Table 2 for the 631 BC Cancer patients identified with COVID-19. The median age was 62 (interquartile range 49–73). The cohort included a slightly higher number of females (n = 333, or 52.8%). Five-hundred and nine patients with confirmed COVID-19 diagnosis also had cancer (80.7%), and the median age for patients in this group was 66 years (interquartile range 53–76). The rate of hospitalizations was significantly higher in patients with cancer (18.8%) compared to those without cancer (0.8%, P < 0.0001). Figure 2 shows that 122 out of 628 patients (19.4%) were listed as having no cancer and were seen for hereditary counselling. Among patients who were alive at the end of the study, the median follow-up time was 62 days (n = 561).
Patients with active or prior cancer were more likely to die from COVID-19 than patients without cancer (HR 7.59, 95% CI 3.37–17.12, P = 0.018) after censoring patients who died due to non-COVID-19-related reasons (Figure 2A). Thirty-day COVID-19 specific mortality was significantly higher (P = 0.0008) for patients with cancer (11.6%) compared to those without cancer (1.6%) (Table 3).
Table 3.
30-day mortality of patients in British Columbia, Canada seen at BC Cancer following diagnosis of COVID-19 compared by presence or absence of cancer, type of cancer, and type of treatment.
| Patients with confirmed COVID-19 (n = 628) |
Patients with confirmed COVID-19 by Cancer Type (n = 628) |
Patients with cancer and confirmed COVID-19 by Treatment Type (n = 506) |
|||
|---|---|---|---|---|---|
| Cohort | 30-Day mortality (%) | Cohort | 30-Day mortality (%) | Cohort | 30-Day mortality (%) |
| Cancer (n = 506) | 11.6% | Breast (n = 96) | 7.0% | Chemotherapy (n = 64) | 23.0% |
| No cancer (n = 122) | 1.6% | CNS1 (n = 20) | 9.1% | Hormonal treatment (n = 55) | 2.2% |
| GI2 (n = 58) | 16.0% | Immunotherapy (n = 6) | 16.7% | ||
| GU3 (n = 66) | 18.1% | Radiation (n = 46) | 12.3% | ||
| Gynecologic (n = 32) | 9.6% | Surgery (n = 10) | 0.0% | ||
| Leukemia (n = 25) | 4.5% | Rituximab (n = 10) | 22.2% | ||
| Lung (n = 49) | 25.0% | Other treatment (n = 26) | 7.7% | ||
| Lymphoma (n = 60) | 8.6% | No treatment (n = 292) | 10.6% | ||
| Other (n = 100) | 7.1% | ||||
| No cancer (n = 122) | 1.6% | ||||
1 Central Nervous System. 2 Gastrointestinal. 3 Genitourinary.
The effects of cancer type and anticancer treatment are shown in Figure 2B and C. Ninety-six patients had active or prior breast cancer, comprising the largest subset of cancer, followed by genitourinary (n = 66) and other cancers (n = 100). Patients with lung cancer (n = 49) had the highest 30-day COVID-19 specific mortality (25.0%), followed by GU (n = 66; 18.1%) and GI (n = 58; 16.0%), with the remaining cancer types (n = 455) under 10.0% (Table 3). A high proportion of patients with cancer were not on any anticancer treatments (n = 292; 57.4%) (Table 2). Of the anticancer treatments considered, patients on chemotherapy had the worst COVID-19 specific survival outcomes (30-day COVID-19 specific mortality of 23.0%). Patients on, rituximab, immunotherapy, and radiation had 30-day COVID-19 specific mortality of 22.2%, 16.7% and 12.3%, respectively (Table 3). Similar analyses were performed with overall survival as an endpoint and are summarized in Figure 3.
Factors associated with increased 30-day COVID-19 specific mortality are shown in Figure 4. These include age ≥65 years of age (HR 4.77, 95% CI 2.72–8.35, P < 0.0001), metastatic cancer stage (HR 3.70, 95% CI 1.77–7.73, P < 0.0001), Eastern Cooperative Oncology Group (ECOG) performance status (PS) ≥2 (HR 8.36, 95% CI 2.89–24.16, P < 0.0001), chronic kidney disease (HR 4.29, 95% CI 1.08–16.89, P < 0.0001), and hypertension (HR 3.17, 95% CI 1.77–5.66, P < 0.0001). There was also an increased COVID-19 specific mortality among male patients (HR 2.00, 95% CI 1.14–3.50, P = 0.0018) compared to female patients. Other variables studied including obesity and other cardiopulmonary conditions did not contribute significantly to worse COVID-19 specific survival outcomes following SARS-CoV-2 infection. On multivariate analysis, only ECOG ≥2 (HR 33.90, 95% CI 4.34–265.08, P = 0.001) was significantly associated with COVID-specific survival after controlling for ECOG (0-1 vs ≥ 2), sex, stage (metastatic vs not), cancer type, cancer treatment, smoking history and age (≥65 vs < 65). For overall survival, ECOG ≥2 (HR 21.74, 95% CI 4.79–98.58, P < 0.0001) and presence of metastatic disease (HR 2.93, 95% CI 1.03–8.32, P = 0.044) were both prognostic after controlling for co-variates. All other variables (sex, stage, cancer type, cancer treatment, smoking history and age for COVID-specific survival; and sex, cancer type, cancer treatment, smoking history and age for overall survival) were controlled for but did not remain in the model as they were not significant.
Figure 4.
Impact of baseline characteristics on COVID-specific survival.
Hypertension was found to be the most common comorbidity among all cancers, with 39.5% of cancer patients diagnosed with COVID-19 also having hypertension. Hypertension was the most prevalent among patients with lung cancer, with 60.0% of lung cancer patients also having hypertension compared with all cancers combined (OR 2.29, 95% CI 1.21–4.16, P = 0.0078). Patients with lung cancer were also found to have the highest incidence of chronic kidney disease, at 15.6% compared to 5.1% in all cancers combined (OR 3.46, 95% CI 1.30–8.53, P = 0.0043), although the number of patients with lung cancer and CKD (n = 7/45) is limited. Patients with lung cancer and COVID-19 were also found to have the highest prevalence of COPD, at 40.0% compared to 6.9% in all cancers combined (OR 9.04, 95% CI 4.39–17.70, P < 0.0001). Diabetes was most common among patients with GI cancers and COVID-19, with a prevalence of 38.2%, compared with 20.2% in all patients with cancer combined (OR 2.44, 95% CI 1.37–4.28, P = 0.0023).
4. Discussion
In this population-based observational study, we demonstrated that the diagnosis of cancer is associated with higher rates of hospitalization (18.8% vs 0.8% in those without cancer, P < 0.0001) and worse survival outcomes followed COVID-19 infection, consistent with previous literature ((WHO), 2020; Onder et al., 2020; Robilotti EV, Babady NE, 2021). Prognostic factors identified related to COVID-19 mortality include age ≥65, hypertension, ECOG ≥2, and stage IV cancer. These results are similar to the risk factors identified in the COVID-19 and Cancer Consortium (CCC19) analysis (Kuderer et al., 2020). Smoking status was also associated with worse 30-day mortality in that study but excluded from our analysis due to inconsistent data availability. Given that malignancies are often diagnosed in older patients and can lead to a deterioration in health, there is likely a complex interplay between a patient's baseline comorbid status and the impact of their cancer on their body's ability to deal with a COVID-19 infection.
Patients with lung cancer had a high prevalence of comorbidities compared to other malignancies, which may be a contributing factor to the differences in survival outcomes observed. In the lung cancer population, 60.0% of patients were found to have hypertension, compared with 39.5% in all cancers combined (OR 2.29, 95% CI 1.21–4.16, P = 0.0078). Several studies have suggested that vascular remodelling and associated accumulation of inflammatory immune cells in the context of lung cancer may occur and this could lead to more damage from inflammation due to severe COVID-19, particularly given that immunotherapy is commonly used to treat thoracic malignancies (Battafarano et al., 2002; Guignabert et al., 2013; Pullamsetti et al., 2017a, 2017b, 2014; Tammemagi, C.M.; Neslund-Dudas, C.; Simoff, M.; Kvale, 2003). Unsurprisingly, patients with lung cancer were also found to have the greatest incidence of chronic obstructive pulmonary disease (COPD), at 40.0% compared to 6.9% in all cancers combined (OR 9.04, 95% CI 4.39–17.70, P < 0.0001). Patients with COPD may have decreased pulmonary reserve and a higher susceptibility to respiratory failure due to COVID-19. Overall, these results are consistent with previous studies regarding COVID-19 in patients with lung cancer and may in part explain the poor survival outcomes seen among lung cancer patients following COVID-19 infection (Figure 2C) (Luo et al., 2020; Maringe et al., 2020; Rogado et al., 2020).
Another notable finding in our analysis is that patients receiving chemotherapy had the worst survival outcomes of anticancer therapies, with a predicted 30-day COVID-19 specific mortality of 23.0%. It will be important to determine whether patients will have a robust and lasting response to vaccines to ensure that patients living through several waves of COVID-19 will remain protected given their high risk of death due to the virus. The administration of rituximab, an anti-CD20 antibody, was associated with the second highest 30-day COVID-19 specific mortality out of all the anticancer treatments (22.2%) However, this is limited by the small number of patients, as only 10 out of 628 surveyed patients with cancer received rituximab within one year prior to COVID-19 diagnosis. The immunosuppressive effects of rituximab have been well-studied, especially in the context of long-term use, where it is associated with B cell depletion and decreased antibody production as a result (Avouac et al., 2021; Bingham et al., 2010; Kos et al., 2020; Mehta et al., 2020). It will be important to ascertain whether rituximab interferes with the maintenance of a robust antiviral immune response against SARS-CoV-2 (Avouac et al., 2021; Mehta et al., 2020) in the many serologic studies that are ongoing.
This study should be interpreted in the context of several limitations. First, this was a retrospective study, and some data elements were not fully available and collected in a regimented fashion, such as laboratory investigations. Second, our analysis does not include variables concerning COVID-19 symptoms as these were difficult to capture from outside hospitals when patients presented. We were constrained by the need to rely on discharge summaries for many of these patients which may have had varying degrees of documentation surrounding the initial presentation. As well, it was difficult to determine the level of medical intervention, COVID-19 vaccination status, and if any COVID-directed therapeutics were used while admitted to an outside hospital. COVID vaccination began in the last 2 months of our chosen cohort so most patients would have either not been vaccinated or only had one injection. Additionally, the classification of anticancer therapy as active if the last dose was received within 12 months prior to a COVID-19 diagnosis could impact the generalizability of these findings as the duration of treatment may be an additional factor affecting the severity of toxicity and side effect profiles associated with anticancer therapies. Finally, our study was only conducted in one province during the early stages of the ongoing pandemic. Sample characteristics of the present study may not be fully generalizable to all patients with cancer, as the type and frequency of cancers observed in our population may not be representative of the cancer population, a large proportion of patients were not receiving any treatment, and a significant proportion of patients had additional comorbidities such as HBP. However, taking these considerations into account allows us to provide an accurate snapshot of the early stages of the ongoing COVID-19 pandemic at BC Cancer. These results may not be extrapolated to all health care systems; however, they were drawn from a population-based cohort within a public health care system, which helps limit differences in outcome based on access to care. While disparities in care may still exist within public health care systems, the ability of our study to drawn from a single health care system that provides care to over five million people living in a Canadian province is an important strength. However, there is a possibility our results may have some bias. For example, we did see that the median age of individuals with COVID was 62, while the median age of patients diagnosed with Canada is 66.9 years. This may be due to differences in COVID exposure or differences in the likelihood of a patient receiving cancer treatment changing as one ages (Statistics Canada - Cancer incidence in The Daily - Cancer incidence in Canada, 2018; 2021).
5. Conclusion
Patients with a cancer diagnosis appear at increased risk of 30-day mortality from SARS-Co-V-2 infection and there was significant variability in outcome based on type of cancer and treatment received. This population is often older and has significant comorbidities that put them at increased risk of serious COVID-19 infection. This study adds further support to aggressive vaccination programs and a low threshold for increased monitoring and close follow-up in suspected COVID-19 among patients living with cancer given the potential for adverse outcomes in this group.
Declarations
Author contribution statement
Angela S. Mathews, BSc; Eric Bhang, BSc; Jonathan M. Loree, MD, MS: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.
Ashley Paul, MD; Irene S. Yu, MD; Colleen McGahan, MSc; Diego Villa, MD, MPH; Karen Gelmon, MD; Antonio Avina-Zubieta, MD, MSc, PhD; Alina S. Gerrie, MD; Ursula Lee, MD; Stephen Chia, MD; Ryan R. Woods, MSc, PhD: Contributed reagents, materials, analysis tools or data; Wrote the paper.
Funding statement
Jonathan M Loree was supported by a Michael Smith Health Professional Investigator Award. This work was supported by Philanthropic funds from BC Cancer Foundation.
Data availability statement
The data that has been used is confidential.
Declaration of interest’s statement
The authors declare no conflict of interest.
Additional information
No additional information is available for this paper.
References
- Avouac J., Drumez E., Hachulla E., Seror R., Georgin-Lavialle S., El Mahou S., et al. COVID-19 outcomes in patients with inflammatory rheumatic and musculoskeletal diseases treated with rituximab: a cohort study. Lancet Rheumatol. 2021:419–426. doi: 10.1016/S2665-9913(21)00059-X. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Battafarano R.J., Piccirillo J.F., Meyers B.F., Hsu H.S., Guthrie T.J., Cooper J.D., et al. Impact of comorbidity on survival after surgical resection in patients with stage I non-small cell lung cancer. J. Thorac. Cardiovasc. Surg. 2002;123:280–287. doi: 10.1067/mtc.2002.119338. [DOI] [PubMed] [Google Scholar]
- Bingham C.O., Looney R.J., Deodhar A., Halsey N., Greenwald M., Codding C., et al. Immunization responses in rheumatoid arthritis patients treated with rituximab: results from a controlled clinical trial. Arthritis Rheum. 2010;62:64–74. doi: 10.1002/art.25034. [DOI] [PubMed] [Google Scholar]
- COVID-19 dashboard by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU) Johns Hopkins Univ Med; 2021. https://coronavirus.jhu.edu/map.html accessed. [Google Scholar]
- Garassino M.C., Whisenant J.G., Huang L.C., Trama A., Torri V., Agustoni F., et al. COVID-19 in patients with thoracic malignancies (TERAVOLT): first results of an international, registry-based, cohort study. Lancet Oncol. 2020;21:914–922. doi: 10.1016/S1470-2045(20)30314-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Guignabert C., Tu L., Le Hiress M., Ricard N., Sattler C., Seferian A., et al. Pathogenesis of pulmonary arterial hypertension: lessons from cancer. Eur. Respir. Rev. 2013;22:543–551. doi: 10.1183/09059180.00007513. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Kos I., Balensiefer B., Roth S., Ahlgrimm M., Sester M., Schmidt T., et al. Prolonged course of COVID-19-associated pneumonia in a B-cell depleted patient After rituximab. Front. Oncol. 2020;10:1–5. doi: 10.3389/fonc.2020.01578. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Kuderer N.M., Choueiri T.K., Shah D.P., Shyr Y., Rubinstein S.M., Rivera D.R., et al. Clinical impact of COVID-19 on patients with cancer (CCC19): a cohort study. Lancet. 2020;395:1907–1918. doi: 10.1016/S0140-6736(20)31187-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Lau H., Khosrawipour T., Kocbach P., Ichii H., Bania J., Khosrawipour V. Estimating mortality from COVID-19: a Scientific brief. Pulmonology. 2020:5–8. [Google Scholar]
- Luo J., Rizvi H., Preeshagul I.R., Egger J.V., Hoyos D., Bandlamudi C., et al. COVID-19 in patients with lung cancer. Ann. Oncol. 2020;31:1386–1396. doi: 10.1016/j.annonc.2020.06.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Maringe C., Spicer J., Morris M., Purushotham A., Nolte E., Sullivan R., et al. The impact of the COVID-19 pandemic on cancer deaths due to delays in diagnosis in England, UK: a national, population-based, modelling study. Lancet Oncol. 2020;21:1023–1034. doi: 10.1016/S1470-2045(20)30388-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Mehta P., Porter J., Chambers R., Isenberg D., Reddy V. B-cell depletion with rituximab in the COVID-19 pandemic: where do we stand? Lancet. 2020;2:E589–E590. doi: 10.1016/S2665-9913(20)30270-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Onder G., Rezza G., Brusaferro S. Case-Fatality rate and characteristics of patients dying in relation to COVID-19 in Italy. JAMA, J. Am. Med. Assoc. 2020;323:1775–1776. doi: 10.1001/jama.2020.4683. [DOI] [PubMed] [Google Scholar]
- Pullamsetti S.S., Schermuly R., Ghofrani A., Weissmann N., Grimminger F., Seeger W. Novel and emerging therapies for pulmonary hypertension. Am. J. Respir. Crit. Care Med. 2014;189:394–400. doi: 10.1164/rccm.201308-1543PP. [DOI] [PubMed] [Google Scholar]
- Pullamsetti S.S., Kojonazarov B., Storn S., Gall H., Salazar Y., Wolf J., et al. Lung cancer-Associated pulmonary hypertension: role of microenvironmental inflammation based on tumor cell-immune cell cross-Talk. Sci. Transl. Med. 2017;9:1–17. doi: 10.1126/scitranslmed.aai9048. [DOI] [PubMed] [Google Scholar]
- Pullamsetti S.S., Savai R., Seeger W., Goncharova E.A. 2017. From Cancer Biology to New Pulmonary Arterial Hypertension Therapeutics Targeting Cell Growth and Proliferation Signaling Hubs. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Reboot . Reboot Rx, Inc; 2021. COVID-cancer Project.https://rebootrx.org/covid-cancer accessed. [Google Scholar]
- Robilotti E.V., Babady N.E.M.P. Determinants of severity in cancer patients with COVID-19 illness. Nat. Med. 2021;26:1218–1223. doi: 10.1038/s41591-020-0979-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Rogado J., Pangua C., Serrano-Montero G., Obispo B., Marino A.M., Pérez-Pérez M., et al. Covid-19 and lung cancer: a greater fatality rate? Lung Cancer. 2020;146:19–22. doi: 10.1016/j.lungcan.2020.05.034. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Tammemagi C.M., Neslund-Dudas C., Simoff M., Kvale P. Impact of comorbidity on lung cancer survival | Enhanced Reader. Int. J. Cancer. 2003:792–802. doi: 10.1002/ijc.10882. [DOI] [PubMed] [Google Scholar]
- The Daily - Cancer incidence in Canada Statistics Canada 2021. 2018. https://www150.statcan.gc.ca/n1/daily-quotidien/210519/dq210519b-eng.htm accessed.
- (WHO) WHO. Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19) 2020. https://www.who.int/publications/i/item/report-of-the-who-china-joint-mission-on-coronavirus-disease-2019-(covid-19 (accessed June 14, 2021)
- Williamson E.J., Walker A.J., Bhaskaran K., Bacon S., Bates C., Morton C.E., et al. Factors associated with COVID-19-related death using OpenSAFELY. Nature. 2020;584:430–436. doi: 10.1038/s41586-020-2521-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data that has been used is confidential.