Open Access

Mortality and morbidity of curative and palliative anticancer treatments during the COVID‑19 pandemic: A multicenter population‑based retrospective study

  • Authors:
    • Emad Tashkandi
    • Amal Al‑Abdulwahab
    • Bassam Basulaiman
    • Abdullah Alsharm
    • Marwan Al‑Hajeili
    • Faisal Alshadadi
    • Lamis Halawani
    • Mubarak Al‑Mansour
    • Bushra Alquzi
    • Samar Barnawi
    • Mohammed Alghamdi
    • Nashwa Abdelaziz
    • Ruqayya Azher
  • View Affiliations

  • Published online on: February 26, 2021     https://doi.org/10.3892/mco.2021.2244
  • Article Number: 82
  • Copyright: © Tashkandi et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Administration of effective anticancer treatments should continue during pandemics. However, the outcomes of curative and palliative anticancer treatments during the coronavirus disease (COVID‑19) pandemic remain unclear. The present retrospective observational study aimed to determine the 30‑day mortality and morbidity of curative and palliative anticancer treatments during the COVID‑19 pandemic. Between March 1 and June 30, 2020, all adults (n=2,504) with solid and hematological malignancies irrespective of cancer stage and type of anticancer treatments at five large comprehensive cancer centers in Saudi Arabia were included. The 30‑day mortality was 5.1% (n=127) for all patients receiving anticancer treatment, 1.8% (n=24) for curative intent, 8.6% (n=103) for palliative intent and 13.4% (n=12) for COVID‑19 cases. The 30‑day morbidity was 28.2% (n=705) for all patients, 17.9% (n=234) for curative intent, 39.3% (n=470) for palliative intent and 75% (n=77) for COVID‑19 cases. The 30‑day mortality was significantly increased with male sex [odds ratio (OR), 2.011; 95% confidence interval (CI), 1.141‑3.546; P=0.016], body mass index (BMI) <25 (OR, 1.997; 95% CI, 1.292‑3.087; P=0.002), hormone therapy (OR, 6.315; 95% CI, 0.074‑2.068; P=0.001) and number of cycles (OR, 2.110; 95% CI, 0.830‑0.948; P=0.001), but decreased with Eastern Cooperative Oncology Group performance status (ECOG‑PS) of 0‑1 (OR, 0.157; 95% CI, 0.098‑0.256; P=0.001), stage I‑II cancer (OR, 0.254; 95% CI, 0.069‑0.934; P=0.039) and curative intent (OR, 0.217; 95% CI, 0.106‑0.443; P=0.001). Furthermore, the 30‑day morbidity significantly increased with age >65 years (OR, 1.420; 95% CI, 1.075‑1.877; P=0.014), BMI <25 (OR, 1.484; 95% CI, 1.194‑1.845; P=0.001), chemotherapy (OR, 1.397; 95% CI, 1.089‑5.438; P=0.032), hormone therapy (OR, 1.527; 95% CI, 0.211‑1.322; P=0.038) and immunotherapy (OR, 1.859; 95% CI, 0.648‑4.287; P=0.038), but decreased with ECOG‑PS of 0‑1 (OR, 0.502; 95% CI, 0.399‑0.632; P=0.001), breast cancer (OR, 0.569; 95% CI, 0.387‑0.836; P=0.004) and curative intent (OR, 0.410; 95% CI, 0.296‑0.586; P=0.001). The mortality risk was lowest with curative treatments. Therefore, such treatments should not be delayed. The morbidity risk doubled with palliative treatments and was highest among COVID‑19 cases. Mortality appeared to be driven by male sex, BMI <25, hormonal therapy and number of cycles, while morbidity increased with age >65 years, BMI <25, chemotherapy, hormonal therapy and immunotherapy. Therefore, oncologists should select the most effective anticancer treatments based on the aforementioned factors.

Introduction

Over the past decades, the number of chemotherapy agents has increased, and evidence has shown that chemotherapy improves survival and cancer-related symptoms (1-3). Caring for cancer patients is challenging, and oncologists need to weigh the risks and benefits of anticancer treatments and identify factors that could predict mortality or morbidity to improve clinical decision-making. There are no universally agreed-upon benchmark figures for early mortality due to anticancer treatments. However, preliminarily establishing a mortality rate of 3-9% with a mean of 5% as a reference has allowed comparisons between different institutions (4).

Globally, as of September 12, 2020, the coronavirus disease (COVID-19) has caused >28.5 million confirmed cases and 916,000 confirmed deaths and affected 216 countries (5). Patients with cancer are susceptible to COVID-19 infections because of the immunosuppressive effect of cancer and anticancer treatments (6). Moreover, it is assumed that receiving anticancer treatments will increase the mortality risk from COVID-19. Hence, many concerns have been raised regarding the management of this specific population during the pandemic. Resource utilization and allocation during the COVID-19 pandemic have been modified by implementing strategies and creating frameworks for prioritizing anticancer treatments. For instance, in Italy, high priority was given to patients receiving curative anticancer treatment to minimize treatment interruption (7). Hanna et al (8) proposed a conceptual framework for prioritizing anticancer treatments, wherein palliative chemotherapy was considered a low priority compared to curative chemotherapy. Another suggestion was to change the route to oral anticancer therapy without compromising oncological outcomes (9). Studies have also shown that delayed adjuvant treatment is associated with inferior survival in colon cancer (10) and breast cancer (11).

Ohe et al (12) retrospectively studied the risk factors for mortality in lung cancer and found that 2.3% of patients died from chemotherapy-related toxicity. Similarly, in small-cell lung cancer, the mortality associated with sepsis was 5%, as reported by Radford et al (13). Stephens et al (14) found that the mortality was 10% within 3 weeks of chemotherapy. Another study found that the mortality was 13% in patients with non-Hodgkin's lymphoma (15).

A proportion of patients dying within 30 days of receiving anticancer treatments may be linked to poor clinical decisions. This study aimed to determine the 30-day mortality and morbidity of curative and palliative anticancer treatments during the COVID-19 pandemic and examine possible risk factors for mortality and morbidity.

Materials and methods

Study design and population

From March 1 to June 30, 2020 we retrospectively collected data of the target population: Adults aged ≥18 years who were histologically diagnosed with cancer, irrespective of the cancer stage and class of anticancer treatment received in five large comprehensive cancer centers in Saudi Arabia, namely, King Abdullah Medical City in Makkah, King Fahad Medical City in Riyadh, King Abdulaziz University in Jeddah, Princess Nora Cancer Center in Jeddah, and King Saud University Oncology Center in Riyadh. The convenience sampling method was used. The study protocol was approved by the Institutional Research Ethics Boards of the above participating centers (IRB number 20-616, April 23, 2020). Pharmacy administration provided the list of patients who received at least one cycle of anticancer treatment in the outpatient setting; a total of 2,504 patients were identified and were eligible in the study.

The inclusion criteria were adult patients with solid or hematological tumors who were receiving anticancer treatments in the outpatient setting during the study period. Both routes of anticancer treatments, oral and parenteral, were included. Patients were followed up until July 30, 2020 to assess treatment outcomes. Patients were excluded if they were on regular follow-up or surveillance; received other treatment modalities such as curative surgeries, radiation treatments alone, and best supportive care; or were under treatment with bone-modifying agents such as bisphosphonate or denosumab. The number of patients with missing variables or lost to follow-up was <1%; they were included in the analysis when appropriate.

Study procedures

Electronic health records (EHRs) were reviewed by senior oncology physicians to identify patients who met inclusion criteria and to collect the data. Each data entry was assigned a code number to ensure data anonymity. Other than the serial code number, patient characteristics comprised age, sex, and body mass index (BMI). Clinical characteristics included the presence of comorbidities, Eastern Cooperative Oncology Group performance status (ECOG-PS), cancer type, and cancer stage. Treatment characteristics included the protocol name, type (chemotherapy, immunotherapy, hormone therapy, or targeted therapy), route (intravenous, subcutaneous, or oral), intent of treatment (curative or palliative), type of curative treatment (neoadjuvant or adjuvant), line of palliative treatment (first-line, second-line, third-line, or fourth-line and beyond), and number of cycles.

The primary outcome was 30-day mortality after administration of curative and palliative anticancer treatments during the COVID-19 pandemic, which was defined as death within 30 days of the last anticancer treatment cycle (excluding road traffic accident and trauma as the cause of death). The secondary outcome was 30-day morbidity, defined as morbidity within 30 days of the last anticancer treatment cycle, which included any of the following: Hospitalizations, emergency room visits, intensive care unit admissions, delay in chemotherapy or dose reduction, COVID-19 incidence, and associations between the outcome and potential prognostic variables.

We calculated the national 30-day mortality rate by dividing the number of patients who received anticancer treatment within 30 days of their death by the total number of patients who received anticancer treatment during the study period. If a patient received multiple cycles of anticancer treatment during the study period, 30-day mortality was computed using the most recent cycle. Patients receiving multiple treatments in this period were counted only once in the dataset. Data were transferred securely to be analyzed and stored in a secure place.

Statistical analysis

All data were analyzed using the Statistical Package for the Social Sciences (SPSS) version 25. Descriptive statistics (percentage, mean, and standard deviation) were calculated for continuous variables, and frequencies for categorical variables. The chi-squared test for categorical variables and independent t-test for continuous variables were conducted to determine any associations between demographic, clinical, tumor, and anticancer treatment characteristics. We used logistic regression analyses to assess any associations of the explanatory variables with 30-day mortality and 30-day morbidity (dependent variables) and with all other variables (independent variables). As none of the variables had a missing rate of >10%, all were included in the analysis. The results of the logistic regression analyses are presented as odds ratios (ORs) and 95% confidence intervals (CIs) that reflect the effect of each variable in our regression model. A P-value of <0.05 was considered statistically significant.

Results

Characteristics of the cancer patients and their outcome

Table I shows the characteristics of the cancer patients. Overall, 2,504 patients received anticancer treatments from March 1 to June 30, 2020. Among them, 1,305 were treated with curative intent and 1,195 were treated with palliative intent. In total, 2,069 (83%) were ≤65 years old, 1,743 (70%) were female, 945 (37.8%) had comorbidities, 1,832 (73%) had an ECOG-PS of 0-1, 1,266 (51.2%) had stage IV cancer, and 1,175 (46.9%) had breast cancer, which was the most common diagnosis.

Table I

Demographic, clinical, tumor and anticancer treatment characteristics.

Table I

Demographic, clinical, tumor and anticancer treatment characteristics.

Patient characteristicsAll patients (n=2,504)Curative intent (n=1,305; 52%)Palliative intent (n=1,195; 48%)P-value
Age, n (%)   <0.05
     >65 years435 (17.3)181 (41.7)253 (58.3) 
     ≤65 years2,069 (82.7)1,124 (54.4)942 (45.6) 
Sex, n (%)   <0.05
     Male751 (30.0)307 (41.0)441 (59.0) 
     Female1,753 (70.0)998 (57.0)754 (43.0) 
BMI, n (%)   <0.05
     <25854 (34.1)367 (43.0)486 (57.0) 
     ≥251,648 (65.8)937 (96.9)709 (43.1) 
Comorbidities, n (%)    
     Yes945 (37.8)426 (48.0)462 (59.0) 
     No1,556 (62.2)772 (53.7)666 (41.0) 
Cause of comorbidity, n (%)   <0.05
     DM329 (35.0)155 (47.1)174 (52.9) 
     HTN239 (25.5)128 (53.6)111 (46.4) 
     IHD53 (5.6)27 (50.9)26 (49.1) 
     DVT29 (2.9)15 (55.6)12 (44.6) 
     CKD22 (2.3)9 (40.9)13 (59.1) 
ECOG-PS, n (%)   <0.05
     0-11,832 (73.3)1,113 (60.8)717 (39.2) 
     >1668 (26.7)191 (28.6)476 (71.4) 
Cancer stage, n (%)   <0.05
     I-II548 (22.2)501 (91.4)47 (8.6) 
     III659 (26.6)577 (87.7)81 (12.3) 
     IV1,266 (51.2)200 (15.8)1,064 (84.2) 
Cancer diagnosis, n (%)   <0.05
     Breast1,175 (46.9)768 (65.4)407 (34.6) 
     Gastrointestinal499 (19.9)146 (29.3)353 (70.7) 
     Hematological252 (10.1)208 (82.9)43 (17.1) 
     Gynecological173 (6.9)65 (37.8)107 (62.2) 
     Lung86 (3.4)11 (12.8)75 (87.2) 
     Urological66 (2.6)10 (15.4)55 (84.6) 
     Other253 (10.1)97 (38.5)155 (61.5) 
Type of therapy, n (%)   <0.05
     Chemotherapy1,538 (61.4)740 (48.2)796 (51.8) 
     Hormone therapy458 (18.3)363 (79.3)95 (20.7) 
     Targeted therapy362 (14.5)147 (40.6)215 (59.4) 
     Immunotherapy85 (3.4)9 (10.7)75 (89.3) 
Route, n (%)   <0.05
     IV1,723 (68.8)831 (48.3)890 (51.7) 
     Oral688 (27.5)417 (60.7)270 (39.3) 
     SC91 (3.6)56 (61.5)35 (38.5) 
Type of curative treatment, n (%)    
     Neoadjuvant-259 (20.3)--
     Adjuvant-805 (63.0)--
     Not applicable-214 (16.7)--
Line of palliative treatment, n (%)    
     First-line--608 (50.9)-
     Second-line--372 (31.1)-
     Third-line--139 (11.6)-
     Fourth-line and beyond--76 (6.4)-
Number of cycles, mean ± SD5.91±9.104.46±5.127.50±11.85<0.05

[i] Data were analyzed using a t-test or a χ2 test, as appropriate, and expressed as mean ± SD or n (%). Due to rounding of values, some variables may not add up to 100%. BMI, body mass index; DM, diabetes mellitus; HTN, hypertension; IHD, ischemic heart disease; DVT, deep vein thrombosis; CKD, chronic kidney disease; ECOG-PS, Eastern Cooperative Oncology Group performance status; IV, intravenous; SC, subcutaneous; SD, standard deviation.

With regard to curative anticancer treatment characteristics, most of the patients received chemotherapy (740 patients, 48.2%), the most common route was intravenous (831 patients, 48.3%), the most common type of treatment was adjuvant (805 patients, 63%), and patients received four cycles of treatment on average. As with palliative treatment, most of the patients received chemotherapy (796 patients, 51.8%), the most common route was intravenous (890 patients, 51.7%), the majority of patients were on first-line treatment (608 patients, 50.9%), and patients received eight cycles of treatment on average.

Table II summarizes the outcomes of interest. In total, 127 (5.1%) patients died within 30 days of receiving anticancer treatments, 24 (1.8%) of whom received curative anticancer treatments, while 103 (8.6%) received palliative treatments. Among the 24 patients who received curative anticancer treatments, sepsis was the most common cause of death (11 patients, 40.7%), whereas among the 103 patients who received palliative treatments, disease progression was the most common cause of death (61 patients, 88.4%). Meanwhile, morbidity was evident in 705 (28.2%) patients within 30 days of receiving anticancer treatments. Among these patients, 234 (17.9%) had curable anticancer treatments, while 470 (39.3%) had palliative anticancer treatments.

Table II

Summary of 30-day mortality and morbidity rates and causes.

Table II

Summary of 30-day mortality and morbidity rates and causes.

VariablesAll patients, n (%) (n=2,504)Curative intent, n (%) (n=1,305)Palliative intent, n (%) (n=1,195)COVID-19-positive, n (%) (n=89)
30-day mortality rate127 (5.1)24 (1.8)103 (8.6)12 (13.4)
Cause of 30-day mortality    
     Disease progression69 (60.0)8 (11.6)61 (88.4)1 (8.3)
     Sepsis27 (23.5)11 (40.7)16 (59.3)5 (41.7)
     Pneumonia7 (6.1)0 (0.0)7 (100.0)3(25)
     Other6 (5.2)1 (16.7)5 (83.3)2 (16.7)
     Febrile neutropenia2 (1.7)1 (50.0)1 (50.0)1 (8.3)
     Stroke2 (1.7)0 (0.0)2 (100.0)0 (0.0)
30-day morbidity rate705 (28.2)234 (17.9)470 (39.3)67 (75.0)
Cause of 30-day morbidity    
     ER visits407 (29.7)136 (33.5)270 (66.5)54 (13.5)
     Hospitalizations367 (26.8)115 (31.4)251 (68.6)54 (15.0)
     Delay in chemotherapy327 (23.9)97 (29.8)229 (70.2)47 (14.7)
     Dose reduction211 (15.4)54 (25.6)157 (74.4)11 (5.3)
     ICU admission58 (4.2)23 (39.7)35 (60.3)8 (14.3)

[i] Due to rounding of values, some variables may not add up to 100%. COVID-19, coronavirus disease; ER, emergency room; ICU, intensive care unit.

In patients who tested positive for COVID-19, the 30-day mortality was 13.4% (n=12), and the 30-day morbidity was 75% (n=77).

Factors associated with mortality and morbidity

Table III displays the results of the multivariate regression analysis of factors associated with mortality. Thirty-day mortality significantly increased with male sex (OR 2.011, 95% CI 1.141-3.546; P=0.016), BMI <25 (OR 1.997, 95% CI 1.292-3.087; P=0.002), hormone therapy compared to targeted therapy (OR 6.315, 95% CI 0.074-2.068; P=0.001), and a greater number of cycles (OR 2.110, CI 0.830-0.948; P=0.001). However, 30-day mortality significantly decreased in patients with an ECOG-PS of 0-1 (OR 0.157, 95% CI 0.098-0.256; P=0.001), stage I-II cancer (OR 0.254, 95% CI 0.069-0.934; P=0.039), and curative treatment (OR 0.217, CI 0.106-0.443; P=0.001).

Table III

Regression analysis of potential prognostic variables associated with 30-day mortality.

Table III

Regression analysis of potential prognostic variables associated with 30-day mortality.

VariableORP-value95% CI for OR
Age (≤65 years)Reference group  
     Age (>65 years)1.0530.8400.636-1.745
Sex (female)Reference group  
     Sex (male)2.0110.0161.141-3.546
BMI (≥25)Reference group  
     BMI (<25)1.9970.0021.292-3.087
ECOG-PS (>1)Reference group  
     ECOG-PS (0-1)0.1570.0010.098-0.253
Stage IVReference group  
     Stage I-II0.2540.0390.069-0.934
     Stage III1.1290.7000.610-2.090
Diagnosis (others)Reference group  
     Breast cancer1.6140.0560.725-3.594
     Hematologic cancer2.3750.9260.977-5.774
     Gynecologic cancer1.0330.4990.523-2.041
     Gastrointestinal cancer1.4050.8580.524-3.764
     Lung cancer1.0910.1860.421-2.829
     Urologic cancer0.3920.2410.098-1.569
Type (targeted therapy)Reference group  
     Type (chemotherapy)2.1100.0620.250-3.485
     Type (hormone therapy)6.3150.0010.074-2.068
     Type (immunotherapy)1.2390.7740.262-5.253
Number of cycles2.1100.0010.830-0.948
Route (SC)Reference group  
     Route (IV)1.4120.5960.395-5.043
     Route (oral)0.4700.2820.119-1.861
Intention (curative)0.2170.0010.106-0.443

[i] OR, odds ratio; CI, confidence interval; BMI, body mass index; ECOG-PS, Eastern Cooperative Oncology Group performance status; SC, subcutaneous; IV, intravenous.

Table IV shows the results of the multivariate regression analysis of factors associated with morbidity. Thirty-day morbidity significantly increased with age >65 years (OR 1.420, 95% CI 1.075-1.877; P=0.014), BMI <25 (OR 1.484, 95% CI 1.194-1.845; P=0.001), chemotherapy (OR 1.397, 1.089-5.438; P=0.032), hormone therapy (OR 1.527, 95% CI 0.211-1.322; P=0.038), and immunotherapy (OR 1.859, 95% CI 0.648-4.287; P=0.038). However, 30-day morbidity significantly decreased with an ECOG-PS of 0-1 (OR 0.502, 95% CI 0.399-0.632; P=0.001), breast cancer (OR 0.569, 95% CI 0.387-0.836; P=0.004), urologic cancer (OR 0.505, 95% CI 0.255-0.999; P=0.050), a greater number of cycles (OR 0.964, CI 0.848-0.980; P=0.001), and curative intent (OR 0.410, CI 0.296-0.586; P=0.001).

Table IV

Regression analysis of potential prognostic variables associated with 30-day morbidity.

Table IV

Regression analysis of potential prognostic variables associated with 30-day morbidity.

VariableORP-value95% CI for OR
Age (≤65 years)Reference group  
     Age (>65 years)1.4200.0141.075-1.877
Sex (female)Reference group  
     Sex (male)0.9630.7870.730-1.270
BMI (≥25)Reference group  
     BMI (<25)1.4840.0011.194-1.845
ECOG-PS (>1)Reference group  
     ECOG-PS (0-1)0.5020.0010.399-0.632
Stage IVReference group  
     Stage I-II0.7780.1950.533-1.137
     Stage III1.0580.7340.765-1.461
Diagnosis (others)Reference group  
     Breast cancer0.5690.0040.387-0.836
     Hematologic cancer1.0460.8450.667-1.639
     Gynecologic cancer1.1700.3760.826-1.658
     Gastrointestinal cancer0.8660.5600.534-1.405
     Lung cancer0.7630.3410.438-1.331
     Urologic cancer0.5050.0500.255-0.999
Type (targeted therapy)Reference group  
     Type (chemotherapy)1.3970.0321.089-5.438
     Type (hormone therapy)1.5270.0380.211-1.322
     Type (immunotherapy)1.8590.0380.648-4.287
Number of cycles0.9640.0010.948-0.980
Route (SC)Reference group  
     Route (IV)1.4240.2510.779-2.602
     Route (oral)0.7790.4370.415-1.462
Intention (curative)0.4100.0010.296-0.568

[i] OR, odds ratio; CI, confidence interval; BMI, body mass index; ECOG-PS, Eastern Cooperative Oncology Group performance status; SC, subcutaneous; IV, intravenous.

Anti-cancer drugs used

Table V presents the anticancer drugs used in the study population. The most common drug was hormone in 463 (18.5%), followed by alkylating agents in 361 (14.4%), Her 2-based drugs in 253 (10.1%), taxanes in 218 (8.7%) and antimetabolites in 214 (8.6%). Multi-mechanism drugs comprised 179 (7.2%) and included protocols such as (fluorouracil, carboplatin, trastuzumab), (fluorouracil, cyclophosphamide, docetaxel), ABVD (doxorubicin, bleomycin, vinblastine, dacarbazine), AVD (doxorubicin, vinblastine, dacarbazine), (bortezomib, pomalidomide, dexamethasone), (carboplatin, paclitaxel, bevacizumab), (carboplatin, paclitaxel, gemcitabine), (carboplatin, paclitaxel, pembrolizumab), RCHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, prednisolone), CVD (cyclophosphamide, vincristine, dacarbazine), CVP (cyclophosphamide, vincristine, prednisone), CYBORD (cyclophosphamide, bortezomib, dexamethasone), (cyclophosphamide, methotrexate, fluorouracil), (vincristine, doxorubicin, cytarabine), DA-R-EPOCH (etoposide, prednisone, vincristine, cyclophosphamide, doxorubicin, rituximab), (dasatinib, dexamethasone, vincristine), DFCP (Dana Farber Consortium Protocol), DRD (daratumumab, lenalidomide, dexamethasone), FEC (fluorouracil, epirubucin, cyclophosphamide), FOLFOXIRI (folinic acid, fluorouracil, oxaliplatin and irinotecan), GDP (gemcitabine, dexamethasone, cisplatin), HIDAC (high-dose Ara-C), VAC-IE (vincristine, adriamycin, cyclophosphamide, ifosfamide and etoposide), (methotrexate, dactinomycine, etoposide), THP (docetaxel, trastuzumab, pertuzumab), TPF (cisplatin, fluorouracil, docetaxel), (vincristine, doxorubicin, cytarabine), and VIP. Additional protocols included ATRA (all-trans retinoic acid), IVIG (intravenous immune globulin), mesna, octreotide, and zoledronic acid.

Table V

Characteristics of anticancer drugs.

Table V

Characteristics of anticancer drugs.

 Curative intent (n=1,307; 52%)Palliative intent (n=1,195; 48%) 
Class of anti-cancer drugsN (%)IVOralSCIVOralSCSchedule of administrationInterval of doses
Hormone463 (18.5)53483128312Daily4 weeks, 8 weeks, 12 weeks
Alkylating agents361 (14.4)169110169120OnceWeekly, 3 weeks, 4 weeks
Her-2 based group253 (10.1)84333111814Once3 weeks
Taxanes218 (8.7)129518210Once or dailyWeekly, 3 weeks
Antimetabolites214 (8.6)3746343823Once or 5 days2 weeks, 3 weeks, 4 weeks
Oxaliplatin based drugs204 (8.2)1020010110Once2 weeks, 3 weeks
Multi-mechanism179 (7.2)134004410VariableVariable
Other118 (4.7)721823104VariableVariable
Irinotecan based drugs110 (4.4)60010400Once or 5 days2 weeks, 3 weeks
Check point inhibitors107 (4.3)19008611Once2 weeks,3 weeks
Monoclonal antibodies85 (3.4)46082911Once or dailyWeekly, 2 weeks, 3 weeks, 4 weeks, 8 weeks
Gemcitabine based drugs80 (3.2)11106710Once1 week, 3 weeks
CDK inhibitors46 (1.8)0203410Daily4 weeks
Anthracyclines35 (1.4)19001600Once3 weeks
Kinase inhibitors29 (1.2)0100280Daily2 weeks, 3 weeks, 4 weeks

[i] Due to rounding of values, some variables may not add up to 100%. IV, intravenous; SC, subcutaneous; CDK, cyclin-dependent kinase.

Incidence of COVID-19

Table VI presents the incidence of COVID-19 in the study population. A total of 89 (3.6%) patients developed COVID-19 after receiving anticancer treatments. Among them, 12 (9.5%) patients died with within 30 days of receiving anticancer treatments, and morbidity was evident in 67 (9.7%) patients.

Table VI

Incidence of COVID-19 and association with 30-day mortality and morbidity.

Table VI

Incidence of COVID-19 and association with 30-day mortality and morbidity.

 COVID-19, n (%) 
VariableYesNoP-value
30-day mortality   
     Yes12 (9.5)114 (90.5)<0.05
     No77 (3.3)2,256 (96.7) 
30-day morbidity   
     Yes67 (9.7)622 (90.3)<0.05
     No22 (1.2)1,748 (98.8) 
Total89 (3.6)2,370 (96.4) 

[i] Data were analyzed using a χ2 test. COVID-19, coronavirus disease.

Characteristics of COVID-19 patients

Table VII presents the characteristics of COVID-19 patients in the study population. A total of 79 (88.8%) patients were older than 65 years, 62 (69.7%) were female, 53 (59.6%) had BMI ≥25, and 60 (67.4%) had ECOG-PS 0-1. Stage IV was the most common in 54 (63.6%), and breast and gastrointestinal were the most frequent cancers in 32 (36%) and 25 (28.1%), respectively. Palliative intent was the aim in 53 (59.6%), intravenous route was the most common in 72 (81%), chemotherapy drugs were used in 72 (81%), and alkylating agents, multi-mechanism, and oxaliplatin-based drugs were used in 16 (18%), 14 (15.7%), and 12 (13.5%), respectively.

Table VII

Characteristics of COVID-19 patients.

Table VII

Characteristics of COVID-19 patients.

VariablesN (%)
Age, years 
     >6579 (88.8)
     ≤6510 (11.2)
Sex 
     Male27 (30.3)
     Female62 (69.7)
BMI 
     <2536 (40.4)
     ≥2553 (59.6)
ECOG-PS 
     0-160 (67.4)
     >129 (32.6)
Cancer stage 
     Stage II16 (18.8)
     Stage III15 (17.6)
     Stage IV54 (63.6)
Cancer diagnosis 
     Breast32 (36.0)
     GI25 (28.1)
     Haematological malignancy13 (14.6)
     Other9 (10.1)
     Gynaecological6 (6.7)
     Lung2 (2.2)
     Neurological malignancy2 (2.2)
Intention 
     Curative36 (40.4)
     Palliative53 (59.6)
Route 
     IV78 (87.6)
     Oral8 (9.0)
     SC3 (3.4)
Class 
     Chemotherapy72 (81.0)
     Hormonal5 (5.6)
     Immunotherapy2 (2.2)
     Targeted10 (11.2)
Class of anti-cancer drugs 
     Alkylating agents16 (18.0)
     Multi-Mechanism14 (15.7)
     Oxaliplatin based drugs12 (13.5)
     Antimetabolites8 (9.0)
     Taxanes8 (9.0)
     Her 2 based drugs6 (6.7)
     Irinotecan based drugs6 (6.7)
     Hormone4 (4.5)
     Gemcitabine based drugs3 (3.4)
     Monoclonal antibodies3 (3.4)
     Check point inhibitor2 (2.2)
     CDK inhibitors2 (2.2)
     Antibiotics1 (1.1)
     Anthracyclines1 (1.1)
     Topoisomerase inhibitors1 (1.1)
     Vinca alkaloids1 (1.1)
     Proteasome inhibitor1 (1.1)

[i] Due to rounding of values, some variables may not add up to 100%. BMI, body mass index; ECOG-PS, Eastern Cooperative Oncology Group performance status; GI, Gastrointestinal; IV, intravenous; SC, subcutaneous. CDK, cyclin-dependent kinase.

Discussion

To our knowledge, this is the first study to investigate the outcomes of curative and palliative anticancer treatments during the COVID-19 pandemic. The data were collected from large comprehensive cancer centers to support the assumption of the risks of mortality and morbidity associated with anticancer treatments during pandemics.

Our population-based study demonstrated that 30-day mortality for all patients who received anticancer treatments was 5.1%, of which 1.8% accounted for curative intent, 8.6% for palliative intent, and 13.4% for COVID-19-positive cases. The 30-day mortality rate of 5.1% in this study could be established as a benchmark at the national level and is comparable to those reported in Australia, UK, and New Zealand (5.6, 4 and 5.17%, respectively) (16-19). For curative and palliative intent, we examined all patients with different cancers-unlike other studies that focused only on certain types of tumors, such as the Systemic Anti-Cancer Therapy Dataset collated by Public Health England, which reported 30-day mortality rates of 3 and 10% for curative and palliative chemotherapy, respectively, for patients with lung cancer. For breast cancer, the 30-day mortality rates were 1 and 7% for curative and palliative chemotherapy, respectively (20). Moreover, the Royal Marsden Hospital reported 30-day mortality rates of 0.5 and 1.5% for curative chemotherapy in breast cancers and for curative chemotherapy in gastrointestinal malignancies, respectively (21).

Our study highlights that important subgroups may be at higher risk of mortality, such as male patients, those with BMI <25, and those receiving hormone therapy. The number of cycles also significantly increased the risk of mortality. We also found that ECOG-PS 0-1, cancer stages I and II, and curative intent significantly decreased the mortality risk. For COVID-19 cases, similar to the results of the TERAVOLT registry (22), our study showed that receiving chemotherapy was associated with an increased mortality risk. However, the patients enrolled in the TERAVOLT registry were older, had lung cancer only, and were COVID-19-positive; this differs from our study where we included patients regardless of the cancer type and the majority of patients were aged <65 years. Likewise, similar to data from the CCC19 database (23), male sex and having an ECOG-PS of ≥2 in this study were associated with increased 30-day mortality. Our study included all patients on active anticancer treatments, in contrast to the CCC19 database where only 39% of patients were on active anticancer treatment. Our observed mortality rate for COVID-19 was 13.4%, which is comparable to that reported in China (14%) (24), the CCC19 database (13%) (23), and the Mount Sinai Health System (11%) (25). However, contrary to international reports, we had a lower incidence of COVID-19 in our cohort, and this needs to be explored in future studies.

Thus far, no studies have described the 30-day morbidity associated with all types of anticancer treatments. Our study results showed that the 30-day morbidity was 28.2% for all patients receiving anticancer treatments, of which 17.9% accounted for curative intent, 39.3% for palliative intent, and 75% for COVID-19 cases. The factors significantly associated with an increased risk of morbidity were age >65 years, BMI <25, chemotherapy, hormone therapy, and immunotherapy. We also found that a significant decrease in morbidity was associated with an ECOG-PS of 0-1, breast cancer, urologic cancer, and curative intent of treatment. The significant increase in the 30-day morbidity of anticancer treatments suggests that oncologists should carefully consider selecting the best regimen, dose, schedule, route, and follow-up for patients receiving anticancer treatments. This must be coupled with an appropriate healthcare system and quality indicators to identify patients who need continuous support (e.g., day care, home care visit, or telemedicine), along with supportive medications to avoid potential harm.

Anti-cancer drugs show promising potential and could be useful as antiviral tools against COVID-19. Nitulescu et al (26) reviewed potential treatments and mechanistic characteristics of drugs that may suppress transmission or ameliorate COVID-19. They found that due to the diversity of clinical studies, using a repurposing strategy for drugs is a rapid response solution. Drug repurposing is the use of approved drugs in an off-label use, which may reduce the cost of drug development and identify potential targetable pathways. Moreover, El Bairi et al (27) highlighted 20 anticancer drugs that have the potential and are currently being tested such as Janus kinase (JAK) pathways, monoclonal antibodies that targets vascular endothelial growth factor (VEGF), antiprotease that targets multiple receptors, inhibition of viral cellular transcription with antibiotics that have anticancer activity, immune check point inhibitors (antiprogrammed cell death), and kinase inhibitors to inhibit the cell cycle and viral life cycle. Whether a single drug or combined treatment may exhibit synergistic action against COVID-19 remains unknown and is an active area of investigation. Similar to the aforementioned studies, many of our patients have been exposed to anticancer drugs with antiviral activity against COVID-19. Further, our multicenter observational study demonstrates lower rates of COVID-19 cases; this may be attributed to the type of anticancer drugs that have antiviral activity and therefore, these could be future drugs to treat COVID-19. Nonetheless, this hypothesis needs to be tested in larger, controlled, prospective studies. Additionally, because of the lower rates of COVID-19, involvement in adaptive clinical trials is encouraged to enrich the field with an international collaborative group to accelerate drug repurposing and development.

Our cancer centers have adopted the international and national guidelines for management of cancer patients during the COVID-19 pandemic (28-30). Cancer care prioritization should include the following: Providing curative and palliative intent based on the risks/benefits assessment, minimizing interruptions or delays, providing COVID-19 testing for cancer patients, expanding use of granulocyte colony-stimulating factor (GCSF) and low molecular weight heparin (LMWH) prophylaxes, switching intravenous anticancer treatment to acceptable alternative oral drugs, increasing intervals between doses, and modifying the schedule and clinic visits using telemedicine. Patients who recover completely from COVID-19 infection will gradually be able to resume full anticancer treatments.

Similar to the CCC19 database (23), the majority of COVID-19 patients in our study exhibited the following characteristics: Older than 65 years, obese, breast cancer as the most common malignancy, and chemotherapy as the most commonly prescribed anticancer drug. Moreover, palliative intravenous chemotherapy drugs were alkylating agents, multi-mechanism drugs and oxaliplatin based drugs were the most common classes used. These are possible factors contributing to COVID-19 infection and mortality.

This study has several strengths. First, we described the 30-day mortality and morbidity of curative and palliative anticancer treatments in the outpatient setting during the COVID-19 pandemic, which have not been reported previously. Second, our population was diverse in terms of age distribution, stage and type of cancer, curative and palliative intent, and presence of solid versus hematological malignancies. Lastly, we included all types of anticancer treatments such as chemotherapy, immunotherapy, targeted therapy, and hormone therapy as well as the most common routes of treatment such as intravenous, subcutaneous, and oral.

However, there are limitations to be considered. First, the study has a retrospective design. Second, the study was restricted to Saudi Arabia, which limits the inferences that can be drawn from the findings. Third, the majority of patients were younger than 65 years and were female patients with breast cancer. However, we attempted to control for these factors by inviting more centers to participate, which could yield a real difference in findings between our study and those of others. Finally, there was a lower incidence of COVID-19 cases in our cohort, which might be related to patients having no or mild symptoms. Prospective cancer registries for COVID-19 cases can capture more accurate data, which would be a possible avenue for future research.

In conclusion, our findings add to previous knowledge regarding the outcomes of curative and palliative anticancer treatments for solid and hematological malignancies during the COVID-19 pandemic. Our data strongly indicated that curative intent was associated with a lower 30-day mortality than was palliative intent, and COVID-19 cases had the highest risk of mortality. Additionally, mortality appeared to be driven by male sex, BMI <25, hormonal therapy, and number of cycles, while morbidity doubled with palliative treatments and reached 75% with COVID-19 cases. Morbidity was driven by age>65 years, BMI <25, chemotherapy, hormonal therapy, and immunotherapy. These data support the conclusion that curative and selected palliative anticancer treatments can be safely continued, thereby reducing the burden of accumulated delays in elective cancer surgeries. Avoiding delays in treatment could relieve pressure among oncologists and maintain good oncological outcomes among cancer patients.

Our data do not necessarily suggest that curative and palliative anticancer treatments can increase the COVID-19 infection risk, as only 3.6% (n=89) out of 96.4% (n=2,370) of patients developed COVID-19 infection. This may provide confidence to oncologists to continue administering anticancer treatments during pandemics assuming appropriate protective measures are undertaken along with tele-oncology care. Our study highlights the importance of informed decision-making between oncologists and cancer patients concerning whether to withhold or continue anticancer treatments during pandemics. This study can contribute to existing literature by providing a benchmark that can be used as a reference for comparing the mortality and morbidity rates of curative and palliative anticancer treatments.

The 30-day mortality rate after anticancer treatment might be a useful clinical indicator for most anticancer treatment protocols. Stopping or delaying anticancer treatments during pandemics can lead to adverse oncological outcomes. Hence, understanding the outcomes of curative and palliative anticancer treatments as well as the outcomes for COVID-19 is urgently needed to help in clinical decision-making.

Acknowledgements

Not applicable.

Funding

No funding was received.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Authors' contributions

ET and RA analyzed and interpreted the patient data. ET, MAH, MAM and RA wrote the manuscript. AAA, BB, AA and MA made substantial contributions in data analysis and interpretation. AA, FA, LH, BA, SB and NA helped in acquiring the data for the work. ET, AAA, MA, MAH and MAM conceived the concept and designed the study. ET, MAH and RA were responsible for confirming the authenticity of the raw data. All authors read and approved the final manuscript.

Ethics approval and consent to participate

The present study was approved by the Institutional Research Ethics Board at King Abdullah Medical City (no. 20-616; Makkah, Saudi Arabia).

Patient consent for publication

The requirement for written informed consent from patients was waived due to the retrospective design of the study.

Competing interests

The authors declare that they have no competing interests.

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April-2021
Volume 14 Issue 4

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Spandidos Publications style
Tashkandi E, Al‑Abdulwahab A, Basulaiman B, Alsharm A, Al‑Hajeili M, Alshadadi F, Halawani L, Al‑Mansour M, Alquzi B, Barnawi S, Barnawi S, et al: Mortality and morbidity of curative and palliative anticancer treatments during the COVID‑19 pandemic: A multicenter population‑based retrospective study. Mol Clin Oncol 14: 82, 2021.
APA
Tashkandi, E., Al‑Abdulwahab, A., Basulaiman, B., Alsharm, A., Al‑Hajeili, M., Alshadadi, F. ... Azher, R. (2021). Mortality and morbidity of curative and palliative anticancer treatments during the COVID‑19 pandemic: A multicenter population‑based retrospective study. Molecular and Clinical Oncology, 14, 82. https://doi.org/10.3892/mco.2021.2244
MLA
Tashkandi, E., Al‑Abdulwahab, A., Basulaiman, B., Alsharm, A., Al‑Hajeili, M., Alshadadi, F., Halawani, L., Al‑Mansour, M., Alquzi, B., Barnawi, S., Alghamdi, M., Abdelaziz, N., Azher, R."Mortality and morbidity of curative and palliative anticancer treatments during the COVID‑19 pandemic: A multicenter population‑based retrospective study". Molecular and Clinical Oncology 14.4 (2021): 82.
Chicago
Tashkandi, E., Al‑Abdulwahab, A., Basulaiman, B., Alsharm, A., Al‑Hajeili, M., Alshadadi, F., Halawani, L., Al‑Mansour, M., Alquzi, B., Barnawi, S., Alghamdi, M., Abdelaziz, N., Azher, R."Mortality and morbidity of curative and palliative anticancer treatments during the COVID‑19 pandemic: A multicenter population‑based retrospective study". Molecular and Clinical Oncology 14, no. 4 (2021): 82. https://doi.org/10.3892/mco.2021.2244