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One‐year mortality and consequences of COVID‐19 in cancer patients: A cohort study

Abstract The 1‐year mortality and health consequences of COVID‐19 in cancer patients are relatively underexplored. In this multicenter cohort study, 166 COVID‐19 patients with cancer were compared with 498 non‐cancer COVID‐19 patients and 498 non‐COVID cancer patients. The 1‐year all‐cause mortality and hospital mortality rates in Cancer COVID‐19 Cohort (30% and 20%) were significantly higher than those in COVID‐19 Cohort (9% and 8%, both P < .001) and Cancer Cohort (16% and 2%, both P < 0.001). The 12‐month all‐cause post‐discharge mortality rate in survival discharged Cancer COVID‐19 Cohort (8%) was higher than that in COVID‐19 Cohort (0.4%, P < .001) but similar to that in Cancer Cohort (15%, P = .084). The incidence of sequelae in Cancer COVID‐19 Cohort (23%, 26/114) is similar to that in COVID‐19 Cohort (30%, 130/432, P = .13). The 1‐year all‐cause mortality was high among patients with hematologic malignancies (59%), followed by those who have nasopharyngeal, brain, and skin tumors (45%), digestive system neoplasm (43%), and lung cancers (32%). The rate was moderate among patients with genitourinary (14%), female genital (13%), breast (11%), and thyroid tumors (0). COVID‐19 patients with cancer showed a high rate of in‐hospital mortality and 1‐year all‐cause mortality, but the 12‐month all‐cause post‐discharge mortality rate in survival discharged cancer COVID‐19 patients was similar to that in Cancer Cohort. Comparing to COVID‐19 Cohort, risk stratification showed that hematologic, nasopharyngeal, brain, digestive system, and lung tumors were high risk (44% vs 9%, P < 0.001), while genitourinary, female genital, breast, and thyroid tumors had moderate risk (10% vs 9%, P = .85) in COVID‐19 Cancer Cohort. Different tumor subtypes had different effects on COVID‐19. But if cancer patients with COVID‐19 manage to survive their COVID‐19 infections, then long‐term mortality appears to be similar to the cancer patients without COVID‐19, and their long‐term clinical sequelae were similar to the COVID‐19 patients without cancer.


| INTRODUCTION
As of 30 March 2021, the number of COVID-19 cases and deaths continued to rise with 127.3 million cumulative cases and 2.8 million deaths globally. 1 The global cancer burden is estimated to have risen to 19.3 million new cases and 10.0 million deaths in 2020. 2 Some initial reports suggested that patients with an active malignancy might be more likely to contract SARS-CoV-2 and have an increased risk of short-term mortality. [3][4][5][6] However, these initial reports were restricted by the follow-up time. 6 As some patients remained hospitalized for some time, longer-term follow-up is needed to better understand the effect of COVID-19 on cancer patients. 3,6,7 Cancer encompasses a diverse array of primary tumor subtypes with different outcomes. 8,9 To the best of our knowledge, there are no reports to study the long-term prognosis of cancer patients with COVID-19, and few studies compare them with cancer patients without COVID-19 as well as different tumor subtypes.
Herein, 166 cancer patients with COVID-19, 498 COVID-19 patients without cancer, and 498 cancer patients without COVID-19 were compared. All of the patients were from the four hospitals in Wuhan, a COVID-19 "hot spot" in China. All survival patients were followed up for at least 12 months after hospital admission.

| Study design and patients
In this multicenter ambidirectional comparative cohort study, we enrolled sequential cancer patients who were admitted with COVID-19 (Cancer COVID-19 Cohort) to the four hospitals in Wuhan, Hubei Province, China, the epicenter of the first COVID-19 pandemic, between January 1, 2020, and March 18, 2020 (Table S1). Eligibility criteria for enrollment were laboratory confirmation of SARS-CoV-2 virus infection by RT-PCR test and hospitalized patients with active cancer (Table S2). COVID-19 disease severity was defined according to World Health Organization (WHO) guidelines. 10 Primary tumor subtypes were classified by the WHO Classification of Tumors series. 11 Each COVID-19 patient with cancer was matched to patients without cancer from the same COVID-19-positive population (COVID-19 Cohort) in a ratio of 1:3 based on age (±5 years) and sex. The patients served as uninfected controls were chosen at a ratio of 1:3 (based on age, gender, and cancer subtype) from cancer patients admitted to the same four hospitals between 1 January 2019 and 17 March 17 2020. All data were deidentified. This study was done in accordance with the STROBE statement. 12

| Study variables and outcomes
We define the acute phase as the time between symptom onset and hospital discharge. Data were collected on site by trained coordinators manually reviewing the electronic medical records and importing them into a secure online database using a standardized case report form (supplementary case report form, Data S1). Demographic characteristics, coexisting conditions, presenting symptoms, vital signs, biochemical findings, treatment practices, and a variety of hospital outcome data were collected. To ensure the enrollment of an unbiased population, the eligible patients were consecutively recruited from each site from 1 January 2020 to 17 March 2020.
All survivors who had been discharged from the hospital were selected into the follow-up study to observe their health consequences in recovery from COVID-19 (supplementary follow-up studies, Data S1). Some patients were followed up in the outpatient clinic and asked to complete a series of questionnaires. We followed up with the patients until death or 18 February 2021, whichever came first.
The primary outcomes included the 1-year all-cause mortality rate and sequelae. Secondary outcomes included the hospital mortality rate, the length of hospital stay, and the 12-month all-cause post-discharge mortality rate.

| Statistical analysis
We aimed to generate a representative sample of cancer patients with COVID-19 by starting with at least 5500 COVID-19 patients and 40 900 cancer patients from the four hospitals ( Figure S1). To describe baseline characteristics, treatment, and outcomes, we prespecified the following covariates for inclusion in the models. Descriptive statistics were used to describe the baseline data of the patients. Categorical variables were presented as percentages. Continuous variables were expressed as mean ± SD if they were normally distributed or as median (IQR) if they were not. Proportions for categorical variables were compared using the χ 2 test.
The Kaplan-Meier method with log-rank test was used for comparing survival curves between Cancer COVID-19 Cohort and control groups. Cox proportional hazards regression was used to investigate the effect of several risk factors on survival. Univariable and multivariable analyses between 1-year mortality and risk factors were performed. The risk factor variables were included in the stepwise Cox proportional hazards regression. A backward stepwise technique was used to evaluate all potential univariable correlates (P < .05) and create a multivariable model containing variables with P < .05.
Analyses were performed using SPSS Statistics 26.0 software (International Business Machines Corp, IBM) and Graph Pad Prism (Version 8.0.2, GraphPad Software, Inc.).

| Hospital mortality
The

| Twelve-month post-discharge mortality
The median follow-up time of cancer COVID-19 patients after discharge was 11.2 (IQR 10.8-11.6) months among those who were alive (Table 3 and Figure 1B)

| Risk factors for outcomes of Cancer COVID-19 cohort
In the multivariate cox regression model (Figure 3)

| DISCUSSION
The current COVID-19 pandemic, a novel coronavirus first detected in Wuhan, China, has created a global crisis. The cancer center of Union Hospital of Huazhong University of Science and Technology was enlisted as a designated hospital to fight against COVID-19. The Asian National Cancer Centers have also implemented pragmatic strategies, distancing strategies, and telemedicine  in challenges and countermeasures against COVID-19. 14 Judging from recent studies, patients with active malignancies might be more likely to contract SARS-CoV-2 and have an increased risk of short-term mortality. However, the 1-year mortality and health consequences of COVID-19 in cancer patients have remained elusive.
To our knowledge, this study represents the longest follow-up on the mortality and health consequences of hospitalized cancer patients with COVID-19. One-year mortality (30%) of Cancer COVID-19 Cohort was nearly two times higher than that of Cancer Cohort (16%, P < .001) and more than three times higher than that of COVID-19 Cohort (9%, P < .001). As it was difficult to determine whether COVID-19 was the direct cause of death or an inevitably terminal event of cancer caused the death, we are reporting the all-cause case fatality rate. All COVID-19 patients in our cohort had finished their clinical treatment. The hospital mortality of the Cancer COVID-19 Cohort was 20%, 2.5 times higher than that of the COVID-19 Cohort (8%, P < .001) and 10 times higher than that of the Cancer Cohort (2%, P < .001). The difference in the hospital mortality among the three cohorts shows the strong adverse effect of COVID-19 on both cancer and non-cancer patients. A cohort study reported a death rate of 20% (40/205) in cancer patients with COVID-19 in Hubei, China. 3 A series of 218 cancer patients with COVID-19 from a New York Hospital System reported a case fatality rate of 28% because of a bias towards more severe cases. 5 The 12-month all-cause postdischarge mortality rate in survival discharged Cancer COVID-19 Cohort (8%) was higher than that in COVID-19 Cohort (0.4%, P < .001) but similar to that in Cancer Cohort (15%, P = .084). Therefore, if cancer patients manage to survive their COVID-19 infections, then long-term sequelae appear to be similar to other cancer patients. The patients served as uninfected controls were based on age, gender, and cancer subtype from cancer patients admitted to the same four hospitals. The risk factors of age, gender, and cancer subtype had been excluded.
We found that at 1 year after hospital admission, 23% of cancer COVID-19 patients endorsed at least one symptom, a similar risk to that of COVID-19 Cohort (30%, P = .13). Cough (9%) or dyspnea (8%) were common among Cancer COVID-19 Cohort, while fatigue (12%) or chest congestion (9%) were common in COVID-19 Cohort. Huang and colleagues found that fatigue or muscle weakness (63%) were the most common symptoms in a 6-month follow-up survey of 1733 COVID-19 patients. 15 A 3-month follow-up survey of 538 COVID-19 patients reported that fatigue (28%) was the most common symptom. 16 Our results showed that different tumor subtypes among Cancer COVID-19 Cohort had different outcomes.
Many of the predictive risk factors for mortality in the Cancer COVID-19 Cohort were similar to data reported in COVID-19 patients. 20 In the adjusted Cox proportional hazards model, we observed significant associations of death with chronic diseases including hypertension, diabetes, hyperuricemia, COPD, cardiovascular disease, and cerebrovascular disease in COVID-19 patients. 21,22 Serologic predictors for mortality in our dataset included anemia, and elevated LDH, D-dimer, and lactic acid, which correlated with available data from all COVID-19 patients. 23

| LIMITATION
This study has several limitations. First of all, this study population only included hospitalized COVID-19 patients within Hubei Province, China. Therefore, the cohort might not exactly represent all cancer patient populations, such as the patients who were on end-of-life care and/or residing in nursing homes, which may potentially bias our study to more severe cases of COVID-19. Secondly, our Cancer Cohort was admitted into hospital for active cancer during a different time frame (1 January 2019 to 17 March 2020) from the COVID-19 Cohorts (1 January 2020 to 17 March 2020), the hospitals might be under different stress levels and operation modes during these two time frames, although it should not affect the patient care significantly. Nonetheless, it would be desired to use contemporaneous control groups when these data become available. Lastly, it would be ideal to have cancer stages identified and matched between cohorts in addition to the cancer subtypes. However, the cancer staging information required extensive manual review and verifications, eventually becoming prohibitively time-consuming and technically difficult for the involved COVID-19 clinicians to collect and confirm on, especially on the deceased patients. Also, the sample size of stratified populations would be too limited to be meaningful. Hopefully, our larger sample size of controls compensates for some of the potential skews.

| CONCLUSION
If cancer patients with COVID-19 manage to survive their COVID-19 infections and immediate sequelae, then long-term mortality appears to be similar to the cancer patients without COVID-19, and their long-term clinical sequelae were similar to the COVID-19 patients without cancer. Different tumor subtypes had different effects on COVID-19. Comparing to the COVID-19 Cohort, risk stratification showed that hematologic, nasopharyngeal, brain, digestive system, and lung tumors were high risk (44% vs 9%, P < .001), while genitourinary, female genital, breast, and thyroid tumors had moderate risk (10% vs 9%, P = .85) in the COVID-19 Cancer Cohort.