Clinical features and death risk factors in COVID-19 patients with cancer: a retrospective study

Background Coronavirus disease 2019 (COVID-19) has spread around the world. This retrospective study aims to analyze the clinical features of COVID-19 patients with cancer and identify death outcome related risk factors. Methods From February 10th to April 15th, 2020, 103 COVID-19 patients with cancer were enrolled. Difference analyses were performed between severe and non-severe patients. A propensity score matching (PSM) analysis was performed, including 103 COVID-19 patients with cancer and 206 matched non-cancer COVID-19 patients. Next, we identified death related risk factors and developed a nomogram for predicting the probability. Results In 103 COVID-19 patients with cancer, the main cancer categories were breast cancer, lung cancer and bladder cancer. Compared to non-severe patients, severe patients had a higher median age, and a higher proportion of smokers, diabetes, heart disease and dyspnea. In addition, most of the laboratory results between two groups were significantly different. PSM analysis found that the proportion of dyspnea was much higher in COVID-19 patients with cancer. The severity incidence in two groups were similar, while a much higher mortality was found in COVID-19 patients with cancer compared to that in COVID-19 patients without cancer (11.7% vs. 4.4%, P = 0.028). Furthermore, we found that neutrophil-to-lymphocyte ratio (NLR) and C-reactive protein (CRP) were related to death outcome. And a nomogram based on the factors was developed. Conclusion In COVID-19 patients with cancer, the clinical features and laboratory results between severe group and non-severe group were significantly different. NLR and CRP were the risk factors that could predict death outcome. Supplementary Information The online version contains supplementary material available at 10.1186/s12879-021-06495-9.

People with diabetes [3], hypertension [4], and heart disease [5] were thought to be susceptible to COVID-19 and had poor clinical outcome. Cancer patients were considered to have lower immunity than normal people. Therefore, they were susceptible to infected with bacteria and viruses [6,7]. Among patients with advanced cancer, 20% had lymphopenia [8]. Therefore, immunosuppression status of cancer patients put them at an increased risk of COVID-19.
In the early stage (before February 2020) of COVID-19 outbreak in Wuhan, China, medical staff and materials were inadequate, leading to a high mortality rate. By February 2020, with the arrival of a large number of medical supplies and more than 40,000 medical workers in Wuhan, the patients with COVID-19 got timely treatment, and the mortality reduced gradually [9]. Some studies reported that cancer patients were more susceptible to COVID-19 and more likely to deteriorate into a severe form [9][10][11][12]. However, most of the cases they reported were in the early stage of the outbreak, and there was a lack of knowledge and response measures of the disease. In this study, we collected cases admitted to hospital in the late stage of the outbreak in Wuhan, China, reflecting the real status of cancer patients with COVID-19 whom were admitted under adequate treatment. We aimed to determine the risk factors of disease severity and death, and sought to develop a nomogram to predict the risk.

Patients selection
This study was approved by the Ethics Committee of Taikang Hospital (TKTJLL-007). The Ethics Committee of the Taikang Hospital waived the need for informed consent of each patient. From February 10th to April 15th, 2020, a total of 2980 patients with confirmed COVID-19 infection by RT-PCR were enrolled from Taikang hospital and other temporary hospitals of Wuhan in this retrospective study, including 103 cancer patients. All patients were divided into severe or non-severe group according to the Diagnosis and Treatment Protocol for COVID-19 (Trial Version 7) published by the National Health Commission of China. The severe patients were defined as following: the respiratory rate is more than 30 breaths per minute, the oxygen saturation is lower than 93% in rest state, oxygenation index is no greater than 300 mmHg, respectively. Propensity score matching (MatchIt package, R, version 3.5.2) was used to match each COVID-19 patient with cancer with two COVID-19 patients without cancer for further analysis. The following predetermined factors were considered: age, gender and comorbidities, such as diabetes, hypertension and heart disease. The flow chart of the study design was shown in Additional file 1: Figure S1.

Data collection and difference analyses
Clinical features, and laboratory results of all patients with COVID-19 were obtained from the hospitals through the way of desk review of patient medical records. All data were extracted manually. Clinical features included demographics, comorbidities and symptoms. The laboratory examinations included routine blood tests, inflammation-related biomarkers, renal and liver function, biochemical tests, and coagulation function. Difference analyses between 103 COVID-19 patients with cancer and 206 matched COVID-19 patients without cancer were performed. In addition, we performed difference analyses between severe and nonsevere groups, as well as between survivors and non-survivors in COVID-19 patients with cancer.

Development and validation of a nomogram
First of all, factors were excluded if missing values of the factor reached more than 20%. Then, missing values were imputed using multivariate imputation if factors had missing values less than 20%. Generalized linear model was used to select significant risk factors (P < 0.05) that affect death outcome. A nomogram was developed with significant risk factors. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve, calibration plot, decision curve and clinical impact curve were used to validate the nomogram. R packages used in

Statistical analysis
Continuous variables were expressed by median and interquartile range (IQR), and categorical variables were represented as frequencies. All statistical analyses were performed using SPSS (version 17.0). The Mann-Whitney U test was used to compare continuous data and Pearson's Χ 2 test used to compare contingency data. A two-sided P value < 0.05 was considered statistically significant.

Comparison between severe and non-severe patients
The patients consisted of 47 females (45.6%) and 56 males (54.4%), and 36 (35%) in severe group and 67 (65%) in non-severe group. The median age of cancer patients was 66 (ranging from 24 to 90), and the age in the severe group was older than that in non-severe group. The smoking people accounted for a higher proportion in the severe group than in the non-severe group ( Table 2). There were 42 (40.8%) patients who had comorbidities, such as hypertension (35.9%), diabetes (13.6%), and heart disease (10.7%), 14 of whom had two or more comorbidities. Among the 36 severe patients, 18 (50%) cases had comorbidities, while in the 67 non-severe patients 24 (35.8%) had comorbidities. A higher proportion of patients with diabetes or heart disease were observed in the severe group than in the non-severe group ( Table 2).
The common clinical symptoms of cancer patients were fever (70.9%), cough (68%), dyspnea (23.3%), diarrhea (6.8%) and vomiting (4.9%). The percentage of patients with cough, vomiting or diarrhea showed no significant difference between the two groups except for a higher percentage of patients had dyspnea in the severe group than in the non-severe group (41.7% vs. 13.4%, P = 0.003) ( Table 2).
The median level of white blood cell (WBC) count was 5.29 × 10 9 /L in all cancer patients, 4.77 × 10 9 /L in nonsevere patients and 7.16 × 10 9 /L in severe patients. Similar results were found for neutrophil count. The median level of lymphocyte count was significantly lower in the severe group than in the non-severe group (0.80 × 10 9 /L vs. 1.23 × 10 9 /L, P = 0.003). In addition, a higher neutrophil-to-lymphocyte ratio (NLR) in severe patients was found (6.93 vs. 2.29, P < 0.001). The level of procalcitonin, C-reactive protein (CRP) and interleukin-6 (IL-6) was higher in severe patients than in non-severe patients ( Table 2). When comparing hepatic function, renal function and biochemical indexes between severe and non-severe patients with cancer, we found that lactate dehydrogenase (LDH), alpha-hydroxybutyric dehydrogenase (αHBDH), D-dimer, N-terminal pro brain natriuretic peptide (NTproBNP), aspartate transaminase (AST) and Uric acid were higher while total protein, albumin and albumin/globulin were lower in the severe group (Table 2).

Propensity score matching analysis
We performed a 1:2 (COVID-19 with cancer: COVID-19 without cancer) matched case-control analysis. Age, gender and comorbidities were used for matching. Then, differences in demographics, comorbidities, symptoms, severity, clinical outcome and laboratory findings were compared (Table 3). We found that COVID-19 patients with cancer had a higher proportion of dyspnea than COVID-19 patients without cancer (23.3% vs. 10.2%, P = 0.003). No other significant differences were found. When comparing laboratory results between two groups, we found that the median lymphocyte count, monocytes count, eosinophils count and alanine transaminase (ALT) were lower in COVID-19 patients with cancer than in COVID-19 patients without cancer (Table 3). In contrast, the median NLR was higher in COVID-19 patients with cancer compared to counterpart. No significant differences were found in other laboratory results. The proportion of severe patients was similar between two groups (35% vs. 31%, P = 0.520). The proportion of deaths was much higher in COVID-19 patients with cancer than WBC white blood cell, NLR leukocyte to lymphocyte ratio, RBC red blood cell, HB hemoglobin, PLT platelet, CRP C-reactive protein, ALT alanine transaminase, AST aspartate transaminase, A/G albumin to globulin ratio, BUN blood urea nitrogen, ALP alkaline phosphatase, γ-GT gamma-glutamyl transpeptidase, CK creatine kinase, CKMB MB isoenzyme of creatine kinase, LDH lactate dehydrogenase, α-HBDH alpha-hydroxybutyric dehydrogenase, NT-proBNP N-terminal pro brain natriuretic peptide, IL-6 interleukin-6  in COVID-19 patients without cancer (11.7% vs. 4.4%, P = 0.028) ( Table 3).

Nomogram for predicting death outcome
As of April 15th, 2020, 12 out of 103 COVID-19 patients with cancer died in this study. To identify risk factors for death outcome and develop a nomogram, we excluded factors if the missing values were more than 20%. Hence, age, gender, smoking, comorbidities, symptoms, WBC count, Neutrophil count, Lymphocyte count, NLR, CRP, ALT and AST were included for further analysis. Comparison between survivors and non-survivors were performed. As shown in Table 4, when comparing clinical features, a higher proportion of dyspnea was found in non-survivors. No significant differences were found in other clinical features between two groups. When comparing laboratory results, we found that WBC count, neutrophil count, NLR and CRP were higher in non-survivors, while lymphocyte count was lower in non-survivors. Age, gender, smoking, number of comorbidities, number of symptoms, WBC, NLR, CRP, ALT, and AST were included in the generalized linear model. The results indicated that NLR and CRP were significant risk factors WBC white blood cell, NLR leukocyte to lymphocyte ratio, RBC red blood cell, HB hemoglobin, PLT platelet, CRP C-reactive protein, ALT alanine transaminase, AST aspartate transaminase, A/G albumin to globulin ratio, BUN blood urea nitrogen, ALP alkaline phosphatase, γ-GT gamma-glutamyl transpeptidase, CK creatine kinase, CKMB MB isoenzyme of creatine kinase, LDH lactate dehydrogenase, α-HBDH alpha-hydroxybutyric dehydrogenase, NT-proBNP N-terminal pro brain natriuretic peptide, IL-6 interleukin-6   Table 4 Comparison between survivors and non-survivors in COVID-19 patients with cancer    (Table 5). A nomogram to predict the probability of death was developed based on the two factors (Fig. 1a). The AUC of the nomogram was 0.918 (95% confidence interval [CI] 0.860-0.977), which was higher than that of NLR (0.872, 95%CI 0.784-0.961) and CRP (0.880, 95%CI 0.803-0.958) (Fig. 1b). The calibration plot showed that the predicted outcomes were in high agreement with observed outcomes (Fig. 1c). The decision curve and clinical impact curve showed that the nomogram had superior net benefit and influence on the death outcome of patients (Fig. 1d-e). For severe outcome, only NLR was significant factor in the generalized linear model (Table 5), hence we didn't develop a nomogram for predicting the probability of severity.

Discussion
The World Health Organization had declared COVID-19 to be a public health emergency of the world [11]. Patients with cancer are at higher risk of COVID-19, because they have low immunity and myelosuppressive effects caused by chemotherapy, surgery or radiotherapy for the cancer. Some studies reported that about 1-5.6% COVID-19 patients had cancer [9,10,13,14]. In our study, we found that approximately 3.5% COVID-19 patients had cancer, which was consistent with that in previous reports.
A multicentre cohort study from China published earlier reported that COVID-19 patients with cancer had 24 different types of cancer, with breast cancer, colorectal cancer and lung cancer being the most common types [9]. Another small cohort study from China found that the most common cancer with COVID-19 was lung cancer, followed by breast cancer and rectal cancer [10]. In this study, we reported a total of 17 different types of cancer, of which breast cancer, lung cancer and bladder cancer were the three most common types of cancer.
The main symptoms of COVID-19 patients with cancer were fever, cough, dyspnea, vomiting, diarrhea, and so on, which were similar to those without cancer. When comparing cancer patients with severe COVID-19 with cancer patients with non-severe COVID-19, we found that the severe group had a higher median age and a higher proportion of smoker, diabetes, heart disease and dyspnea. Blood tests can real-time indicate the patient's condition. In severe group, the WBC count and neutrophil count were higher and lymphocyte count was lower, so the NLR is a significant factor to predict the severity of patients. We also found that the levels of pro-inflammatory cytokines and infection-related biomarkers were higher in severe COVID-19 patients with cancer, including procalcitonin, CRP and IL-6. In addition, the COVID-19 might injure the liver, heart, kidney and other vital organs. Hence, LDH, α-HBDH, D-dimer, NTproBNP, AST were higher in severe COVID-19 patients with cancer.
Wang and colleagues reported a much higher proportion of severe COVID-19 in cancer patients than in noncancer patients [9]. But our study found that the severity rate in cancer patients was similar to that in non-cancer patients. The reason may be that the patients in our study were admitted to hospital after February 2020 and the patients reported in those studies were admitted to hospital mostly before February 2020. Patients included in this study received better treatment because of sufficient medical resources. So the severe rate of cancer patients in this study was lower than that in previous reports.
The mortality in COVID-19 patients with cancer ranged from 15 to 28.6% in previous studies [9,10,15]. In this study, we reported a mortality of 11.7% in COVID-19 patients with cancer, which was higher than that in COVID-19 patients without cancer (4.4%). The global outbreak of COVID-19 brings huge challenges to the rational use of medical resources. Therefore, how to use effective markers to screen patients who need intensive care or have a high risk of death will help allocate medical resources effectively and reasonably, as well as reduce the mortality. Liang and his colleagues found that X-ray abnormality, age, hemoptysis, dyspnea, unconsciousness, number of comorbidities, cancer history, NLR, LDH and direct bilirubin at admission were significant predictors of critical illness, and developed a clinical tool with these 10 variables to predict the probability of developing critical illness for COVID-19 patients [16]. The tool had a good performance of AUC (0.88). Yan and his colleagues used LDH, lymphocytes and high-sensitivity CRP to develop a model to identify patients at high risk of death [17]. Liu reported that the severity of the patients  with NLR > 7.90 and CRP > 34.62 died (Fig. 2). The risk stratification would help us to take care of the high-risk patients and reduce the mortality. There were several limitations in this study. First, the clinical records were incomplete. Especially no dynamic changes of laboratory results were recorded, which might help us to better identify severity or death outcome related risk factors. Second, the sample of COVID-19 patient with cancer was limited. No external validation of the nomogram was performed.

Conclusion
This study presented the details of clinical features and laboratory results in 103 COVID-19 patients with cancer. The main cancer categories were breast cancer, lung cancer, bladder cancer. The mortality was 11.7%. In addition, this study found that NLR and CRP were death risk factors. An algorithm based on the two factors could help to screen high risk patients and give adequate treatment and protective measures in advance.