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.

Indications for and contraindications of immune checkpoint inhibitors in cancer patients with COVID-19 vaccination.

The COVID-19 pandemic has lasted over 1 year and will not disappear in a short time. There is no specific remedy against the virus as yet. Vaccination is thus far one of the most important strategies for preventing COVID-19. Cancer patients with COVID-19 have a higher mortality because of immunosuppression. Immune checkpoint inhibitors (ICIs) are a novel anticancer strategy for blocking inhibitory pathways, which are related to the immune response. There is a question regarding whether COVID-19 vaccination and ICI treatment impact each other in cancer patients. This review explores both sides of the relationship between ICI treatment and COVID-19 vaccination and suggests good efficacy and safety of ICI treatment after COVID-19 vaccination as well as little impact on the virus protection and toxicity associated with COVID-19 vaccination during ICI treatment.

Lay abstract The COVID-19 pandemic has lasted over 1 year. Vaccination is a promising strategy for preventing COVID-19. Cancer patients are prone to infection with COVID-19, and these patients have high mortality. Immune checkpoint inhibitors (ICIs) are a novel anticancer strategy. Whether COVID-19 vaccination and ICI treatment impact each other in cancer patients remains unknown. This review explores both sides of the relationship between ICI treatment and COVID-19 vaccination and suggests good efficacy and safety of ICI treatment after COVID-19 vaccination as well as little impact on the virus protection and toxicity associated with COVID-19 vaccination during ICI treatment.

Clinical characteristics and a decision tree model to predict death outcome in severe COVID-19 patients.

BACKGROUND: The novel coronavirus disease 2019 (COVID-19) spreads rapidly among people and causes a pandemic. It is of great clinical significance to identify COVID-19 patients with high risk of death. METHODS: A total of 2169 adult COVID-19 patients were enrolled from Wuhan, China, from February 10th to April 15th, 2020. Difference analyses of medical records were performed between severe and non-severe groups, as well as between survivors and non-survivors. In addition, we developed a decision tree model to predict death outcome in severe patients. RESULTS: Of the 2169 COVID-19 patients, the median age was 61 years and male patients accounted for 48%. A total of 646 patients were diagnosed as severe illness, and 75 patients died. An older median age and a higher proportion of male patients were found in severe group or non-survivors compared to their counterparts. Significant differences in clinical characteristics and laboratory examinations were found between severe and non-severe groups, as well as between survivors and non-survivors. A decision tree, including three biomarkers, neutrophil-to-lymphocyte ratio, C-reactive protein and lactic dehydrogenase, was developed to predict death outcome in severe patients. This model performed well both in training and test datasets. The accuracy of this model were 0.98 in both datasets. CONCLUSION: We performed a comprehensive analysis of COVID-19 patients from the outbreak in Wuhan, China, and proposed a simple and clinically operable decision tree to help clinicians rapidly identify COVID-19 patients at high risk of death, to whom priority treatment and intensive care should be given.