Estimating the Risk of Death from COVID-19 in Adult Cancer Patients

Remdesivir Decreases Mortality in COVID-19 Patients with Active Malignancy

Data on the use of remdesivir, the first antiviral agent against SARS-CoV-2, are limited in oncologic patients. We aimed to analyze contributing factors for mortality in patients with malignancies in the real-world CSOVID-19 study. In total, 222 patients with active oncological disorders were selected from a nationwide COVID-19 study of 4890 subjects. The main endpoint of the current study was the 28-day in-hospital mortality. Approximately half of the patients were male, and the majority had multimorbidity (69.8%), with a median age of 70 years. Baseline SpO2 < 85% was observed in 25%. Overall, 59 (26.6%) patients died before day 28 of hospitalization: 29% due to hematological, and 20% due to other forms of cancers. The only factor increasing the odds of death in the multivariable model was eGFR < 60 mL/min/m2 (4.621, p = 0.02), whereas SpO2 decreased the odds of death at baseline (0.479 per 5%, p = 0.002) and the use of remdesivir (0.425, p = 0.03). This study shows that patients with COVID-19 and malignancy benefit from early remdesivir therapy, resulting in a decrease in early mortality by 80%. The prognosis was worsened by low glomerular filtration rate and low peripheral oxygen saturation at baseline underlying the role of kidney protection and early hospitalization.

Mortality in patients with cancer and SARS-CoV-2 infection: Results from the Argentinean Network of Hospital-Based Cancer Registries

BACKGROUND

Cancer is an important risk factor in patients with COVID-19. We aimed to describe the clinical and demographic characteristics associated with mortality in patients with cancer who were infected with SARS-CoV-2.

METHODS

We conducted a retrospective longitudinal study of 1206 patients with confirmed SARS-CoV-2 infection and cancer, registered in the Argentinean Network of Hospital-Based Cancer Registries (RITA) from March 31, 2020 to January 31, 2021. Demographic and clinical differences between survivors and non-survivors were summarized using descriptive statistics. The primary endpoint was all-cause mortality within 30 days of COVID-19 diagnosis. Risk factors for mortality were identified using logistic regression models.

RESULTS

1206 patients with cancer and confirmed SARS-CoV-2 infection were included, median age was 54 years (interquartile range: 42-65); 793 (65.8%) were female. 1101 (91.3%) had solid tumors and 105(8.7%) had hematological malignancies. The most frequent solid tumor was breast (278, 23.1%), while lymphoma was the main hematological one (59, 4.9%). Cervical cancer was more frequent in survivors, while lung cancer predominated in non-survivors. 275 (22.8%) patients were diagnosed with cancer within the past year. A total of 129 (10.7%) patients died within 30 days after COVID-19 diagnosis, with a case fatality rate of 15.2% (16/105) for hematologic malignancies and 10.3% (113/1101) for solid tumors. Multivariable regression analysis showed that age 60-79 (odds ratio [OR]: 4.69, 95% confidence interval [CI]: 2.72-9.70), age ≥ 80 (OR: 12.86, 95%CI: 5.08-32.54), time since cancer diagnosis < 1 year (OR: 2.49, 95%CI: 1.57-3.93) and 1-2 years (OR: 2.20, 95%CI: 1.36-3.57), and lung cancer (OR: 4.35, 95%CI: 2.02-9.36) were risk factors for death.

CONCLUSION

Patients with cancer and SARS-CoV-2 infection had a high case-fatality rate. Identified risk factors (older age, recent diagnosis and lung type) could guide prevention strategies aimed at reducing the risk of dying from COVID-19 in cancer patients.

Decision support analysis for risk identification and control of patients affected by COVID-19 based on Bayesian Networks

In the context of the outbreak of coronavirus disease (COVID-19), this paper proposes an innovative and systematic decision support model based on Bayesian networks (BNs) to identify and control the risk of COVID-19 patients spreading the virus, which requires the following three steps. First, by consulting the related literature and combining this with expert knowledge, we identify and classify the characteristics (risk factors) of COVID-19 and obtain a conceptual framework for COVID-19 Risk Assessment Bayesian Networks (CRABNs). Second, data on COVID-19 patients with expert scoring results on patient risk levels were collected from hospitals in Hubei Province of China and are used as the training set, and the structure and parameters of the CRABNs model are obtained through machine learning. Finally, we propose two indicators, namely, Model Bias and Model Accuracy, and use the remaining data to verify the feasibility and effectiveness of the CRABNs model to ensure that there are no significant differences between the predicted results of the model and the actual results provided by experts who have relevant experience in treating COVID-19. At the same time, we compared the CRABNs model with the support vector machine (SVM), random forest (RF), and k-nearest neighbour (KNN) models through four indicators: accuracy, sensitivity, specificity, and F-score. The results suggest the reliability of the model and show that it has promising application potential. The proposed model can be used globally by doctors in hospitals as a decision support tool to improve the accuracy of assessing the severity of COVID-19 symptoms in patients. Furthermore, with the further improvement of the model in the future, it can be used for risk assessments in the field of epidemics.

Heterogeneity and Risk of Bias in Studies Examining Risk Factors for Severe Illness and Death in COVID-19: A Systematic Review and Meta-Analysis

This systematic review and meta-analysis synthesized the evidence on the impacts of demographics and comorbidities on the clinical outcomes of COVID-19, as well as the sources of the heterogeneity and publication bias of the relevant studies. Two authors independently searched the literature from PubMed, Embase, Cochrane library, and CINAHL on 18 May 2021; removed duplicates; screened the titles, abstracts, and full texts by using criteria; and extracted data from the eligible articles. The variations among the studies were examined by using Cochrane, Q.; I2, and meta-regression. Out of 11,975 articles that were obtained from the databases and screened, 559 studies were abstracted, and then, where appropriate, were analyzed by meta-analysis (n = 542). COVID-19-related severe illness, admission to the ICU, and death were significantly correlated with comorbidities, male sex, and an age older than 60 or 65 years, although high heterogeneity was present in the pooled estimates. The study design, the study country, the sample size, and the year of publication contributed to this. There was publication bias among the studies that compared the odds of COVID-19-related deaths, severe illness, and admission to the ICU on the basis of the comorbidity status. While an older age and chronic diseases were shown to increase the risk of developing severe illness, admission to the ICU, and death among the COVID-19 patients in our analysis, a marked heterogeneity was present when linking the specific risks with the outcomes.

Understanding the experiences of patients with cancers in hospitals during COVID-19 pandemic in China: A qualitative research study

AIM

To explore the experiences of patients with cancers in hospitals during the COVID-19 pandemic.

DESIGN

A qualitative research study.

METHODS

Using a phenomenological approach, we enrolled 22 patients with cancers in the Hunan Cancer Hospital from 20 February 2020 to 10 April 2020. The interviews were conducted face-to-face and were analysed by Colaizzi's 7-step method. This study aligns with the COREQ checklist.

RESULTS

The experiences of patients with cancers in hospitals during the COVID-19 pandemic can be categorized into four major themes: (1) emotional changes; (2) delays in visiting hospital; (3) barriers to accessing medical care services, and (4) inconvenience related to logistics services.

COVID-19 in 96 Patients With Hematologic Disease: The First Single-center Experience From the Czech Republic

Background

Coronavirus disease 2019 (COVID-19) represents an important infectious complication associated with high mortality rates in patients with hematologic diseases. There have not been published any epidemiologic studies from Czech Republic so far.

Patients and Methods

This study is the first analysis of patients with hematologic malignancies and bone marrow failure syndromes treated at single hematology center in the Czech Republic between March 1 and December 31, 2020, in whom COVID-19 infection was confirmed.

Results

The sample comprised 96 patients aged 26 to 84 years (median, 66.0 years). At the time of their COVID-19 diagnosis, 75 patients (78.1%) were treated for hematologic diseases. Twenty-seven patients (28.1%) in the sample had complete remission (CR) of their hematologic disease. They were nonsignificantly more likely to have asymptomatic to moderate COVID-19 infection than those who failed to achieve CR (74.1% vs. 56.5%; P = .06). A more severe course of the infection was significantly correlated with older age (P = .047). Lung involvement was also statistically significantly associated with older age (P = .045). Over the study period, a total of 15 patients died. Age greater than 60 years was significantly associated with deaths from COVID-19 (P = .036), with failure to achieve CR having a statistically nonsignificant impact on mortality (P = .22).

Conclusion

These results confirm the prognostic significance of age for achieving treatment response of hematologic disease as well as the severity and mortality of COVID-19 in hematology patients.

Maintaining Standards in Colorectal Cancer Surgery During the Global Pandemic: A Cohort Study

Aim

Cancer surgery in the COVID-19 pandemic presents many new challenges. For each patient, the risk of contracting COVID-19 during the perioperative period, with the potential for life-threatening sequelae (1), has to be weighed against the risk of delaying treatment. We assessed the response and short-term outcomes from elective colorectal cancer surgery during the pandemic at our institution.

Method

We report a prospective cohort study of all elective colorectal surgery cases performed at our Trust during the 11 weeks following the national UK lockdown on 23rd March 2020, compared with the same time period in 2019.

Results

Eighty-five colorectal operations were performed during the 2020 (COVID) time period, and 179 performed in the 2019 (non-COVID) time period. A significantly higher proportion of cases during the COVID period were cancer-related (66% vs 26%, p < 0.00001). There was no difference in length of hospital stay, complications or readmissions. There were no mortalities in either cohort. Among the cancer patients, there were no differences in TMN staging, R1 resection rate or lymph node yields. No elective patient tested positive for COVID-19 during the perioperative period.

Conclusion

At the height of the COVID pandemic, we maintained delivery the of high-quality elective colorectal cancer surgery, with no worsening of short-term outcomes and no compromise in the quality of cancer resections. Ongoing monitoring of this cohort is essential. The risks associated with COVID-19 will continue for some time, necessitating adaptive responses to maintain high-quality cancer services.