COVID-19 Risk Factors for Cancer Patients: A First Report with Comparator Data from COVID-19 Negative Cancer Patients

Risk and Protective Factors for COVID-19 Morbidity, Severity, and Mortality

The outbreak of the coronavirus disease 2019 (COVID-19), caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has become an evolving global health crisis. Currently, a number of risk factors have been identified to have a potential impact on increasing the morbidity of COVID-19 in adults, including old age, male sex, pre-existing comorbidities, and racial/ethnic disparities. In addition to these factors, changes in laboratory indices and pro-inflammatory cytokines, as well as possible complications, could indicate the progression of COVID-19 into a severe and critical stage. Children predominantly suffer from mild illnesses due to COVID-19. Similar to adults, the main risk factors in pediatric patients include age and pre-existing comorbidities. In contrast, supplementation with a healthy diet and sufficient nutrition, COVID-19 vaccination, and atopic conditions may act as protective factors against the infection of SARS-CoV-2. COVID-19 vaccination not only protects vulnerable individuals from SARS-CoV-2 infection, more importantly, it may also reduce the development of severe disease and death due to COVID-19. Currently used therapies for COVID-19 are off-label and empiric, and their impacts on the severity and mortality of COVID-19 are still unclear. The interaction between asthma and COVID-19 may be bidirectional and needs to be clarified in more studies. In this review, we highlight the clinical evidence supporting the rationale for the risk and protective factors for the morbidity, severity, and mortality of COVID-19.

An explanatory analytics framework for early detection of chronic risk factors in pandemics

Timely decision-making in national and global health emergencies such as pandemics is critically important from various aspects. Especially, early identification of risk factors of contagious viral diseases can lead to efficient management of limited healthcare resources and saving lives by prioritizing at-risk patients. In this study, we propose a hybrid artificial intelligence (AI) framework to identify major chronic risk factors of novel, contagious diseases as early as possible at the time of pandemics. The proposed framework combines evolutionary search algorithms with machine learning and the novel explanatory AI (XAI) methods to detect the most critical risk factors, use them to predict patients at high risk of mortality, and analyze the risk factors at the individual level for each high-risk patient. The proposed framework was validated using data from a repository of electronic health records of early COVID-19 patients in the US. A chronological analysis of the chronic risk factors identified using our proposed approach revealed that those factors could have been identified months before they were determined by clinical studies and/or announced by the United States health officials.

Animal models for COVID-19: advances, gaps and perspectives

COVID-19, caused by SARS-CoV-2, is the most consequential pandemic of this century. Since the outbreak in late 2019, animal models have been playing crucial roles in aiding the rapid development of vaccines/drugs for prevention and therapy, as well as understanding the pathogenesis of SARS-CoV-2 infection and immune responses of hosts. However, the current animal models have some deficits and there is an urgent need for novel models to evaluate the virulence of variants of concerns (VOC), antibody-dependent enhancement (ADE), and various comorbidities of COVID-19. This review summarizes the clinical features of COVID-19 in different populations, and the characteristics of the major animal models of SARS-CoV-2, including those naturally susceptible animals, such as non-human primates, Syrian hamster, ferret, minks, poultry, livestock, and mouse models sensitized by genetically modified, AAV/adenoviral transduced, mouse-adapted strain of SARS-CoV-2, and by engraftment of human tissues or cells. Since understanding the host receptors and proteases is essential for designing advanced genetically modified animal models, successful studies on receptors and proteases are also reviewed. Several improved alternatives for future mouse models are proposed, including the reselection of alternative receptor genes or multiple gene combinations, the use of transgenic or knock-in method, and different strains for establishing the next generation of genetically modified mice.

Infection and reinfection with SARS-CoV-2 in cancer patients: A cohort study

COVID-19 reinfection, although a controversial issue, is an important clinical problem in cancer patients and beyond. The present study aimed to identify the risk factors associated with worse outcomes in cancer patients with Covid-19 in both first infection and reinfection and to describe the involvement of vaccines in reinfection outcome. The present study enrolled 85 patients with solid tumors who had Covid-19 infection and had not been previously vaccinated. Classical risk factors associated with worse outcomes in cancer patients with second SARS-Cov infection were considered. The patients were followed up retrospectively, measuring mortality at the first and second infection and the vaccination rate after the first infection. The factors associated with the highest risk of mortality at the first infection were, in order of importance: intensive care unit (ICU) admission, unfavorable performance status, radiologically quantifiable presence of oncological disease, and administration of cytotoxic chemotherapy in the period immediately before infection. The risk factors associated with higher mortality from reinfection were ECOG 3-4 performance status and administration of cytotoxic chemotherapy in the period immediately before infection. In the studied patients, mortality from reinfection was not affected by prior vaccination. Thus, bearing in mind all of these risk factors for poor outcomes in cancer patients with solid tumors presenting with Covid-19 can help the treating oncologists make personalized decisions about patient care during the pandemic.

Clinical Characteristics and Outcomes of COVID-19 in 1290 New Cancer Patients: Single-center, Prospective Cohort Study from Iran

OBJECTIVE

To determine the clinical characteristics and outcomes of COVID-19 in a large cohort of new cancer patients referred to an oncology clinic in the north of Iran.

METHODS

During the 20-months COVID-19 pandemic, new cancer patients were followed-up. Demographic, pathologic, and clinical variables were collected for each patient. COVID-19 was confirmed based on a positive polymerase chain reaction test. Analyses were performed using the STATA version 14.0 at a significance level of 0.05.

RESULTS

In this study, 1294 new cancer patients were followed for 24 months (mean age: 58.7 years [range 10 to 95]). During the study period, COVID-19 was diagnosed in 9.4% of the patients with hospitalization rate of 3.4%, an ICU admission rate of 0.7%, and COVID-19 mortality rate of 4.9%. Hematological malignancies (OR_U= 2.6, CI95% 1.28- 5.34), receiving palliative treatments (OR_A=3.03, CI95% 1.6-5.45) and receiving radiotherapy (OR_A=2.07, 1.17-3.65) were the most common predictive factors of COVID infection in cancer patients. Also, the COVID mortality was higher in brain cancer patients (P = 0.07), metastatic disease (P = 0.01) and patients receiving palliative treatments (P = 0.02).

CONCLUSION

In patients suffering from cancer, COVID-19 infection can be predicted by cancer type, palliative care, and radiotherapy in cancer patients. Furthermore, brain cancers, metastasis, and palliative care were all associated with COVID-19 related mortality.

Milano Policlinico ONCOVID modified Score for risk evaluation in oncology during the COVID-19 pandemic: a prospective monocentric study

Purpose

Methods

Results

Conclusion

COVID-19 Pandemic Impact on Surgical Treatment Methods for Early-Stage Cervical Cancer: A Population-Based Study in Romania

Being one of the most common malignancies in young women, cervical cancer is frequently successfully screened around the world. Early detection enables for an important number of curative options that allow for more than 90% of patients to survive more than three years without cancer relapse. Unfortunately, the COVID-19 pandemic put tremendous pressure on healthcare systems and access to cancer care, determining us to develop a study on the influence the pandemic had on surgical care of cervical cancer, and to assess changes in its management and outcomes. A retrospective study design allowed us to compare cervical cancer trends of the last 48 months of the pre-pandemic period with the first 24 months during the COVID-19 pandemic, using the database from the Timis County Emergency Clinical Hospital. New cases of cervical cancer presented to our clinic in more advanced stages (34.6% cases of FIGO stage III during the pandemic vs. 22.4% before the pandemic, p-value = 0.047). These patients faced significantly more changes in treatment plans, postponed surgeries, and postponed radio-chemotherapy treatment. From the full cohort of cervical cancer patients, 160 were early stages eligible for curative intervention who completed a three-year follow-up period. The disease-free survival and overall survival were not influenced by the surgical treatment of choice, or by the SARS-CoV-2 infection (log-rank p-value = 0.449, respectively log-rank p-value = 0.608). The individual risk factors identified for the three-year mortality risk were independent of the SARS-CoV-2 infection and treatment changes during the COVID-19 pandemic. We observed significantly fewer cases of cervical cancer diagnosed per year during the first 24 months of the COVID-19 pandemic, blaming the changes in healthcare system regulations that failed to offer the same conditions as before the pandemic. Even though we did not observe significant changes in disease-free survival of early-stage cervical cancers, we expect the excess of cases diagnosed in later stages to have lower survival rates, imposing the healthcare systems to consider different strategies for these patients while the pandemic is still ongoing.

The Impact of COVID-19 on the Delivery of Systemic Anti-Cancer Treatment at Guy's Cancer Centre

BACKGROUND

This study aimed to assess the outcome of cancer patients undergoing systemic anti-cancer treatment (SACT) at our centre to help inform future clinical decision-making around SACT during the COVID-19 pandemic.

METHODS

Patients receiving at least one episode of SACT for solid tumours at Guy's Cancer Centre between 1 March and 31 May 2020 and the same period in 2019 were included in the study. Data were collected on demographics, tumour type/stage, treatment type (chemotherapy, immunotherapy, biological-targeted) and SARS-CoV2 infection.

RESULTS

A total of 2120 patients received SACT in 2020, compared to 2449 in 2019 (13% decrease). From 2019 to 2020, there was an increase in stage IV disease (62% vs. 72%), decrease in chemotherapy (42% vs. 34%), increase in immunotherapy (6% vs. 10%), but similar rates of biologically targeted treatments (37% vs. 38%). There was a significant increase in 1st and 2nd line treatments in 2020 (68% vs. 81%; p < 0.0001) and reduction in 3rd and subsequent lines (26% vs. 15%; p = 0.004) compared to 2019. Of the 2020 cohort, 2% patients developed SARS-CoV2 infections.

CONCLUSIONS

These real-world data from a tertiary Cancer Centre suggest that despite the challenges faced due to the COVID-19 pandemic, SACT was able to be continued without any significant effects on the mortality of solid-tumour patients. There was a low rate (2%) of SARS-CoV-2 infection which is comparable to the 1.4%-point prevalence in our total cancer population.