ABSTRACT
The global coronavirus pandemic (COVID-19) started in 2020 and is still ongoing today. Among the numerous insights the community has learned from the COVID-19 pandemic is the value of robust healthcare inventory management. The main cause of many casualties around the world is the lack of medical resources for those who need them. To inhibit the spread of COVID-19, it is therefore imperative to simulate the demand for desirable medical goods at the proper time. The estimation of the incidence of infections using the right epidemiological criteria has a significant impact on the number of medical supplies required. Modeling susceptibility, exposure, infection, hospitalization, isolation, and recovery in relation to the COVID-19 pandemic is indeed crucial for the management of healthcare inventories. The goal of this research is to examine the various inventory policies such as reorder point, periodic order, and just-in-time in order to minimize the inventory management cost for medical commodities. To accomplish this, a SEIHIsRS model has been employed to comprehend the dynamics of COVID-19 and determine the hospitalized percentage of infected people. Based on this information, various situations are developed, considering the lockdown, social awareness, etc., and an appropriate inventory policy is recommended to reduce inventory management costs. It is observed that the just-in-time inventory policy is found to be the most cost-effective when there is no lockdown or only a partial lockdown. When there is a complete lockdown, the periodic order policy is the best inventory policy. The periodic order and reorder policies are cost-effective strategies to apply when social awareness is high. It has also been noticed that periodic order and reorder policies are the best inventory strategies for uncertain vaccination efficacy. This effort will assist in developing the best healthcare inventory management strategies to ensure that the right healthcare requirements are available at a minimal cost. © 2023 by the authors.
ABSTRACT
Background: Immunodeficiency in patients (pts) with cancer can lead to the progression of common respiratory viral infections to lower respiratory tract disease (LRTD) with potentially high mortality. Understanding risk factors of SARS-CoV-2 related LRTD in pts with cancer is imperative for the development of preventive measures. Methods: We examined all patients aged 18 years or older with cancer and laboratory-confirmed SARS-CoV-2 infection reported between March 16, 2020 and February 6, 2021 in the international CCC19 registry. We examined frequency of LRTD (pneumonia, pneumonitis, acute respiratory distress syndrome, or respiratory failure), demographic and clinicopathologic factors associated with LRTD, and 30-day and overall mortality in pts with and without LRTD. Results: Of 7,289 pts with a median follow-up time of 42 (21-90) days, 2187 (30%) developed LRTD. Pts of older age (65 yrs or older), male sex, pre-existing comorbidities, baseline immunosuppressants, baseline corticosteroids, and ECOG performance status of 2 or more had substantially higher rates of LRTD compared to those without these risk factors (Table). We did not observe differences in LRTD rates between pts of different racial/ethnic groups, smoking history, hypertension, obesity, cancer status, timing or type of anti-cancer therapy. LRTD was more likely in pts with thoracic malignancy (39%), hematological malignancy (39%) compared to those with other solid tumors (27%). The majority of pts (86%) had symptomatic presentation;however, 8% of pts with asymptomatic presentation developed LRTD. 30-day and overall mortality rates were significantly higher in pts with LRTD than those without LRTD (31% vs. 4% and 38% vs. 6%, P < 0.05). Conclusions: COVID-19 related LRTD rate is high and associated with worse mortality rates in pts with cancer. The majority of risk factors associated with LRTD demonstrate underlying immunodeficiency or lung structural damage as a driving force in this population. Identifying pts at high-risk for developing LRTD can help guide clinical management, improve pt outcomes, increase the cost-effectiveness of antiviral therapy, and direct future clinical trial designs for vaccine or antiviral agents. (Table Presented).
ABSTRACT
Background: COVID-19 has been associated with immune modulation that may predispose infected patients to bacterial, viral, or fungal coinfections. Due to critical illness, > 70% of patients with severe COVID-19 receive empiric antibacterial or antifungal therapy, along with standard anti-COVID-19 treatments. However, the frequency of proven or probable secondary infections is < 10%. To our knowledge, there are no studies evaluating co-infections in patients with cancer and COVID-19, a vulnerable group with multiple risk factors for co-infections. We aim to describe the prevalence of bacterial, viral, and fungal co-infections, identify risk factors for coinfection, and investigate the potential impact of co-infections on mortality, in patients with a history of cancer and COVID-19. Methods: The CCC19 registry (NCT04354701) includes patients with active or prior hematologic or invasive solid malignancies reported across academic and community sites. We captured bacterial, fungal, or viral coinfections diagnosed within ±2 weeks from diagnosis of COVID-19, identified factors associated with an increased risk of having a coinfection, and evaluated the association of coinfections with 30-day all-cause mortality. Results: We examined 6732 patients with a history of cancer and a laboratory-confirmed diagnosis of SARS-CoV-2 reported to CCC19 by 82 sites between March 17, 2020 and February 3, 2021, with complete data on coinfection status. Median age was 65 (interquartile range: 55-75) years with 48% male, 52% non-Hispanic white, 19% non-Hispanic black, and 16% Hispanic. 5448 (81%) had solid tumors and 1466 (22%) had hematologic malignancies. Bacterial infections were reported in 823 patients (12%), including 296 Gram+ and 245 Gram- bacterial events. Documented viral (176 patients, 3%) and fungal (59 patients, 0.9%) co-infections were rare. The risk for co-infections increased with age, and they were more frequent among men, older patients, and those with diabetes, pulmonary or renal comorbid conditions, active progressive cancer, or hematologic malignancies (unadjusted P< 0.01). The frequency of reported co-infections decreased over the study period (divided into quartiles, Mantel-Haenszel P< 0.01). All-cause mortality rates were higher among those with bacterial (24% vs. 10%), viral (22% vs. 12%), and fungal (37% vs. 12%) coinfections compared to those without (unadjusted P< 0.01). Conclusions: The frequency of bacterial infections in patients with cancer and COVID-19 is relatively low. Viral and fungal co-infections are uncommon. Coinfections are associated with higher mortality rates. Several patient and tumor factors can be used for risk stratification and guide early empiric antimicrobial agent selection, which may improve clinical outcomes. These data could inform antimicrobial stewardship interventions in this tenuous patient population.
ABSTRACT
Coronavirus is a potentially fatal disease that normally occurs in mammals and birds. Generally, in humans, the virus spreads through aerial droplets of any type of fluid secreted from the body of an infected person. Coronavirus is a family of viruses that is more lethal than other unpremeditated viruses. In December 2019, a new variant, i.e., a novel coronavirus (COVID-19) developed in Wuhan province, China. Since January 23, 2020, the number of infected individuals has increased rapidly, affecting the health and economies of many countries, including Pakistan. The objective of this research is to provide a system to classify and categorize the COVID-19 outbreak in Pakistan based on the data collected every day from different regions of Pakistan. This research also compares the performance of machine learning classifiers (i.e., Decision Tree (DT), Naive Bayes (NB), Support Vector Machine, and Logistic Regression) on the COVID-19 dataset collected in Pakistan. According to the experimental results, DT and NB classifiers outperformed the other classifiers. In addition, the classified data is categorized by implementing a Bayesian Regularization Artificial Neural Network (BRANN) classifier. The results demonstrate that the BRANN classifier outperforms state-of-the-art classifiers. © 2021 Tech Science Press. All rights reserved.
ABSTRACT
BACKGROUND: Patients with cancer may be at high risk of adverse outcomes from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. We analyzed a cohort of patients with cancer and coronavirus 2019 (COVID-19) reported to the COVID-19 and Cancer Consortium (CCC19) to identify prognostic clinical factors, including laboratory measurements and anticancer therapies. PATIENTS AND METHODS: Patients with active or historical cancer and a laboratory-confirmed SARS-CoV-2 diagnosis recorded between 17 March and 18 November 2020 were included. The primary outcome was COVID-19 severity measured on an ordinal scale (uncomplicated, hospitalized, admitted to intensive care unit, mechanically ventilated, died within 30 days). Multivariable regression models included demographics, cancer status, anticancer therapy and timing, COVID-19-directed therapies, and laboratory measurements (among hospitalized patients). RESULTS: A total of 4966 patients were included (median age 66 years, 51% female, 50% non-Hispanic white); 2872 (58%) were hospitalized and 695 (14%) died; 61% had cancer that was present, diagnosed, or treated within the year prior to COVID-19 diagnosis. Older age, male sex, obesity, cardiovascular and pulmonary comorbidities, renal disease, diabetes mellitus, non-Hispanic black race, Hispanic ethnicity, worse Eastern Cooperative Oncology Group performance status, recent cytotoxic chemotherapy, and hematologic malignancy were associated with higher COVID-19 severity. Among hospitalized patients, low or high absolute lymphocyte count; high absolute neutrophil count; low platelet count; abnormal creatinine; troponin; lactate dehydrogenase; and C-reactive protein were associated with higher COVID-19 severity. Patients diagnosed early in the COVID-19 pandemic (January-April 2020) had worse outcomes than those diagnosed later. Specific anticancer therapies (e.g. R-CHOP, platinum combined with etoposide, and DNA methyltransferase inhibitors) were associated with high 30-day all-cause mortality. CONCLUSIONS: Clinical factors (e.g. older age, hematological malignancy, recent chemotherapy) and laboratory measurements were associated with poor outcomes among patients with cancer and COVID-19. Although further studies are needed, caution may be required in utilizing particular anticancer therapies. CLINICAL TRIAL IDENTIFIER: NCT04354701.