Recommendations and guidelines for the diagnosis and management of Coronavirus Disease-19 (COVID-19) associated bacterial and fungal infections in Taiwan.

Coronavirus disease-19 (COVID-19) is an emerging infectious disease caused by SARS-CoV-2 that has rapidly evolved into a pandemic to cause over 600 million infections and more than 6.6 million deaths up to Nov 25, 2022. COVID-19 carries a high mortality rate in severe cases. Co-infections and secondary infections with other micro-organisms, such as bacterial and fungus, further increases the mortality and complicates the diagnosis and management of COVID-19. The current guideline provides guidance to physicians for the management and treatment of patients with COVID-19 associated bacterial and fungal infections, including COVID-19 associated bacterial infections (CABI), pulmonary aspergillosis (CAPA), candidiasis (CAC) and mucormycosis (CAM). Recommendations were drafted by the 7th Guidelines Recommendations for Evidence-based Antimicrobial agents use Taiwan (GREAT) working group after review of the current evidence, using the grading of recommendations assessment, development, and evaluation (GRADE) methodology. A nationwide expert panel reviewed the recommendations in March 2022, and the guideline was endorsed by the Infectious Diseases Society of Taiwan (IDST). This guideline includes the epidemiology, diagnostic methods and treatment recommendations for COVID-19 associated infections. The aim of this guideline is to provide guidance to physicians who are involved in the medical care for patients with COVID-19 during the ongoing COVID-19 pandemic.

Coronavirus disease 2019 (COVID-19) associated bacterial coinfection: Incidence, diagnosis and treatment.

Coronavirus disease 2019 (COVID-19) emerged as a pandemic that spread rapidly around the world, causing nearly 500 billion infections and more than 6 million deaths to date. During the first wave of the pandemic, empirical antibiotics was prescribed in over 70% of hospitalized COVID-19 patients. However, research now shows a low incidence rate of bacterial coinfection in hospitalized COVID-19 patients, between 2.5% and 5.1%. The rate of secondary infections was 3.7% in overall, but can be as high as 41.9% in the intensive care units. Over-prescription of antibiotics to treat COVID-19 patients fueled the ongoing antimicrobial resistance globally. Diagnosis of bacterial coinfection is challenging due to indistinguishable clinical presentations with overlapping lower respiratory tract symptoms such as fever, cough and dyspnea. Other diagnostic methods include conventional culture, diagnostic syndromic testing, serology test and biomarkers. COVID-19 patients with bacterial coinfection or secondary infection have a higher in-hospital mortality and longer length of stay, timely and appropriate antibiotic use aided by accurate diagnosis is crucial to improve patient outcome and prevent antimicrobial resistance.

Identifying predictors for bacterial and fungal coinfection on chest computed tomography in patients with Pneumocystis pneumonia.

BACKGROUND: Pneumocystis pneumonia (PCP) is a common opportunistic infection with high mortality in individuals with decreased immunity. Pulmonary coinfections with PCP are associated with poor prognosis. The study aims to identify radiological predictors for pulmonary coinfections in patients with PCP and risk factors for mortality. METHODS: This is a retrospective, five-year study was conducted in a medical center, enrolling patients diagnosed with PCP, who received a chest computed tomography (CT) scan. The radiological findings and medical records of all participants were reviewed carefully by 2 independent doctors. Univariable and multivariable analysis was performed to identify radiological predictors for pulmonary coinfection and clinical risk factors for poor prognosis. RESULTS: A total of 101 participants were included, of which 39 were HIV-infected and 62 were non-HIV-infected. In multivariable analysis, radiologic predictors on chest CT for coinfection with bacteria pneumonia included lack of ground glass opacity (adjusted odds ratio [aOR], 6.33; 95% confidence interval [CI], 2.03-19.77; p = 0.001) and presence of pleural effusion (aOR, 3.74; 95% CI, 1.27-10.99; p = 0.017). Predictors for fungal pneumonia included diffuse consolidation (adjusted OR, 6.27; 95% CI, 1.72-22.86; p = 0.005) and presence of pleural effusion (adjusted OR, 5.26; 95% CI, 1.44-19.17; p = 0.012). A significantly higher in-hospital mortality was associated with older age, recent corticosteroid exposure, cytomegalovirus coinfection, and acute respiratory failure. CONCLUSION: Early identification of pulmonary coinfections in PCP using radiological features on the CT scans, will enable appropriate treatment which is crucial to improve the prognosis.

Combinational reasoning of quantitative fuzzy topological relations for simple fuzzy regions.

In recent years, formalization and reasoning of topological relations have become a hot topic as a means to generate knowledge about the relations between spatial objects at the conceptual and geometrical levels. These mechanisms have been widely used in spatial data query, spatial data mining, evaluation of equivalence and similarity in a spatial scene, as well as for consistency assessment of the topological relations of multi-resolution spatial databases. The concept of computational fuzzy topological space is applied to simple fuzzy regions to efficiently and more accurately solve fuzzy topological relations. Thus, extending the existing research and improving upon the previous work, this paper presents a new method to describe fuzzy topological relations between simple spatial regions in Geographic Information Sciences (GIS) and Artificial Intelligence (AI). Firstly, we propose a new definition for simple fuzzy line segments and simple fuzzy regions based on the computational fuzzy topology. And then, based on the new definitions, we also propose a new combinational reasoning method to compute the topological relations between simple fuzzy regions, moreover, this study has discovered that there are (1) 23 different topological relations between a simple crisp region and a simple fuzzy region; (2) 152 different topological relations between two simple fuzzy regions. In the end, we have discussed some examples to demonstrate the validity of the new method, through comparisons with existing fuzzy models, we showed that the proposed method can compute more than the existing models, as it is more expressive than the existing fuzzy models.