Can artificial intelligence improve the management of pneumonia

The use of artificial intelligence (AI) to support clinical medical decisions is a rather promising concept. There are two important factors that have driven these advances: the availability of data from electronic health records (EHR) and progress made in computational performance. These two concepts are interrelated with respect to complex mathematical functions such as machine learning (ML) or neural networks (NN). Indeed, some published articles have already demonstrated the potential of these approaches in medicine. When considering the diagnosis and management of pneumonia, the use of AI and chest X-ray (CXR) images primarily have been indicative of early diagnosis, prompt antimicrobial therapy, and ultimately, better prognosis. Coupled with this is the growing research involving empirical therapy and mortality prediction, too. Maximizing the power of NN, the majority of studies have reported high accuracy rates in their predictions. As AI can handle large amounts of data and execute mathematical functions such as machine learning and neural networks, AI can be revolutionary in supporting the clinical decision-making processes. In this review, we describe and discuss the most relevant studies of AI in pneumonia

Emergence of Progressive Mutations in SARS-CoV-2 From a Hematologic Patient With Prolonged Viral Replication

We documented a hematologic patient with prolonged SARS-CoV-2 viral replication in whom emergence of viral mutations was documented after the consecutive use of antivirals and convalescent plasma. The virus detected in the last of 12 clinical samples (day 237) had accumulated 22 changes in amino acids and 29 in nucleotides. Some of these changes, such as the E484Q, were mutations of concern as defined by WHO. This finding represents an enormous epidemiological threat and poses a major clinical challenge. Combined antiviral strategies, as well as specific strategies related to the diagnostic approach of prolonged infections for this specific population, may be needed.This work has been financed by funds for research ad hoc COVID-19 from patronage provided by citizens and organizations to Hospital Clínic de Barcelona-Fundació Clínic per a la Recerca Biomèdica. This work received support from FONDO-COVID19 (ISCIII Grant number: COV20-00679), Instituto de Salud Carlos III (PI21/01640) and by European Region (ERDF, “A way to make Europe”). PP-A [JR20/00012 and PI21/00498], NG-P [FI19/00133], have also received research grants from the Ministerio de Sanidad y Consumo, Instituto de Salud Carlos III. The funders had neither a specific role in study design or collection of data, nor in writing of the paper or decision to submit.S

Incidence of co-infections and superinfections in hospitalised patients with COVID-19: a retrospective cohort study

Objectives: To describe the burden, epidemiology and outcomes of co-infections and superinfections occurring in hospitalized patients with coronavirus disease 2019 (COVID-19). Methods: We performed an observational cohort study of all consecutive patients admitted for ≥48 hours to the Hospital Clinic of Barcelona for COVID-19 (28 February to 22 April 2020) who were discharged or dead. We describe demographic, epidemiologic, laboratory and microbiologic results, as well as outcome data retrieved from electronic health records. Results: Of a total of 989 consecutive patients with COVID-19, 72 (7.2%) had 88 other microbiologically confirmed infections: 74 were bacterial, seven fungal and seven viral. Community-acquired co-infection at COVID-19 diagnosis was uncommon (31/989, 3.1%) and mainly caused by Streptococcus pneumoniae and Staphylococcus aureus. A total of 51 hospital-acquired bacterial superinfections, mostly caused by Pseudomonas aeruginosa and Escherichia coli, were diagnosed in 43 patients (4.7%), with a mean (SD) time from hospital admission to superinfection diagnosis of 10.6 (6.6) days. Overall mortality was 9.8% (97/989). Patients with community-acquired co-infections and hospital-acquired superinfections had worse outcomes. Conclusions: Co-infection at COVID-19 diagnosis is uncommon. Few patients developed superinfections during hospitalization. These findings are different compared to those of other viral pandemics. As it relates to hospitalized patients with COVID-19, such findings could prove essential in defining the role of empiric antimicrobial therapy or stewardship strategies

Impact of Inflammatory Response Modifiers on the Incidence of Hospital-Acquired Infections in Patients with COVID-19.

The study aim was to assess the influence of inflammatory response modifiers, including anti-interleukin-6 (IL-6) biologics and corticosteroids, on the incidence of hospital-acquired infections in patients with coronavirus disease 2019 (COVID-19). METHODS: Case-control study performed at a university hospital from February 26 to May 26, 2020. Cases were defined as patients with COVID-19 who developed hospital-acquired infections. For each case, two controls were selected among patients without infections. Cases and controls were matched obeying three criteria in a hierarchical sequence: length of hospital stay up until the first infection; comorbidity; and need for Intensive care unit (ICU) admission. Conditional logistic regression analysis was used to estimate the association of exposures with being a case. RESULTS: A total of 71 cases and 142 controls were included. Independent predictors for acquiring a hospital infection were chronic liver disease [odds ratio (OR) 16.56, 95% CI 1.87-146.5, p = 0.012], morbid obesity (OR 6.11, 95% CI 1.06-35.4, p = 0.043), current or past smoking (OR 4.15, 95% CI 1.45-11.88, p = 0.008), exposure to hydroxychloroquine (OR 0.2, 95% CI 0.041-1, p = 0.053), and invasive mechanical ventilation (OR 61.5, 95% CI 11.08-341, p ≤ 0.0001). CONCLUSIONS: Inflammatory response modifiers had no influence on acquisition of nosocomial infections in admitted patients with COVID-19. Hospital-acquired infections primarily occurred in the critically ill and invasive mechanical ventilation was the main exposure conferring risk

Prolonged viral replication in patients with hematologic malignancies hospitalized with COVID-19

[No abstract available