Search

Background: Automated analysis of lung computed tomography (CT) scans may help characterize subphenotypes of acute respiratory illness. We integrated lung CT features measured via deep learning with clinical and laboratory data in spontaneously breathing subjects to enhance the identification of COVID-19 subphenotypes., Methods: This is a multicenter observational cohort study in spontaneously breathing patients with COVID-19 respiratory failure exposed to early lung CT within 7 days of admission. We explored lung CT images using deep learning approaches to quantitative and qualitative analyses; latent class analysis (LCA) by using clinical, laboratory and lung CT variables; regional differences between subphenotypes following 3D spatial trajectories., Results: Complete datasets were available in 559 patients. LCA identified two subphenotypes (subphenotype 1 and 2). As compared with subphenotype 2 (n = 403), subphenotype 1 patients (n = 156) were older, had higher inflammatory biomarkers, and were more hypoxemic. Lungs in subphenotype 1 had a higher density gravitational gradient with a greater proportion of consolidated lungs as compared with subphenotype 2. In contrast, subphenotype 2 had a higher density submantellar-hilar gradient with a greater proportion of ground glass opacities as compared with subphenotype 1. Subphenotype 1 showed higher prevalence of comorbidities associated with endothelial dysfunction and higher 90-day mortality than subphenotype 2, even after adjustment for clinically meaningful variables., Conclusions: Integrating lung-CT data in a LCA allowed us to identify two subphenotypes of COVID-19, with different clinical trajectories. These exploratory findings suggest a role of automated imaging characterization guided by machine learning in subphenotyping patients with respiratory failure., Trial Registration: ClinicalTrials.gov Identifier: NCT04395482. Registration date: 19/05/2020., (© 2024. The Author(s).)

Tuberculosis (TB) remains a significant global health challenge. Indeed, according to the World Health Organization (WHO), TB is classified as the second most common cause of death worldwide due to a single infectious agent in 2022, following COVID-19. To effectively manage tuberculosis patients, it is necessary to ensure accurate diagnosis, prompt treatment initiation, and vigilant monitoring of patients' progress. In 2017, the TB Ge network was implemented and launched in two primary hospitals within the Liguria Region in Italy, with the main purpose to manage tuberculosis infections. This system, organized as a web-based tool, simplifies the manual input of patient's data and therapies, while automating the integration of test results from hospitals' Laboratory Information Systems (LIS), without requiring human intervention. The goal of this paper is to highlight the outcomes achieved through the implementation of the TB Ge network in a period seriously affected by the COVID-19 pandemia and outline future directions. More specifically, the aim is to extend its adoption to all hospitals in the Liguria Region, thus improving the management of tuberculosis infections across healthcare facilities.

Background: Hospitalized patients with coronavirus disease 2019 (COVID-19) can be classified into different clinical phenotypes based on their demographic, clinical, radiology, and laboratory features. We aimed to validate in an external cohort of hospitalized COVID-19 patients the prognostic value of a previously described phenotyping system (FEN-COVID-19) and to assess the reproducibility of phenotypes development as a secondary analysis., Methods: Patients were classified in phenotypes A, B or C according to the severity of oxygenation impairment, inflammatory response, hemodynamic and laboratory tests according to the FEN-COVID-19 method., Results: Overall, 992 patients were included in the study, and 181 (18%), 757 (76%) and 54 (6%) of them were assigned to the FEN-COVID-19 phenotypes A, B, and C, respectively. An association with mortality was observed for phenotype C vs. A (hazard ratio [HR] 3.10, 95% confidence interval [CI] 1.81-5.30, p  < 0.001) and for phenotype C vs. B (HR 2.20, 95% CI 1.50-3.23, p  < 0.001). A non-statistically significant trend towards higher mortality was also observed for phenotype B vs. A (HR 1.41; 95% CI 0.92-2.15, p  = 0.115). By means of cluster analysis, three different phenotypes were also identified in our cohort, with an overall similar gradient in terms of prognostic impact to that observed when patients were assigned to FEN-COVID-19 phenotypes., Conclusions: The prognostic impact of FEN-COVID-19 phenotypes was confirmed in our external cohort, although with less difference in mortality between phenotypes A and B than in the original study.

Clinical trials demonstrated that SARS-CoV-2 vaccines reduce COVID-19-related mortality and morbidity. We describe the effect of vaccination on COVID-19-patients admitted at our hospital. Retrospective, single-center study conducted in Genoa, Italy, including patients ≥18years hospitalized for COVID-19 from May to December 2021. Demographical and clinical data were collected, vaccinated (group-A) and not-vaccinated (group-B) patients were compared. Impact of vaccination on mortality, ICU admission, and oxygen need was studied using Cox proportional hazards and logistic regression models after adjusting for propensity scores. Overall, 395 patients SARS-CoV-2 infected were included, of which 150 (38%) were vaccinated and 245 (62%) were not vaccinated. Patients in group-A were older, more disable, and with higher morbidity. Overall, 64 patients (16%) died within 30 days from admission, 34 in Group A (23%), and 30 in group B (12%). However, no statistically significant differences were observed (group-A versus group-B: HR 0.83, 95% CI 0.49-1.40, p  = 0.483). On the other hand, vaccination was protective in terms of ICU admission (OR = 0.23, p  = 0.046) and oxygen need (OR = 0.33, p  = 0.008). Our study confirms that SARS-CoV-2 vaccination reduces morbidity among patients hospitalized for COVID-19. The still high mortality in our cohort of vaccinated individuals could be partially due to vulnerable conditions of our patients.

Patients with severe SARS-CoV-2 infection have an overwhelming inflammatory response characterized by remarkable organs monocyte infiltration. We performed an immunophenotypic analysis on circulating monocytes in 19 COVID-19 patients in comparison with 11 naïve HIV-1 patients and 10 healthy subjects. Reduced frequency of classical monocytes and increased frequency of intermediate monocytes characterized COVID-19 patients with respect to both HIV naïve patients and healthy subjects. Intensity of C-C motif chemokine receptor 2 (CCR2) monocyte expression highly correlated with parameters of kidney dysfunction. Our data indicate that highly activated monocytes of COVID-19 patients may be pathogenically associated with the development of renal disease., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Objective: There is evidence of a bidirectional association between COVID-19 disease and psychiatric disorders. We aimed to assess whether exposure to psychotropic medications prior to hospitalization was associated with mortality or discharge within 30 days after hospital admission., Methods: In this prospective study, we included all individuals with a laboratory-confirmed COVID-19 infection who were admitted to the Bologna University Hospital between 1st March 2020 and 31st January 2021. We collected data about pre-existing psychiatric disorders and the use of psychotropic medications at the admission. As univariate analyses, we estimated cumulative incidence functions for 30-day mortality and discharge stratifying by exposure to each of the psychotropic medication classes. Finally, we fitted Cox regression models to estimate cause-specific Hazard Ratios (HR) of 30-day mortality and discharge. Results were adjusted for sociodemographic (age, sex), clinically relevant variables (comorbidity, c-reactive protein levels, severity of disease at presentation, history of smoking, study period), and psychiatric variables (psychiatric disorder diagnosis, number of psychotropic medications)., Results: Out of a total of 1238 hospitalized patients, 316 were prescribed psychotropic medications at the time of admission. Among these, 45 (3.6%) were taking a first-generation antipsychotics (FGA) and 66 (5.3%) a second generation antipsychotic (SGA). Exposure to SGA was associated with increased rates of 30-day mortality (HR = 2.01, 95%CI = 1.02-3.97) and exposure to FGA was associated with decreased rates of 30-day discharge (HR = 0.55, 95%CI = 0.33-0.90)., Conclusion: Patients with COVID-19 infection exposed to FGA and SGA may have worse COVID-19 infection outcomes., Competing Interests: Declaration of Competing Interest The authors have no competing interests to report., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Background: It is 30 years since evidence-based medicine became a great support for individual clinical expertise in daily practice and scientific research. Electronic systems can be used to achieve the goal of collecting data from heterogeneous datasets and to support multicenter clinical trials. The Ligurian Infectious Diseases Network (LIDN) is a web-based platform for data collection and reuse originating from a regional effort and involving many professionals from different fields., Objectives: The objective of this work is to present an integrated system of ad hoc interfaces and tools that we use to perform pseudonymous clinical data collection, both manually and automatically, to support clinical trials., Methods: The project comprehends different scenarios of data collection systems, according to the degree of information technology of the involved centers. To be compliant with national regulations, the last developed connection is based on the standard Clinical Document Architecture Release 2 by Health Level 7 guidelines, interoperability is supported by the involvement of a terminology service., Results: Since 2011, the LIDN platform has involved more than 8,000 patients from eight different hospitals, treated or under treatment for at least one infectious disease among human immunodeficiency virus (HIV), hepatitis C virus , severe acute respiratory syndrome coronavirus 2 , and tuberculosis. Since 2013, systems for the automatic transfer of laboratory data have been updating patients' information for three centers, daily. Direct communication was set up between the LIDN architecture and three of the main national cohorts of HIV-infected patients., Conclusion: The LIDN was originally developed to support clinicians involved in the project in the management of data from HIV-infected patients through a web-based tool that could be easily used in primary-care units. Then, the developed system grew modularly to respond to the specific needs that arose over a time span of more than 10 years., Competing Interests: None declared., (Thieme. All rights reserved.)

Diagnosing people living with chronic viral hepatitis is challenging due to the absence of symptoms as long as liver decompensated cirrhosis come out. The aim of this retrospective study was to evaluate the prevalence of HBV and/or HCV infections in a non-selected population, hospitalised for SARS-CoV-2 infection in a tertiary care hospital in Northern Italy. During the study period 1,429 patients were admitted to hospital for SARS-CoV-2 infection, serologic tests for HBV and/or HCV were available for 382 (27%) patients and 3 were excluded due to their previous known serologic status. Among 379 patients, 235 (62%) were male, median age was 70 years (range 21-103), 360 (95%) were Caucasian. Among them, 372/379 (98%) were screened for HBsAg, 320/379 (84%) for HBcAb. HBsAg was positive in 2/372 (0.5%, 95% CI 0.0006-0.02) patients (only in one HBV-DNA was performed that was negative), while HBcAb was found positive in 55/320 (17%, 95% CI 0.13-0.22). Among 370/379 (98%) patients screened for HCV, 11/370 (3%, 95% CI 0.02-0.05) had positive HCV-Ab. Five out of 11 (45%) were tested for HCV-RNA that resulted positive in two patients (0.5%, 95% CI 0.0006-0.02). Considering this data, even though the screening was performed in only 27% of study population, a tailored screening in people with known risk factors for hepatitis might be preferable to universal screening in low prevalence areas. Also a prompt diagnostic workout should begin in case of clinical or laboratory suspicion of hepatitis and in those starting immunosuppressive treatments., (© 2022. The Author(s).)

The Italian health system is organised on a regional basis and services are provided by both public and private operators, affecting the planning of services, access to services by citizens and their health rights. The creation of an observatory monitoring the methods and times of access to healthcare services has been pursued. The preliminary phase of the project is presented, which will lead to the comparison of the data obtained from 2019, with an eye on the Covid-19 pandemic impact.

The current Covid-19 pandemic makes necessary to identify people affected by SARS-CoV-2. To do this, the most reliable method is the use of the molecular test that is the gold standard to detect positive peoples.Here, we provide a comprehensive review on the diagnostic processes through molecular tests for SARS-CoV-2 infection. First, we have obtained information about the testing technologies in the Liguria region's hospitals to find and describe the most common technologies used and to calculate the molecular test's average cost. Second, we have evaluated the sensitivity, the specificity, the safety with respect to the data reported on scientific literature (Real Word Data VS Registrative Studies) and the organizational aspects of the molecular tests.Clinical Relevance- This study aims to provide support to the decision makers on clinical, economic, organizational, social and ethical issues related to the use of molecular test for SARS-CoV-2.

In this observational study, 13 patients with severe COVID-19 and 10 healthy controls were enrolled. The data concerning the analysis of circulating T cells show that, in severe COVID-19 patients, the expansion of these cell compartments is prone to induce antibody response, inflammation (CCR4+ and CCR6+ TFH) and regulation (CD8+ Treg). This pathogenic mechanism could lead us to envision a possible new form of biological target therapy., (© 2021 Wiley Periodicals LLC.)