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Objective: The purpose of this study was to determine the effectiveness of a new AI-based tool called NAIF (NAFLD-AI-Fibrosis) in identifying individuals from the general population with advanced liver fibrosis (stage F3/F4). We compared NAIF's performance to two existing risk score calculators, aspartate aminotransferase-to-platelet ratio index (APRI) and fibrosis-4 (Fib4). Methods: To set up the algorithm for diagnosing severe liver fibrosis (defined as Fibroscan® values E ≥ 9.7 KPa), we used 19 blood biochemistry parameters and two demographic parameters in a group of 5,962 individuals from the NHANES population (2017–2020 pre-pandemic, public database). We then assessed the algorithm's performance by comparing its accuracy, precision, sensitivity, specificity, and F1 score values to those of APRI and Fib4 scoring systems. Results: In a kept-out sub dataset of the NHANES population, NAIF achieved a predictive precision of 72 %, a sensitivity of 61 %, and a specificity of 77 % in correctly identifying adults (aged 18–79 years) with severe liver fibrosis. Additionally, NAIF performed well when tested with two external datasets of Italian patients with a Fibroscan® score E ≥ 9.7 kPa, and with an external dataset of patients with diagnosis of severe liver fibrosis through biopsy. Conclusions: The results of our study suggest that NAIF, using routinely available parameters, outperforms in sensitivity existing scoring methods (Fib4 and APRI) in diagnosing severe liver fibrosis, even when tested with external validation datasets. NAIF uses routinely available parameters, making it a promising tool for identifying individuals with advanced liver fibrosis from the general population. Word count abstract: 236.

Rheumatic disease patients are at greater risk of infection due to their disease, comorbidities, and immunosup-pressive therapy. COVID-19 outcomes in this patient setting appeared to be similar to those of the general population. However, data on this topic were mainly related to small studies on a limited number of patients. Consequently, to date, this field remains poorly explored, particularly in the pre-vaccine era. This monocen-tric study aimed to describe the intrahospital mortality in rheumatic patients with SARS-CoV-2 consecutively hospitalized from 21 February to 31 December 2020, before anti-SARS-CoV-2 vaccine administration spread, compared with non-rheumatic patients. Of 2491 included patients, 65 [3%, median (interquartile range) age 75 (64.76-82.239 years, 65% women] were suffering from rheumatic diseases. A total of 20 deaths were reported [case fatality rate 31%, 95% confidence interval (CI): 19-42] compared with 433 deaths (19%, 95% CI: 17-20) in patients without rheumatic diseases (p=0.024). However, the rheumatic disease was not associated with a significant increase in univariate mortality hazards (hazard ratio 1.374, 95% CI: 0.876-2.154), and after adjust-ment (hazard ratio 1.199, 95% CI: 0.759-1.894) by age, sex and Charlson comorbidity index. The incidence of intensive care unit admission, death, and discharge in the case-control study was comparable between rheumatic and non-rheumatic patients. The presence of rheumatic diseases in SARS-CoV-2-hospitalized patients did not represent an independent risk factor for severe disease or mortality.

Background and Objectives: Remdesivir is an antiviral agent, which was shown to be safe and effective in treating early COVID-19, but its favourable impact in hospitalised patients with non-critical disease is still under investigation. The present study aimed to assess the effectiveness and safety of remdesivir as a treatment for hospitalised patients with COVID-19 by a propensity score analysis of observational data. Methods: In this monocentric retrospective cohort study, the effectiveness and safety of a 5-day course of remdesivir (200 mg intravenously at Day 1, then 100 mg from Days 2–5) in association with the standard of care were assessed in comparison with the standard of care only. The primary endpoint was the proportion of recovery on Day 14. Results: Of 3662 eligible inpatients who tested positive for the severe acute respiratory syndrome coronavirus 2 genome by nasopharyngeal swab at admission, 861 (24%) non-critical patients were included in a propensity score analysis and 281 (33%) were exposed to remdesivir. In total, 242/281 (86.1%) and 435/580 (75.0%) patients recovered in exposed and non-exposed, respectively, with a relative improvement of 11.1% (95% CI + 5.8 to 16.5%; unadjusted odds ratio: 2.07, 95% CI 1.40–3.05, p = 0.0001; after adjustment by propensity score weighting, odds ratio: 1.92, 95% CI 1.30–2.83, p = 0.001). In treated patients, 1 (0.03%) anaphylactic reaction and 1 (0.03%) acute reaction during drug injection were reported, and 24 (8.5%) patients stopped the treatment due to adverse reactions. No significant differences were found with respect to the secondary efficacy endpoints (in-hospital all-cause death, need for intensive care treatments, clinical improvement score at Day 28) and safety endpoints (any and serious adverse reactions). Conclusion: A 5-day course of remdesivir in association with the standard of care effectively promoted recovery from COVID-19 among non-critical in-hospital patients and had an acceptable safety pro

Background: Robust data on case fatality rate (CFR) among inpatients with COVID-19 are still lacking, and the role of patient characteristics in in-hospital deaths remains under-investigated. This study quantified the overall CFR and described its trend in a cohort of hospitalized patients with SARS-CoV-2 in Italy. Admission to ICU, death, or discharge were the secondary outcomes. Methods: This retrospective study is based on administrative health data and electronic case records of inpatients consecutively admitted to Niguarda Hospital between 21 February and 8 November 2020. Results: An overall CFR of 18% was observed. CFR was significantly reduced during the second wave of contagion (1 June to 30 September, 16%) compared with the first wave (21 February to 31 May, 21% p = 0.015). Such reduction was mainly observed among male inpatients between 40 and 80 years with limited comorbidities. Admission to ICU was associated with a high risk of mortality in both waves. The incidence of severe disease and the need for ICU admission were lower in the second wave. Conclusion: CFR in SARS-CoV-2 inpatients was demonstrated to decrease over time. This reduction may partly reflect the changes in hospital strategy and clinical practice. The reasons for this improvement should be further investigated to plan an exit strategy in case of future outbreaks. Key messages: What is already known on this topic Before the advent of anti-COVID-19 vaccines, a multi-wave pattern of contagion was observed, and this trend conditioned the inpatient case fatality rate (CFR), which varied over time accordingly to the waves of contagion. Only preliminary results on the in-hospital mortality trend are available, along with a partial analysis of its determinants. Consequently, robust data on CFR among inpatients with SARS-CoV-2 infection are still lacking, and the role of patient characteristics in in-hospital deaths remains under-investigated. What this study adds This study shows that t

Background The progress of digital transformation in clinical practice opens the door to transforming the current clinical line for liver disease diagnosis from a late-stage diagnosis approach to an early-stage based one. Early diagnosis of liver fibrosis can prevent the progression of the disease and decrease liver-related morbidity and mortality. We developed here a machine learning (ML) algorithm containing standard parameters that can identify liver fibrosis in the general US population. Materials and methods Starting from a public database (National Health and Nutrition Examination Survey, NHANES), representative of the American population with 7265 eligible subjects (control population n = 6828, with Fibroscan values E < 9.7 KPa; target population n = 437 with Fibroscan values E ≥ 9.7 KPa), we set up an SVM algorithm able to discriminate for individuals with liver fibrosis among the general US population. The algorithm set up involved the removal of missing data and a sampling optimization step to managing the data imbalance (only ∼ 5 % of the dataset is the target population). Results For the feature selection, we performed an unbiased analysis, starting from 33 clinical, anthropometric, and biochemical parameters regardless of their previous application as biomarkers of liver diseases. Through PCA analysis, we identified the 26 more significant features and then used them to set up a sampling method on an SVM algorithm. The best sampling technique to manage the data imbalance was found to be oversampling through the SMOTE-NC. For final model validation, we utilized a subset of 300 individuals (150 with liver fibrosis and 150 controls), subtracted from the main dataset prior to sampling. Performances were evaluated on multiple independent runs. Conclusions We provide proof of concept of an ML clinical decision support tool for liver fibrosis diagnosis in the general US population. Though the presented ML model represents at this stage only a prototype, in

OBJECTIVE: This study aimed to improve the post-marketing surveillance on mRNA anti-SARS-CoV-2 vaccines, characterizing the adverse events (AEs) after the first dose of mRNA BNT162b vaccine. The associations between the AEs and individuals' characteristics were explored. PATIENTS AND METHODS: All adult healthcare workers at Niguarda Hospital (Milan, Italy) who were referred for the first dose of vaccine were offered to participate in a cross-sectional survey during the second-dose administration, between 18 January and 7 February 2021. All participants completed a questionnaire about age, gender, weight, height, medical history, concurrent therapies, employment status, previous diagnosis/testing for SARS-CoV-2 infection, and a list of 24 AEs (solicited AEs). The development of at least one solicited AEs was the main outcome. AEs were stratified by the presence of injection-site symptoms, systemic symptoms or both, and the differences between strata were assessed as a secondary outcome. Biometric data and reports of a previous diagnosis of SARS-CoV-2 infection were also explored, as predictors of the main outcome. RESULTS: 7,014 healthcare workers were included. An incidence of 3 per 10.000 persons for serious AEs following the first administration of the mRNA BNT162b vaccine was found. An association between the development of non-serious AEs with young age, female gender, low body mass index, and previous history of SARSCoV- 2 was described. CONCLUSIONS: This real-life study supported data on the safety profile of the BNT162b2 mRNA vaccine. Our findings on the associations between the development of non-serious AEs with some individual characteristics may help physicians and patients make educated and informed medical decisions towards anti-COVID-19 vaccination.