Safety and effectiveness of isavuconazole in real-life non-neutropenic patients

Objectives: Information is scarce on clinical experiences with non-neutropenic patients with invasive fungal infection (IFI) receiving isavuconazole. We aimed to report the safety and effectiveness of this drug as a first-line treatment or rescue in real life. Methods: A retrospective, observational multicentric study of non-neutropenic patients who received isavuconazole as an IFI treatment at 12 different university hospitals (January 2018-2022). All patients met criteria for proven, probable or possible IFI according to EORTC-MSG. Results: A total of 238 IFIs were treated with isavuconazole during the study period. Combination therapy was administered in 27.7% of cases. The primary IFI was aspergillosis (217, 91.2%). Other IFIs treated with isavuconazole were candidemia (n = 10), mucormycosis (n = 8), histoplasmosis (n = 2), cryptococcosis (n = 2), and others (n = 4). Median time of isavuconazole treatment was 29 days. Only 5.9% (n = 14) of cases developed toxicity, mainly hepatic-related (10 patients, 4.2%). Nine patients (3.8%) had treatment withdrawn. Successful clinical response at 12 weeks was documented in 50.5% of patients. Conclusion: Isavuconazole is an adequate treatment for non-neutropenic patients with IFIs. Toxicity rates were low and its effectiveness was comparable to other antifungal therapies previously reported. (c) 2024 The Authors. Published by Elsevier Ltd on behalf of International Society for Infectious Diseases. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/

Lopinavir/Ritonavir and Darunavir/Cobicistat in Hospitalized COVID-19 Patients: Findings From the Multicenter Italian CORIST Study

Background: Protease inhibitors have been considered as possible therapeutic agents for COVID-19 patients. Objectives: To describe the association between lopinavir/ritonavir (LPV/r) or darunavir/cobicistat (DRV/c) use and in-hospital mortality in COVID-19 patients. Study Design: Multicenter observational study of COVID-19 patients admitted in 33 Italian hospitals. Medications, preexisting conditions, clinical measures, and outcomes were extracted from medical records. Patients were retrospectively divided in three groups, according to use of LPV/r, DRV/c or none of them. Primary outcome in a time-to event analysis was death. We used Cox proportional-hazards models with inverse probability of treatment weighting by multinomial propensity scores. Results: Out of 3,451 patients, 33.3% LPV/r and 13.9% received DRV/c. Patients receiving LPV/r or DRV/c were more likely younger, men, had higher C-reactive protein levels while less likely had hypertension, cardiovascular, pulmonary or kidney disease. After adjustment for propensity scores, LPV/r use was not associated with mortality (HR = 0.94, 95% CI 0.78 to 1.13), whereas treatment with DRV/c was associated with a higher death risk (HR = 1.89, 1.53 to 2.34, E-value = 2.43). This increased risk was more marked in women, in elderly, in patients with higher severity of COVID-19 and in patients receiving other COVID-19 drugs. Conclusions: In a large cohort of Italian patients hospitalized for COVID-19 in a real-life setting, the use of LPV/r treatment did not change death rate, while DRV/c was associated with increased mortality. Within the limits of an observational study, these data do not support the use of LPV/r or DRV/c in COVID-19 patients

Covid-19 And Rheumatic Autoimmune Systemic Diseases: Role of Pre-Existing Lung Involvement and Ongoing Treatments

The Covid-19 pandemic may have a deleterious impact on patients with autoimmune systemic diseases (ASD) due to their deep immune-system alterations

How future surgery will benefit from SARS-COV-2-related measures: a SPIGC survey conveying the perspective of Italian surgeons

COVID-19 negatively affected surgical activity, but the potential benefits resulting from adopted measures remain unclear. The aim of this study was to evaluate the change in surgical activity and potential benefit from COVID-19 measures in perspective of Italian surgeons on behalf of SPIGC. A nationwide online survey on surgical practice before, during, and after COVID-19 pandemic was conducted in March-April 2022 (NCT:05323851). Effects of COVID-19 hospital-related measures on surgical patients' management and personal professional development across surgical specialties were explored. Data on demographics, pre-operative/peri-operative/post-operative management, and professional development were collected. Outcomes were matched with the corresponding volume. Four hundred and seventy-three respondents were included in final analysis across 14 surgical specialties. Since SARS-CoV-2 pandemic, application of telematic consultations (4.1% vs. 21.6%; p < 0.0001) and diagnostic evaluations (16.4% vs. 42.2%; p < 0.0001) increased. Elective surgical activities significantly reduced and surgeons opted more frequently for conservative management with a possible indication for elective (26.3% vs. 35.7%; p < 0.0001) or urgent (20.4% vs. 38.5%; p < 0.0001) surgery. All new COVID-related measures are perceived to be maintained in the future. Surgeons' personal education online increased from 12.6% (pre-COVID) to 86.6% (post-COVID; p < 0.0001). Online educational activities are considered a beneficial effect from COVID pandemic (56.4%). COVID-19 had a great impact on surgical specialties, with significant reduction of operation volume. However, some forced changes turned out to be benefits. Isolation measures pushed the use of telemedicine and telemetric devices for outpatient practice and favored communication for educational purposes and surgeon-patient/family communication. From the Italian surgeons' perspective, COVID-related measures will continue to influence future surgical clinical practice

Age, Successive Waves, Immunization, and Mortality in Elderly COVID-19 Haematological Patients: EPICOVIDEHA Findings

Introduction: elderly patients with haematologic malignancies face the highest risk of severe COVID-19 outcomes. The infection impact in different age groups remains unstudied in detail. Methods: We analysed elderly patients (age groups: 65-70, 71-75, 76-80 and >80 years old) with hematologic malignancies included in the EPICOVIDEHA registry between January 2020 and July 2022. Univariable and multivariable Cox regression models were conducted to identify factors influencing death in COVID-19 patients with haematological malignancy. results: the study included data from 3,603 elderly patients (aged 65 or older) with haematological malignancy, with a majority being male (58.1%) and a significant proportion having comorbidities. The patients were divided into four age groups, and the analysis assessed COVID-19 outcomes, vaccination status, and other variables in relation to age and pandemic waves.tThe 90-day survival rate for patients with COVID-19 was 71.2%, with significant differences between groups. The pandemic waves had varying impacts, with the first wave affecting patients over 80 years old, the second being more severe in 65-70, and the third being the least severe in all age groups. factors contributing to 90-day mortality included age, comorbidities, lymphopenia, active malignancy, acute leukaemia, less than three vaccine doses, severe COVID-19, and using only corticosteroids as treatment. Conclusions: These data underscore the heterogeneity of elderly haematological patients, highlight the different impact of COVID waves and the pivotal importance of vaccination, and may help in planning future healthcare efforts

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino de tector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower-or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Dexamethasone treatment for COVID-19 is related to increased mortality in hematologic malignancy patients : results from the EPICOVIDEHA Registry

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