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[...]it should be considered that the ISTH SSC on Fibrinolysis group has identified several technical pitfalls detected in current studies on D-dimer in COVID-19 cases. [...]D-Dimer guided-anticoagulation management does not seem supported enough by evidence-based recommendations. Rights and permissions Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.

To analyze the application of lung ultrasound (LUS) diagnostic approach in obstetric patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and compare LUS score and symptoms of the patients. A single-center observational retrospective study from October 31, 2020 to March 31, 2021. Department of Ob/Gyn at the University-Hospital of Udine, Italy. Pregnant women with SARS-CoV-2 diagnosed with reverse transcription-PCR (RT-PCR) swab test were subdivided as symptomatic and asymptomatic patients with COVID-19. Lung ultrasound evaluation both through initial evaluation upon admission and through serial evaluations. Reporting LUS findings and LUS score characteristics. Symptomatic patients with COVID-19 showed a higher LUS (median 3.5 vs. 0, < 0.001). LUS was significantly correlated with COVID-19 biomarkers as C-reactive protein (CPR; = 0.011), interleukin-6 ( = 0.013), and pro-adrenomedullin ( = 0.02), and inversely related to arterial oxygen saturation ( = 0.004). The most frequent ultrasound findings were focal B lines (14 vs. 2) and the light beam (9 vs. 0). Lung ultrasound can help to manage pregnant women with SARS-CoV-2 infection during a pandemic surge. ClinicalTrials.gov, NCT04823234. Registered on March 29, 2021.

In patients intubated for hypoxemic acute respiratory failure (ARF) related to novel coronavirus disease (COVID-19), we retrospectively compared two weaning strategies, early extubation with immediate non-invasive ventilation (NIV) versus standard weaning encompassing spontaneous breathing trial (SBT), with respect to IMV duration (primary endpoint), extubation failures and reintubations, rate of tracheostomy, intensive care unit (ICU) length of stay and mortality (additional endpoints). All COVID-19 adult patients, intubated for hypoxemic ARF and subsequently extubated, were enrolled. Patients were included in two groups, early extubation followed by immediate NIV application, and conventionally weaning after passing SBT. 121 patients were enrolled and analyzed, 66 early extubated and 55 conventionally weaned after passing an SBT. IMV duration was 9 [6–11] days in early extubated patients versus 11 [6–15] days in standard weaning group ( p  = 0.034). Extubation failures [12 (18.2%) vs. 25 (45.5%), p  = 0.002] and reintubations [12 (18.2%) vs. 22 (40.0%) p  = 0.009] were fewer in early extubation compared to the standard weaning groups, respectively. Rate of tracheostomy, ICU mortality, and ICU length of stay were no different between groups. Compared to standard weaning, early extubation followed by immediate NIV shortened IMV duration and reduced the rate of extubation failure and reintubation.

Background: During the COVID-19 pandemic, lung ultrasound (LUS) has been widely used since it can be performed at the patient’s bedside, does not produce ionizing radiation, and is sufficiently accurate. The LUS score allows for quantifying lung involvement; however, its clinical prognostic role is still controversial. Methods: A retrospective observational study on 103 COVID-19 patients with respiratory failure that were assessed with an LUS score at intensive care unit (ICU) admission and discharge in a tertiary university COVID-19 referral center. Results: The deceased patients had a higher LUS score at admission than the survivors (25.7 vs. 23.5; p-value = 0.02; cut-off value of 25; Odds Ratio (OR) 1.1; Interquartile Range (IQR) 1.0−1.2). The predictive regression model shows that the value of LUSt0 (OR 1.1; IQR 1.0–1.3), age (OR 1.1; IQR 1.0−1.2), sex (OR 0.7; IQR 0.2−3.6), and days in spontaneous breathing (OR 0.2; IQR 0.1–0.5) predict the risk of death for COVID-19 patients (Area under the Curve (AUC) 0.92). Furthermore, the surviving patients showed a significantly lower difference between LUS scores at admission and discharge (mean difference of 1.75, p-value = 0.03). Conclusion: Upon entry into the ICU, the LUS score may play a prognostic role in COVID-19 patients with ARDS. Furthermore, employing the LUS score as a monitoring tool allows for evaluating the patients with a higher probability of survival.

BackgroundAntimicrobial de-escalation (ADE) is a strategy of antimicrobial stewardship, aiming at preventing the emergence of antimicrobial resistance (AMR) by decreasing the exposure to broad-spectrum antimicrobials. There is no high-quality research on ADE and its effects on AMR. Its definition varies and there is little evidence-based guidance for clinicians to use ADE in the intensive care unit (ICU).MethodsA task force of 16 international experts was formed in November 2016 to provide with guidelines for clinical practice to develop questions targeted at defining ADE, its effects on the ICU population and to provide clinical guidance. Groups of 2 experts were assigned 1–2 questions each within their field of expertise to provide draft statements and rationale. A Delphi method, with 3 rounds and an agreement threshold of 70% was required to reach consensus.ResultsWe present a comprehensive document with 13 statements, reviewing the evidence on the definition of ADE, its effects in the ICU population and providing guidance for clinicians in subsets of clinical scenarios where ADE may be considered.ConclusionADE remains a topic of controversy due to the complexity of clinical scenarios where it may be applied and the absence of evidence to the effects it may have on antimicrobial resistance.

Severe or life threatening infections are common among patients in the intensive care unit (ICU). Most infections in the ICU are bacterial or fungal in origin and require antimicrobial therapy for clinical resolution. Antibiotics are the cornerstone of therapy for infected critically ill patients. However, antibiotics are often not optimally administered resulting in less favorable patient outcomes including greater mortality. The timing of antibiotics in patients with life threatening infections including sepsis and septic shock is now recognized as one of the most important determinants of survival for this population. Individuals who have a delay in the administration of antibiotic therapy for serious infections can have a doubling or more in their mortality. Additionally, the timing of an appropriate antibiotic regimen, one that is active against the offending pathogens based on in vitro susceptibility, also influences survival. Thus not only is early empiric antibiotic administration important but the selection of those agents is crucial as well. The duration of antibiotic infusions, especially for β-lactams, can also influence antibiotic efficacy by increasing antimicrobial drug exposure for the offending pathogen. However, due to mounting antibiotic resistance, aggressive antimicrobial de-escalation based on microbiology results is necessary to counterbalance the pressures of early broad-spectrum antibiotic therapy. In this review, we examine time related variables impacting antibiotic optimization as it relates to the treatment of life threatening infections in the ICU. In addition to highlighting the importance of antibiotic timing in the ICU we hope to provide an approach to antimicrobials that also minimizes the unnecessary use of these agents. Such approaches will increasingly be linked to advances in molecular microbiology testing and artificial intelligence/machine learning. Such advances should help identify patients needing empiric antibiotic therapy at an earlier time point as well as the specific antibiotics required in order to avoid unnecessary administration of broad-spectrum antibiotics.

Proper categorisation of light vehicles is crucial for analysing and comprehending the developments taking place in the road transport sector, that impact the environment, road safety, transport operation, and urban planning. However, current vehicle classification methods in Europe are based on empirical or legacy approaches, sometimes founded on obsolete criteria, and do not fully reflect recent changes in the vehicle fleet and market. This paper aims to establish a scientific approach for the classification of light vehicles by introducing a Bayesian statistical method to define vehicle segments in an explicit and reproducible way. Contrarily to previous studies that mostly depend on machine learning techniques, which, despite their high accuracy typically lack explainability, the proposed approach prioritises the transparency of classification decisions. Through an in-depth examination of vehicle physical attributes, key variables were identified and utilised to determine clear boundaries between segments. These boundaries were articulated through linear relationships of the chosen variables, thus providing well-defined criteria open for interpretation and verification. The algorithm could assign up to 82% of the vehicles to the original segments. The accuracy demonstrated is comparable to that of several unsupervised machine learning models and transparently reveals the boundaries among different segments. The findings can be used by researchers and modellers to update existing vehicle fleet models, particularly those treating environmental and energy consumption impacts, or used as a possible standard for multi-purpose vehicle classification.

The massive consumption of antibiotics in the ICU is responsible for substantial ecological side effects that promote the dissemination of multidrug-resistant bacteria (MDRB) in this environment. Strikingly, up to half of ICU patients receiving empirical antibiotic therapy have no definitively confirmed infection, while de-escalation and shortened treatment duration are insufficiently considered in those with documented sepsis, highlighting the potential benefit of implementing antibiotic stewardship programs (ASP) and other quality improvement initiatives. The objective of this narrative review is to summarize the available evidence, emerging options, and unsolved controversies for the optimization of antibiotic therapy in the ICU. Published data notably support the need for better identification of patients at risk of MDRB infection, more accurate diagnostic tools enabling a rule-in/rule-out approach for bacterial sepsis, an individualized reasoning for the selection of single-drug or combination empirical regimen, the use of adequate dosing and administration schemes to ensure the attainment of pharmacokinetics/pharmacodynamics targets, concomitant source control when appropriate, and a systematic reappraisal of initial therapy in an attempt to minimize collateral damage on commensal ecosystems through de-escalation and treatment-shortening whenever conceivable. This narrative review also aims at compiling arguments for the elaboration of actionable ASP in the ICU, including improved patient outcomes and a reduction in antibiotic-related selection pressure that may help to control the dissemination of MDRB in this healthcare setting.