ECLS-SHOCK and DanGer Shock: Implications for optimal temporary mechanical circulatory support use for cardiogenic shock due to acute myocardial infarction.

Despite concerted efforts to rapidly identify patients with cardiogenic shock complicating acute myocardial infarction (AMI-CS) and provide timely revascularization, early mortality remains stubbornly high. While artificially augmenting systemic flow through the use of temporary mechanical circulatory support (tMCS) devices would be expected to reduce the rate of progression to multi-organ dysfunction and thereby enhance survival, reliable evidence for benefit has remained elusive with lingering questions regarding the appropriate selection of both patients and devices, as well as the timing of device implantation relative to other critical interventions. Further complicating matters are the resource-intensive multidisciplinary systems of care that must be brought to bear in this complex patient population.

From Escalate to Elevate: A New Paradigm for Comprehensive Cardiogenic Shock Management.

Patients with cardiogenic shock (CS) present with critical hemodynamic compromise with low cardiac output (CO) resulting in end-organ dysfunction. Prognosis is closely related to the severity of shock and treatment of patients with CS is resource intensive. In this review, we consider the current treatment paradigms alongside the evidence that underpins them.

Rapid Antibacterial Assessments for Plastic and Textile Materials Against .

: Standard test methods for evaluating the antibacterial performance of plastic (non-porous) and textile (porous) materials are accurate and reliable, but completing a standard assessment generally requires at least several days to a week. Well-trained and experienced technicians are also required to conduct the standard tests consistently and analyse the samples and test results systemically. These costs are often not favourable for the performance assurance of antimicrobial products in industrial production, nor for meeting the fast-return demands in research and development of antimicrobial materials nowadays.

Machine learning and clinician predictions of antibiotic resistance in Enterobacterales bloodstream infections.

Background: Patients with Gram-negative bloodstream infections are at risk of serious adverse outcomes without active treatment, but identifying who has antimicrobial resistance (AMR) to target empirical treatment is challenging.

Methods: We used XGBoost machine learning models to predict antimicrobial resistance to seven antibiotics in patients with Enterobacterales bloodstream infection. Models were trained using hospital and community data from Oxfordshire, UK, for patients with positive blood cultures between 01-January-2017 and 31-December-2021.

Temporal incremental healthcare costs associated with complications in Hong Kong Chinese patients with type 2 diabetes: A prospective study in Joint Asia diabetes evaluation (JADE) Register (2007-2019).

Objective: We examined incremental healthcare costs (inpatient and outpatient) related to complications in Chinese patients with type 2 diabetes (T2D) during the year of occurrence and post-event years, utilizing the Joint Asia Diabetes Evaluation (JADE) Register cohort of Hong Kong Chinese patients with T2D between 2007 and 2019.

Research Design And Methods: 19,440 patients with T2D underwent structured evaluation utilizing the JADE platform with clinical outcomes data retrieved from territory-wide electronic medical records including inpatient, outpatient and emergency care. Two-part model was adopted to account for skewed healthcare costs distribution.