Occurrence of Corynebacterium striatum as an emerging antibiotic-resistant nosocomial pathogen in a Tunisian hospital

Corynebacterium striatum is a nosocomial opportunistic pathogen increasingly associated with a wide range of human infections and is often resistant to several antibiotics. We investigated the susceptibility of 63 C. striatum isolated at the Farhat-Hached hospital, Sousse (Tunisia), during the period 2011?2014, to a panel of 16 compounds belonging to the main clinically relevant classes of antimicrobial agents. All strains were susceptible to vancomycin, linezolid, and daptomycin. Amikacin and gentamicin also showed good activity (MICs90 = 1 and 2 mg/L, respectively). High rates of resistance to penicillin (82.5%), clindamycin (79.4%), cefotaxime (60.3%), erythromycin (47.6%), ciprofloxacin (36.5%), moxifloxacin (34.9%), and rifampicin (25.4%) were observed. Fifty-nine (93.7%) out of the 63 isolates showed resistance to at least one compound and 31 (49.2%) were multidrug-resistant. Twenty-nine resistance profiles were distinguished among the 59 resistant C. striatum. Most of the strains resistant to fluoroquinolones showed a double mutation leading to an amino acid change in positions 87 and 91 in the quinolone resistance-determining region of the gyrA gene. The 52 strains resistant to penicillin were positive for the gene bla, encoding a class A ?-lactamase. Twenty-two PFGE patterns were identified among the 63 C. striatum, indicating that some clones have spread within the hospital

A Comprehensive Reengineering Of The Hospital Emergency Triage System

Hospital emergency triage and specifically Mass Casualty Incidents (MCIs) are of major concern with regard to treatment and patient outcomes. Traditional emergency department triage models are oversimplified and often lead to over/under triaging of patients. Furthermore, triage models do not account for the full spectrum of different types of MCIs which often results in misclassification. In this thesis, we begin by looking at traditional triage models currently being used in hospital systems and identify several shortcomings of using these models within the context of a chemical related MCI. I will then move to describe a new approach to creating a dynamically adaptive multi-phase triage system capable of managing patients regardless of the MCI scenario. The new system utilizes modern mobile technology and is capable of deploying artificial intelligence algorithms to assist caregivers with decision making. I discuss the data analytics and machine learning techniques necessary to create deployable AI and compare these models to current resources available for emergency decision support, WISER and CHEMM-ist. Finally, I will conclude by describing the Human-Computer Interaction (HCI) design of computational software capable of quickly collecting patient data, performing data analysis and provide caregivers with decision logic and situational awareness. This patient management system has the potential to improve patient treatment and outcomes with the added advantage of being integrated into current hospital Electronic Health Records vii (EHR). Current hospital resources and triage models can be easily implemented and should be considered for Emergency Department (ED) deployment