Influence of real-world characteristics on outcomes for patients with methicillin-resistant Staphylococcal skin and soft tissue infections:a multi-country medical chart review in Europe

BACKGROUND: Patient-related (demographic/disease) and treatment-related (drug/clinician/hospital) characteristics were evaluated as potential predictors of healthcare resource use and opportunities for early switch (ES) from intravenous (IV)-to-oral methicillin-resistant Staphylococcus aureus (MRSA)-active antibiotic therapy and early hospital discharge (ED). METHODS: This retrospective observational medical chart study analyzed patients (across 12 European countries) with microbiologically confirmed MRSA complicated skin and soft tissue infections (cSSTI), ≥3 days of IV anti-MRSA antibiotics during hospitalization (July 1, 2010-June 30, 2011), and discharged alive by July 31, 2011. Logistic/linear regression models evaluated characteristics potentially associated with actual resource use (length of IV therapy, length of hospital stay [LOS], IV-to-oral antibiotic switch), and ES and ED (using literature-based and expert-verified criteria) outcomes. RESULTS: 1542 patients (mean ± SD age 60.8 ± 16.5 years; 61.5% males) were assessed with 81.0% hospitalized for MRSA cSSTI as the primary reason. Several patient demographic, infection, complication, treatment, and hospital characteristics were predictive of length of IV therapy, LOS, IV-to-oral antibiotic switch, or ES and ED opportunities. Outcomes and ES and ED opportunities varied across countries. Length of IV therapy and LOS (r = 0.66, p < 0.0001) and eligibilities for ES and ED (r = 0.44, p < 0.0001) showed relatively strong correlations. IV-to-oral antibiotic switch patients had significantly shorter length of IV therapy (−5.19 days, p < 0.001) and non-significantly shorter LOS (−1.86 days, p > 0.05). Certain patient and treatment characteristics were associated with increased odds of ES (healthcare-associated/ hospital-acquired infection) and ED (patient living arrangements, healthcare-associated/ hospital-acquired infection, initiating MRSA-active treatment 1–2 days post cSSTI index date, existing ED protocol), while other factors decreased the odds of ES (no documented MRSA culture, ≥4 days from admission to cSSTI index date, IV-to-oral switch, IV line infection) and ED (dementia, no documented MRSA culture, initiating MRSA-active treatment ≥3 days post cSSTI index date, existing ES protocol). CONCLUSIONS: Practice patterns and opportunity for further ES and ED were affected by several infection, treatment, hospital, and geographical characteristics, which should be considered in identifying ES and ED opportunities and designing interventions for MRSA cSSTI to reduce IV days and LOS while maintaining the quality of care. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2334-14-476) contains supplementary material, which is available to authorized users

Economic burden of inpatient and outpatient antibiotic treatment for methicillin-resistant Staphylococcus aureus complicated skin and soft-tissue infections

Previous economic analyses evaluating treatment of methicillin-resistant Staphylococcus aureus (MRSA) complicated skin and soft-tissue infections (cSSTI) failed to include all direct treatment costs such as outpatient parenteral antibiotic therapy (OPAT). Our objective was to develop an economic model from a US payer perspective that includes all direct inpatient and outpatient costs incurred by patients with MRSA cSSTI receiving linezolid, vancomycin, or daptomycin. A 4-week decision model was developed for this economic analysis. Published literature and database analyses with validation by experts provided clinical, resource use, and cost inputs on data such as efficacy rate, length of stay, adverse events, and OPAT services. Base-case analysis assumed equal efficacy and equal length of stay for treatments. We conducted several sensitivity analyses where assumptions on resource use or efficacy were varied. Costs were reported in year-end 2011 US dollars. Total treatment costs in the base-case were lower for linezolid ($10,571) than vancomycin ($11,096), and daptomycin ($13,612). Inpatient treatment costs were$740 more, but outpatient costs, $1,266 less with linezolid than vancomycin therapy due to a switch to oral linezolid when the patient was discharged. Compared with daptomycin, both inpatient and outpatient treatment costs were lower with linezolid by$87 and $2,954 respectively. In sensitivity analyses, linezolid had lower costs compared with vancomycin and daptomycin when using differential length of stay data from a clinical trial, and using success rates from a meta-analysis. In a scenario without peripherally inserted central catheter line costs, linezolid became slightly more expensive than vancomycin (by$285), but remained less costly than daptomycin (by $2,316). Outpatient costs of managing MRSA cSSTI may be reduced by 30%–50% with oral linezolid compared with vancomycin or daptomycin. Results from this analysis support potential economic benefit and cost savings of using linezolid versus traditional OPAT when total inpatient and outpatient medical costs are evaluated

Machine Learning-Based Forecasting Active Power Loss in Distribution Systems

This paper presents an ensemble learning approach to predict the active power losses during the allocation and sizing of distributed generation (DG) units in power distribution networks. The forecast model incorporates the Gradient Boosting Machine Regression (GBMR) to estimate DG location, bus voltages, DG size, and active losses without conventional power flow calculations. The results demonstrate that the suggested estimations of power losses and DG sizing are effective, practical, and adaptable for power system management. The accuracy of the proposed model has been validated using key performance metrics and tested on the standard IEEE 33 bus system. In the case of fixed load, the GBMR outperforms other machine learning techniques with the R-squared 0.9997, with a very low mean absolute percentage error (MAPE) (0.2216%) and a root mean square error (RMSE) of 1.0673 in predicting active power losses. This approach is promising in enabling grid operators to effectively manage DG unit integration of distributed energy resources from precise and reliable estimates of the power loss

Central Nervous System Complications in Civilians’ Blast – Induced Head Injuries

Objective: To evaluate the frequency of central nervous system complications in civilians with explosive related head injuries.Material and Methods: This observational study was conducted at Neurosurgery Department, Post Graduate Medical Institute, Lady Reading Hospital, Peshawar – Pakistan, from January 2009 to December 2011 (3 years). We included all the patients with explosive related head injuries who were hospitalized, of all age groups and both genders, and excluded those patients who died before hospitalization. We also excluded patients with head injuries having neurovascular complications or had neurological deficit.Results: We had total of 191 patients with bomb blast head injuries treated in Lady Reading Hospital. One sixty six (86.9%) patients were male and 25 (13.1%) female with male / female ratio of 6.6:1. Their age ranged from 2 months of 71 years. Almost 65% of the patients were in their 2nd, 3rd and 4th decades of life. The common CNS complications in our patients were infections (12.04%), cerebrospinal fluid (CSF) leak (8.38%), epilepsy (5.2%) and post-traumatic hydrocephalous (3.7%). Mortality rate was 11%.Conclusion: We conclude from this study that central nervous system infections are the common (12.04%) complication of civilian explosive related head injuries followed by cerebrospinal fluid leak, seizures and hydro-cephalous respectively. Over all morbidity is 29.32% and mortality rate is 11% in those head injured patients who reach to hospital

β

A number of xenobiotic-inducible cytochrome P450s (CYPs) are now known to be localized in the mitochondrial compartment, though their pharmacological or toxicological roles remain unclear. Here, we show that BNF treatment markedly inhibits liver mitochondrial O2 consumption rate (OCR), ADP-dependent OCR, and also reserve OCR, in wild-type mice but not in Cyp1a1/1a2(−/−) double knockout mice. BNF treatment markedly affected mitochondrial complex I and complex IV activities and also attenuated mitochondrial gene expression. Furthermore, under in vitro conditions, BNF treatment induced cellular ROS production, which was inhibited by mitochondria-targeted antioxidant Mito-CP and CYP inhibitor proadefin, suggesting that most of the ROS production was intramitochondrial and probably involved the catalytic activity of mitochondrial CYP1 enzymes. Interestingly, our results also show that the AHR antagonist resveratrol, markedly attenuated BNF-induced liver mitochondrial defects in wild-type mice, confirming the role of AHR and AHR-regulated CYP1 genes in eliciting mitochondrial dysfunction. These results are consistent with reduced BNF-induced mitochondrial toxicity in Cyp1a1/1a2(−/−) mice and elevated ROS production in COS cells stably expressing CYP1A1. We propose that increased mitochondrial ROS production and respiratory dysfunction are part of xenobiotic toxicity. Resveratrol, a chemopreventive agent, renders protection against BNF-induced toxicity

Breastfeeding patterns among women visited on the St. Justine territory (1986-1988) : Does ethnicity play a role?

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal

Predictive data modelling for biomedical data and imaging

In this book, we embark on a journey into the realm of predictive data modeling for biomedical data and imaging in healthcare. It explores the potential of predictive analytics in the field of medical science through utilizing various tools and techniques to unravel insights and enhance patient care. This volume creates a medium for an interchange of knowledge from expertise and concerns in the field of predictive data modeling. In detail, the research work on this will include the effective use of predictive data modeling algorithms to run image analysis tasks for understanding. Predictive Data Modelling for Biomedical Data and Imaging is divided into three sections, namely Section I - Beginning of Predictive Data Modeling for Biomedical Data and Imaging/Healthcare, Section II - Data Design and Analysis for Biomedical Data and Imaging/Healthcare, and Section III - Case Studies of Predictive Analytics for Biomedical Data and Imaging/Healthcare. We hope this book will inspire further research and innovation in the field of predictive data modeling for biomedical data and imaging in healthcare. By exploring diverse case studies and methodologies, this book contributes to the advancement of healthcare practices, ultimately improving patient outcomes and well-being