Telemedicine coverage for post-operative ICU patients.

Introduction There is an increased demand for intensive care unit (ICU) beds. We sought to determine if we could create a safe surge capacity model to increase ICU capacity by treating ICU patients in the post-anaesthesia care unit (PACU) utilizing a collaborative model between an ICU service and a telemedicine service during peak ICU bed demand. Methods We evaluated patients managed by the surgical critical care service in the surgical intensive care unit (SICU) compared to patients managed in the virtual intensive care unit (VICU) located within the PACU. A retrospective review of all patients seen by the surgical critical care service from January 1st 2008 to July 31st 2011 was conducted at an urban, academic, tertiary centre and level 1 trauma centre. Results Compared to the SICU group ( n = 6652), patients in the VICU group ( n = 1037) were slightly older (median age 60 (IQR 47-69) versus 58 (IQR 44-70) years, p = 0.002) and had lower acute physiology and chronic health evaluation (APACHE) II scores (median 10 (IQR 7-14) versus 15 (IQR 11-21), p \u3c 0.001). The average amount of time patients spent in the VICU was 13.7 + /-9.6 hours. In the VICU group, 750 (72%) of patients were able to be transferred directly to the floor; 287 (28%) required subsequent admission to the surgical intensive care unit. All patients in the VICU group were alive upon transfer out of the PACU while mortality in the surgical intensive unit cohort was 5.5%. Discussion A collaborative care model between a surgical critical care service and a telemedicine ICU service may safely provide surge capacity during peak periods of ICU bed demand. The specific patient populations for which this approach is most appropriate merits further investigation

Guidelines for Perioperative Care for Emergency Laparotomy Enhanced Recovery After Surgery (ERAS) Society Recommendations: Part 1—Preoperative: Diagnosis, Rapid Assessment and Optimization

BackgroundEnhanced Recovery After Surgery (ERAS) protocols reduce length of stay, complications and costs fora large number of elective surgical procedures. A similar, structured approach appears to improve outcomes, including mortality, for patients undergoing high-risk emergency general surgery, and specifically emergency laparotomy. These are the first consensus guidelines for optimal care of these patients using an ERAS approach.MethodsExperts in aspects of management of the high-risk and emergency general surgical patient were invited to contribute by the International ERAS Society. Pubmed, Cochrane, Embase, and MEDLINE database searches on English language publications were performed for ERAS elements and relevant specific topics. Studies on each item were selected with particular attention to randomized controlled trials, systematic reviews, meta-analyses and large cohort studies, and reviewed and graded using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) system. Recommendations were made on the best level of evidence, or extrapolation from studies on non-emergency patients when appropriate. The Delphi method was used to validate final recommendations. The guideline has been divided into two parts: Part 1—Preoperative Care and Part 2—Intraoperative and Postoperative management. This paper provides guidelines for Part 1.ResultsTwelve components of preoperative care were considered. Consensus was reached after three rounds.ConclusionsThese guidelines are based on the best available evidence for an ERAS approach to patients undergoing emergency laparotomy. Initial management is particularly important for patients with sepsis and physiological derangement. These guidelines should be used to improve outcomes for these high-risk patients

Combinatorial Development of Solid Catalytic Materials: Design of High-Throughput Experiments, Data Analysis, Data Mining

The book provides a comprehensive treatment of combinatorial development of heterogeneous catalysts. In particular, two computer-aided approaches that have played a key role in combinatorial catalysis and high-throughput experimentation during the last decade - evolutionary optimization and artificial neural networks - are described. The book is unique in that it describes evolutionary optimization in a broader context of methods of searching for optimal catalytic materials, including statistical design of experiments, as well as presents neural networks in a broader context of data analysis.It is the first book that demystifies the attractiveness of artificial neural networks, explaining its rational fundamental - their universal approximation capability. At the same time, it shows the limitations of that capability and describes two methods for how it can be improved. The book is also the first that presents two other important topics pertaining to evolutionary optimization and artificial neural networks: automatic generating of problem-tailored genetic algorithms, and tuning evolutionary algorithms with neural networks. Both are not only theoretically explained, but also well illustrated through detailed case studies.Introduction to Approaches in the Development of Heterogeneous Catalysts; Methods of Searching for Optimal Catalytic Materials; Analysis and Mining of Data Gathered in Catalytic Experiments; Artificial Neural Networks in the Study of Catalytic Performance

Statistical Analysis of Past Catalytic Data on Oxidative Methane Coupling for New Insights into the Composition of High-Performance Catalysts

A database consisting of 1870 data sets on catalyst compositions and their performances in the oxidative coupling of methane was compiled. For this goal, about 1000 full-text references from the last 30 years have been analyzed and about 420 of them, which contained all the necessary information, were selected for the data extraction. The accumulated data were subject to statistical analysis: analysis of variance, correlation analysis, and decision tree. On the basis of the results, 18 catalytic key elements were selected from originally 68 elements. All oxides of the selected elements, which positively affect the selectivity to C2 products, show strong basicity. Analysis of binary and ternary interactions between the selected key elements shows that high-performance catalysts are mainly based on Mg and La oxides. Alkali (Cs, Na) and alkalineearth (Sr, Ba) metals used as dopants increase the selectivity of the host oxides, whereas dopants such as Mn, W, and the Cl anion have positive effects on the catalyst activity. The maximal C2 selectivities for the proposed catalyst compositions range from 72 to 82%, and the respective C2 yields range from 16 to 26%

Developing catalytic materials for the oxidative coupling of methane through statistical analysis of literature data

Based on available 1870 literature data for the oxidative coupling of methane (OCM), various statistical models were applied i) to design three-component catalysts consisting of one host metal oxide (La2O3 or MgO) and two oxide (Li, Na, Cs, Sr, Ba, La, or Mn) dopants and ii) to predict their OCM performance. To validate this approach for catalyst design, selected materials were prepared and experimentally tested for their activity and selectivity in the target reaction. The effects of kinds of host oxides, dopants and their interplay on the OCM performance of differently composed catalysts were statistically evaluated