An outbreak of Multi-Drug Resistant Acinetobacter in the burn intensive care unit of a large tertiary care center

Risk factors for Multi-Drug Resistant Acinetobacter (MDRA) acquisition were studied in patients in a burn intensive care unit (ICU) where there was an outbreak of MDRA. Forty cases were matched with eighty controls based on length of stay in the Burn ICU and statistical analysis was performed on data for several different variables. Matched analysis showed that mechanical ventilation, transport ventilation, number of intubations, number of bronchoscopy procedures, total body surface area burn, and prior Methicillin Resistant Staphylococcus aureus colonization were all significant risk factors for MDRA acquisition. ^ MDRA remains a significant threat to the burn population. Treatment for burn patients with MDRA is challenging as resistance to antibiotics continues to increase. This study underlined the need to closely monitor the most critically ill ventilated patients during an outbreak of MDRA as they are the most at risk for MDRA acquisition.^

TIME SERIES ANALYSIS AS INPUT FOR PREDICTIVE MODELING: PREDICTING CARDIAC ARREST IN A PEDIATRIC INTENSIVE CARE UNIT

The first manuscript, entitled "Time-Series Analysis as Input for Clinical Predictive Modeling: Modeling Cardiac Arrest in a Pediatric ICU" lays out the theoretical background for the project. There are several core concepts presented in this paper. First, traditional multivariate models (where each variable is represented by only one value) provide single point-in-time snapshots of patient status: they are incapable of characterizing deterioration. Since deterioration is consistently identified as a precursor to cardiac arrests, we maintain that the traditional multivariate paradigm is insufficient for predicting arrests. We identify time series analysis as a method capable of characterizing deterioration in an objective, mathematical fashion, and describe how to build a general foundation for predictive modeling using time series analysis results as latent variables. Building a solid foundation for any given modeling task involves addressing a number of issues during the design phase. These include selecting the proper candidate features on which to base the model, and selecting the most appropriate tool to measure them. We also identified several unique design issues that are introduced when time series data elements are added to the set of candidate features. One such issue is in defining the duration and resolution of time series elements required to sufficiently characterize the time series phenomena being considered as candidate features for the predictive model. Once the duration and resolution are established, there must also be explicit mathematical or statistical operations that produce the time series analysis result to be used as a latent candidate feature. In synthesizing the comprehensive framework for building a predictive model based on time series data elements, we identified at least four classes of data that can be used in the model design. The first two classes are shared with traditional multivariate models: multivariate data and clinical latent features. Multivariate data is represented by the standard one value per variable paradigm and is widely employed in a host of clinical models and tools. These are often represented by a number present in a given cell of a table. Clinical latent features derived, rather than directly measured, data elements that more accurately represent a particular clinical phenomenon than any of the directly measured data elements in isolation. The second two classes are unique to the time series data elements. The first of these is the raw data elements. These are represented by multiple values per variable, and constitute the measured observations that are typically available to end users when they review time series data. These are often represented as dots on a graph. The final class of data results from performing time series analysis. This class of data represents the fundamental concept on which our hypothesis is based. The specific statistical or mathematical operations are up to the modeler to determine, but we generally recommend that a variety of analyses be performed in order to maximize the likelihood that a representation of the time series data elements is produced that is able to distinguish between two or more classes of outcomes. The second manuscript, entitled "Building Clinical Prediction Models Using Time Series Data: Modeling Cardiac Arrest in a Pediatric ICU" provides a detailed description, start to finish, of the methods required to prepare the data, build, and validate a predictive model that uses the time series data elements determined in the first paper. One of the fundamental tenets of the second paper is that manual implementations of time series based models are unfeasible due to the relatively large number of data elements and the complexity of preprocessing that must occur before data can be presented to the model. Each of the seventeen steps is analyzed from the perspective of how it may be automated, when necessary. We identify the general objectives and available strategies of each of the steps, and we present our rationale for choosing a specific strategy for each step in the case of predicting cardiac arrest in a pediatric intensive care unit. Another issue brought to light by the second paper is that the individual steps required to use time series data for predictive modeling are more numerous and more complex than those used for modeling with traditional multivariate data. Even after complexities attributable to the design phase (addressed in our first paper) have been accounted for, the management and manipulation of the time series elements (the preprocessing steps in particular) are issues that are not present in a traditional multivariate modeling paradigm. In our methods, we present the issues that arise from the time series data elements: defining a reference time; imputing and reducing time series data in order to conform to a predefined structure that was specified during the design phase; and normalizing variable families rather than individual variable instances. The final manuscript, entitled: "Using Time-Series Analysis to Predict Cardiac Arrest in a Pediatric Intensive Care Unit" presents the results that were obtained by applying the theoretical construct and its associated methods (detailed in the first two papers) to the case of cardiac arrest prediction in a pediatric intensive care unit. Our results showed that utilizing the trend analysis from the time series data elements reduced the number of classification errors by 73%. The area under the Receiver Operating Characteristic curve increased from a baseline of 87% to 98% by including the trend analysis. In addition to the performance measures, we were also able to demonstrate that adding raw time series data elements without their associated trend analyses improved classification accuracy as compared to the baseline multivariate model, but diminished classification accuracy as compared to when just the trend analysis features were added (ie, without adding the raw time series data elements). We believe this phenomenon was largely attributable to overfitting, which is known to increase as the ratio of candidate features to class examples rises. Furthermore, although we employed several feature reduction strategies to counteract the overfitting problem, they failed to improve the performance beyond that which was achieved by exclusion of the raw time series elements. Finally, our data demonstrated that pulse oximetry and systolic blood pressure readings tend to start diminishing about 10-20 minutes before an arrest, whereas heart rates tend to diminish rapidly less than 5 minutes before an arrest.

Intensive care unit utilization among patients with cancer at the end of life

Over the last 2 decades, survival rates in critically ill cancer patients have improved. Despite the increase in survival, the intensive care unit (ICU) continues to be a location where end-of-life care takes place. More than 20% of deaths in the United States occur after admission to an ICU, and as baby boomers reach the seventh and eighth decades of their lives, the volume of patients in the ICU is predicted to rise. The aim of this study was to evaluate intensive care unit utilization among patients with cancer who were at the end of life. End of life was defined using decedent and high-risk cohort study designs. The decedent study evaluated characteristics and ICU utilization during the terminal hospital stay among patients who died at The University of Texas MD Anderson Cancer Center during 2003-2007. The high-risk cohort study evaluated characteristics and ICU utilization during the index hospital stay among patients admitted to MD Anderson during 2003-2007 with a high risk of in-hospital mortality. Factors associated with higher ICU utilization in the decedent study included non-local residence, hematologic and non-metastatic solid tumor malignancies, malignancy diagnosed within 2 months, and elective admission to surgical or pediatric services. Having a palliative care consultation on admission was associated with dying in the hospital without ICU services. In the cohort of patients with high risk of in-hospital mortality, patients who went to the ICU were more likely to be younger, male, with newly diagnosed non-metastatic solid tumor or hematologic malignancy, and admitted from the emergency center to one of the surgical services. A palliative care consultation on admission was associated with a decreased likelihood of having an ICU stay. There were no differences in ethnicity, marital status, comorbidities, or insurance status between patients who did and did not utilize ICU services. Inpatient mortality probability models developed for the general population are inadequate in predicting in-hospital mortality for patients with cancer. The following characteristics that differed between the decedent study and high-risk cohort study can be considered in future research to predict risk of in-hospital mortality for patients with cancer: ethnicity, type and stage of malignancy, time since diagnosis, and having advance directives. Identifying those at risk can precipitate discussions in advance to ensure care remains appropriate and in accordance with the wishes of the patient and family.^

Transformation of the Rice Marketing System and Myanmar's Transition to a Market Economy

Creating a rice marketing system has been one of the central policy issues in Myanmar's move to a market economy since the end of the 1980s. Two liberalizations of rice marketing were implemented in 1987 and 2003. This paper examines the essential aspects of the liberalizations and the subsequent transformation of Myanmar's rice marketing sector. It attempts to bring into clearer focus the rationale of the government's rice marketing reforms which is to maintain a stable supply of rice at a low price to consumers. Under this rationale, however, the state rice marketing sector continued to lose efficiency while the private sector was allowed to develop on condition that it did not jeopardize the rationale of stable supply at low price. The paper concludes that the prospect for the future development of the private rice marketing sector is dim since a change in the rice market's rationale is unlikely. Private rice exporting is unlikely to be permitted, while the domestic market is approaching the saturation point. Thus, there is little momentum for the private rice sector to undertake any substantial expansion of investment.

Myanmar's two decades of partial transition to a market economy : a negative legacy for the new government

Despite more than two decades of transition from a centrally planned to a market-oriented economy, Myanmar’s economic transition is still only partly complete. The government’s initial strategy for dealing with the swelling deficits of the state economic enterprises (SEEs) was to put them under direct control in order to scrutinize their expenditures. This policy change postponed restructuring and exacerbated the soft budget constraint problem of the SEEs. While the installation of a new government in March 2011 has increased prospects for economic development, sustainable growth still requires full-scale structural reform of the SEEs and institutional infrastructure building. Myanmar can learn from the gradual approaches to economic transition in China and Vietnam, where partial reforms weakened further impetus for reforms.

Transition to a cyclable city: policies and variables affecting cycling commuting

The growing interest in achieving the objectives of cycling policies has increased the need to know the key variables that influence the use of the bicycle for daily mobility. This paper makes a contribution in this research line by examining a varying nature of variables – objective and psychological - and their influence on cycling commuting in the context of a “climber cycling city”: Vitoria-Gasteiz (Spain). Statistical differences of the variables were determined between cycling commuters and commuters by other modes. The objective variables analyzed allowed us to identify the cycling commuting profile in Vitoria-Gasteiz, but showed a small effect on cycling commuting. However, analyses on seven cycling psychological variables identified and defined, showed a higher influence, especially “Individual capacities” and “Non-commuting cycling habit”. Their results allowed recommending a wide et of policy initiatives. These policy recommendations were made considering that Vitoria-Gasteiz is a “city in transition” towards cycling: a high level of cycling share for the Spanish contex t and the safety issue not being the main barrier for cycling. However the psychological latent variable “Non-commuting cycling habit” indicates that normalization of the bicycle as a mode of transport needs more progress.

Environmental Wireless Sensor Network Deployment in Food Industry: from Theory to Practice

The difficulty behind Wireless Sensor Network deployments in industrial environments not only resides in the number of nodes or the communication protocols but also in the real location of the sensor nodes and the parameters to be monitored. Sensor soiling, high humidity and unreachable locations, among others, make real deployments a very difficult task to plan. Even though it is possible to find myriad approaches for floor planners and deployment tools in the state of the art, most of these problems are very difficult to model and foresee before actually deploying the network in the final scenario. This work shows two real deployments in food factories and how their problems are found and overcome.

Annual league tables of mortality in neonatal intensive care units: longitudinal study

Objective: To assess whether crude league tables of mortality and league tables of risk adjusted mortality accurately reflect the performance of hospitals.

Respiratory effects of dexmedetomidine in the surgical patient requiring intensive care

The respiratory effects of dexmedetomidine were retrospectively examined in 33 postsurgical patients involved in a randomised, placebo-controlled trial after extubation in the intensive care unit (ICU). Morphine requirements were reduced by over 50% in patients receiving dexmedetomidine. There were no differences in respiratory rates, oxygen saturations, arterial pH and arterial partial carbon dioxide tension (PaCO2) between the groups. Interestingly the arterial partial oxygen tension (PaO2) : fractional inspired oxygen (FIO2) ratios were statistically significantly higher in the dexmedetomidine group. Dexmedetomidine provides important postsurgical analgesia and appears to have no clinically important adverse effects on respiration in the surgical patient who requires intensive care.

Emergency airway management by intensive care unit nurses with the intubating laryngeal mask airway and the laryngeal tube

When using the laryngeal tube and the intubating laryngeal mask airway (ILMA), the medium-size (maximum volume 1100 ml) versus adult (maximum volume 1500 ml) self-inflating bags resulted in significantly lower lung tidal volumes. No gastric inflation occurred when using both devices with either ventilation bag. The newly developed medium-size self-inflating bag may be an option to further reduce the risk of gastric inflation while maintaining sufficient lung ventilation. Both the ILMA and laryngeal tube proved to be valid alternatives for emergency airway management in the experimental model used.

Are the returns to technological change in health care declining?

Whether the U.S. health care system supports too much technological change—so that new technologies of low value are adopted, or worthwhile technologies become overused—is a controversial question. This paper analyzes the marginal value of technological change for elderly heart attack patients in 1984–1990. It estimates the additional benefits and costs of treatment by hospitals that are likely to adopt new technologies first or use them most intensively. If the overall value of the additional treatments is declining, then the benefits of treatment by such intensive hospitals relative to other hospitals should decline, and the additional costs of treatment by such hospitals should rise. To account for unmeasured changes in patient mix across hospitals that might bias the results, instrumental–variables methods are used to estimate the incremental mortality benefits and costs. The results do not support the view that the returns to technological change are declining. However, the incremental value of treatment by intensive hospitals is low throughout the study period, supporting the view that new technologies are overused.

Clinical pathway intervention compliance and effectiveness when used in the treatment of patients with severe sepsis and septic shock at an Intensive Care Unit in Spain

The purpose of this quasi-experimental study was to assess levels of compliance with the intervention bundles contained in a clinical pathway used in the treatment of patients with severe sepsis and septic shock, and to analyze the pathway’s impact on survival and duration of hospital stays. We used data on 125 patients in an Intensive Care Unit, divided into a control group (N=84) and an intervention group (N=41). Levels of compliance increased from 13.1% to 29.3% in 5 resuscitation bundle interventions and from 14.3% to 22% in 3 monitoring bundle interventions. In-hospital mortality at 28 days decreased by 11.2% and the duration of hospital stay was reduced by 5 days. Although compliance was low, the intervention enhanced adherence to the instructions given in the clinical pathway and we observed a decline in mortality at 28 days and shorter hospital stays.

Statistical Appendices to “Performance of Long-Term Care Systems in Europe”. ENEPRI Research Report 117A, 21 December 2012

This document provides statistical appendices underpinning the research presented in ENEPRI Research Report No. 117, “Performance of Long-Term Care Systems in Europe”, December 2012. Esther Mot is Senior Researcher in the Netherlands Bureau for Economic Policy Analysis (CPB) and Riemer Faber is researcher at CPB. Joanna Geerts is researcher and Peter Willemé is health economist in the Social Security Research Group at the Federal Planning Bureau (FPB).