Oxygen therapy, continuous positive airway pressure, or noninvasive bilevel positive pressure ventilation in the treatment of acute cardiogenic pulmonary edema

OBJECTIVE: To compare the effects of 3 types of noninvasive respiratory support systems in the treatment of acute pulmonary edema: oxygen therapy (O2), continuous positive airway pressure, and bilevel positive pressure ventilation. METHODS: We studied prospectively 26 patients with acute pulmonary edema, who were randomized into 1 of 3 types of respiratory support groups. Age was 69±7 years. Ten patients were treated with oxygen, 9 with continuous positive airway pressure, and 7 with noninvasive bilevel positive pressure ventilation. All patients received medicamentous therapy according to the Advanced Cardiac Life Support protocol. Our primary aim was to assess the need for orotracheal intubation. We also assessed the following: heart and respiration rates, blood pressure, PaO2, PaCO2, and pH at begining, and at 10 and 60 minutes after starting the protocol. RESULTS: At 10 minutes, the patients in the bilevel positive pressure ventilation group had the highest PaO2 and the lowest respiration rates; the patients in the O2 group had the highest PaCO2 and the lowest pH (p<0.05). Four patients in the O2 group, 3 patients in the continuous positive pressure group, and none in the bilevel positive pressure ventilation group were intubated (p<0.05). CONCLUSION: Noninvasive bilevel positive pressure ventilation was effective in the treatment of acute cardiogenic pulmonary edema, accelerated the recovery of vital signs and blood gas data, and avoided intubation.

Predicting triage waiting time in maternity emergency care by means of data mining

Healthcare organizations often benefit from information technologies as well as embedded decision support systems, which improve the quality of services and help preventing complications and adverse events. In Centro Materno Infantil do Norte (CMIN), the maternal and perinatal care unit of Centro Hospitalar of Oporto (CHP), an intelligent pre-triage system is implemented, aiming to prioritize patients in need of gynaecology and obstetrics care in two classes: urgent and consultation. The system is designed to evade emergency problems such as incorrect triage outcomes and extensive triage waiting times. The current study intends to improve the triage system, and therefore, optimize the patient workflow through the emergency room, by predicting the triage waiting time comprised between the patient triage and their medical admission. For this purpose, data mining (DM) techniques are induced in selected information provided by the information technologies implemented in CMIN. The DM models achieved accuracy values of approximately 94% with a five range target distribution, which not only allow obtaining confident prediction models, but also identify the variables that stand as direct inducers to the triage waiting times.

Optimization techniques to detect early ventilation extubation in intensive care units

The decision support models in intensive care units are developed to support medical staff in their decision making process. However, the optimization of these models is particularly difficult to apply due to dynamic, complex and multidisciplinary nature. Thus, there is a constant research and development of new algorithms capable of extracting knowledge from large volumes of data, in order to obtain better predictive results than the current algorithms. To test the optimization techniques a case study with real data provided by INTCare project was explored. This data is concerning to extubation cases. In this dataset, several models like Evolutionary Fuzzy Rule Learning, Lazy Learning, Decision Trees and many others were analysed in order to detect early extubation. The hydrids Decision Trees Genetic Algorithm, Supervised Classifier System and KNNAdaptive obtained the most accurate rate 93.2%, 93.1%, 92.97% respectively, thus showing their feasibility to work in a real environment.

A clinical recommendation system to maternity care

Nowadays in healthcare, the Clinical Decision Support Systems are used in order to help health professionals to take an evidence-based decision. An example is the Clinical Recommendation Systems. In this sense, it was developed and implemented in Centro Hospitalar do Porto a pre-triage system in order to group the patients on two levels (urgent or outpatient). However, although this system is calibrated and specific to the urgency of obstetrics and gynaecology, it does not meet all clinical requirements by the general department of the Portuguese HealthCare (Direção Geral de Saúde). The main requirement is the need of having priority triage system characterized by five levels. Thus some studies have been conducted with the aim of presenting a methodology able to evolve the pre-triage system on a Clinical Recommendation System with five levels. After some tests (using data mining and simulation techniques), it has been validated the possibility of transformation the pre-triage system in a Clinical Recommendation System in the obstetric context. This paper presents an overview of the Clinical Recommendation System for obstetric triage, the model developed and the main results achieved.

Improving appropriateness of antibiotic therapy: randomized trial of an intervention to foster reassessment of prescription after 3 days.

OBJECTIVES: Reassessment of ongoing antibiotic therapy is an important step towards appropriate use of antibiotics. This study was conducted to evaluate the impact of a short questionnaire designed to encourage reassessment of intravenous antibiotic therapy after 3 days. PATIENTS AND METHODS: Patients hospitalized on the surgical and medical wards of a university hospital and treated with an intravenous antibiotic for 3-4 days were randomly allocated to either an intervention or control group. The intervention consisted of mailing to the physician in charge of the patient a three-item questionnaire referring to possible adaptation of the antibiotic therapy. The primary outcome was the time elapsed from randomization until a first modification of the initial intravenous antibiotic therapy. It was compared within both groups using Cox proportional-hazard modelling. RESULTS: One hundred and twenty-six eligible patients were randomized in the intervention group and 125 in the control group. Time to modification of intravenous antibiotic therapy was 14% shorter in the intervention group (adjusted hazard ratio for modification 1.28, 95% CI 0.99-1.67, P = 0.06). It was significantly shorter in the intervention group compared with a similar group of 151 patients observed during a 2 month period preceding the study (adjusted hazard ratio 1.17, 95% CI 1.03-1.32, P = 0.02). CONCLUSION: The results suggest that a short questionnaire, easily adaptable to automatization, has the potential to foster reassessment of antibiotic therapy.

Sistema intel•ligent d’ajuda a la decisió pel control remot de tecnologia de membranes per la depuració d’aigües

El principal objectiu del projecte consisteix en desenvolupar l’anàlisi, disseny,desenvolupament i implementació d’un sistema d’ajuda a la decisió (SAD) basat en elconeixement pel control remot i la supervisió de l’operació integrada d’estacionsdepuradores BRM (bioreactor de membranes) pe ra la depuració d’aigües residuals ambexigències de qualitat de reutilització de l’aigua tractada

Adaptive business intelligence for an open negotiation environment

Engineering of negotiation model allows to develop effective heuristic for business intelligence. Digital ecosystems demand open negotiation models. To define in advance effective heuristics is not compliant with the requirement of openness. The new challenge is to develop business intelligence in advance exploiting an adaptive approach. The idea is to learn business strategy once new negotiation model rise in the e-market arena. In this paper we present how recommendation technology may be deployed in an open negotiation environment where the interaction protocol models are not known in advance. The solution we propose is delivered as part of the ONE Platform, open source software that implements a fully distributed open environment for business negotiation

A Negotiation Style Recommender Based on Computational Ecology in Open Negotiation Environments

The system described herein represents the first example of a recommender system in digital ecosystems where agents negotiate services on behalf of small companies. The small companies compete not only with price or quality, but with a wider service-by-service composition by subcontracting with other companies. The final result of these offerings depends on negotiations at the scale of millions of small companies. This scale requires new platforms for supporting digital business ecosystems, as well as related services like open-id, trust management, monitors and recommenders. This is done in the Open Negotiation Environment (ONE), which is an open-source platform that allows agents, on behalf of small companies, to negotiate and use the ecosystem services, and enables the development of new agent technologies. The methods and tools of cyber engineering are necessary to build up Open Negotiation Environments that are stable, a basic condition for predictable business and reliable business environments. Aiming to build stable digital business ecosystems by means of improved collective intelligence, we introduce a model of negotiation style dynamics from the point of view of computational ecology. This model inspires an ecosystem monitor as well as a novel negotiation style recommender. The ecosystem monitor provides hints to the negotiation style recommender to achieve greater stability of an open negotiation environment in a digital business ecosystem. The greater stability provides the small companies with higher predictability, and therefore better business results. The negotiation style recommender is implemented with a simulated annealing algorithm at a constant temperature, and its impact is shown by applying it to a real case of an open negotiation environment populated by Italian companies

Medical futility at the end of life: the perspectives of intensive care and palliative care clinicians.

Objectives Medical futility at the end of life is a growing challenge to medicine. The goals of the authors were to elucidate how clinicians define futility, when they perceive life-sustaining treatment (LST) to be futile, how they communicate this situation and why LST is sometimes continued despite being recognised as futile. Methods The authors reviewed ethics case consultation protocols and conducted semi-structured interviews with 18 physicians and 11 nurses from adult intensive and palliative care units at a tertiary hospital in Germany. The transcripts were subjected to qualitative content analysis. Results Futility was identified in the majority of case consultations. Interviewees associated futility with the failure to achieve goals of care that offer a benefit to the patient's quality of life and are proportionate to the risks, harms and costs. Prototypic examples mentioned are situations of irreversible dependence on LST, advanced metastatic malignancies and extensive brain injury. Participants agreed that futility should be assessed by physicians after consultation with the care team. Intensivists favoured an indirect and stepwise disclosure of the prognosis. Palliative care clinicians focused on a candid and empathetic information strategy. The reasons for continuing futile LST are primarily emotional, such as guilt, grief, fear of legal consequences and concerns about the family's reaction. Other obstacles are organisational routines, insufficient legal and palliative knowledge and treatment requests by patients or families. Conclusion Managing futility could be improved by communication training, knowledge transfer, organisational improvements and emotional and ethical support systems. The authors propose an algorithm for end-of-life decision making focusing on goals of treatment.

Supply chain management: An opportunity for metaheuristics

In today s highly competitive and global marketplace the pressure onorganizations to find new ways to create and deliver value to customersgrows ever stronger. In the last two decades, logistics and supply chainhas moved to the center stage. There has been a growing recognition thatit is through an effective management of the logistics function and thesupply chain that the goal of cost reduction and service enhancement canbe achieved. The key to success in Supply Chain Management (SCM) requireheavy emphasis on integration of activities, cooperation, coordination andinformation sharing throughout the entire supply chain, from suppliers tocustomers. To be able to respond to the challenge of integration there isthe need of sophisticated decision support systems based on powerfulmathematical models and solution techniques, together with the advancesin information and communication technologies. The industry and the academiahave become increasingly interested in SCM to be able to respond to theproblems and issues posed by the changes in the logistics and supply chain.We present a brief discussion on the important issues in SCM. We then arguethat metaheuristics can play an important role in solving complex supplychain related problems derived by the importance of designing and managingthe entire supply chain as a single entity. We will focus specially on theIterated Local Search, Tabu Search and Scatter Search as the ones, but notlimited to, with great potential to be used on solving the SCM relatedproblems. We will present briefly some successful applications.

Development and validation of a clinical decision rule for the diagnosis of influenza.

INTRODUCTION: A clinical decision rule to improve the accuracy of a diagnosis of influenza could help clinicians avoid unnecessary use of diagnostic tests and treatments. Our objective was to develop and validate a simple clinical decision rule for diagnosis of influenza. METHODS: We combined data from 2 studies of influenza diagnosis in adult outpatients with suspected influenza: one set in California and one in Switzerland. Patients in both studies underwent a structured history and physical examination and had a reference standard test for influenza (polymerase chain reaction or culture). We randomly divided the dataset into derivation and validation groups and then evaluated simple heuristics and decision rules from previous studies and 3 rules based on our own multivariate analysis. Cutpoints for stratification of risk groups in each model were determined using the derivation group before evaluating them in the validation group. For each decision rule, the positive predictive value and likelihood ratio for influenza in low-, moderate-, and high-risk groups, and the percentage of patients allocated to each risk group, were reported. RESULTS: The simple heuristics (fever and cough; fever, cough, and acute onset) were helpful when positive but not when negative. The most useful and accurate clinical rule assigned 2 points for fever plus cough, 2 points for myalgias, and 1 point each for duration <48 hours and chills or sweats. The risk of influenza was 8% for 0 to 2 points, 30% for 3 points, and 59% for 4 to 6 points; the rule performed similarly in derivation and validation groups. Approximately two-thirds of patients fell into the low- or high-risk group and would not require further diagnostic testing. CONCLUSION: A simple, valid clinical rule can be used to guide point-of-care testing and empiric therapy for patients with suspected influenza.

Analysis, modelling and classification of geospatial data using machine learning

The research considers the problem of spatial data classification using machine learning algorithms: probabilistic neural networks (PNN) and support vector machines (SVM). As a benchmark model simple k-nearest neighbor algorithm is considered. PNN is a neural network reformulation of well known nonparametric principles of probability density modeling using kernel density estimator and Bayesian optimal or maximum a posteriori decision rules. PNN is well suited to problems where not only predictions but also quantification of accuracy and integration of prior information are necessary. An important property of PNN is that they can be easily used in decision support systems dealing with problems of automatic classification. Support vector machine is an implementation of the principles of statistical learning theory for the classification tasks. Recently they were successfully applied for different environmental topics: classification of soil types and hydro-geological units, optimization of monitoring networks, susceptibility mapping of natural hazards. In the present paper both simulated and real data case studies (low and high dimensional) are considered. The main attention is paid to the detection and learning of spatial patterns by the algorithms applied.

FERTILIZER RECOMMENDATION SYSTEM FOR MELON BASED ON NUTRITIONAL BALANCE

Melon is one of the most demanding cucurbits regarding fertilization, requiring knowledge of soils, crop nutritional requirements, time of application, and nutrient use efficiency for proper fertilization. Developing support systems for decision-making for fertilization that considers these variables in nutrient requirement and supply is necessary. The objective of this study was parameterization of a fertilizer recommendation system for melon (Ferticalc-melon) based on nutritional balance. To estimate fertilizer recommendation, the system considers the requirement subsystem (REQ), which includes the demand for nutrients by the plant, and the supply subsystem (SUP), which corresponds to the supply of nutrients through the soil and irrigation water. After determining the REQtotal and SUPtotal, the system calculates the nutrient balances for N, P, K, Ca, Mg, and S, recommending fertilizer application if the balance is negative (SUP < REQ), but not if the balance is positive or zero (SUP ≥ REQ). Simulations were made for different melon types (Yellow, Cantaloupe, Galia and Piel-de-sapo), with expected yield of 45 t ha-1. The system estimated that Galia type was the least demanding in P, while Piel-de-sapo was the most demanding. Cantaloupe was the least demanding for N and Ca, while the Yellow type required less K, Mg, and S. As compared to other fertilizer recommendation methods adopted in Brazil, the Ferticalc system was more dynamic and flexible. Although the system has shown satisfactory results, it needs to be evaluated under field conditions to improve its recommendations.

Analysis of Existing Work-Zone Devices with MASH Safety Performance Criteria, TPF-5(018), 2009

Crashworthy, work-zone, portable sign support systems accepted under NCHRP Report No. 350 were analyzed to predict their safety peformance according to the TL-3 MASH evaluation criteria. An analysis was conducted to determine which hardware parameters of sign support systems would likely contribute to the safety performance with MASH. The acuracy of the method was evaluated through full-scale crash testing. Four full-scale crash tests were conducted with a pickup truck. Two tall-mounted, sign support systems with aluminum sign panels failed the MASH criteria due to windshield penetration. One low-mounted system with a vinyl, roll-up sign panel failed the MASH criteria due to windshield and floorboard penetration. Another low-mounted system with an aluminum sign panel successfully met the MASH criteria. Four full-scale crash tests were conducted with a small passenger car. The low-mounted tripod system with an aluminum sign panel failed the MASH criteria due to windshield penetration. One low-mounted system with aluminum sign panel failed the MASH criteria due to excessive windshield deformation, and another similar system passed the MASH criteria. The low-mounted system with a vinyl, roll-up sign panel successfully met the MASH criteria. Hardware parameters of work-zone sign support systems that were determined to be important for failure with MASH include sign panel material, the height to the top of the mast, the presence of flags, sign-locking mechanism, base layout and system orientation. Flowcharts were provided to assist manufacturers when designing new sign support systems.

"A renewed sense of purpose": Mothers' and fathers' experience of having a child following a recent stillbirth.

Background Most research has focused on mothers¿ experiences of perinatal loss itself or on the subsequent pregnancy, whereas little attention has been paid to both parents¿ experiences of having a child following late perinatal loss and the experience of parenting this child. The current study therefore explored mothers¿ and fathers' experiences of becoming a parent to a child born after a recent stillbirth, covering the period of the second pregnancy and up to two years after the birth of the next baby.MethodIn depth interviews were conducted with 7 couples (14 participants). Couples were eligible if they previously had a stillbirth (after 24 weeks of gestation) and subsequently had another child (their first live baby) who was now under the age of 2 years. Couples who had more than one child after experiencing a stillbirth and those who were not fluent in English were excluded. Qualitative analysis of the interview data was conducted using Interpretive Phenomenological Analysis.ResultsFive superordinate themes emerged from the data: Living with uncertainty; Coping with uncertainty; Relationship with the next child; The continuing grief process; Identity as a parent. Overall, fathers' experiences were similar to those of mothers', including high levels of anxiety and guilt during the subsequent pregnancy and after the child was born. Coping strategies to address these were identified. Differences between mothers and fathers regarding the grief process during the subsequent pregnancy and after their second child was born were identified. Despite difficulties with bonding during pregnancy and at the time when the baby was born, parents' perceptions of their relationship with their subsequent child were positive.ConclusionsFindings highlight the importance of tailoring support systems not only according to mothers' but also to fathers' needs. Parents¿, and particularly fathers', reported lack of opportunities for grieving as well as the high level of anxiety of both parents about their baby's wellbeing during pregnancy and after birth implies a need for structured support. Difficulties experienced in bonding with the subsequent child during pregnancy and once the child is born need to be normalised.