Critical events in mechanically ventilated patients

Mechanical Ventilation is an artificial way to help a Patient to breathe. This procedure is used to support patients with respiratory diseases however in many cases it can provoke lung damages, Acute Respiratory Diseases or organ failure. With the goal to early detect possible patient breath problems a set of limit values was defined to some variables monitored by the ventilator (Average Ventilation Pressure, Compliance Dynamic, Flow, Peak, Plateau and Support Pressure, Positive end-expiratory pressure, Respiratory Rate) in order to create critical events. A critical event is verified when a patient has a value higher or lower than the normal range defined for a certain period of time. The values were defined after elaborate a literature review and meeting with physicians specialized in the area. This work uses data streaming and intelligent agents to process the values collected in real-time and classify them as critical or not. Real data provided by an Intensive Care Unit were used to design and test the solution. In this study it was possible to understand the importance of introduce critical events for Mechanically Ventilated Patients. In some cases a value is considered critical (can trigger an alarm) however it is a single event (instantaneous) and it has not a clinical significance for the patient. The introduction of critical events which crosses a range of values and a pre-defined duration contributes to improve the decision-making process by decreasing the number of false positives and having a better comprehension of the patient condition.

Pervasive patient timeline for intensive care units

This research work explores a new way of presenting and representing information about patients in critical care, which is the use of a timeline to display information. This is accomplished with the development of an interactive Pervasive Patient Timeline able to give to the intensivists an access in real-time to an environment containing patients clinical information from the moment in which the patients are admitted in the Intensive Care Unit (ICU) until their discharge This solution allows the intensivists to analyse data regarding vital signs, medication, exams, data mining predictions, among others. Due to the pervasive features, intensivists can have access to the timeline anywhere and anytime, allowing them to make decisions when they need to be made. This platform is patient-centred and is prepared to support the decision process allowing the intensivists to provide better care to patients due the inclusion of clinical forecasts.

Pervasive adaptive data acquisition gateway for critical healthcare

The data acquisition process in real-time is fundamental to provide appropriate services and improve health professionals decision. In this paper a pervasive adaptive data acquisition architecture of medical devices (e.g. vital signs, ventilators and sensors) is presented. The architecture was deployed in a real context in an Intensive Care Unit. It is providing clinical data in real-time to the INTCare system. The gateway is composed by several agents able to collect a set of patients’ variables (vital signs, ventilation) across the network. The paper shows as example the ventilation acquisition process. The clients are installed in a machine near the patient bed. Then they are connected to the ventilators and the data monitored is sent to a multithreading server which using Health Level Seven protocols records the data in the database. The agents associated to gateway are able to collect, analyse, interpret and store the data in the repository. This gateway is composed by a fault tolerant system that ensures a data store in the database even if the agents are disconnected. The gateway is pervasive, universal, and interoperable and it is able to adapt to any service using streaming data.

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.

Step towards to improve the voluntary interruption of pregnancy by means of business intelligence

With the implementation of Information and Communication Technologies in the health sector, it became possible the existence of an electronic record of information for patients, enabling the storage and the availability of their information in databases. However, without the implementation of a Business Intelligence (BI) system, this information has no value. Thus, the major motivation of this paper is to create a decision support system that allows the transformation of information into knowledge, giving usability to the stored data. The particular case addressed in this chapter is the Centro Materno Infantil do Norte, in particular the Voluntary Interruption of Pregnancy unit. With the creation of a BI system for this module, it is possible to design an interoperable, pervasive and real-time platform to support the decision-making process of health professionals, based on cases that occurred. Furthermore, this platform enables the automation of the process for obtaining key performance indicators that are presented annually by this health institution. In this chapter, the BI system implemented in the VIP unity in CMIN, some of the KPIs evaluated as well as the benefits of this implementation are presented.

Real-Time models to predict the use of vasopressors in monitored patients

The needs of reducing human error has been growing in every field of study, and medicine is one of those. Through the implementation of technologies is possible to help in the decision making process of clinics, therefore to reduce the difficulties that are typically faced. This study focuses on easing some of those difficulties by presenting real-time data mining models capable of predicting if a monitored patient, typically admitted in intensive care, will need to take vasopressors. Data Mining models were induced using clinical variables such as vital signs, laboratory analysis, among others. The best model presented a sensitivity of 94.94%. With this model it is possible reducing the misuse of vasopressors acting as prevention. At same time it is offered a better care to patients by anticipating their treatment with vasopressors.

Predicting pre-triage waiting time in a maternity emergency room through data mining

An unsuitable patient flow as well as prolonged waiting lists in the emergency room of a maternity unit, regarding gynecology and obstetrics care, can affect the mother and child’s health, leading to adverse events and consequences regarding their safety and satisfaction. Predicting the patients’ waiting time in the emergency room is a means to avoid this problem. This study aims to predict the pre-triage waiting time in the emergency care of gynecology and obstetrics of Centro Materno Infantil do Norte (CMIN), the maternal and perinatal care unit of Centro Hospitalar of Oporto, situated in the north of Portugal. Data mining techniques were induced using information collected from the information systems and technologies available in CMIN. The models developed presented good results reaching accuracy and specificity values of approximately 74% and 94%, respectively. Additionally, the number of patients and triage professionals working in the emergency room, as well as some temporal variables were identified as direct enhancers to the pre-triage waiting time. The imp lementation of the attained knowledge in the decision support system and business intelligence platform, deployed in CMIN, leads to the optimization of the patient flow through the emergency room and improving the quality of services.

Real-Time decision support using data mining to predict blood pressure critical events in intensive medicine patients

Patient blood pressure is an important vital signal to the physicians take a decision and to better understand the patient condition. In Intensive Care Units is possible monitoring the blood pressure due the fact of the patient being in continuous monitoring through bedside monitors and the use of sensors. The intensivist only have access to vital signs values when they look to the monitor or consult the values hourly collected. Most important is the sequence of the values collected, i.e., a set of highest or lowest values can signify a critical event and bring future complications to a patient as is Hypotension or Hypertension. This complications can leverage a set of dangerous diseases and side-effects. The main goal of this work is to predict the probability of a patient has a blood pressure critical event in the next hours by combining a set of patient data collected in real-time and using Data Mining classification techniques. As output the models indicate the probability (%) of a patient has a Blood Pressure Critical Event in the next hour. The achieved results showed to be very promising, presenting sensitivity around of 95%.

A Real-Time intelligent system for tracking patient condition

Hospitals have multiple data sources, such as embedded systems, monitors and sensors. The number of data available is increasing and the information are used not only to care the patient but also to assist the decision processes. The introduction of intelligent environments in health care institutions has been adopted due their ability to provide useful information for health professionals, either in helping to identify prognosis or also to understand patient condition. Behind of this concept arises this Intelligent System to track patient condition (e.g. critic events) in health care. This system has the great advantage of being adaptable to the environment and user needs. The system is focused in identifying critic events from data streaming (e.g. vital signs and ventilation) which is particularly valuable for understanding the patient’s condition. This work aims to demonstrate the process of creating an intelligent system capable of operating in a real environment using streaming data provided by ventilators and vital signs monitors. Its development is important to the physician because becomes possible crossing multiple variables in real-time by analyzing if a value is critic or not and if their variation has or not clinical importance.

Sistemática filogenética y biología reproductiva con énfasis en solanum y capsicum (solanaceae). Systematics, phylogeny and reproductive biology with emphasis in solanum and capsicum (solanaceae).

Muchas respuestas a preguntas básicas sobre relaciones evolutivas, ubicación sistemática y evolución de caracteres morfológicos y ecológicos pueden ser obtenidas a través de las reconstrucciones filogenéticas. Sobre este contexto se pretende encarar en este proyecto estudios de filogenia molecular, revisiones sistemáticas, biología reproductiva y citogenética en Solanáceas americanas. Se intentará resolver la delimitación específica de Solanum sect. Solanum y Geminata, y Capsicum, y establecer relaciones filogenéticas en estos grupos. Se harán revisiones analizándose caracteres vegetativos y reproductivos críticos para evaluar su variabilidad y definir su valor taxonómico; para los estudios moleculares se utilizarán los marcadores ndhF, trnT-L, trnL-F y waxy. En base a los resultados se propondrán agrupamientos y relaciones de parentesco. Además, se hará un estudio cariosistemático para caracterizar y circunscribir especies en Solanum y miembros de la tribu Physaleae, y hasta variedades y/o cultivares en Capsicum, mediante técnicas clásicas y de bandeos de fluorescencia y AgNOR e hibridación in situ fluorescente (FISH). A nivel reproductivo, se estudiará la ecofisiología en las estructuras masculinas y su incidencia en la fructificación en Capscium baccatum. El desarrollo de esta temática comprende experiencias in vivo (a campo y en laboratorio) así como estudios histológicos y químicos.Se espera avanzar en la resolución de algunos problemas: 1) la complicada delimitación de especies de los taxones en estudio; 2) las relaciones filogenéticas en algunos de ellos; 3) la falta de conocimiento de la organización genómica; 4) el origen de las especies cultivadas de Capsicum. En cuanto a la biología reproductiva, para C. baccatum se pretende avanzar en el conocimiento de variables de relevancia en la reproducción, en especial los efectos del ambiente.

Estudios evolutivos en especies de plantas nativas.

Se indagará principalmente acerca del rol de los procesos neutrales, como la deriva génica, de procesos selectivos, como la selección natural mediada por polinizadores y de procesos históricos (geológicos y climáticos del pasado) en la diversificación floral tanto a escala microevolutiva como macroevolutiva. La heterogeneidad ambiental que se presenta en amplios rangos geográficos puede promover la diferenciación entre poblaciones debido a las diferencias en condiciones físicas y biológicas. De esta manera, especies ampliamente distribuidas ofrecen la oportunidad de explorar la dinámica de los procesos evolutivos que tienen lugar a nivel interpoblacional (Dobzhansky 1970, Thompson 1999). El estudio comparativo entre especies hermanas permite comprender cómo la selección natural (adaptación) y la inercia filogenética (herencia ancestral) han modelado los rasgos de las especies que observamos en la actualidad (Díaz 2002, Schluter 2000, Futuyma 2005). Uno de los usos más importantes de la información filogenética es el de reconstruir la historia del cambio evolutivo en caracteres adaptativos mediante su mapeo en la filogenia y la reconstrucción del estado de estos caracteres en el ancestro. Así, la asociación entre transición de caracteres y transiciones en grupos funcionales es una evidencia directa de la hipótesis adaptativa de que los rasgos son seleccionados por grupos funcionales de polinizadores. Una aproximación filogenética puede permitir identificar la dirección y el tiempo de evolución. Todos estos aspectos señalan la necesidad de adoptar una perspectiva conceptualmente integrada (morfológica, genética, filogenética, filogeográfica y ecológica) en el estudio de la biología evolutiva de las flores. Estudiar como actúan los procesos micro- y macroevolutivos en las interacciones planta-polinizador, en una dimensión espacial y temporal, arrojará resultados importantes tanto en el campo teórico como en el de la conservación. Por una parte, permitirá poner a prueba hipótesis relevantes sobre la adaptación de caracteres, mientras que explorará los procesos evolutivos que subyacen a las tramas de las interacciones planta-polinizador; por otro lado, comprender el rol de los cambios climáticos pasados en la diversificación biológica es interesante tanto desde una aproximación evolutiva como desde la biología de la conservación (Avise 2000; Moritz et al. 2000; Petit et al. 2003; Hewitt 2004). Géneros a ser estudiados en este proyecto: 1- Anarthrophyllum (Fabaceae,15 spp), 2- Monttea (Plantaginaceae, 3 spp), 3- Caleolaria (Calceolariaceae 3 spp), 4- Centris (Apidae, 1 spp), 5- Jaborosa (Solanaceae, 23 spp). Metodología: Mapeado de las poblaciones. Elenco de polinizadores, frecuencia. Obtención y medición de caracteres fenotípicos florales. Néctar: concentración y vol. Aceites (peso); Morfometría geométrica (Zelditch et al. 2005). Éxito reproductivo (Dafni & Kevan 2003). Caracteres genéticos: extracción, amplificación y secuenciación: en Calceolaria se utilizarán 2 genes de cloroplasto trnH-psbA y trnS-trnG y genes anónimos nucleares de copia única (scnADN), para Jaborosa se utilizarán 3 genes de cloroplasto (trnH-psbA, TrnD-trnT y ndhF-rp32) y el gen nuclear GBSSI waxy. Finalmente para Centris cineraria se usaría el tRNA ILE y NADH Deshidrogenada subunidad 2. Análisis filogenéticos de parsimonia (Goloboff et al. 2000, Kitching et al. 1998, Nixon 2002, Farris et al. 1996, Sorenson 1999); Filogeografía: reconstrucción de redes por parsimonia (Clement et al. 2000; Posada et al. 2000), análisis de clados anidados (NCPA). Se usarán las claves de inferencia (Templeton 2004). Para todos estos análisis se utilizarán los siguientes programas: DnaSP, Network, Arlequin, MrBayes, Paup, ModelTest, Beast, TNT, WinClada TCS y GeoDis. Estadística multivariada: Los diferentes rasgos florales mencionados se analizarán utilizando distancias de Gower (datos cualitativos) y euclídeas (datos cuantitativos) mediante la técnica multivariada ACoP.

Histologia e ultra-estrutura do vaso dorsal de Scaptotrigona postica (Hymenoptera, Apidae) em operárias e rainhas de diferentes idades

O vaso dorsal é um órgão tubular localizado na região mediano-dorsal do corpo dos insetos, abaixo do tegumento. Fez-se um estudo de microscopia de luz e eletrônica de transmissão da porção abdominal do vaso dorsal, o coração, em uma espécie de abelha indígena. Foram estudadas operárias e rainhas em diferentes idades. O coração está localizado no sinus pericárdico. A parede cardíaca é formada por fibras musculares estriadas e apresenta aberturas ou ostíolos providos de válvulas. A fibra cardíaca contém miofibrilas arranjadas irregularmente, núcleos alongados ou redondos, mitocôndrias grandes e numerosas, e depósitos de glicogênio. Em operárias e rainhas longevas, as fibras encontram-se em degeneração, evidenciada por vacúolos autofágicos, alterações mitocondriais e acúmulo de corpos mielínicos. Em conclusão, o coração de Scaptotrigona postica é semelhante ao de outros insetos estudados. As alterações encontradas estão relacionadas ao processo de envelhecimento e mantêm relação temporal com a expectativa de vida da casta.

Atividades relacionadas à construção e aprovisionamento de ninhos de Xylocopa subcyanea (Hymenoptera, Apidae) em uma área de restinga na Bahia, Brasil

As abelhas do gênero Xylocopa Latreille, 1802 são comuns em ecossistemas de restingas em acelerada fase de degradação e são importantes polinizadores deste ecossistema. Elas nidificam especialmente em madeira morta ou apodrecida. As atividades relacionadas à construção e estrutura dos ninhos de Xylocopa (Schoenherria) subcyanea Perez, 1901 na restinga do litoral norte da Bahia, Brasil, foram observadas em 43 ninhos ativos de X. subcyanea, em diferentes fases de desenvolvimento, em dois troncos apodrecidos. A fase de fundação ou reuso no primeiro tronco foi em agosto e a fase de provisionamento das células nos ninhos, em ambos troncos, em janeiro. As principais atividades foram escavações no tronco, entrada e saída dos ninhos, permanência na entrada do ninho, entrada com pólen e desidratação de néctar. Foi observado horário preferencial ao longo do dia para as atividades de entrada e saída dos ninhos, sendo estas influenciadas pelos horários do nascer e pôr do sol. Ninhos abandonados foram reusados por X. subcyanea e Centris tarsata SMITH, 1874. Os ninhos ativos eram ocupados por quatro diferentes abelhas adultas. Geralmente, uma abelha ficava na entrada do ninho. A estrutura do ninho foi descrita.