Схема когенерации с размещением противодавленческой и гидропаровой турбин на общем валу с газопоршневой установкой

Показана перспективність використання когенераційних технологій для підвищення рентабельності вугільних підприємств. Розглянуто схему з розміщенням турбіни з противотиском і гідропарової турбіни на одному валу з газопоршневою установкою. Використання даної схеми для утилізації надлишкового тепла шахтних енергокомплексів дозволить отримати коефіцієнт корисної дії 64 % та зменшити витрати палива.In this paper the perspective use of cogeneration technology enhance the profitability of coal enterprises was discussed. The scheme with setting back-pressures and steam-water turbines on one shaft of gas engine was considered. Using this scheme for utilization of surplus heat mine energy complexes will provide efficiency of 64% and reduce fuel

Patient and anesthesia characteristics of children with low pre-incision blood pressure: A retrospective observational study

Background: Intraoperative blood pressure has been suggested as a key factor for safe pediatric anesthesia. However, there is not much insight into factors that discriminate between children with low and normal pre-incision blood pressure. Our aim was to explore whether children who have a low blood pressure during anesthesia are different than those with normal blood pressure. The focus of the present study was on the pre-incision period. Methods: This retrospective study included pediatric patients undergoing anesthesia for non-cardiac surgery at a tertiary pediatric university hospital, between 2012 and 2016. We analyzed the association between pre-incision blood pressure and patient- and anesthesia characteristics, comparing low with normal pre-incision blood pressure. This association was further explored with a multivariable linear regression. Results: In total, 20 962 anesthetic cases were included. Pre-incision blood pressure was associated with age (beta −0.04 SD per year), gender (female −0.11), previous surgery (−0.15), preoperative blood pressure (+0.01 per mm Hg), epilepsy (0.12), bronchial hyperactivity (−0.18), emergency surgery (0.10), loco-regional technique (−0.48), artificial airway device (supraglottic airway device instead of tube 0.07), and sevoflurane concentration (0.03 per sevoflurane %). Conclusions: Children with low pre-incision blood pressure do not differ on clinically relevant factors from children with normal blood pressure. Although the present explorative study shows that pre-incision blood pressure is partly dependent on patient characteristics and partly dependent on anesthetic technique, other unmeasured variables might play a more important role

Use of anesthesia data for research

In CHAPTER 2 we have developed reference curves for blood pressure during anesthesia in children. We collected pediatric anesthesia data from 10 different hospitals in two different countries. With 116,362 anesthetic procedures we constructed sex specific references, adjusted for age, weight or height. The curves provided in CHAPTER 2, help clinicians with a better reference for intra-operative blood pressure monitoring compared to what was previously available. These references can also be used in research, as we illustrated in CHAPTER 3. We applied the references to a single-center pediatric cohort, to explore which children get a relatively low blood pressure during anesthesia. The blood pressure was adjusted for sex and height using the references from CHAPTER 2. We studied the association of patient and anesthesia procedure characteristics with this normalized blood pressure. Part of the variation in blood pressure could be explained by the procedure characteristics, such as application of a loco-regional technique. Only small effects of patient characteristics on blood pressure were estimated, therefore we could not define a typical child that develops low blood pressure during anesthesia. In PART II of this dissertation we focused on artifacts in physiologic measurements (i.e. heart rate, saturation, end tidal carbon dioxide, non-invasive blood pressure and invasive blood pressure) during anesthesia. First, in CHAPTER 4 we observed the incidence of artifacts in pediatric cases during 170 hours of anesthesia. Incidence of artifacts ranges from 0.5% for heart rate to 7.5% for end tidal carbon dioxide. We found that these artifacts did not occur at random and were dependent on different factors, e.g. type of measurement, age of the patient, type of surgery or the phase of surgery at which the measurement was done. As some of these factors could also be related to outcome, we hypothesized that artifacts or the way artifacts are filtered, could act as a confounder. Second, in CHAPTER 5 we tested this hypothesis by studying the effect of different artifact filtering methods on the result of an example study. The artifact filtering methods were identified with a systematic literature search. As an example we used hospital data from adults older than 60 who underwent medium to high risk surgery and analyzed the relation between hypotension and postoperative myocardial injury. We showed that there is indeed a small systematic effect of artifact filtering methods on the estimated relation between hypotension and myocardial injury. Finally, in CHAPTER 6 we focused in more detail on the artifacts in invasive blood pressure data and formulated a method to identify artifacts automatically. We showed that besides dependency on factors (e.g. type of surgery and phase of surgery) it also matters when and how artifacts are identified. When someone observes the procedure live, similar to CHAPTER 4, the artifact identification was different than when someone reviewed the data retrospectively, which is customary in database research. The information or context available live and retrospective is different, and therefore the conclusion drawn by an observer is different. Therefore we cannot simply use live or retrospective manual annotations as a golden standard for artifact identification. A clear definition of what is assumed to be an artifact should first be formulated and reported by a researcher. Despite the differences we hypothesized that the process of artifact identification could still be automated. In CHAPTER 6 we applied different learning algorithms to model the identification of artifacts. In this study, the performance of these algorithms remained mediocre at best. Future research could focus on development of better performing algorithms using additional information and further optimization of the training of such algorithms to reduce the manual work required

Incidence of Artifacts and Deviating Values in Research Data Obtained from an Anesthesia Information Management System in Children

BACKGROUND: Vital parameter data collected in anesthesia information management systems are often used for clinical research. The validity of this type of research is dependent on the number of artifacts. METHODS: In this prospective observational cohort study, the incidence of artifacts in anesthesia information management system data was investigated in children undergoing anesthesia for noncardiac procedures. Secondary outcomes included the incidence of artifacts among deviating and nondeviating values, among the anesthesia phases, and among different anesthetic techniques. RESULTS: We included 136 anesthetics representing 10,236 min of anesthesia time. The incidence of artifacts was 0.5% for heart rate (95% CI: 0.4 to 0.7%), 1.3% for oxygen saturation (1.1 to 1.5%), 7.5% for end-tidal carbon dioxide (6.9 to 8.0%), 5.0% for noninvasive blood pressure (4.0 to 6.0%), and 7.3% for invasive blood pressure (5.9 to 8.8%). The incidence of artifacts among deviating values was 3.1% for heart rate (2.1 to 4.4%), 10.8% for oxygen saturation (7.6 to 14.8%), 14.1% for end-tidal carbon dioxide (13.0 to 15.2%), 14.4% for noninvasive blood pressure (10.3 to 19.4%), and 38.4% for invasive blood pressure (30.3 to 47.1%). CONCLUSIONS: Not all values in anesthesia information management systems are valid. The incidence of artifacts stored in the present pediatric anesthesia practice was low for heart rate and oxygen saturation, whereas noninvasive and invasive blood pressure and end-tidal carbon dioxide had higher artifact incidences. Deviating values are more often artifacts than values in a normal range, and artifacts are associated with the phase of anesthesia and anesthetic technique. Development of (automatic) data validation systems or solutions to deal with artifacts in data is warranted

Artifact processing methods influence on intraoperative hypotension quantification and outcome effect estimates

Background: Physiologic data that is automatically collected during anesthesia is widely used for medical record keeping and clinical research. These data contain artifacts, which are not relevant in clinical care, but may influence research results. The aim of this study was to explore the effect of different methods of filtering and processing artifacts in anesthesiology data on study findings in order to demonstrate the importance of proper artifact filtering. Methods: The authors performed a systematic literature search to identify artifact filtering methods. Subsequently, these methods were applied to the data of anesthesia procedures with invasive blood pressure monitoring. Different hypotension measures were calculated (i.e., presence, duration, maximum deviation below threshold, and area under threshold) across different definitions (i.e., thresholds for mean arterial pressure of 50, 60, 65, 70 mmHg). These were then used to estimate the association with postoperative myocardial injury. results: After screening 3,585 papers, the authors included 38 papers that reported artifact filtering methods. The authors applied eight of these methods to the data of 2,988 anesthesia procedures. The occurrence of hypotension (defined with a threshold of 50 mmHg) varied from 24% with a median filter of seven measurements to 55% without an artifact filtering method, and between 76 and 90% with a threshold of 65 mmHg. Standardized odds ratios for presence of hypotension ranged from 1.16 (95% CI, 1.07 to 1.26) to 1.24 (1.14 to 1.34) when hypotension was defined with a threshold of 50 mmHg. Similar variations in standardized odds ratios were found when applying methods to other hypotension measures and definitions. conclusions: The method of artifact filtering can have substantial effects on estimates of hypotension prevalence. The effect on the association between intraoperative hypotension and postoperative myocardial injury was relatively small. Nevertheless, the authors recommend that researchers carefully consider artifacts handling and report the methodology used

Artifacts annotations in anesthesia blood pressure data by man and machine

Physiologic data from anesthesia monitors are automatically captured. Yet erroneous data are stored in the process as well. While this is not interfering with clinical care, research can be affected. Researchers should find ways to remove artifacts. The aim of the present study was to compare different artifact annotation strategies, and to assess if a machine learning algorithm is able to accept or reject individual data points. Non-cardiac procedures requiring invasive blood pressure monitoring were eligible. Two trained research assistants observed procedures live for artifacts. The same procedures were also retrospectively annotated for artifacts by a different person. We compared the different ways of artifact identifications and modelled artifacts with three different learning algorithms (lasso restrictive logistic regression, neural network and support vector machine). In 88 surgical procedures including 5711 blood pressure data points, the live observed incidence of artifacts was 2.1% and the retrospective incidence was 2.2%. Comparing retrospective with live annotation revealed a sensitivity of 0.32 and specificity of 0.98. The performance of the learning algorithms which we applied ranged from poor (kappa 0.053) to moderate (kappa 0.651). Manual identification of artifacts yielded different incidences in different situations, which were not comparable. Artifact detection in physiologic data collected during anesthesia could be automated, but the performance of the learning algorithms in the present study remained moderate. Future research should focus on optimization and finding ways to apply them with minimal manual work. The present study underlines the importance of an explicit definition for artifacts in database research

Artifacts annotations in anesthesia blood pressure data by man and machine

Physiologic data from anesthesia monitors are automatically captured. Yet erroneous data are stored in the process as well. While this is not interfering with clinical care, research can be affected. Researchers should find ways to remove artifacts. The aim of the present study was to compare different artifact annotation strategies, and to assess if a machine learning algorithm is able to accept or reject individual data points. Non-cardiac procedures requiring invasive blood pressure monitoring were eligible. Two trained research assistants observed procedures live for artifacts. The same procedures were also retrospectively annotated for artifacts by a different person. We compared the different ways of artifact identifications and modelled artifacts with three different learning algorithms (lasso restrictive logistic regression, neural network and support vector machine). In 88 surgical procedures including 5711 blood pressure data points, the live observed incidence of artifacts was 2.1% and the retrospective incidence was 2.2%. Comparing retrospective with live annotation revealed a sensitivity of 0.32 and specificity of 0.98. The performance of the learning algorithms which we applied ranged from poor (kappa 0.053) to moderate (kappa 0.651). Manual identification of artifacts yielded different incidences in different situations, which were not comparable. Artifact detection in physiologic data collected during anesthesia could be automated, but the performance of the learning algorithms in the present study remained moderate. Future research should focus on optimization and finding ways to apply them with minimal manual work. The present study underlines the importance of an explicit definition for artifacts in database research

Reference Values for Noninvasive Blood Pressure in Children during Anesthesia : A Multicentered Retrospective Observational Cohort Study

BACKGROUND: Although noninvasive blood pressure (NIBP) monitoring during anesthesia is a standard of care, reference ranges for blood pressure in anesthetized children are not available. We developed sex- and age-specific reference ranges for NIBP in children during anesthesia and surgery. METHODS: In this retrospective observational cohort study, we included NIBP data of children with no or mild comorbidity younger than 18 yr old from the Multicenter Perioperative Outcomes Group data set. Sex-specific percentiles of the NIBP values for age were developed and extrapolated into diagrams and reference tables representing the 50th percentile (0 SD), +1 SD, -1 SD, and the upper (+2 SD) and lower reference ranges (-2 SD). RESULTS: In total, 116,362 cases from 10 centers were available for the construction of NIBP age- and sex-specific reference curves. The 0 SD of the mean NIBP during anesthesia varied from 33 mmHg at birth to 67 mmHg at 18 yr. The low cutoff NIBP (2 SD below the 50th percentile) varied from 17 mmHg at birth to 47 mmHg at 18 yr old. CONCLUSIONS: This is the first study to present reference ranges for blood pressure in children during anesthesia. These reference ranges based on the variation of values obtained in daily care in children during anesthesia could be used for rapid screening of changes in blood pressure during anesthesia and may provide a consistent reference for future blood pressure-related pediatric anesthesia research

Étude statistique des formes dérivées et de leurs valeurs sémantiques à travers un corpus d’arabe moderne de presse

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Reference Values for Noninvasive Blood Pressure in Children during Anesthesia: A Multicentered Retrospective Observational Cohort Study

Background: Although noninvasive blood pressure (NIBP) monitoring during anesthesia is a standard of care, reference ranges for blood pressure in anesthetized children are not available. We developed sex- and age-specific reference ranges for NIBP in children during anesthesia and surgery. Methods: In this retrospective observational cohort study, we included NIBP data of children with no or mild comorbidity younger than 18 yr old from the Multicenter Perioperative Outcomes Group data set. Sex-specific percentiles of the NIBP values for age were developed and extrapolated into diagrams and reference tables representing the 50th percentile (0 SD), +1 SD, -1 SD, and the upper (+2 SD) and lower reference ranges (-2 SD). Results: In total, 116,362 cases from 10 centers were available for the construction of NIBP age- and sex-specific reference curves. The 0 SD of the mean NIBP during anesthesia varied from 33 mmHg at birth to 67 mmHg at 18 yr. The low cutoff NIBP (2 SD below the 50th percentile) varied from 17 mmHg at birth to 47 mmHg at 18 yr old. Conclusions: This is the first study to present reference ranges for blood pressure in children during anesthesia. These reference ranges based on the variation of values obtained in daily care in children during anesthesia could be used for rapid screening of changes in blood pressure during anesthesia and may provide a consistent reference for future blood pressure-related pediatric anesthesia research