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

Management and outcomes in critically ill nonagenarian versus octogenarian patients.

BACKGROUND: Intensive care unit (ICU) patients age 90 years or older represent a growing subgroup and place a huge financial burden on health care resources despite the benefit being unclear. This leads to ethical problems. The present investigation assessed the differences in outcome between nonagenarian and octogenarian ICU patients. METHODS: We included 7900 acutely admitted older critically ill patients from two large, multinational studies. The primary outcome was 30-day-mortality, and the secondary outcome was ICU-mortality. Baseline characteristics consisted of frailty assessed by the Clinical Frailty Scale (CFS), ICU-management, and outcomes were compared between octogenarian (80-89.9 years) and nonagenarian (> 90 years) patients. We used multilevel logistic regression to evaluate differences between octogenarians and nonagenarians. RESULTS: The nonagenarians were 10% of the entire cohort. They experienced a higher percentage of frailty (58% vs 42%; p < 0.001), but lower SOFA scores at admission (6 + 5 vs. 7 + 6; p < 0.001). ICU-management strategies were different. Octogenarians required higher rates of organ support and nonagenarians received higher rates of life-sustaining treatment limitations (40% vs. 33%; p < 0.001). ICU mortality was comparable (27% vs. 27%; p = 0.973) but a higher 30-day-mortality (45% vs. 40%; p = 0.029) was seen in the nonagenarians. After multivariable adjustment nonagenarians had no significantly increased risk for 30-day-mortality (aOR 1.25 (95% CI 0.90-1.74; p = 0.19)). CONCLUSION: After adjustment for confounders, nonagenarians demonstrated no higher 30-day mortality than octogenarian patients. In this study, being age 90 years or more is no particular risk factor for an adverse outcome. This should be considered- together with illness severity and pre-existing functional capacity - to effectively guide triage decisions. TRIAL REGISTRATION: NCT03134807 and NCT03370692

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Quantification of contrast kinetics in clinical imaging

This book provides a comprehensive survey of the pharmacokinetic models used for the quantitative interpretation of contrast-enhanced imaging. It discusses all the available imaging technologies and the problems related to the calibration of the imaging system and accuracy of the estimated physiological parameters. Enhancing imaging modalities using contrast agents has opened up new opportunities for going beyond morphological information and enabling minimally invasive assessment of tissue and organ functionality down to the molecular level. In combination with mathematical modeling of the contrast agent kinetics, contrast- enhanced imaging has the potential to provide clinically valuable additional information by estimating quantitative physiological parameters. The book presents the broad spectrum of diagnostic possibilities provided by quantitative contrast-enhanced imaging, with a particular focus on cardiology and oncology, as well as novel developments in the area of quantitative molecular imaging along with their potential clinical applications. Given the variety of available techniques, the choice of the appropriate imaging modality and the most suitable pharmacokinetic model is often challenging. As such, the book provides a valuable technical guide for researchers, clinical scientists, and experts in the field who wish to better understand and properly apply tracer-kinetic modeling for quantitative contrast-enhanced imaging

Langetermijnuitkomsten van IC-behandeling

Patients admitted to an intensive care unit (ICU) comprise of a heterogeneous population with substantial differences in admission diagnosis, length of stay and comorbidity. Therefore, very often the prognosis for each patient differs. In the Netherlands, over 20% of the more than 80,000 patients treated in ICU annually will die within a year of admission. Some of those who survive and are discharged from ICU experience persistent physical, mental and cognitive health problems post-discharge; this is called post-intensive care syndrome (PICS). One year following discharge, circa 50% of patients continue to report physical symptoms, including muscle weakness and walking difficulties. Approximately one in five patients discharged from ICU will develop symptoms akin to post-traumatic stress disorder, and one third will experience depressive symptoms for some time. It remains unclear to what extent the actual ICU admission may potentially contribute to the decline in performance status and quality of life

Langetermijnuitkomsten van IC-behandeling: stand van zaken

Patients admitted to an intensive care unit (ICU) comprise of a heterogeneous population with substantial differences in admission diagnosis, length of stay and co-morbidity. Therefore, very often the prognosis for each patient differs. In the Netherlands, over 20% of the more than 80,000 patients treated in ICU annually will die within a year of admission. Some of those who survive and are discharged from ICU experience persistent physical, mental and cognitive health problems post-discharge; this is called post-intensive care syndrome (PICS). One year following discharge, circa 50% of patients continue to report physical symptoms, including muscle weakness and walking difficulties. Approximately one in five patients discharged from ICU will develop symptoms akin to post-traumatic stress disorder, and one third will experience depressive symptoms for some time. It remains unclear to what extent the actual ICU admission may potentially contribute to the decline in performance status and quality of life

The performance of acute versus antecedent patient characteristics for 1-year mortality prediction during intensive care unit admission: a national cohort study

Background: Multiple factors contribute to mortality after ICU, but it is unclear how the predictive value of these factors changes during ICU admission. We aimed to compare the changing performance over time of the acute illness component, antecedent patient characteristics, and ICU length of stay (LOS) in predicting 1-year mortality. Methods: In this retrospective observational cohort study, the discriminative value of four generalized mixed-effects models was compared for 1-year and hospital mortality. Among patients with increasing ICU LOS, the models included (a) acute illness factors and antecedent patient characteristics combined, (b) acute component only, (c) antecedent patient characteristics only, and (d) ICU LOS. For each analysis, discrimination was measured by area under the receiver operating characteristics curve (AUC), calculated using the bootstrap method. Statistical significance between the models was assessed using the DeLong method (p value < 0.05). Results: In 400,248 ICU patients observed, hospital mortality was 11.8% and 1-year mortality 21.8%. At ICU admission, the combined model predicted 1-year mortality with an AUC of 0.84 (95% CI 0.84-0.84). When analyzed separately, the acute component progressively lost predictive power. From an ICU admission of at least 3 days, antecedent characteristics significantly exceeded the predictive value of the acute component for 1-year mortality, AUC 0.68 (95% CI 0.68-0.69) versus 0.67 (95% CI 0.67-0.68) (p value < 0.001). For hospital mortality, antecedent characteristics outperformed the acute component from a LOS of at least 7 days, comprising 7.8% of patients and accounting for 52.4% of all bed days. ICU LOS predicted 1-year mortality with an AUC of 0.52 (95% CI 0.51-0.53) and hospital mortality with an AUC of 0.54 (95% CI 0.53-0.55) for patients with a LOS of at least 7 days. Conclusions: Comparing the predictive value of factors influencing 1-year mortality for patients with increasing ICU LOS, antecedent patient characteristics are more predictive than the acute component for patients with an ICU LOS of at least 3 days. For hospital mortality, antecedent patient characteristics outperform the acute component for patients with an ICU LOS of at least 7 days. After the first week of ICU admission, LOS itself is not predictive of hospital nor 1-year mortality