32 research outputs found

    Prevalence, associated factors and outcomes of pressure injuries in adult intensive care unit patients: the DecubICUs study

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    Funder: European Society of Intensive Care Medicine; doi: http://dx.doi.org/10.13039/501100013347Funder: Flemish Society for Critical Care NursesAbstract: Purpose: Intensive care unit (ICU) patients are particularly susceptible to developing pressure injuries. Epidemiologic data is however unavailable. We aimed to provide an international picture of the extent of pressure injuries and factors associated with ICU-acquired pressure injuries in adult ICU patients. Methods: International 1-day point-prevalence study; follow-up for outcome assessment until hospital discharge (maximum 12 weeks). Factors associated with ICU-acquired pressure injury and hospital mortality were assessed by generalised linear mixed-effects regression analysis. Results: Data from 13,254 patients in 1117 ICUs (90 countries) revealed 6747 pressure injuries; 3997 (59.2%) were ICU-acquired. Overall prevalence was 26.6% (95% confidence interval [CI] 25.9–27.3). ICU-acquired prevalence was 16.2% (95% CI 15.6–16.8). Sacrum (37%) and heels (19.5%) were most affected. Factors independently associated with ICU-acquired pressure injuries were older age, male sex, being underweight, emergency surgery, higher Simplified Acute Physiology Score II, Braden score 3 days, comorbidities (chronic obstructive pulmonary disease, immunodeficiency), organ support (renal replacement, mechanical ventilation on ICU admission), and being in a low or lower-middle income-economy. Gradually increasing associations with mortality were identified for increasing severity of pressure injury: stage I (odds ratio [OR] 1.5; 95% CI 1.2–1.8), stage II (OR 1.6; 95% CI 1.4–1.9), and stage III or worse (OR 2.8; 95% CI 2.3–3.3). Conclusion: Pressure injuries are common in adult ICU patients. ICU-acquired pressure injuries are associated with mainly intrinsic factors and mortality. Optimal care standards, increased awareness, appropriate resource allocation, and further research into optimal prevention are pivotal to tackle this important patient safety threat

    Research on protocol migration

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    Heat Load Forecasting of Marine Diesel Engine Based on Long Short-Term Memory Network

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    High heat load on diesel engines is a main cause of ship failure, which can lead to ship downtime and pose a risk to personal safety and the environment. As such, predictive detection and maintenance measures are highly important. During the operation of marine diesel engines, operating data present strong dynamic, time lag, and nonlinear characteristics, and traditional models and prediction methods cause difficulties in accurately predicting the heat load. Therefore, the prediction of its heat load is a challenging and significant task. The continuously developing machine learning technology provides methods and ideas for intelligent detection and diagnosis maintenance. The prediction of diesel engine exhaust temperature using long short-term memory network (LSTM) is analyzed in this study to determine the diesel engine heat load and introduce an effective method. Spearman correlation coefficient method with the addition of artificial experience is utilized for feature selection to obtain the optimal input for the LSTM model. The model is applied to validate the ship data of the Shanghai Fuhai ship, and results show that the mean absolute percentage error (MAPE) of the model is lowest at 0.089. Compared with other models, the constructed prediction model presents higher accuracy and stability, as well as an optimal evaluation index. A new idea is thus provided for combining artificial knowledge experience with data-driven applications in engineering practice

    An estimation of distribution algorithm with resampling and local improvement for an operation optimization problem in steelmaking process

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    This paper studies an operation optimization problem in a steelmaking process. Shortly before the tapping of molten steel from the converter furnace, end-point control measures are applied to achieve the required final molten steel quality. While it is difficult to build an exact mathematical model for this process, the control inputs and the corresponding outputs are available by collecting production data. We build a data-driven model for the process. To optimize the control parameters, an improved estimation of distribution algorithm (EDA) is developed using a probabilistic model comprising different distributions. A resampling mechanism is incorporated into the EDA to guide the new population to a broader and more promising area when the search becomes ineffective. To further enhance the solution quality, we add a local improvement to update the current best individual through simplified gravitational search and information learning. Experiments are conducted using real data from a converter steelmaking process. The results show that the algorithm can help to achieve the specified molten steel quality. To evaluate the proposed algorithm as a general optimization algorithm, we test it on some complex benchmark functions. The results illustrate that it outperforms other state-of-the-art algorithms across a wide range of problems.</div
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