Purpose. To investigate the factors contributing to mortality in coronavirus disease 2019 (COVID-19) patients admitted in the intensive care unit (ICU) and design a model to predict the mortality ...rate. Method. We retrospectively evaluated the medical records and CT images of the ICU-admitted COVID-19 patients who had an on-admission chest CT scan. We analyzed the patients’ demographic, clinical, laboratory, and radiologic findings and compared them between survivors and nonsurvivors. Results. Among the 121 enrolled patients (mean age, 62.2 ± 14.0 years; male, 82 (67.8%)), 41 (33.9%) survived, and the rest succumbed to death. The most frequent radiologic findings were ground-glass opacity (GGO) (71.9%) with peripheral (38.8%) and bilateral (98.3%) involvement, with lower lobes (94.2%) predominancy. The most common additional findings were cardiomegaly (63.6%), parenchymal band (47.9%), and crazy-paving pattern (44.4%). Univariable analysis of radiologic findings showed that cardiomegaly p:0.04, pleural effusion p:0.02, and pericardial effusion p:0.03 were significantly more prevalent in nonsurvivors. However, the extension of pulmonary involvement was not significantly different between the two subgroups (11.4 ± 4.1 in survivors vs. 11.9 ± 5.1 in nonsurvivors, p:0.59). Among nonradiologic factors, advanced age p:0.002, lower O2 saturation p:0.01, diastolic blood pressure p:0.02, and hypertension p:0.03 were more commonly found in nonsurvivors. There was no significant difference between survivors and nonsurvivors in terms of laboratory findings. Three following factors remained significant in the backward logistic regression model: O2 saturation (OR: 0.91 (95% CI: 0.84–0.97), p:0.006), pericardial effusion (6.56 (0.17–59.3), p:0.09), and hypertension (4.11 (1.39–12.2), p:0.01). This model had 78.7% sensitivity, 61.1% specificity, 90.0% positive predictive value, and 75.5% accuracy in predicting in-ICU mortality. Conclusion. A combination of underlying diseases, vital signs, and radiologic factors might have prognostic value for mortality rate prediction in ICU-admitted COVID-19 patients.
Background
Since December 2019, the world has struggled with the COVID‐19 pandemic. Even after the introduction of various vaccines, this disease still takes a considerable toll. In order to improve ...the optimal allocation of resources and communication of prognosis, healthcare providers and patients need an accurate understanding of factors (such as obesity) that are associated with a higher risk of adverse outcomes from the COVID‐19 infection.
Objectives
To evaluate obesity as an independent prognostic factor for COVID‐19 severity and mortality among adult patients in whom infection with the COVID‐19 virus is confirmed.
Search methods
MEDLINE, Embase, two COVID‐19 reference collections, and four Chinese biomedical databases were searched up to April 2021.
Selection criteria
We included case‐control, case‐series, prospective and retrospective cohort studies, and secondary analyses of randomised controlled trials if they evaluated associations between obesity and COVID‐19 adverse outcomes including mortality, mechanical ventilation, intensive care unit (ICU) admission, hospitalisation, severe COVID, and COVID pneumonia. Given our interest in ascertaining the independent association between obesity and these outcomes, we selected studies that adjusted for at least one factor other than obesity. Studies were evaluated for inclusion by two independent reviewers working in duplicate.
Data collection and analysis
Using standardised data extraction forms, we extracted relevant information from the included studies. When appropriate, we pooled the estimates of association across studies with the use of random‐effects meta‐analyses. The Quality in Prognostic Studies (QUIPS) tool provided the platform for assessing the risk of bias across each included study. In our main comparison, we conducted meta‐analyses for each obesity class separately. We also meta‐analysed unclassified obesity and obesity as a continuous variable (5 kg/m2 increase in BMI (body mass index)). We used the GRADE framework to rate our certainty in the importance of the association observed between obesity and each outcome. As obesity is closely associated with other comorbidities, we decided to prespecify the minimum adjustment set of variables including age, sex, diabetes, hypertension, and cardiovascular disease for subgroup analysis.
Main results
We identified 171 studies, 149 of which were included in meta‐analyses. As compared to 'normal' BMI (18.5 to 24.9 kg/m2) or patients without obesity, those with obesity classes I (BMI 30 to 35 kg/m2), and II (BMI 35 to 40 kg/m2) were not at increased odds for mortality (Class I: odds ratio OR 1.04, 95% confidence interval CI 0.94 to 1.16, high certainty (15 studies, 335,209 participants); Class II: OR 1.16, 95% CI 0.99 to 1.36, high certainty (11 studies, 317,925 participants)). However, those with class III obesity (BMI 40 kg/m2 and above) may be at increased odds for mortality (Class III: OR 1.67, 95% CI 1.39 to 2.00, low certainty, (19 studies, 354,967 participants)) compared to normal BMI or patients without obesity. For mechanical ventilation, we observed increasing odds with higher classes of obesity in comparison to normal BMI or patients without obesity (class I: OR 1.38, 95% CI 1.20 to 1.59, 10 studies, 187,895 participants, moderate certainty; class II: OR 1.67, 95% CI 1.42 to 1.96, 6 studies, 171,149 participants, high certainty; class III: OR 2.17, 95% CI 1.59 to 2.97, 12 studies, 174,520 participants, high certainty). However, we did not observe a dose‐response relationship across increasing obesity classifications for ICU admission and hospitalisation.
Authors' conclusions
Our findings suggest that obesity is an important independent prognostic factor in the setting of COVID‐19. Consideration of obesity may inform the optimal management and allocation of limited resources in the care of COVID‐19 patients.
Smoking is a modifiable risk factor for six of the eight leading causes of death. Despite the great burden, there is lack of data regarding the trend of cigarette smoking in Iran. We described the ...national and provincial prevalence of cigarette smoking and its 12-year time trend utilizing six rounds of Iranian stepwise approach for surveillance of non-communicable disease (STEPS) surveys.
We gathered data from six STEPS surveys done in 2005, 2007, 2008, 2009, 2011, and 2016 in Iran. To estimate the data of missing years, we used two separate statistical models including the mixed model and spatio-temporal analysis.
The overall prevalence rate of cigarette smoking was 14.65% (12.81‒16.59) in 2005 and 10.63% (9.00‒12.57) in 2016 in Iran. The prevalence of cigarette smoking in 2005 and 2016 was 25.15% (23.18‒27.11) and 19.95% (17.93%‒21.97%) for men and 4.13% (2.43‒6.05) and 1.31% (0.06-3.18) for women, respectively. The prevalence of smoking in different provinces of Iran ranged from 20.73% (19.09‒22.47) to 9.67% (8.24‒11.34) in 2005 and from 15.34% (13.68‒17.12) to 6.41% (5.31‒7.94) in 2016. The overall trend of smoking was downward, which was true for both sexes and all 31 provinces. The declining annual percent change (APC) of the prevalence trend was -2.87% in total population, -9.91% in women, and -2.08% in men from 2005 to 2016.
Although the prevalence of smoking had a decreasing trend in Iran, this trend showed disparities among sexes and provinces and this epidemiological data can be used to modify smoking prevention programs.
It is increasingly common to collect and store specimens for future unspecified research. However, the effects of prolonged storage on the stability and quality of analytes in serum have not been ...well investigated. We aimed to determine whether the stability of liver enzymes extracted from frozen bio-samples stored at the baseline is affected by storage conditions.
A total of four liver enzymes in the sera of 400 patients were examined following storage. After deter-mining the baseline measurements, the serum of each patient was aliquoted and stored at -70°C for three and six months, as well as one, two, and five years after collecting the original sample. The percent change from baseline measurements was calculated both statistically and clinically. Linear models were also used to correct the results of the samples based on the time they were frozen.
In almost all samples, liver enzymes were detectable until two years after the baseline, while in a signifi-cant proportion of samples, enzymes were not ultimately detectable five years after the baseline. Linear regression analysis on log-transformed levels of enzymes shows that the performance is acceptable until one year after the baseline. The performance of the prediction model declines substantially two and five years after the baseline, except for GGT.
Long-term storage of serum samples significantly decreases the concentration of the liver enzymes from the baseline, except for GGT. It is not recommended to store samples for more than two years, as liver en-zymes are not detectable afterwards.
Improving the accuracy of solar power forecasting has become crucial for dealing with the negative effects of the integration of continually increasing solar power into power systems. This is a more ...challenging task when historical solar radiation data has not been recorded and no specific sky imaging equipment is available. This paper proposes a univariate data-driven method to improve the accuracy of very short-term electrical solar power forecasting. This approach includes defining new features that efficiently tackle the nonlinear characteristics of electrical solar power. An instance-based variable selection is also used to identify the best relevant features. Three state-of-the-art learning algorithms (i.e. neural networks, support vector regression, and random forest) have been used and compared as prediction algorithms of the proposed method. The effectiveness of the proposed approach is evaluated in a 15-min ahead prediction trial using a real solar power dataset and against three evaluation measures. The results show the proposed method significantly enhances the performance of very short-term solar power forecasting.
•An accurate very short-term method for solar power forecasting is developed.•The method merely relies on historical solar power data.•The method requires no specific equipment, sensor data, or weather predictions.•The method enhances the performance of very short-term solar power forecasting.
With the emergence of smart grids, accurate very short-term load forecasting (VSTLF) has become a crucial tool for competitive energy markets. The number of behind-the-meter photovoltaic solar ...panels, which usually are not monitored are increasing. This could reduce the load visibility and also affects the VSTLF accuracy. While most of the research works focus on demand forecasting of large areas, the performance of VSTLF methods for the future scenario of high solar power penetration in the microgrid environment is unclear. This paper investigates the impact of high solar power integration on the forecasting accuracy of machine learning VSTLF models for microgrids. The performance of Neural Network, Support Vector Regression are evaluated for high penetration scenario and no solar penetration. The accuracy of these models is examined for three different forecasting horizons through various comparative experiments for two real-world microgrid datasets. According to simulation results, using the most relevant variables is highly recommended. Furthermore, the results demonstrate that machine-learning methods can tackle the nonlinear characteristics of net load forecasting as well as total load forecasting. These simulations show that high penetration of solar power generation could not significantly affect the accuracy of machine learning VSTLF if most relevant variables are selected and applied as model inputs.
Purpose
This research aimed to visualize and analyze the co-word network and thematic clusters of the intellectual structure in the field of linked data during 1900–2021.
Design/methodology/approach
...This applied research employed a descriptive and analytical method, scientometric indicators, co-word techniques, and social network analysis. VOSviewer, SPSS, Python programming, and UCINet software were used for data analysis and network structure visualization.
Findings
The top ranks of the Web of Science (WOS) subject categorization belonged to various fields of computer science. Besides, the USA was the most prolific country. The keyword ontology had the highest frequency of co-occurrence. Ontology and semantic were the most frequent co-word pairs. In terms of the network structure, nine major topic clusters were identified based on co-occurrence, and 29 thematic clusters were identified based on hierarchical clustering. Comparisons between the two clustering techniques indicated that three clusters, namely semantic bioinformatics, knowledge representation, and semantic tools were in common. The most mature and mainstream thematic clusters were natural language processing techniques to boost modeling and visualization, context-aware knowledge discovery, probabilistic latent semantic analysis (PLSA), semantic tools, latent semantic indexing, web ontology language (OWL) syntax, and ontology-based deep learning.
Originality/value
This study adopted various techniques such as co-word analysis, social network analysis network structure visualization, and hierarchical clustering to represent a suitable, visual, methodical, and comprehensive perspective into linked data.
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