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Background: Lung ultrasound (LUS) and procalcitonin (PCT) are independently used to improve accuracy when diagnosing lung infections. The aim of the study was to evaluate the accuracy of a new algorithm combining LUS and PCT for the diagnosis of bacterial pneumonia.
Methods: Randomized, blinded, comparative effectiveness clinical trial. Children <18 years old with suspected pneumonia admitted to pediatric intensive care unit were included, and randomized into experimental group (EG) or control group (CG) if LUS or chest X-Ray (CXR) were done as the first pulmonary image, respectively. PCT was determined. In patients with bacterial pneumonia, sensitivity, specificity, and predictive values of LUS, CXR, and of both combined with PCT were analyzed and compared. Concordance between the final diagnosis and the diagnosis concluded through the imaging test was assessed.
Results: A total of 194 children, with a median age of 134 (interquartile range [IQR]: 39-554) days, were enrolled, 96 randomized into the EG and 98 into the CG. Bacterial pneumonia was diagnosed in 97 patients. Sensitivity and specificity for bacterial pneumonia diagnosis were 78% (95% confidence interval [CI]: 70-85) and 98% (95% CI: 93-99) for LUS, 85% (95% CI: 78-90) and 53% (95% CI: 43-62) for CXR, 90% (95% CI: 83-94) and 85% (95% CI: 76-91) when combining LUS and PCT, and 95% (95% CI: 90-98) and 41% (95% CI: 31-52) when combining CXR and PCT. The positive predictive value for LUS and PCT was 88% (95% C:I 79%-93%) versus 68% (95% CI: 60-75) for CXR and PCT. The concordance between the final diagnosis and LUS had a kappa value of 0.69 (95% CI: 0.62-0.75) versus 0.34 (95% CI: 0.21-0.45) for CXR, (p < 0.001).
Conclusions: The combination of LUS and PCT presented a better accuracy for bacterial pneumonia diagnosis than combining CXR and PCT. Therefore, its implementation could be a reliable tool for pneumonia diagnosis in critically ill children.
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http://dx.doi.org/10.1002/ppul.25790 | DOI Listing |
Introduction: Low back pain (LBP) is a significant global health burden, with variable treatment outcomes and an unclear underlying molecular mechanism. Effective prediction of treatment responses remains a challenge. In this study, we aimed to develop gene signature-based machine learning models using transcriptomic data from peripheral immune cells to predict treatment outcomes in patients with LBP.
View Article and Find Full Text PDFBMC Bioinformatics
December 2024
Department of Software Science, Dankook University, Youngin, Gyeonggi, South Korea.
Background: High-dimensional datasets with low sample sizes (HDLSS) are pivotal in the fields of biology and bioinformatics. One of core objective of HDLSS is to select most informative features and discarding redundant or irrelevant features. This is particularly crucial in bioinformatics, where accurate feature (gene) selection can lead to breakthroughs in drug development and provide insights into disease diagnostics.
View Article and Find Full Text PDFNurse Educ Today
December 2024
Department of Fundamental Nursing, School of Nursing, Jilin University, Changchun 130021, Jilin Province, PR China. Electronic address:
Objective: To summarise and compare the applications and effectiveness of virtual reality in cultivating clinical thinking among nursing students and to further analyse the active ingredients of virtual reality applications.
Design: Systematic review.
Review Methods: A systematic and comprehensive search of PubMed, Web of Science, Cochrane, Scopus, Embase, and CINAHL was conducted from the inception until 5th of February 2024.
J Environ Manage
December 2024
College of Geography and Remote Sensing Sciences, Xinjiang Key Laboratory of Oasis Ecology, Xingjiang University, Urumqi, 830017, China. Electronic address:
The accumulation of plastic waste from various sources into marine and inland water is considered a global problem due to its serious impacts on aquatic ecosystems and human health. In the past decade, remote sensing has played an important role in monitoring of plastic pollution in marine and inland water sources and has achieved a series of research results in this field. In this study, a comprehensive review was conducted on the development, opportunities, and challenges of datasets and methods in Marine and Inland Water Plastics Remote Sensing (MIWPRS) monitoring over the past decade, based on the Web of Science (WOS) core database.
View Article and Find Full Text PDFEur J Radiol
December 2024
Division of Endocrinology and Metabolism, Department of Medicine III, Medical University of Vienna, Austria.
Objectives: To explore texture analysis' ability on T and T relaxation maps to classify liver fibrosis into no-to-mild liver fibrosis (nmF) versus severe fibrosis (sF) group using machine learning algorithms and histology as reference standard.
Materials And Methods: In this single-center study, patients undergoing 3 T MRI who also had histology examination were retrospectively enrolled. SNAPSHOT-FLASH sequence for T1 mapping, radial turbo-spin-echo sequence for T2 mapping and spin-echo echo-planar-imaging magnetic resonance elastography (MRE) sequences were analyzed.
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