Background: Moderate and deep sedation are well-established techniques in many developed countries, and several guidelines have been published. However, they have received attention in China only in recent years. The aim of this study is to investigate current paediatric sedation practices in tertiary children's hospitals and tertiary maternity and children hospitals in China.
Methods: All tertiary children's hospitals and tertiary maternity and children hospitals registered with the National Health Commission of the People's Republic of China were invited to participate in an electronic survey, which included information on the sedation caseload, facility availability, staff structure, clinical skill requirements for sedation providers, fasting guidelines, patient-monitoring practices, and choice of sedatives.
Results: Fifty-eight of the 63 hospitals that completed the survey (92.1%) provided moderate and deep sedation. Dedicated sedation rooms and post-sedation recovery rooms were found in 14 (24.1%) and 19 (32.8%) hospitals, respectively. Sedation for non-invasive procedures was primarily performed by anaesthesiologists (69.0%); however, 75.9% of the sedation providers had not received paediatric basic or advanced life-support training. Children were asked to fast from clear liquids for at least 2 h in 44.8% of hospitals and up to 6 h in 5.2% of hospitals; they were asked to fast from solid food/milk for at least 4 h in 27.6% of hospitals and more than 8 h in 1.7% of hospitals. The most commonly used sedative in all groups was chloral hydrate. For rescue, propofol was the most widely used sedative, particularly for children older than 4 years.
Conclusions: Moderate and deep sedation practices vary widely in tertiary children's hospitals and tertiary maternity and children hospitals in China. Optimised practices should be established to improve the quality of moderate and deep sedation.
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http://dx.doi.org/10.1186/s12913-019-4885-4 | DOI Listing |
J Pathol Inform
January 2025
Cincinnati Children's AI Imaging Research (CAIIR) Center, Cincinnati, OH, United States.
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Purpose: To study the clustering-constrained attention multiple instance learning (CLAM) approach for staging liver fibrosis on trichrome whole slide images (WSIs) of children and young adults.
Int J Oral Sci
January 2025
School of Cyber Science and Engineering, Sichuan University, Chengdu, China.
The presence of a positive deep surgical margin in tongue squamous cell carcinoma (TSCC) significantly elevates the risk of local recurrence. Therefore, a prompt and precise intraoperative assessment of margin status is imperative to ensure thorough tumor resection. In this study, we integrate Raman imaging technology with an artificial intelligence (AI) generative model, proposing an innovative approach for intraoperative margin status diagnosis.
View Article and Find Full Text PDFLancet
January 2025
Faculty of Medicine, Wroclaw University of Science and Technology, Wrocław, Poland.
Hidradenitis suppurativa is a chronic inflammatory disease characterised by painful, deep-seated nodules, abscesses, and draining tunnels in the skin of axillary, inguinal, genitoanal, or inframammary areas. In recent years, the body of knowledge in hidradenitis suppurativa has advanced greatly. This disorder typically starts in the second or third decade of life.
View Article and Find Full Text PDFComput Methods Programs Biomed
January 2025
Regional Institute of Ophthalmology, Indira Gandhi Institute of Medical Sciences, Patna, 800025, Bihar, India.
Background And Objectives: Hypertensive Retinopathy (HR) is a retinal manifestation resulting from persistently elevated blood pressure. Severity grading of HR is essential for patient risk stratification, effective management, progression monitoring, timely intervention, and minimizing the risk of vision impairment. Computer-aided diagnosis and artificial intelligence (AI) systems play vital roles in the diagnosis and grading of HR.
View Article and Find Full Text PDFClin Oral Investig
January 2025
State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, 610041, China.
Objectives: To develop a platform including a deep convolutional neural network (DCNN) for automatic segmentation of the maxillary sinus (MS) and adjacent structures, and automatic algorithms for measuring 3-dimensional (3D) clinical parameters.
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