Nurses' perceptions of using an evidence-based care bundle for initial emergency nursing management of patients with severe traumatic brain injury: A qualitative study.

Evidence to guide initial emergency nursing care of patients with severe traumatic brain injury (TBI) in Thailand is currently not available in a useable form. A care bundle was used to summarise an evidence-based approach to the initial emergency nursing management of patients with severe TBI and was implemented in one Thai emergency department. The aim of this study was to describe Thai emergency nurses' perceptions of care bundle use. A descriptive qualitative study was used to describe emergency nurses' perceptions of care bundle use during the implementation phase (Phase-One) and then post-implementation (Phase-Two). Ten emergency nurses participated in Phase-One, while 12 nurses participated in Phase-Two. In Phase-One, there were five important factors identified in relation to use of the care bundle including quality of care, competing priorities, inadequate equipment, agitated patients, and teamwork. In Phase Two, participants perceived that using the care bundle helped them to improve quality of care, increased nurses' knowledge, skills, and confidence. Care bundles are one strategy to increase integration of research evidence into clinical practice and facilitate healthcare providers to deliver optimal patient care in busy environments with limited resources

Use of an evidence-based care bundle by Thai emergency nurses.

 Implementation of a care bundle for nursing management of patients with severe traumatic brain injury was feasible in the Thai context. Use of an evidence-based care bundle increased emergency nurses’ knowledge regarding severe TBI management and improved the care delivered during the initial emergency nursing management of these patients

kasikrit/PCa-binary-segmentation: Version 1.0.1

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DisVar: an R library for identifying variants associated with diseases using large-scale personal genetic information

Background Genetic variants may potentially play a contributing factor in the development of diseases. Several genetic disease databases are used in medical research and diagnosis but the web applications used to search these databases for disease-associated variants have limitations. The application may not be able to search for large-scale genetic variants, the results of searches may be difficult to interpret and variants mapped from the latest reference genome (GRCH38/hg38) may not be supported. Methods In this study, we developed a novel R library called “DisVar” to identify disease-associated genetic variants in large-scale individual genomic data. This R library is compatible with variants from the latest reference genome version. DisVar uses five databases of disease-associated variants. Over 100 million variants can be simultaneously searched for specific associated diseases. Results The package was evaluated using 24 Variant Call Format (VCF) files (215,054 to 11,346,899 sites) from the 1000 Genomes Project. Disease-associated variants were detected in 298,227 hits across all the VCF files, taking a total of 63.58 m to complete. The package was also tested on ClinVar’s VCF file (2,120,558 variants), where 20,657 hits associated with diseases were identified with an estimated elapsed time of 45.98 s. Conclusions DisVar can overcome the limitations of existing tools and is a fast and effective diagnostic and preventive tool that identifies disease-associated variations from large-scale genetic variants against the latest reference genome

Thai emergency nurses’ management of patients with severe traumatic brain injury: comparison of knowledge and clinical management with best available evidence

Background:In Thailand, the rate of TBI-related hospitalisation is increasing, however, little is known about the evidence-based management of severe TBI in the developing world. The aim of this study was to explore Thai emergency nurses’ management of patients with severe TBI.Methods:An exploratory descriptive mixed method design was used to conduct this two stage study: survey methods were used to examine emergency nurses’ knowledge regarding management of patients with severe TBI (Stage 1) and observational methods were used to examine emergency nurses’ clinical management of patients with severe TBI (Stage 2). The study setting was the emergency department (ED) at a regional hospital in Southern Thailand.Results:34 nurses participated in Stage 1 (response rate 91.9%) and the number of correct responses ranged from 33.3% to 95.2%. In Stage 2, a total of 160 points of measurement were observed in 20 patients with severe TBI over 40 h. In this study there were five major areas identified for the improvement of care of patients with severe TBI: (i) end-tidal carbon dioxide (ETCO2) monitoring and targets; (ii) use of analgesia and sedation; (iii) patient positioning; (iv) frequency of nursing assessment; and (v) dose of Mannitol diuretic.Conclusions:There is variation in Thai nurses’ knowledge and care practices for patients with severe TBI. To increase consistency of evidence-based TBI care in the Thai context, a knowledge translation intervention that is ecologically valid, appropriate to the Thai healthcare context and acceptable to the multidisciplinary care team is needed

PAIN INTENSITY AND PAIN INTERFERENCE AMONG TRAUMA PATIENTS: A LITERATURE REVIEW

Background: The incidence of trauma has been high and has gained attention worldwide. The energy involved in trauma results in specific tissue damage. Such tissue damage generally leads to pain. The high pain intensity possibly is consequence of trauma due to transfer energy to the body from external force and absorbed in wide area. This pain affected patients’ physical and psychological function, in which well known as pain interference. Objective: The aim of this review is to describe the pain intensity and pain interference among trauma patients. Method: A systematic search of electronic databases (CINHAL, ProQuest, Science Direct, and Google scholar) was conducted for quantitative and qualitative studies measuring pain intensity and pain interference. The search limited to hospitalized trauma patients in adult age. Results: The search revealed 678 studies. A total of 10 descriptive studies examined pain intensity and pain interference and met inclusion criteria. The pain intensity and pain interference was assessed using Brief Pain Inventory (BPI). Pain intensity of hospitalized trauma patients were moderate to severe. These including 6 studies in orthopedic trauma, one study in musculoskeletal, two in studies in combinational between orthopedic and musculoskeletal, and two studies in burn injury. Moreover, the patients also reported pain was relentless & unbearable. In accordance, data showed that pain interference was moderate to severe from six studies. These studies result in vary of functional interference. However, those studies examined pain interference on sleep, enjoyment of life, mood, relationship with other, walking, general activity, and walking. Conclusion: The evidence from 10 studies included in this review indicates that hospitalized trauma patients perceived moderate to severe pain intensity and pain interference. Further research is needed to better evaluate the pain of hospitalized trauma patients

DeepVAQ : an adaptive deep learning for prediction of vascular access quality in hemodialysis patients

Abstract Background Chronic kidney disease is a prevalent global health issue, particularly in advanced stages requiring dialysis. Vascular access (VA) quality is crucial for the well-being of hemodialysis (HD) patients, ensuring optimal blood transfer through a dialyzer machine. The ultrasound dilution technique (UDT) is used as the gold standard for assessing VA quality; however, its limited availability due to high costs impedes its widespread adoption. We aimed to develop a novel deep learning model specifically designed to predict VA quality from Photoplethysmography (PPG) sensors. Methods Clinical data were retrospectively gathered from 398 HD patients, spanning from February 2021 to February 2022. The DeepVAQ model leverages a convolutional neural network (CNN) to process PPG sensor data, pinpointing specific frequencies and patterns that are indicative of VA quality. Meticulous training and fine-tuning were applied to ensure the model’s accuracy and reliability. Validation of the DeepVAQ model was carried out against established diagnostic standards using key performance metrics, including accuracy, specificity, precision, F-score, and area under the receiver operating characteristic curve (AUC). Result DeepVAQ demonstrated superior performance, achieving an accuracy of 0.9213 and a specificity of 0.9614. Its precision and F-score stood at 0.8762 and 0.8364, respectively, with an AUC of 0.8605. In contrast, traditional models like Decision Tree, Naive Bayes, and kNN demonstrated significantly lower performance across these metrics. This comparison underscores DeepVAQ's enhanced capability in accurately predicting VA quality compared to existing methodologies. Conclusion Exemplifying the potential of artificial intelligence in healthcare, particularly in the realm of deep learning, DeepVAQ represents a significant advancement in non-invasive diagnostics. Its precise multi-class classification ability for VA quality in hemodialysis patients holds substantial promise for improving patient outcomes, potentially leading to a reduction in mortality rates

Evidence–practice gaps in initial neuro-protective nursing care: a mixed methods study of Thai patients with moderate or severe traumatic brain injury

AIMS: This paper aims to identify the frequency and nature of evidence-practice gaps in the initial neuro-protective nursing care of patients with moderate or severe traumatic brain injury provided by Thai trauma nurses. BACKGROUND: Little is known about how Thai trauma nurses use evidence-based practice when providing initial neuro-protective nursing care to patients with moderate or severe traumatic brain injury. DESIGN: A mixed methods design was used to conduct this study. METHODS: Data were collected from January to March 2017 using observations and audits of the clinical care of 22 patients by 35 nurses during the first 4 h of admission to trauma ward. The study site was a regional hospital in Southern Thailand. RESULTS: The major evidence-practice gaps identified were related to oxygen and carbon dioxide monitoring and targets, mean arterial pressure and systolic blood pressure targets and management of increased intracranial pressure through patient positioning and pain and agitation management. CONCLUSION: There were evidence-practice gaps in initial neuro-protective nursing care provided by Thai trauma nurses that need to be addressed to improve the safety and quality of care for Thai patients with moderate or severe traumatic brain injury

Initial emergency nursing management of patients with severe traumatic brain injury : development of an evidence-based care bundle for the Thai emergency department context.

Thai emergency nurses play a vital role in caring for patients with severe TBI, and are an important part of the healthcare team throughout the resuscitation phase. They are also responsible for continuous physiological monitoring, and detecting deterioration associated with increased intracranial pressure and preventing secondary brain injury. However, there is known variation in Thai nurses\u27 knowledge and care practices for patients with severe TBI. In addition, there are no specific evidence-based practice guidelines available for emergency nursing management of patients with severe TBI