Clinical Practice of High-Flow Nasal Cannula Therapy in ARDS Patients: A Cross-Sectional Survey of Respiratory Therapists.

Background: High-flow nasal cannula (HFNC) is an essential non-invasive oxygen therapy in acute respiratory distress syndrome (ARDS) patients. Despite its wide use, research assessing the knowledge, practice, and barriers to using HFNC among respiratory therapists (RT) is lacking. Methods: A cross-sectional questionnaire was conducted among RTs in Saudi Arabia between December 19, 2022, and July 15, 2023. Data were analyzed as means and standard deviation or frequency and percentages. A Chi-square test was used to compare the differences between groups. Results: A total of 1001 RTs completed the online survey. Two-thirds of the respondents 659 (65.8%) had received training in using HFNC and 785 (78.4%) had used HFNC in clinical settings. The top conditions for HFNC indication were COVID-19 (78%), post-extubation (65%), and do-not-intubate patients (64%). Participants strongly agreed that helping maintain conversation and eating abilities (32.95%) and improving shortness of breath (34.1%) were advantages of HFNC. Surprisingly, 568 (57%) of RT staff did not follow a protocol for HFNC with ARDS patients. When starting HFNC, 40.2% of the participants started with FiO2 of 61% to 80%. Additionally, high percentages of RT staff started with a flow rate between 30 L/minute and 40 L/minute (40.6%) and a temperature of 37°C (57.7%). When weaning ARDS patients, 482 (48.1%) recommended first reducing gas flow by 5-10 L/minute every two to four hours. Moreover, 549 (54.8%) believed that ARDS patients could be disconnected from HFNC if they achieved a flow rate of <20 L/minute and FiO2 of <35%. Lack of knowledge was the most common challenge concerning HFNC implementation. Conclusion: The findings revealed nuanced applications marked by significant endorsement in certain clinical scenarios and a lack of protocol adherence, underscoring the need for uniform, evidence-based guidelines and enhanced training for RTs. Addressing these challenges is pivotal to optimizing the benefits of HFNC across varied clinical contexts.

Virtual reality headsets for perimetry testing: a systematic review.

Standard automated perimetery is considered the gold standard for evaluating a patient's visual field. However, it is costly and requires a fixed testing environment. In response, perimetric devices using virtual reality (VR) headsets have emerged as an alternative way to measure visual fields in patients. This systematic review aims to characterize both novel and established VR headsets in the literature and explore their potential applications within visual field testing. A search was conducted using MEDLINE, Embase, CINAHL, and the Core Collection (Web of Science) for articles published until January 2023. Subject headings and keywords related to virtual reality and visual field were used to identify studies specific to this topic. Records were first screened by title/abstract and then by full text using predefined criteria. Data was extracted accordingly. A total of 2404 records were identified from the databases. After deduplication and the two levels of screening, 64 studies describing 36 VR headset perimetry devices were selected for extraction. These devices encompassed various visual field measurement techniques, including static and kinetic perimetry, with some offering vision rehabilitation capabilities. This review reveals a growing consensus that VR headset perimetry devices perform comparably to, or even better than, standard automated perimetry. They are better tolerated by patients in terms of gaze fixation, more cost-effective, and generally more accessible for patients with limited mobility.

Utilizing a responsive web portal for studying disc tracing agreement in retinal images.

Glaucoma is a leading cause of blindness worldwide whose detection is based on multiple factors, including measuring the cup to disc ratio, retinal nerve fiber layer and visual field defects. Advances in image processing and machine learning have allowed the development of automated approached for segmenting objects from fundus images. However, to build a robust system, a reliable ground truth dataset is required for proper training and validation of the model. In this study, we investigate the level of agreement in properly detecting the retinal disc in fundus images using an online portal built for such purposes. Two Doctors of Optometry independently traced the discs for 159 fundus images obtained from publicly available datasets using a purpose-built online portal. Additionally, we studied the effectiveness of ellipse fitting in handling misalignments in tracing. We measured tracing precision, interobserver variability, and average boundary distance between the results provided by ophthalmologists, and optometrist tracing. We also studied whether ellipse fitting has a positive or negative impact on properly detecting disc boundaries. The overall agreement between the optometrists in terms of locating the disc region in these images was 0.87. However, we found that there was a fair agreement on the disc border with kappa = 0.21. Disagreements were mainly in fundus images obtained from glaucomatous patients. The resulting dataset was deemed to be an acceptable ground truth dataset for training a validation of models for automatic detection of objects in fundus images.

Glaucoma detection using image processing techniques: A literature review.

The term glaucoma refers to a group of heterogeneous diseases that cause the degeneration of retinal ganglion cells (RGCs). The degeneration of RGCs leads to two main issues: (i) structural changes to the optic nerve head as well as the nerve fiber layer, and (ii) simultaneous functional failure of the visual field. These two effects of glaucoma may lead to peripheral vision loss and, if the condition is left to progress it may eventually lead to blindness. No cure for glaucoma exists apart from early detection and treatment by optometrists and ophthalmologists. The degeneration of RGCs is normally detected from retinal images which are assessed by an expert. These retinal images also provide other vital information about the health of an eye. Thus, it is essential to develop automated techniques for extracting this information. The rapid development of digital images and computer vision techniques have increased the potential for analysis of eye health from images. This paper surveys current approaches to detect glaucoma from 2D and 3D images; both the limitations and possible future directions are highlighted. This study also describes the datasets used for retinal analysis along with existing evaluation algorithms. The main topics covered by this study may be enumerated as follows.

Multiple sequence alignment with affine gap by using multi-objective genetic algorithm.

Multiple sequence alignment is of central importance to bioinformatics and computational biology. Although a large number of algorithms for computing a multiple sequence alignment have been designed, the efficient computation of highly accurate and statistically significant multiple alignments is still a challenge. In this paper, we propose an efficient method by using multi-objective genetic algorithm (MSAGMOGA) to discover optimal alignments with affine gap in multiple sequence data. The main advantage of our approach is that a large number of tradeoff (i.e., non-dominated) alignments can be obtained by a single run with respect to conflicting objectives: affine gap penalty minimization and similarity and support maximization. To the best of our knowledge, this is the first effort with three objectives in this direction. The proposed method can be applied to any data set with a sequential character. Furthermore, it allows any choice of similarity measures for finding alignments. By analyzing the obtained optimal alignments, the decision maker can understand the tradeoff between the objectives. We compared our method with the three well-known multiple sequence alignment methods, MUSCLE, SAGA and MSA-GA. As the first of them is a progressive method, and the other two are based on evolutionary algorithms. Experiments on the BAliBASE 2.0 database were conducted and the results confirm that MSAGMOGA obtains the results with better accuracy statistical significance compared with the three well-known methods in aligning multiple sequence alignment with affine gap. The proposed method also finds solutions faster than the other evolutionary approaches mentioned above.