Frequency of extra pulmonary complications in critically ill COVID-19 patients and their association with inflammatory markers and hypoxia: Retrospective analysis at a tertiary care hospital in Karachi, Pakistan.

Background and Objective: This study aimed to determine the incidence of extra pulmonary complications among critically ill COVID-19 patients requiring invasive mechanical ventilation and association of these complications with various inflammatory markers and degree of hypoxia. Methods: A retrospective cohort study was conducted among 173 adults in Karachi having COVID-19 and were admitted to ICU in a tertiary care private hospital between August 2020 to July 2021. Results: The median age of patients included in the analysis was 61 years (IQR; 16). Acute kidney injury, septic shock, cardiac injury, and electrolytes imbalance were the most frequent extra pulmonary complications with proportion of 65.3% (n=113), 63.6% (n=110), 61.8% (n=107) and 33.5% (n=58). Statistically significant differences in the median serum levels of ferritin were observed among male versus female, critically ill covid patients with and without ICU mortality as well as patients with and without hospital mortality (p-value <0.05). Significantly higher serum levels of d-dimer were noted among patients who developed acute liver injury or NSTEMI, or had ICU stay of > 3 days or received mechanical ventilation for >2 days. Conclusion: Acute kidney injury, septic shock, cardiac injury, and electrolytes imbalance were the most common extra pulmonary complications among mechanically ventilated COVID-19 patients with ARDS. Higher serum d-dimer levels were associated with acute liver injury, NSTEMI, ICU stay longer >3 days and invasive mechanical ventilation >2 days. Higher serum ferritin levels are associated with male sex and serve as an important predictor of ICU as well as hospital mortality.

YouTube as a source of patient information for meningitis: A content-quality and audience engagement analysis.

OBJECTIVES: Now that the internet is more accessible to an increasing number of populations worldwide, many rely on the internet for their information about their daily lives. This includes people concerned about their health to students to sometimes also doctors. Since YouTube is the second most visited website, our aim was to evaluate the content-quality of YouTube videos relating to meningitis. METHODS: We chose the first 30 videos for four different search phrases: meningitis, bacterial meningitis, viral meningitis, fungal meningitis and meningitis signs. The validated DISCERN scoring criteria was used to assess the videos by two raters independently. Qualitative data, quantitative data and the source of upload of each video were analyzed for video quality and audience engagement. RESULTS: Out of 150 videos, 84 videos met the inclusion criteria. The mean DISCERN score was 34.6 ± 9.76 (out of a total 75), which indicates that the quality of YouTube videos on meningitis is poor. There is an excellent reliability between the two raters. Videos had a higher audience engagement via a greater number of daily views and comments when they included the definition, symptoms, prognosis, animation, diagrams, and an anatomical explanation of the meninges (P < 0.0001 for all). CONCLUSION: The quality of YouTube videos on meningitis is poor, however, we have listed the videos which may be most effective for patient education for reference. Our quality and engagement analysis may help content creators make better health content on meningitis.

Region-Based Segmentation and Wiener Pilot-Based Novel Amoeba Denoising Scheme for CT Imaging.

Computed tomography (CT) is one of the most common and beneficial medical imaging schemes, but the associated high radiation dose injurious to the patient is always a concern. Therefore, postprocessing-based enhancement of a CT reconstructed image acquired using a reduced dose is an active research area. Amoeba- (or spatially variant kernel-) based filtering is a strong candidate scheme for postprocessing of the CT image, which adapts its shape according to the image contents. In the reported research work, the amoeba filtering is customized for postprocessing of CT images acquired at a reduced X-ray dose. The proposed scheme modifies both the pilot image formation and amoeba shaping mechanism of the conventional amoeba implementation. The proposed scheme uses a Wiener filter-based pilot image, while region-based segmentation is used for amoeba shaping instead of the conventional amoeba distance-based approach. The merits of the proposed scheme include being more suitable for CT images because of the similar region-based and symmetric nature of the human body anatomy, image smoothing without compromising on the edge details, and being adaptive in nature and more robust to noise. The performance of the proposed amoeba scheme is compared to the traditional amoeba kernel in the image denoising application for CT images using filtered back projection (FBP) on sparse-view projections. The scheme is supported by computer simulations using fan-beam projections of clinically reconstructed and simulated head CT phantoms. The scheme is tested using multiple image quality matrices, in the presence of additive projection noise. The scheme implementation significantly improves the image quality visually and statistically, providing better contrast and image smoothing without compromising on edge details. Promising results indicate the efficacy of the proposed scheme.