Prevalence of the Risk Factors of Delirium Among COVID-19 Patients in the Intensive Care Unit at King Abdullah Medical City, Makkah: A Single-Center Study.

Background Coronavirus disease 2019 (COVID-19) patients admitted to the intensive care unit (ICU) are at a higher risk of developing delirium. In this study, we estimated the incidence of delirium and its risk factors in ICU patients with COVID-19 at King Abdullah Medical City (KAMC), Makkah, Saudi Arabia. Methodology We conducted a retrospective, analytical, cohort study of adult COVID-19 patients admitted to the ICU of KAMC between May 2020 and July 2021. Data were collected from electronic medical records. Results Of the 406 examined patients with COVID-19 aged >18 years, 55 developed delirium in the ICU setting. The incidence rate was 0.59% per 100 ICU days in these 55 patients; the mean age was 62.36 ± 17.9 years, and 65.5% were men. Binary logistic regression showed that age (p = 0.027), nationality (p = 0.045), presence of infectious diseases other than COVID-19 (p = 0.047), and ICU outcome (p = 0.013) were significant risk factors for developing delirium. The clinical presentation and prognosis of patients who developed delirium were assessed using the Acute Physiology and Chronic Health Evaluation II and Sequential Organ Failure Assessment scores, and the mean scores were 16.13 ± 7.96 and 5.25 ± 3.48, respectively. The mean length of ICU stay was 22.2 ± 33.3 days; 39 (70.9%) patients were discharged and 16 (29.1%) died. Conclusions Older age, nationality, infections, and ICU outcomes were risk factors for developing delirium in hospitalized COVID-19 patients at KAMC. Early detection of cognitive comorbidities and delirium in these patients is important.

Prevalence of the Risk Factors of Delirium Among COVID-19 Patients in the Intensive Care Unit at King Abdullah Medical City, Makkah: A Single-Center Study

Background Coronavirus disease 2019 (COVID-19) patients admitted to the intensive care unit (ICU) are at a higher risk of developing delirium. In this study, we estimated the incidence of delirium and its risk factors in ICU patients with COVID-19 at King Abdullah Medical City (KAMC), Makkah, Saudi Arabia. Methodology We conducted a retrospective, analytical, cohort study of adult COVID-19 patients admitted to the ICU of KAMC between May 2020 and July 2021. Data were collected from electronic medical records. Results Of the 406 examined patients with COVID-19 aged >18 years, 55 developed delirium in the ICU setting. The incidence rate was 0.59% per 100 ICU days in these 55 patients;the mean age was 62.36 ± 17.9 years, and 65.5% were men. Binary logistic regression showed that age (p = 0.027), nationality (p = 0.045), presence of infectious diseases other than COVID-19 (p = 0.047), and ICU outcome (p = 0.013) were significant risk factors for developing delirium. The clinical presentation and prognosis of patients who developed delirium were assessed using the Acute Physiology and Chronic Health Evaluation II and Sequential Organ Failure Assessment scores, and the mean scores were 16.13 ± 7.96 and 5.25 ± 3.48, respectively. The mean length of ICU stay was 22.2 ± 33.3 days;39 (70.9%) patients were discharged and 16 (29.1%) died. Conclusions Older age, nationality, infections, and ICU outcomes were risk factors for developing delirium in hospitalized COVID-19 patients at KAMC. Early detection of cognitive comorbidities and delirium in these patients is important.

Chest X-ray Classification for the Detection of COVID-19 Using Deep Learning Techniques.

Recent technological developments pave the path for deep learning-based techniques to be used in almost every domain of life. The precision of deep learning techniques make it possible for these to be used in the medical field for the classification and detection of various diseases. Recently, the coronavirus (COVID-19) pandemic has put a lot of pressure on the health system all around the world. The diagnosis of COVID-19 is possible by PCR testing and medical imagining. Since COVID-19 is highly contagious, diagnosis using chest X-ray is considered safe in various situations. In this study, a deep learning-based technique is proposed to classify COVID-19 infection from other non-COVID-19 infections. To classify COVID-19, three different pre-trained models named EfficientNetB1, NasNetMobile and MobileNetV2 are used. The augmented dataset is used for training deep learning models while two different training strategies have been used for classification. In this study, not only are the deep learning model fine-tuned but also the hyperparameters are fine-tuned, which significantly improves the performance of the fine-tuned deep learning models. Moreover, the classification head is regularized to improve the performance. For the evaluation of the proposed techniques, several performance parameters are used to gauge the performance. EfficientNetB1 with regularized classification head outperforms the other models. The proposed technique successfully classifies four classes that include COVID-19, viral pneumonia, lung opacity, and normal, with an accuracy of 96.13%. The proposed technique shows superiority in terms of accuracy when compared with recent techniques present in the literature.

Measuring and Assessing Performance of Mobile Broadband Networks and Future 5G Trends

Mobile broadband (MBB) is one of the critical goals in fifth-generation (5G) networks due to rising data demand. MBB provides very high-speed internet access with seamless connections. Existing MBB, including third-generation (3G) and fourth-generation (4G) networks, also requires monitoring to ensure good network performance. Thus, performing analysis of existing MBB assists mobile network operators (MNOs) in further improving their MBB networks’ capabilities to meet user satisfaction. In this paper, we analyzed and evaluated the multidimensional performance of existing MBB in Oman. Drive test measurements were carried out in four urban and suburban cities: Muscat, Ibra, Sur and Bahla. This study aimed to analyze and understand the MBB performance, but it did not benchmark the performance of MNOs. The data measurements were collected through drive tests from two MNOs supporting 3G and 4G technologies: Omantel and Ooredoo. Several performance metrics were measured during the drive tests, such as signal quality, throughput (downlink and unlink), ping and handover. The measurement results demonstrate that 4G technologies were the dominant networks in most of the tested cities during the drive test. The average downlink and uplink data rates were 18 Mbps and 13 Mbps, respectively, whereas the average ping and pong loss were 53 ms and 0.9, respectively, for all MNOs.