Susceptible exposed infectious recovered-machine learning for COVID-19 prediction in Saudi Arabia

Susceptible exposed infectious recovered (SEIR) is among the epidemiological models used in forecasting the spread of disease in large populations. SEIR is a fitting model for coronavirus disease (COVID-19) spread prediction. Somehow, in its original form, SEIR could not measure the impact of lockdowns. So, in the SEIR equations system utilized in this study, a variable was included to evaluate the impact of varying levels of social distance on the transmission of COVID-19. Additionally, we applied artificial intelligence utilizing the deep neural network machine learning (ML) technique. On the initial spread data for Saudi Arabia that were available up to June 25th, 2021, this improved SEIR model was used. The study shows possible infection to around 3.1 million persons without lockdown in Saudi Arabia at the peak of spread, which lasts for about 3 months beginning from the lockdown date (March 21st). On the other hand, the Kingdom's current partial lockdown policy was estimated to cut the estimated number of infections to 0.5 million over nine months. The data shows that stricter lockdowns may successfully flatten the COVID-19 graph curve in Saudi Arabia. We successfully predicted the COVID-19 epidemic's peaks and sizes using our modified deep neural network (DNN) and SEIR model

Dermatological Emergencies in Family Medicine: Recognition, Management, and Referral Considerations

Numerous people with skin disorders who have real dermatologic crises show up at the emergency room. Family doctors need to be able to identify potentially fatal dermatological disorders quickly since they could be the first to encounter patients with these illnesses. The purpose of this review is to provide guidance for early recognition, help identify distinct symptoms, and enable early diagnosis of emerging dermatological conditions. Necrotizing fasciitis, Stevens-Johnson syndrome, toxic epidermal necrolysis, Rocky Mountain spotted fever, and other possible emergencies that might manifest as dermatological symptoms are examples of these conditions. In this article we will be discussing the dermatological emergencies present at primary care settings and encountered by family physician, recognition and management of those emergencies, referral considerations, role of family medicine in dermatological emergencies and other topics

HIV-Care Outcome in Saudi Arabia; a Longitudinal Cohort

Background: Clinical characteristics of HIV-1 infection in people inhabiting Western, Sub-Saharan African, and South-East Asian countries are well recognized. However, very little information is available with regard to HIV-1 infection and treatment outcome in MENA countries including the Gulf Cooperation Council (GCC) states. Methods: Clinical, demographic and epidemiologic characteristics of 602 HIV-1 infected patients followed in the adult Infectious Diseases Clinic of King Faisal Specialist Hospital and Research Centre, in Riyadh, Kingdom of Saudi Arabia a tertiary referral center were longitudinally collected from 1989 to 2010. Results: Of the 602 HIV-1 infected patients in this observation period, 70% were male. The major mode of HIV-1 transmission was heterosexual contact (55%). At diagnosis, opportunistic infections were found in 49% of patients, most commonly being pneumocysitis. AIDS associated neoplasia was also noted in 6% of patients. A hundred and forty-seven patients (24%) died from the cohort by the end of the observation period. The mortality rate peaked in 1992 at 90 deaths per 1000 person-year, whereas the mortality rate gradually decreased to <1% from 1993-2010. In 2010, 71% of the patients were receiving highly active retroviral therapy. Conclusions: These data describe the clinical characteristic of HIV-1-infected patients at a major tertiary referral hospital in KSA over a 20-year period. Initiation of antiretroviral therapy resulted in a significant reduction in both morbidity and mortality. Future studies are needed in the design and implementation of targeted treatment and prevention strategies for HIV-1 infection in KSA

Data-Driven Prediction for COVID-19 Severity in Hospitalized Patients

Clinicians urgently need reliable and stable tools to predict the severity of COVID-19 infection for hospitalized patients to enhance the utilization of hospital resources and supplies. Published COVID-19 related guidelines are frequently being updated, which impacts its utilization as a stable go-to resource for informing clinical and operational decision-making processes. In addition, many COVID-19 patient-level severity prediction tools that were developed during the early stages of the pandemic failed to perform well in the hospital setting due to many challenges including data availability, model generalization, and clinical validation. This study describes the experience of a large tertiary hospital system network in the Middle East in developing a real-time severity prediction tool that can assist clinicians in matching patients with appropriate levels of needed care for better management of limited health care resources during COVID-19 surges. It also provides a new perspective for predicting patients&rsquo; COVID-19 severity levels at the time of hospital admission using comprehensive data collected during the first year of the pandemic in the hospital. Unlike many previous studies for a similar population in the region, this study evaluated 4 machine learning models using a large training data set of 1386 patients collected between March 2020 and April 2021. The study uses comprehensive COVID-19 patient-level clinical data from the hospital electronic medical records (EMR), vital sign monitoring devices, and Polymerase Chain Reaction (PCR) machines. The data were collected, prepared, and leveraged by a panel of clinical and data experts to develop a multi-class data-driven framework to predict severity levels for COVID-19 infections at admission time. Finally, this study provides results from a prospective validation test conducted by clinical experts in the hospital. The proposed prediction framework shows excellent performance in concurrent validation (n=462 patients, March 2020&ndash;April 2021) with highest discrimination obtained with the random forest classification model, achieving a macro- and micro-average area under receiver operating characteristics curve (AUC) of 0.83 and 0.87, respectively. The prospective validation conducted by clinical experts (n=185 patients, April&ndash;May 2021) showed a promising overall prediction performance with a recall of 78.4&ndash;90.0% and a precision of 75.0&ndash;97.8% for different severity classes

Successful treatment of multi-focal XDR tuberculous osteomyelitis

Summary: We herein describe the nosocomial transmission of a pre-XDR or MDR case of pulmonary tuberculosis in a HIV-negative health care worker in an area endemic for MDR and XDR tuberculosis. Following inadequate therapy and non-compliance, he presented with extra-pulmonary XDR tuberculosis in the form of multi-focal osteomyelitis and encysted pleural effusion. He was cured after two years of treatment with various anti-tuberculous drugs in addition to interferon gamma. Keywords: XDR tuberculosis, Osteomyelitis, Therap