Disparity Analysis for Cardiac Surgical Outcomes: The Citizenship Factor.

(1) Background: Disparity in clinical care on the basis of gender, socioeconomic status, ethnic and racial variation is an established phenomenon. The focus on health disparities was led on by the report of the Secretary's Task Force on Black & Minority Health, which emphasized that the burden of death and illness was in excess among black people and other minorities. In Saudi Arabia, cardiac health care is being provided to a heterogeneous group of patients during pilgrimage time. This mixed population comprises different socio-economic backgrounds, demographics, ethnicities and languages. This study was carried out to assess for any disparities in cardiac surgical outcomes after isolated CABG surgery between Saudi citizens and non-Saudi patients. (2) Methods: The data of 2178 patients who underwent isolated coronary artery bypass surgery at King Abdullah Medical City from December 2014 to July 2020 were extracted. Patient demographics, clinical features, comorbidities, diagnoses, surgical procedures, complications, length of hospital stay and mortality were included in the data. The primary outcome was mortality after coronary artery bypass grafting surgery. (3) Results: A total of 2178 isolated CABG procedures were conducted during the study period with almost 57.5% of patients being Saudi citizens in comparison with 42.5% of non-Saudi citizens. The male gender represented the majority of the population, with a total of 1584 patients, representing 72.7% of the total study population. The rate of mortality had no statistical significance with the mortality rate of 5% vs. 5.3% (p < 0.786). The postoperative morbidities were comparable for all the parameters except for postoperative extracorporeal membrane oxygenation (ECMO). (4) Conclusions: In the present study, the chances of survival and postoperative outcomes are not associated with nationality per se, but with underlying comorbidities.

Comprehensive Highlights of the Universal Efforts towards the Development of COVID-19 Vaccine.

The world has taken proactive measures to combat the pandemic since the coronavirus disease 2019 (COVID-19) outbreak, which was caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). These measures range from increasing the production of personal protective equipment (PPE) and highlighting the value of social distancing to the emergency use authorization (EUA) of therapeutic drugs or antibodies and their appropriate use; nonetheless, the disease is still spreading quickly and is ruining people's social lives, the economy, and public health. As a result, effective vaccines are critical for bringing the pandemic to an end and restoring normalcy in society. Several potential COVID-19 vaccines are now being researched, developed, tested, and reviewed. Since the end of June 2022, several vaccines have been provisionally approved, whereas others are about to be approved. In the upcoming years, a large number of new medications that are presently undergoing clinical testing are anticipated to hit the market. To illustrate the advantages and disadvantages of their technique, to emphasize the additives and delivery methods used in their creation, and to project potential future growth, this study explores these vaccines and the related research endeavors, including conventional and prospective approaches.

Prevalence of coronary artery disease and its risk factors in Majmaah City, Kingdom of Saudi Arabia.

Objective: This study was carried out with an aim to outline the prevalence of coronary artery diseases, its risk of one region of the Saudi Arabia. Methods: A retrospective observational study conducted across five secondary medical centers located in the city of Majmaah. Hospital medical records and ministry of health records were screened over a 6-month period for data on patients admitted for Coronary artery disease (CAD). Data collected included sociodemographic characteristics, medical profile, and laboratory findings. Results: A total of 327 participants were included in this study with a median age of 64 and the majority being male participants (59.8%). The majority were married, held a primary school degree and earned a salary for living. A large number (82.9%) were hypertensive and diabetic (66.7%) and one-fourth had a previous MI (25.1%). A large number (73.7%) had heart failure with a mean ejection fraction of 44% (SD = 13). The causes of heart failure were mainly ischemic (56.3%) and hypertensive (28.1%). Readmission rates at 30 and 90 days then at 6 and 12 months were 22, 53.8, 68.8, and 75.8%, respectively. The mortality rates at the same time intervals were 5.5, 8.9, 14.1, and 22.9%, respectively. Predictors of readmission are age, CCI, and NYHA class. Conclusion: Coronary artery disease is the leading cause of heart failure. End stage CAD can have similar results in terms of readmission and mortality as heart failure. Future research should target patients in different stages of the condition and monitor their comorbidities which may impact the study findings.

Exploration of Black Boxes of Supervised Machine Learning Models: A Demonstration on Development of Predictive Heart Risk Score.

Machine learning (ML) often provides applicable high-performance models to facilitate decision-makers in various fields. However, this high performance is achieved at the expense of the interpretability of these models, which has been criticized by practitioners and has become a significant hindrance in their application. Therefore, in highly sensitive decisions, black boxes of ML models are not recommended. We proposed a novel methodology that uses complex supervised ML models and transforms them into simple, interpretable, transparent statistical models. This methodology is like stacking ensemble ML in which the best ML models are used as a base learner to compute relative feature weights. The index of these weights is further used as a single covariate in the simple logistic regression model to estimate the likelihood of an event. We tested this methodology on the primary dataset related to cardiovascular diseases (CVDs), the leading cause of mortalities in recent times. Therefore, early risk assessment is an important dimension that can potentially reduce the burden of CVDs and their related mortality through accurate but interpretable risk prediction models. We developed an artificial neural network and support vector machines based on ML models and transformed them into a simple statistical model and heart risk scores. These simplified models were found transparent, reliable, valid, interpretable, and approximate in predictions. The findings of this study suggest that complex supervised ML models can be efficiently transformed into simple statistical models that can also be validated.