Factors Influencing Patients to Decide to Discharge Themselves against Medical Advice at Tertiary Hospitals: A Cross-Sectional Study

Background: Discharged against medical advice (DAMA) is defined as any instance when a patient wants to leave the hospital against the managing physician’s decision. This study aimed to identify factors that influence patients to decide to be DAMA. Methods: A descriptive, cross-sectional study. The study was conducted in the emergency department (ED) of King Fahad Medical City (KFMC)-Saudi Arabia-Riyadh city. A questionnaire in both Arabic and English was distributed to all participants to fill in either English or Arabic. Results: Between 1 March and 30 April 2021, 510 responses were collected. Most of the study participants (31.4%) were over the age of 54. Our findings showed that 12.5% of our participants had taken discharge against medical advice in the past. Results Regarding Factors That Influence Patients to Decide on DAMA Showed: Regarding Inappropriate behavior and disrespect of the physician or staff to the patient and his relatives, 262 (51.4%) participants, 85 (16.7%) participants, and 163 (32%) participants agreed, neutral, and disagreed, respectively. Regarding the Lack of physicians’ and nurses’ attention to the patient and his relatives (emotionally), our result showed that 278 (54.5%) participants, 95 (18.6%) participants, and 137 (26.9%) participants agreed, neutral, and disagree, respectively. Regarding failure to inform the patient or his relatives of his condition, it showed that 257 (50.4%) participants, 95 (18.6%) participants, and 158 (31%) participants agreed, neutral, and disagreed, respectively. Regarding feeling better from DAMA, our result showed 226 (44.3%) participants, 119 (23.3%) participants, and 165 (32.4%) participants agreed, neutral, and disagreed, respectively. Regarding patients’ or their relative’s tiredness of hospital stay, the result showed that 166 (32.5%) participants, 104 (20.4%) participants, and 240 (47.1%) participants agreed, neutral, and disagreed, respectively. Conclusion: The long wait time to be seen by a physician was the major factor that forced patients to leave the emergency department against medical advice.

Analysis of Length of Stay (LOS) Data from the Medical Records of Tertiary Care Hospital in Saudi Arabia for Five Diagnosis Related Groups: Application of Cox Prediction Model

Background: One of the main objectives of hospital managements is to control the length of stay (LOS). Successful control of LOS of inpatients will result in reduction in the cost of care, decrease in nosocomial infections, medication side effects, and better management of the limited number of available patients’ beds. The length of stay (LOS) is an important indicator of the efficiency of hospital management by improving the quality of treatment, and increased hospital profit with more efficient bed management. The purpose of this study was to model the distribution of LOS as a function of patient’s age, and the Diagnosis Related Groups (DRG), based on electronic medical records of a large tertiary care hospital. Materials and Methods: Information related to the research subjects were retrieved from a database of patients admitted to King Faisal Specialist Hospital and Research Center hospital in Riyadh, Saudi Arabia between January 2014 and December 2016. Subjects’ confidential information was masked from the investigators. The data analyses were reported visually, descriptively, and analytically using Cox proportional hazard regression model to predict the risk of long-stay when patients’ age and the DRG are considered as antecedent risk factors. Results: Predicting the risk of long stay depends significantly on the age at admission, and the DRG to which a patient belongs to. We demonstrated the validity of the Cox regression model for the available data as the proportionality assumption is shown to be satisfied. Two examples were presented to demonstrate the utility of the Cox model in this regard.

Predictors of the Aggregate of COVID-19 Cases and Its Case-Fatality: A Global Investigation Involving 120 Countries

Objective: Since the identification of COVID-19 in December 2019 as a pandemic, over 4500 research papers were published with the term “COVID-19” contained in its title. Many of these reports on the COVID-19 pandemic suggested that the coronavirus was associated with more serious chronic diseases and mortality particularly in patients with chronic diseases regardless of country and age. Therefore, there is a need to understand how common comorbidities and other factors are associated with the risk of death due to COVID-19 infection. Our investigation aims at exploring this relationship. Specifically, our analysis aimed to explore the relationship between the total number of COVID-19 cases and mortality associated with COVID-19 infection accounting for other risk factors. Methods: Due to the presence of over dispersion, the Negative Binomial Regression is used to model the aggregate number of COVID-19 cases. Case-fatality associated with this infection is modeled as an outcome variable using machine learning predictive multivariable regression. The data we used are the COVID-19 cases and associated deaths from the start of the pandemic up to December 02-2020, the day Pfizer was granted approval for their new COVID-19 vaccine. Results: Our analysis found significant regional variation in case fatality. Moreover, the aggregate number of cases had several risk factors including chronic kidney disease, population density and the percentage of gross domestic product spent on healthcare. The Conclusions: There are important regional variations in COVID-19 case fatality. We identified three factors to be significantly correlated with case fatality.

A Review of Systemic Lupus Erythematosus (SLE): Symptoms, Risk Factors, Treatment, and Health Related Quality of Life Issues

In this paper, we present a thorough review of one of the most life-threatening autoimmune diseases, Systemic lupus erythematosus (lupus). Symptoms, risk factors, including genetic and epidemiological factors are discussed. Treatment, life expectancies, and Health Related Quality of Life of patients with SLE will be discussed as well. Special attention will be given to Lupus Nephritis.

Analysis of Hospital Mortality Data: The Role of DRG’s

Background: Factors associated with hospital mortality are usually identified and their effects are quantified through statistical modeling. To guide the choice of the best statistical model, we first quantify the predictive ability of each model and then use the CIHI index to see if the hospital policy needs any change. Objectives: The main purpose of this study compared three statistical models in the evaluation of the association between hospital mortality and two risk factors, namely subject’s age at admission and the length of stay, adjusting for the effect of Diagnostic Related Groups (DRG). Methods: We use several SAS procedures to quantify the effect of DRG on the variability in hospital mortality. These procedures are the Logistic Regression model (ignoring the DRG effect), the Generalized Estimating Equation (GEE) that takes into account the within DRG clustering effect (but the within cluster correlation is treated as nuisance parameter), and the Generalized Linear Mixed Model (GLIMMIX). We showed that the GLIMMIX is superior to other models as it properly accounts for the clustering effect of “Diagnostic Related Groups” denoted by DRG. Results: The GLM procedure showed that the proportional contribution of DRG is 16%. All three models showed significant and increasing trend in mortality (P < 0.0001) with respect to the two risk factors (age at admission, and hospital length of stay). It was also clear that the CIHI index was not different under the three models. We re-estimated the models parameters after dichotomizing the risk factors at the optimal cut-off points, using the ROC curve. The parameters estimates and their significance did not change.

Health-related quality of life and perceived social support among cancer palliative patients in Saudi Arabia

Aim:This study aims to describe health-related quality of life(HRQL),identify factors associated with HRQL physical and mental health domains,and explore the association between perceived social supports and HRQL among cancer palliative patients in Saudi Arabia.Methods:A cross-sectional study is applied.The validated European Organization for Cancer Research and Treatment,the EORTC QLQ-15 palliative care scale and the Multidimensional Scale of Perceived Social Support(MSPSS)have been used.A convenience sample of(N=301)palliative cancer patients was collected from two main regional cancer centers in Riyadh.Data were analyzed using Pearson correlation analysis.Results:Results indicate that overall quality of life showed a significant positive correlation with perceived family and friend support,sub-factors of perceived social support.Regression analysis showed that the overall model experienced 69.0%of the variance for global health statutes with F(4,7)=7.149 P<0.001.Physical functioning,emotional functioning,and family support were found to be significant predictors of global health status.Family and Friend support were found to be significant positive predictors of emotional functioning.Conclusions:The inpatient and outpatient treatment can vary at different stages and in different areas,family and friend support has been highlighted as necessary in this context.Physical and emotional factors have been demonstrated in older age(geriatric)patients as they may have debilitating diseases that can limit their functioning hence support the case for more palliative care.