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

Assessment of Medication Compliance and Follow-Up Clinic Attendance Among Patients With Cardiovascular Diseases in the Jazan Region

BACKGROUND AND AIM

Cardiovascular diseases are common causes of mortality in Saudi Arabia and the world. This study aims to assess medication compliance and regularity of follow-up for cardiovascular patients in the Jazan region.

METHODOLOGY

An analytical cross-sectional approach was used to target all registered cardiovascular patients attending the cardio clinic in a Jazan region hospital. Data were collected using an interview questionnaire developed by the researchers with the help of experts. The questionnaire included the patients' sociodemographic data, clinical characteristics, disease-related data, drugs, and appointments.

RESULTS

The study included 259 patients diagnosed with cardiac disease. About 53.7% of the patients were males. All the cases had the disease for one year or more. About 56% of the patients had no difficulty remembering their medications, while 44% had problems remembering to take them. More than half of the patients had good medication adherence, and 79.6% had good appointment adherence. Only 20.4% of patients had a poor adherence rate.

CONCLUSION AND RECOMMENDATIONS

The adherence rate for the patients' medication and appointments was satisfactory due to high patient awareness. On the other hand, poor adherence was related more to non-Saudi patients.

Machine learning-based models for prediction of the risk of stroke in coronary artery disease patients receiving coronary revascularization

BACKGROUND

To construct several prediction models for the risk of stroke in coronary artery disease (CAD) patients receiving coronary revascularization based on machine learning methods.

METHODS

In total, 5757 CAD patients receiving coronary revascularization admitted to ICU in Medical Information Mart for Intensive Care IV (MIMIC-IV) were included in this cohort study. All the data were randomly split into the training set (n = 4029) and testing set (n = 1728) at 7:3. Pearson correlation analysis and least absolute shrinkage and selection operator (LASSO) regression model were applied for feature screening. Variables with Pearson correlation coefficient<9 were included, and the regression coefficients were set to 0. Features more closely related to the outcome were selected from the 10-fold cross-validation, and features with non-0 Coefficent were retained and included in the final model. The predictive values of the models were evaluated by sensitivity, specificity, area under the curve (AUC), accuracy, and 95% confidence interval (CI).

RESULTS

The Catboost model presented the best predictive performance with the AUC of 0.831 (95%CI: 0.811-0.851) in the training set, and 0.760 (95%CI: 0.722-0.798) in the testing set. The AUC of the logistic regression model was 0.789 (95%CI: 0.764-0.814) in the training set and 0.731 (95%CI: 0.686-0.776) in the testing set. The results of Delong test revealed that the predictive value of the Catboost model was significantly higher than the logistic regression model (P<0.05). Charlson Comorbidity Index (CCI) was the most important variable associated with the risk of stroke in CAD patients receiving coronary revascularization.

CONCLUSION

The Catboost model was the optimal model for predicting the risk of stroke in CAD patients receiving coronary revascularization, which might provide a tool to quickly identify CAD patients who were at high risk of postoperative stroke.

Differential Expression of Serum Proinflammatory Cytokine TNF-α and Genetic Determinants of TNF-α, CYP2C19*17, miR-423 Genes and Their Effect on Coronary Artery Disease Predisposition and Progression

Coronary artery disease (CAD) is the leading cause of death and hospitalization worldwide and represents a problem for public health systems everywhere. In Saudi Arabia, the prevalence of CAD is estimated to be 5.5%. Risk factors for CAD include older age, male gender, obesity, high blood pressure, smoking, diabetes, hyperlipidemia, and genetic factors. Reducing the risk factors in susceptible individuals will decrease the prevalence of CAD. Genome wide association studies have helped to reveal the association of many loci with diseases like CAD. In this study, we examined the link between single nucleotide variations (SNVs) of TNF-α-rs1800629 G>A, CYP2C19*17 (rs12248560) C>T, and miR-423 rs6505162 C>A and the expression of TNF-α with CAD. We used the mutation specific PCR, ARMS-PCR, and ELISA. The results showed that the A allele of the TNF-α rs1800629 G>A SNP is linked to CAD with odd ratio (OR) (95% CI) = 2.10, p-value = 0.0013. The T allele of the CYP2C19*17 (rs12248560) C>T is linked to CAD with OR (95% CI) = 2.02, p-value = 0.003. In addition, the A allele of the miR-423 rs6505162 C>A SNV is linked to CAD with OR (95% CI) = 1.49, p-value = 0.036. The ELISA results indicated that the TNF-α serum levels are significantly increased in CAD patients compared to healthy controls. We conclude the TNF-α rs1800629 G>A, CYP2C19*17, and miR-423 rs6505162 C>A are potential genetic loci for CAD in the Saudi population. These findings require further verification in future studies. After being verified, our results might be utilized in genetic testing to identify individuals that are susceptible to CAD and, therefore, for whom reducing modifiable risk factors (e.g., poor diet, diabetes, obesity, and smoking) would result in prevention or delay of CAD.

Factors predicting medication adherence among coronary artery disease patients in Saudi Arabia: A descriptive study

OBJECTIVES

To measure medication adherence among coronary artery disease (CAD) patients and identify sociodemographic factors that are medication adherence predictors.

METHODS

A cross-sectional correlation design was carried out, following the STROBE guidelines. The study was carried out in a specialized cardiac center in the western region of Saudi Arabia between March 2019 and January 2020. A total of 278 patients completed the study survey.

RESULTS

The majority of participants (59.4%) reported moderate medication adherence, and the remainder reported poor (30.6%) and good (10%) medication adherence. It was found that women patients, patients with higher education levels, non-smokers, patients who regularly followed-up with their cardiologist, and patients with family support showed significantly higher medication adherence. Four of the sociodemographic variables (gender, number of doctor visits, family support, and education level) predicted medication adherence.

CONCLUSION

Approximately 30% of the participants reported poor medication adherence. The number of cardiologist visits and the level of family support were 2 of the factors found to be associated with medication adherence.

Use of machine learning to identify risk factors for coronary artery disease

Coronary artery disease (CAD) is the leading cause of death in both developed and developing nations. The objective of this study was to identify risk factors for coronary artery disease through machine-learning and assess this methodology. A retrospective, cross-sectional cohort study using the publicly available National Health and Nutrition Examination Survey (NHANES) was conducted in patients who completed the demographic, dietary, exercise, and mental health questionnaire and had laboratory and physical exam data. Univariate logistic models, with CAD as the outcome, were used to identify covariates that were associated with CAD. Covariates that had a p<0.0001 on univariate analysis were included within the final machine-learning model. The machine learning model XGBoost was used due to its prevalence within the literature as well as its increased predictive accuracy in healthcare prediction. Model covariates were ranked according to the Cover statistic to identify risk factors for CAD. Shapely Additive Explanations (SHAP) explanations were utilized to visualize the relationship between these potential risk factors and CAD. Of the 7,929 patients that met the inclusion criteria in this study, 4,055 (51%) were female, 2,874 (49%) were male. The mean age was 49.2 (SD = 18.4), with 2,885 (36%) White patients, 2,144 (27%) Black patients, 1,639 (21%) Hispanic patients, and 1,261 (16%) patients of other race. A total of 338 (4.5%) of patients had coronary artery disease. These were fitted into the XGBoost model and an AUROC = 0.89, Sensitivity = 0.85, Specificity = 0.87 were observed (Fig 1). The top four highest ranked features by cover, a measure of the percentage contribution of the covariate to the overall model prediction, were age (Cover = 21.1%), Platelet count (Cover = 5.1%), family history of heart disease (Cover = 4.8%), and Total Cholesterol (Cover = 4.1%). Machine learning models can effectively predict coronary artery disease using demographic, laboratory, physical exam, and lifestyle covariates and identify key risk factors.