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Novel cardiovascular risk markers in cardiovascular diseases

Alghibiwi, Hanan (2021) Novel cardiovascular risk markers in cardiovascular diseases. PhD thesis, University of Glasgow.

Introduction:

Cardiovascular disease is the leading cause of death worldwide, and the global prevalence of cardiovascular disease increased every year. Cardiovascular disease (CVD) is a complex multifactorial disease. It is essential to understand the different biological and genetic factors that are associated with the development and progression of CVD in order to improve the diagnosis, prognosis and treatment of the condition and, thereby, reduce mortality and morbidity.

The use of cardiometabolic markers, including blood biomarkers such as troponin, and NT-proBNP, as well as non-invasive imaging markers, such as carotid intima media thickness and pulse wave arterial stiffness, might help to correctly identify patients with a CVD risk at an early stage. These markers may help practitioners understand and monitor disease progression, but whether they provide additional information to the conventional risk factors included in the risk prediction model or disease progression is not currently clear.

In contrast, other biomarkers could help provide insights into pathophysiological processes and help practitioners understand potential drug targets. MicroRNAs (miRNAs) are a family of small, noncoding RNA molecules that regulate gene expression by targeting specific messenger RNA. Dysregulation of specific miRNAs expression have been associated with cardiovascular diseases. Since the discovery of miRNAs in body fluids, including plasma, saliva and urine, a strong body of evidence has been published demonstrating the potential use of circulating miRNAs as markers for several diseases, including CVD.

This thesis studies the possible determinants for imaging markers of cardiovascular disease risk, including carotid intima media thickness and pulse wave arterial stiffness index and further investigates putative circulating miRNAs as novel biomarkers for cardiovascular diseases. The overarching aim is to provide insight into novel biomarkers for monitoring and treatment of cardiovascular disease progression.

The associations of circulating miRNA expression with markers of cardiometabolic diseases: CAMERA trial: Candidate circulating miRNAs were selected on the basis of the findings in the literature review that showed that their dysregulation was associated with a change of cardiometabolic markers such as cardiac enzyme and imaging markers. A cross-sectional analysis was conducted to investigate the associations of circulating miRNAs, miR30c, miR103, miR133a, miR122 and miR146a with cardiometabolic markers, using 60 paired stored plasma samples at baseline and after 18 months from the CAMERA trial. Significant associations were observed for selected circulating miRNAs with biomarkers of cardiovascular risk in a population with coronary heart disease and insulin resistance. A significant association was found between the expression of miR103 with cardiac biomarkers, including troponin T and NT-pro-BNP, and miR122 with carotid intima media thickness. These associations supported previous observations that indicated an association between the upregulated miR122 expression and adverse lipid profile (Willeit et al., 2017a), and the role of lipid in the progression of carotid intima media thickness that was shown in a study by (Huang et al., 2016). In addition, upregulated miR103 expression in response to cardiac necrosis was observed in a study done by Wang et al. (2015a). Therefore, further research is needed to understand the specific role of miR103 in cardiac necrosis.

The effect of metformin on the expression of circulating miRNA: The Carotid Atherosclerosis: MEtformin for insulin ResistAnce (CAMERA) trial : Metformin is the first line therapy for Type 2 diabetes. The effect of metformin on the risk of cardiovascular diseases had been studied in two randomised controlled trials: the UKPDS and the HOME trial. Those studies showed that metformin reduced the risk of CVD through surrogates in patients with diabetes. In this study, the effect of metformin on the expression of these circulating miRNAs was explored using both the baseline samples and the 18-month plasma samples from the CAMERA randomised control trial (RCT). Randomisation to metformin failed to show any effect on the expression of circulating miR30c, miR103, mi133a, miR122 and miR146a in a population without diabetes and with coronary heart disease, although the study was, perhaps, underpowered and the effect of metformin in the study in general was very modest. While no strong evidence for metformin having an effect on these miRNAs was observed, this was one of the first, if not the first, RCT to study miRNA biomarkers and provides a platform for future research in this area.

Association of cardiovascular risk factors with carotid intima media thickness and pulse wave arterial stiffness in UK Biobank; Cross sectional study: More established vascular biomarkers of CVD were studied for the purposes of this thesis, in a much larger study than previously possible, in order to understand whether measurement of the markers might be clinically useful. In this first study, using the UK Biobank (42,727 participants), the determinants of vascular imaging biomarkers (carotid intima media thickness and pulse wave arterial stiffness index) were examined using traditional cardiovascular risk factors including systolic and diastolic blood pressure, HbA1c, lipid markers and anthropometric measurements. A cross-sectional analysis was done to investigate the upstream determinants for carotid intima media thickness and pulse wave arterial stiffness. Generally, systolic blood pressure and age were the strongest independent risk factors for a high cIMT value. It was found that, for every one SD increase in age and systolic blood pressure, the mean cIMT increased by 0.357 and 0.115 SD, respectively. Systolic blood pressure and age were the strongest independent risk factors for high PWASI. It was found that, for every one SD increase in age and systolic blood pressure, the mean PWASI increased by 0.005 and 0.046 SD, respectively. Also, this study showed that the cIMT was higher in males than females, along with other cardiovascular risk factors. This study, therefore, highlights potentially important differences in what these biomarkers indicate about upstream cardiovascular risk. Although both appear to be influenced by established risk factors, in line with the findings of much of the rest of the literature, they are different. This means they may have different clinical utility in different settings.

Carotid intima media thickness and pulse wave arterial stiffness with association of cardiovascular events in UK Biobank: Prospective study: In this UK Biobank based study, the focus was on establishing whether carotid intima media thickness or pulse wave arterial stiffness index was associated with the incidence of cardiovascular diseases, stroke and CHD. In addition, we adjusted for the strongest established determinants based on findings of chapter 5 that include systolic blood pressure and age for cIMT, age and systolic blood pressure for PWASI.

After adjustment, one SD increase of mean cIMT was associated with 50 % increase risk of incidence of CVD events. The incidence of CVD events increased in highest quantile (mean cIMT >748µm) by 3 folds compared to persons in the lowest quantiles (mean cIMT <588µm). While, per one SD change in PWASI, the risk of CVD events decreased by 13% after adjustment with age and gender over a median 3 years of follow up in United Kingdom. Therefore, it appears cIMT is the more convincing biomarker that reflects CVD risk in the short term.

Overall conclusion:

This study has demonstrated the potential utility of circulating miRNAs as novel biomarkers for cardiovascular disease, providing new data for the most promising miRNA biomarkers, in the context of a large and well standardised study relative to much of the rest of the literature. Also, the study reports on the upstream risk factors that are important potential determinants of imaging biomarkers as well as on the association between these imaging markers and incidence of cardiovascular disease in the large UK Biobank population. The understanding of these biomarkers might help in the future to develop the way cardiovascular disease risk is managed in the clinical setting.

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• Published: 12 November 2020

Early and accurate detection and diagnosis of heart disease using intelligent computational model

• Yar Muhammad 1 ,
• Muhammad Tahir 1 ,
• Maqsood Hayat 1 &
• Kil To Chong 2  

Scientific Reports volume  10 , Article number:  19747 ( 2020 ) Cite this article

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• Cardiovascular diseases
• Computational biology and bioinformatics
• Health care
• Heart failure

Heart disease is a fatal human disease, rapidly increases globally in both developed and undeveloped countries and consequently, causes death. Normally, in this disease, the heart fails to supply a sufficient amount of blood to other parts of the body in order to accomplish their normal functionalities. Early and on-time diagnosing of this problem is very essential for preventing patients from more damage and saving their lives. Among the conventional invasive-based techniques, angiography is considered to be the most well-known technique for diagnosing heart problems but it has some limitations. On the other hand, the non-invasive based methods, like intelligent learning-based computational techniques are found more upright and effectual for the heart disease diagnosis. Here, an intelligent computational predictive system is introduced for the identification and diagnosis of cardiac disease. In this study, various machine learning classification algorithms are investigated. In order to remove irrelevant and noisy data from extracted feature space, four distinct feature selection algorithms are applied and the results of each feature selection algorithm along with classifiers are analyzed. Several performance metrics namely: accuracy, sensitivity, specificity, AUC, F1-score, MCC, and ROC curve are used to observe the effectiveness and strength of the developed model. The classification rates of the developed system are examined on both full and optimal feature spaces, consequently, the performance of the developed model is boosted in case of high variated optimal feature space. In addition, P-value and Chi-square are also computed for the ET classifier along with each feature selection technique. It is anticipated that the proposed system will be useful and helpful for the physician to diagnose heart disease accurately and effectively.

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Introduction.

Heart disease is considered one of the most perilous and life snatching chronic diseases all over the world. In heart disease, normally the heart fails to supply sufficient blood to other parts of the body to accomplish their normal functionality 1 . Heart failure occurs due to blockage and narrowing of coronary arteries. Coronary arteries are responsible for the supply of blood to the heart itself 2 . A recent survey reveals that the United States is the most affected country by heart disease where the ratio of heart disease patients is very high 3 . The most common symptoms of heart disease include physical body weakness, shortness of breath, feet swollen, and weariness with associated signs, etc. 4 . The risk of heart disease may be increased by the lifestyle of a person like smoking, unhealthy diet, high cholesterol level, high blood pressure, deficiency of exercise and fitness, etc. 5 . Heart disease has several types in which coronary artery disease (CAD) is the common one that can lead to chest pain, stroke, and heart attack. The other types of heart disease include heart rhythm problems, congestive heart failure, congenital heart disease (birth time heart disease), and cardiovascular disease (CVD). Initially, traditional investigation techniques were used for the identification of heart disease, however, they were found complex 6 . Owing to the non-availability of medical diagnosing tools and medical experts specifically in undeveloped countries, diagnosis and cure of heart disease are very complex 7 . However, the precise and appropriate diagnosis of heart disease is very imperative to prevent the patient from more damage 8 . Heart disease is a fatal disease that rapidly increases in both economically developed and undeveloped countries. According to a report generated by the World Health Organization (WHO), an average of 17.90 million humans died from CVD in 2016. This amount represents approximately 30% of all global deaths. According to a report, 0.2 million people die from heart disease annually in Pakistan. Every year, the number of victimizing people is rapidly increasing. European Society of Cardiology (ESC) has published a report in which 26.5 million adults were identified having heart disease and 3.8 million were identified each year. About 50–55% of heart disease patients die within the initial 1–3 years, and the cost of heart disease treatment is about 4% of the overall healthcare annual budget 9 .

Conventional invasive-based methods used for the diagnosis of heart disease which were based on the medical history of a patient, physical test results, and investigation of related symptoms by the doctors 10 . Among the conventional methods, angiography is considered one of the most precise technique for the identification of heart problems. Conversely, angiography has some drawbacks like high cost, various side effects, and strong technological knowledge 11 . Conventional methods often lead to imprecise diagnosis and take more time due to human mistakes. In addition, it is a very expensive and computational intensive approach for the diagnosis of disease and takes time in assessment 12 .

To overcome the issues in conventional invasive-based methods for the identification of heart disease, researchers attempted to develop different non-invasive smart healthcare systems based on predictive machine learning techniques namely: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Naïve Bayes (NB), and Decision Tree (DT), etc. 13 . As a result, the death ratio of heart disease patients has been decreased 14 . In literature, the Cleveland heart disease dataset is extensively utilized by the researchers 15 , 16 .

In this regard, Robert et al . 17 have used a logistic regression classification algorithm for heart disease detection and obtained an accuracy of 77.1%. Similarly, Wankhade et al . 18 have used a multi-layer perceptron (MLP) classifier for heart disease diagnosis and attained accuracy of 80%. Likewise, Allahverdi et al . 19 have developed a heart disease classification system in which they integrated neural networks with an artificial neural network and attained an accuracy of 82.4%. In a sequel, Awang et al . 20 have used NB and DT for the diagnosis and prediction of heart disease and achieved reasonable results in terms of accuracy. They achieved an accuracy of 82.7% with NB and 80.4% with DT. Oyedodum and Olaniye 21 have proposed a three-phase system for the prediction of heart disease using ANN. Das and Turkoglu 22 have proposed an ANN ensemble-based predictive model for the prediction of heart disease. Similarly, Paul and Robin 23 have used the adaptive fuzzy ensemble method for the prediction of heart disease. Likewise, Tomov et al. 24 have introduced a deep neural network for heart disease prediction and his proposed model performed well and produced good outcomes. Further, Manogaran and Varatharajan 25 have introduced the concept of a hybrid recommendation system for diagnosing heart disease and their model has given considerable results. Alizadehsani et al . 26 have developed a non-invasive based model for the prediction of coronary artery disease and showed some good results regarding the accuracy and other performance assessment metrics. Amin et al . 27 have proposed a framework of a hybrid system for the identification of cardiac disease, using machine learning, and attained an accuracy of 86.0%. Similarly, Mohan et al . 28 have proposed another intelligent system that integrates RF with a linear model for the prediction of heart disease and achieved the classification accuracy of 88.7%. Likewise, Liaqat et al . 29 have developed an expert system that uses stacked SVM for the prediction of heart disease and obtained 91.11% classification accuracy on selected features.

The contribution of the current work is to introduce an intelligent medical decision system for the diagnosis of heart disease based on contemporary machine learning algorithms. In this study, 10 different nature of machine learning classification algorithms such as Logistic Regression (LR), Decision Tree (DT), Naïve Bayes (NB), Random Forest (RF), Artificial Neural Network (ANN), etc. are implemented in order to select the best model for timely and accurate detection of heart disease at an early stage. Four feature selection algorithms, Fast Correlation-Based Filter Solution (FCBF), minimal redundancy maximal relevance (mRMR), Least Absolute Shrinkage and Selection Operator (LASSO), and Relief have been used for selecting the vital and more correlated features that have truly reflect the motif of the desired target. Our developed system has been trained and tested on the Cleveland (S 1 ) and Hungarian (S 2 ) heart disease datasets which are available online on the UCI machine learning repository. All the processing and computations were performed using Anaconda IDE. Python has been used as a tool for implementing all the classifiers. The main packages and libraries used include pandas, NumPy, matplotlib, sci-kit learn (sklearn), and seaborn. The main contribution of our proposed work is given below:

The performance of all classifiers has been tested on full feature spaces in terms of all performance evaluation matrices specifically accuracy.

The performances of the classifiers are tested on selected feature spaces, selected through various feature selection algorithms mentioned above.

The research study recommends that which feature selection algorithm is feasible with which classification algorithm for developing a high-level intelligence system for the diagnosing of heart disease patients.

The rest of the paper is organized as: “ Results and discussion ” section represents the results and discussion, “ Material and methods ” section describes the material and methods used in this paper. Finally, we conclude our proposed research work in “ Conclusion ” section.

Results and discussion

This section of the paper discusses the experimental results of various contemporary classification algorithms. At first, the performance of all used classification models i.e. K-Nearest Neighbors (KNN), Decision Tree (DT), Extra-Tree Classifier (ETC), Random Forest (RF), Logistic Regression (LR), Naïve Bayes (NB), Artificial Neural Network (ANN), Support Vector Machine (SVM), Adaboost (AB), and Gradient Boosting (GB) along with full feature space is evaluated. After that, four feature selection algorithms (FSA): Fast Correlation-Based Filter (FCBF), Minimal Redundancy Maximal Relevance (mRMR), Least Absolute Shrinkage and Selection Operator (LASSO), and Relief are applied to select the prominent and high variant features from feature space. Furthermore, the selected feature spaces are provided to classification algorithms as input to analyze the significance of feature selection techniques. The cross-validation techniques i.e. k-fold (10-fold) are applied on both the full and selected feature spaces to analyze the generalization power of the proposed model. Various performance evaluation metrics are implemented for measuring the performances of the classification models.

Classifiers’ predictive outcomes on full feature space

The experimental outcomes of the applied classification algorithms on the full feature space of the two benchmark datasets by using 10-fold cross-validation (CV) techniques are shown in Tables 1 and 2 , respectively.

The experimental results demonstrated that the ET classifier performed quite well in terms of all performance evaluation metrics compared to the other classifiers using 10-fold CV. ET achieved 92.09% accuracy, 91.82% sensitivity, 92.38% specificity, 97.92% AUC, 92.84% Precision, 0.92 F1-Score and 0.84 MCC. The specificity indicates that the diagnosed test was negative and the individual doesn't have the disease. While the sensitivity indicates the diagnostic test was positive and the patient has heart disease. In the case of the KNN classification model, multiple experiments were accomplished by considering various values for k i.e. k = 3, 5, 7, 9, 13, and 15, respectively. Consequently, KNN has shown the best performance at value k = 7 and achieved a classification accuracy of 85.55%, 85.93% sensitivity, 85.17% specificity, 95.64% AUC, 86.09% Precision, 0.86 F1-Score, and 0.71 MCC. Similarly, DT classifier has achieved accuracy of 86.82%, 89.73% sensitivity, 83.76% specificity, 91.89% AUC, 85.40% Precision, 0.87 F1-Score, and 0.73 MCC. Likewise, GB classifier has yielded accuracy of 91.34%, 90.32% sensitivity, 91.52% specificity, 96.87% AUC, 92.14% Precision, 0.92 F1-Score, and 0.83 MCC. After empirically evaluating the success rates of all classifiers, it is observed that ET Classifier out-performed among all the used classification algorithms in terms of accuracy, sensitivity, and specificity. Whereas, NB shows the lowest performance in terms of accuracy, sensitivity, and specificity. The ROC curve of all classification algorithms on full feature space is represented in Fig.  1 .

ROC curves of all classifiers on full feature space using 10-fold cross-validation on S 1 .

In the case of dataset S 2 , composed of 1025 total instances in which 525 belong to the positive class and 500 instances of having negative class, again ET has obtained quite well results compared to other classifiers using a 10-fold cross-validation test, which are 96.74% accuracy, 96.36 sensitivity, 97.40% specificity, and 0.93 MCC as shown in Table 2 .

Classifiers’ predictive outcomes on selected feature space

Fcbf feature selection technique.

FCBF feature selection technique is applied to select the best subset of feature space. In this attempt, various length of subspaces is generated and tested. Finally, the best results are achieved by classification algorithms on the subset of feature space (n = 6) using a 10-fold CV. Table 3 shows various performance measures of classifiers executed on the selected features space of FCBF.

Table 3 demonstrates that the ET classifier obtained quite good results including accuracy of 94.14%, 94.29% sensitivity, and specificity of 93.98%. In contrast, NB reported the lowest performance compared to the other classification algorithms. The performance of classification algorithms is also illustrated in Fig.  2 by using ROC curves.

ROC curve of all classifiers on selected features by FCBF feature selection algorithm.

mRMR feature selection technique

mRMR feature selection technique is used in order to select a subset of features that enhance the performance of classifiers. The best results reported on a subset of n = 6 of feature space which is shown in Table 4 .

In the case of mRMR, still, the success rates of the ET classifier are well in terms of all performance evaluation metrics compared to the other classifiers. ET has attained 93.42% accuracy, 93.92% sensitivity, and specificity of 93.88%. In contrast, NB has achieved the lowest outcomes which are 81.84% accuracy. Figure  3 shows the ROC curve of all ten classifiers using the mRMR feature selection algorithm.

ROC curve of all classifiers on selected features using the mRMR feature selection algorithm.

LASSO feature selection technique

In order to choose the optimal feature space which not only reduces computational cost but also progresses the performance of the classifiers, LASSO feature selection technique is applied. After performing various experiments on different subsets of feature space, the best results are still noted on the subspace of (n = 6). The predicted outcomes of the best-selected feature space are reported in Table 5 using the 10-fold CV.

Table 5 demonstrated that the predicted outcomes of the ET classifier are considerable and better compared to the other classifiers. ET has achieved 89.36% accuracy, 88.21% sensitivity, and specificity of 90.58%. Likewise, GB has yielded the second-best result which is the accuracy of 88.47%, 89.54% sensitivity, and specificity of 87.37%. Whereas, LR has performed worse results and achieved 80.77% accuracy, 83.46% sensitivity, and specificity of 77.95%. ROC curves of the classifiers are shown in Fig.  4 .

ROC curve of all classifiers on selected feature space using the LASSO feature selection algorithm.

Relief feature selection technique

In a sequel, another feature selection technique Relief is applied to investigate the performance of classifiers on different sub-feature spaces by using the wrapper method. After empirically analyzing the results of the classifiers on a different subset of feature spaces, it is observed that the performance of classifiers is outstanding on the sub-space of length (n = 6). The results of the optimal feature space on the 10-fold CV technique are listed in Table 6 .

Again, the ET classifier performed outstandingly in terms of all performance evaluation metrics as compared to other classifiers. ET has obtained an accuracy of 94.41%, 94.93% sensitivity, and specificity of 94.89%. In contrast, NB has shown the lowest performance and achieved 80.29% accuracy, 81.93% sensitivity, and specificity of 78.55%. The ROC curves of the classifiers are demonstrated in Fig.  5 .

ROC curve of all classifiers on selected features selected by the Relief feature selection algorithm.

After executing classification algorithms along with full and selected feature spaces in order to select the optimal algorithm for the operational engine, the empirical results have revealed that ET performed well not only on all feature space but also on optimal selected feature space among all the used classification algorithms. Furthermore, the ET classifier obtained quite promising accuracy in the case of the Relief feature selection technique which is 94.41%. Overall, the performance of ET is reported better in terms of most of the measures while other classifiers have shown good results in one measure while worse in other measures. In addition, the performance of the ET classifier is also evaluated on a 10-fold CV in combination with different sub-feature spaces of varying length starting from 1 to 12 with a step size of 1 to check the stability and discrimination power of the classifier as described in 30 . Doing so will assist the readers to have a better understanding of the impact, of the number of selected features on the performance of the classifiers. The same process is repeated for another dataset i.e. S 2 (Hungarian heart disease dataset) as well, to know the impact of selected features on the classification performance.

Tables 7 and 8 shows the performance of the ET classifier using 10-fold CV in combination with different feature sub-spaces starting from 1 to 12 with a step size of 1. The experimental results show that the performance of the ET classifier is affected significantly by using the varying length of sub-feature spaces. Finally, it is concluded that all these achievements are ascribed with the best selection of Relief feature selection technique which not only reduces the feature space but also enhances the predictive power of classifiers. In addition, the ET classifier has also played a quite promising role in these achievements because it has clearly and precisely learned the motif of the target class and reflected it truly. In addition, the performance of the ET classifier is also evaluated on 5-fold and 7-fold CV in combination with different sub-spaces of length 5 and 7 to check the stability and discrimination power of the classifier. It is also tested on another dataset S 2 (Hungarian heart disease dataset). The results are shown in supplementary materials .

In Table 9 , P-value and Chi-Square values are also computed for the ET classifier in combination with the optimal feature spaces of different feature selection techniques.

Performance comparison with existing models

Further, a comparative study of the developed system is conducted with other states of the art machine learning approaches discussed in the literature. Table 10 represents, a brief description and classification accuracies of those approaches. The results demonstrate that our proposed model success rate is high compared to existing models in the literature.

Material and methods

The subsections represent the materials and the methods that are used in this paper.

The first and rudimentary step of developing an intelligent computational model is to construct or develop a problem-related dataset that truly and effectively reflects the pattern of the target class. Well organized and problem-related dataset has a high influence on the performance of the computational model. Looking at the significance of the dataset, two datasets i.e. the Cleveland heart disease dataset S 1 and Hungarian heart disease dataset (S 2 ) are used, which are available online at the University of California Irvine (UCI) machine learning repository and UCI Kaggle repository, and various researchers have used it for conducting their research studies 28 , 31 , 32 . The S1 consists of 304 instances, where each instance has distinct 13 attributes along with the target labels and are selected for training. The dataset is composed of two classes, presence or absence of heart disease. The S 2 is composed of 1025 instances in which 525 instances belong to positive class while the rest of 500 instances have negative class. The description of attributes of both the datasets is the same, and both have similar attributes. The complete description and information of the datasets with 13 attributes are given in Table 11 .

Proposed system methodology

The main theme of the developed system is to identify heart problems in human beings. In this study, four distant feature selection techniques namely: FCBF, mRMR, Relief, and LASSO are applied on the provided dataset in order to remove noisy, redundant features and select variant features, consequently may cause of enhancing the performance of the proposed model. Various machine learning classification algorithms are used in this study which include, KNN, DT, ETC, RF, LR, NB, ANN, SVM, AB, and GB. Different evaluation metrics are computed to assess the performance of classification algorithms. The methodology of the proposed system is carried out in five stages which include dataset preprocessing, selection of features, cross-validation technique, classification algorithms, and performance evaluation of classifiers. The framework of the proposed system is illustrated in Fig.  6 .

An Intelligent Hybrid Framework for the prediction of heart disease.

Preprocessing of data

Data preprocessing is the process of transforming raw data into meaningful patterns. It is very crucial for a good representation of data. Various preprocessing approaches such as missing values removal, standard scalar, and Min–Max scalar are used on the dataset in order to make it more effective for classification.

Feature selection algorithms

Feature selection technique selects the optimal features sub-space among all the features in a dataset. It is very crucial because sometimes, the classification performance degrades due to irrelevant features in the dataset. The feature selection technique improves the performance of classification algorithms and also reduces their execution time. In this research study, four feature selection techniques are used and are listed below:

Fast correlation-based filter (FCBF): FCBF feature selection algorithm follows a sequential search strategy. It first selects full features and then uses symmetric uncertainty for measuring the dependencies of the features on each other and how they affect the target output label. After this, it selects the most important features using the backward sequential search strategy. FCBF outperforms on high dimensional datasets. Table 12 shows the results of the selected features (n = 6) by using the FCBF feature selection algorithm. Each attribute is given a weight based on its importance. According to the FCBF feature selection technique, the most important features are THA and CPT as shown in Table 12 . The ranking that the FCBF gives to all the features of the dataset is shown in Fig.  7 .

Minimal redundancy maximal relevance (mRMR): mRMR uses the heuristic approach for selecting the most vital features that have minimum redundancy and maximum relevance. It selects those features which are useful and relevant to the target. As it follows a heuristic approach so, it checks one feature at a time and then computes its pairwise redundancy with the other features. The mRMR feature selection algorithm is not suitable for high domain feature problems 33 . The results of selected features by the mRMR feature selection algorithm (n = 6) are listed in Table 13 . In addition, among these attributes, PES and CPT have the highest score. Figure  7 describes the attributes ranking given by the mRMR feature selection algorithm to all attributes in the feature space.

Features ranking by four feature selection algorithms (FCBF, LASSO, mRMR, Relief).

Least absolute shrinkage and selection operator (LASSO) LASSO selects features based on updating the absolute value of the features coefficient. In updating the features coefficient values, zero becoming values are removed from the features subset. LASSO outperforms with low feature coefficient values. The features having high coefficient values will be selected in the subset of features and the rest will be eliminated. Moreover, some irrelevant features with higher coefficient values may be selected and are included in the subset of features 30 . Table 14 represents the six most profound attributes which have a great correlation with the target and their scores selected by the LASSO feature selection algorithm. Figure 7 represents the important features and their scoring values given by the LASSO feature selection algorithm.

Relief feature selection algorithm Relief utilizes the concept of instance-based learning which allocates weight to each attribute based on its significance. The weight of each attribute demonstrates its capability to differentiate among class values. Attributes are rated by weights, and those attributes whose weight is exceeding a user-specified cutoff, are chosen as the final subset 34 . The relief feature selection algorithm selects the most significant attributes which have more effect on the target 35 . The algorithm operates by selecting instances randomly from the training samples. The nearest instance of the same class (nearest hit) and opposite class (nearest miss) is identified for each sampled instance. The weight of an attribute is updated according to how well its values differentiate between the sampled instance and its nearest miss and hit. If an attribute discriminates amongst instances from different classes and has the same value for instances of the same class, it will get a high weight.

The weight updating of attributes works on a simple idea (line 6). That if instance R i and NH have dissimilar value (i.e. the diff value is large), that means the attribute splits two instances with the same class which is not worthwhile, and thus we reduce the attributes weight. On the other hand, if the instance R i and NM have a distinct value that means the attribute separates the two instances with a different class, which is desirable. The six most important features selected by the Relief algorithm are listed in descending order in Table 15 . Based on weight values the most vital features are CPT and Age. Figure  7 demonstrates the important features and their ranking given by the Relief feature selection algorithm.

Machine learning classification algorithms

Various machine learning classification algorithms are investigated for early detection of heart disease, in this study. Each classification algorithm has its significance and the importance is reported varied from application to application. In this paper, 10 distant nature of classification algorithms namely: KNN, DT, ET, GB, RF, SVM, AB, NB, LR, and ANN are applied to select the best and generalize prediction model.

Classifier validation method

Validation of the prediction model is an essential step in machine learning processes. In this paper, the K-Fold cross-validation method is applied to validating the results of the above-mentioned classification models.

K-fold cross validation (CV)

In K-Fold CV, the whole dataset is split into k equal parts. The (k-1) parts are utilized for training and the rest is used for the testing at each iteration. This process continues for k-iteration. Various researchers have used different values of k for CV. Here k = 10 is used for experimental work because it produces good results. In tenfold CV, 90% of data is utilized for training the model and the remaining 10% of data is used for the testing of the model at each iteration. At last, the mean of the results of each step is taken which is the final result.

Performance evaluation metrics

For measuring the performance of the classification algorithms used in this paper, various evaluation matrices have been implemented including accuracy, sensitivity, specificity, f1-score, recall, Mathew Correlation-coefficient (MCC), AUC-score, and ROC curve. All these measures are calculated from the confusion matrix described in Table 16 .

In confusion matrix True Negative (TN) shows that the patient has not heart disease and the model also predicts the same i.e. a healthy person is correctly classified by the model.

True Positive (TP) represents that the patient has heart disease and the model also predicts the same result i.e. a person having heart disease is correctly classified by the model.

False Positive (FP) demonstrates that the patient has not heart disease but the model predicted that the patient has i.e. a healthy person is incorrectly classified by the model. This is also called a type-1 error.

False Negative (FN) notifies that the patient has heart disease but the model predicted that the patient has not i.e. a person having heart disease is incorrectly classified by the model. This is also called a type-2 error.

Accuracy Accuracy of the classification model shows the overall performance of the model and can be calculated by the formula given below:

Specificity specificity is a ratio of the recently classified healthy people to the total number of healthy people. It means the prediction is negative and the person is healthy. The formula for calculating specificity is given as follows:

Sensitivity Sensitivity is the ratio of recently classified heart patients to the total patients having heart disease. It means the model prediction is positive and the person has heart disease. The formula for calculating sensitivity is given below:

Precision: Precision is the ratio of the actual positive score and the positive score predicted by the classification model/algorithm. Precision can be calculated by the following formula:

F1-score F1 is the weighted measure of both recall precision and sensitivity. Its value ranges between 0 and 1. If its value is one then it means the good performance of the classification algorithm and if its value is 0 then it means the bad performance of the classification algorithm.

MCC It is a correlation coefficient between the actual and predicted results. MCC gives resulting values between − 1 and + 1. Where − 1 represents the completely wrong prediction of the classifier.0 means that the classifier generates random prediction and + 1 represents the ideal prediction of the classification models. The formula for calculating MCC values is given below:

Finally, we will examine the predictability of the machine learning classification algorithms with the help of the receiver optimistic curve (ROC) which represents a graphical demonstration of the performance of ML classifiers. The area under the curve (AUC) describes the ROC of a classifier and the performance of the classification algorithms is directly linked with AUC i.e. larger the value of AUC greater will be the performance of the classification algorithm.

In this study, 10 different machine learning classification algorithms namely: LR, DT, NB, RF, ANN, KNN, GB, SVM, AB, and ET are implemented in order to select the best model for early and accurate detection of heart disease. Four feature selection algorithms such as FCBF, mRMR, LASSO, and Relief have been used to select the most vital and correlated features that truly reflect the motif of the desired target. Our developed intelligent computational model has been trained and tested on two datasets i.e. Cleveland (S1) and Hungarian (S2) heart disease datasets. Python has been used as a tool for implementation and simulating the results of all the utilized classification algorithms.

The performance of all classification models has been tested in terms of various performance metrics on full feature space as well as selected feature spaces, selected through various feature selection algorithms. This research study recommends that which feature selection algorithm is feasible with which classification model for developing a high-level intelligent system for the diagnosis of a patient having heart disease. From simulation results, it is observed that ET is the best classifier while relief is the optimal feature selection algorithm. In addition, P-value and Chi-square are also computed for the ET classifier along with each feature selection algorithm. It is anticipated that the proposed system will be useful and helpful for the doctors and other care-givers to diagnose a patient having heart disease accurately and effectively at the early stages.

Heart disease is one of the most devastating and fatal chronic diseases that rapidly increase in both economically developed and undeveloped countries and causes death. This damage can be reduced considerably if the patient is diagnosed in the early stages and proper treatment is provided to her. In this paper, we developed an intelligent predictive system based on contemporary machine learning algorithms for the prediction and diagnosis of heart disease. The developed system was checked on two datasets i.e. Cleveland (S1) and Hungarian (S2) heart disease datasets. The developed system was trained and tested on full features and optimal features as well. Ten classification algorithms including, KNN, DT, RF, NB, SVM, AB, ET, GB, LR, and ANN, and four feature selection algorithms such as FCBF, mRMR, LASSO, and Relief are used. The feature selection algorithm selects the most significant features from the feature space, which not only reduces the classification errors but also shrink the feature space. To assess the performance of classification algorithms various performance evaluation metrics were used such as accuracy, sensitivity, specificity, AUC, F1-score, MCC, and ROC curve. The classification accuracies of the top two classification algorithms i.e. ET and GB on full features were 92.09% and 91.34% respectively. After applying feature selection algorithms, the classification accuracy of ET with the relief feature selection algorithm increases from 92.09 to 94.41%. The accuracy of GB increases from 91.34 to 93.36% with the FCBF feature selection algorithm. So, the ET classifier with the relief feature selection algorithm performs excellently. P-value and Chi-square are also computed for the ET classifier with each feature selection technique. The future work of this research study is to use more optimization techniques, feature selection algorithms, and classification algorithms to improve the performance of the predictive system for the diagnosis of heart disease.

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This research was supported by the Brain Research Program of the National Research Foundation (NRF) funded by the Korean government (MSIT) (No. NRF-2017M3C7A1044815).

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Muhammad, Y., Tahir, M., Hayat, M. et al. Early and accurate detection and diagnosis of heart disease using intelligent computational model. Sci Rep 10 , 19747 (2020). https://doi.org/10.1038/s41598-020-76635-9

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Cardiovascular disease and depression: a narrative review

Affiliation.

• 1 Institute of Medicinal Plant Development, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China.
• PMID: 38084332
• PMCID: PMC10710900
• DOI: 10.3389/fcvm.2023.1274595

In clinical practice, it is frequently observed that cardiac and psychological disorders frequently co-occur, leading to the emergence of a field known as cardiovascular disease with depression. Depression, in particular, poses a remarkable risk for the evolution of cardiovascular disease and intimately relates to adverse cardiovascular outcomes and mortality. Moreover, individuals who are depressed exhibit a higher susceptibility to developing cardiovascular disease compared to those in good health. Patients diagnosed with cardiovascular disease with depression disease face a heightened risk of mortality within a 5-year timeframe, and their prognosis remains unsatisfactory even after receiving treatment targeting a single disorder, with a notable recurrence rate. Psychological interventions in conjunction with medications are commonly employed in clinical settings for treating patients with cardiovascular disease and depression diseases, albeit with limited effectiveness and unfavorable prognosis. Traditional Chinese medicine (TCM), such as Shuangxinfang, Chaihujialonggumuli, and Yixin Ningshen Tablet, etc., have been reported and have Therapeutic effects in patients with cardiovascular disease combined with depression. Despite numerous articles documenting a notable association between heart disease and depression, there exists a dearth of studies elucidating the precise pathogenesis and target of action for cardiovascular disease with depression diseases. This article endeavors to consolidate the epidemiological data, potential pathogenic mechanisms, and available treatment modalities for cardiovascular disease with depression diseases. Its primary objective is to unveil plausible co-morbid mechanisms and suitable treatment approaches, thereby offering novel insights for the prevention, diagnosis, and management of cardiovascular disease with depression diseases.

Keywords: cardiovascular disease; depression; epidemiology; pathogenesis; treatment.

© 2023 Li, Zhou, Wang, Yang and Sun.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

HPA axis and psycho-cardiology diseases.

Inflammatory factors in cardiovascular disease…

Inflammatory factors in cardiovascular disease with depression diseases CRP, c-reactive protein; MPO, myeloperoxidase;…

5-HT and psycho-cardiology diseases.

Intestinal flora and cardiovascular disease…

Intestinal flora and cardiovascular disease with depression diseases 3b HSD, 3b hydroxysteroid dehydrogenase;…

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Diane Gal, Bart Thijs, Wolfgang Glänzel, Karin R Sipido, Hot topics and trends in cardiovascular research, European Heart Journal , Volume 40, Issue 28, 21 July 2019, Pages 2363–2374, https://doi.org/10.1093/eurheartj/ehz282

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Comprehensive data on research undertaken in cardiovascular medicine can inform the scientific community and can support policy building. We used the publication output from 2004 to 2013 and the 2014 references to these documents, to identify research topics and trends in the field of cardiovascular disease.

Text fragments were extracted from the titles and abstracts of 478 000 publications using natural language processing. Through machine-learning algorithms, these text fragments combined to identify specific topics across all publications. A second method, which included cross-references, assigned each publication document to a specific cluster. Experts named the topics and document clusters based on various outputs from these semi-automatic methods. We identified and labelled 175 cardiovascular topics and 20 large document clusters, with concordance between the approaches. Overarching, strongly growing topics in clinical and population sciences are evidence-based guidance for treatment, research on outcomes, prognosis, and risk factors. ‘Hot’ topics include novel treatments in valve disease and in coronary artery disease, and imaging. Basic research decreases its share over time but sees substantial growth of research on stem cells and tissue engineering, as well as in translational research. Inflammation, biomarkers, metabolic syndrome, obesity, and lipids are hot topics across population, clinical and basic research, supporting integration across the cardiovascular field.

Growth in clinical and population research emphasizes improving patient outcomes through novel treatments, risk stratification, and prevention. Translation and innovation redefine basic research in cardiovascular disease. Medical need, funding and publishing policies, and scientific opportunities are potential drivers for these evolutions.

Current policies for public funding of health research increasingly focus on innovation, with a final goal to improve health outcomes. 1 To support policies, roadmaps are established, for example for diabetes 2 and respiratory 3 diseases. In the USA, the joint Academies developed a document to guide national policy in health 4 with a dedicated document for cardiovascular medicine 5 that includes general directions for research. In Europe, building a roadmap for cardiovascular research is one of the tasks of the ERA-CVD network. 6 Expert opinion guides the exercise but a macro and global-level overview of past cardiovascular research can enrich the debate and strengthen the basis for recommendations. The breadth of cardiovascular research is astounding, 7 with research undertaken across a variety of institutions and with each piece of research having its own scope/focus or topic. It is thus challenging to review and summarize all the research that has been undertaken.

Identifying all the relevant research is the first hurdle to overcome, then classifying or identifying topics of research is the next significant hurdle. Journal classification systems offer little assistance, as they are not granular enough to identify more specific topics within broader fields. Thesauri or medical dictionaries, such as PubMed or the International Classification of Diseases (ICD), do not offer an overview of time-dependent changes in topics or changing concepts.

Identifying key topics using semi-automatic approaches based on text analysis is an alternative solution that takes advantage of recent developments in high-level informatics. As this is not reliant on a predefined classification, it may result in different outcomes. Various methods use natural language processing (NLP) to extract topics or clusters from text. For example, the bibliometric community has compared the results when varying methods are applied to a set of astronomy publications, focusing on the importance having topic expert input throughout the process. 8 The recent CardioScape project analysed abstracts of 2476 research projects awarded 2010–12 as published by funding bodies. The authors assigned research project to topics, based on the abstract text, using a semi-automatic process that tested and trained the data to more quickly allocate abstracts to a topic than depending solely on expert review. They produced a detailed taxonomy or classification of cardiovascular research based on the list of topics of the European Society of Cardiology, creating a hierarchical list of over 600 topics. 9

Here, we aim to identify topics in published cardiovascular research and their evolution between 2004 and 2013, assessing whether they have appeared, disappeared, or changed over time. In a comprehensive approach, we use a combination of existing methods for text mining, network analysis, and clustering, and further develop these tools to handle a large dataset of >400 000 publications.

In our study, we use two different and complementary approaches. A first one detects topics across the collection of publications, counting number of documents, and relations between topics. A second one maps document networks into clusters with an identifiable subject of research. These approaches are described here in brief, with more detail provided in the Supplementary material online .

Data sources

The dataset includes the reference, abstract, address, and citation data for 478 006 cardiovascular publications from 2004 to 2013, including 2014 references to these documents, using an expert informed search strategy and references to core cardiovascular journals, as previously published. 7 The documents span across >5000 journals, and include cardiovascular publications in leading general journals in medical and life sciences ( Supplementary material online , Table S1 ). We obtained the data from Clarivate Analytics Web of Science Core Collection (WoS) through a custom data license held by ECOOM, KU Leuven.

Text pre-processing

We took all titles and abstracts of the above publications, and extracted the noun phrases (text fragments of various lengths) using the NLP framework developed at Stanford. 10   Supplementary material online , Figure S1 illustrates the subsequent data flow for the analysis.

Topic modelling

For this approach, we applied latent Dirichlet allocation (LDA) 11 to the above-mentioned text fragments from the titles and abstracts of all publications. This LDA approach groups the text fragments to identify topics and allocates documents to topics. In this approach, a document contributes to several topics. Of note, general terms or terms that are used frequently across the majority of documents are filtered out as part of the methodology, resulting in groups of highly specific text fragments and, consequently, topics, as illustrated in Supplementary material online , Figure S2 .

At least three cardiovascular experts (listed in the Acknowledgements section) named each topic based on a set of the top 40 text fragments representing a topic. Further rounds of cross-review validated and consolidated the naming process. A final review of all topics ensured naming consistency across the topics and allowed for additional expert-based classification as clinical, basic, or population research.

We then calculated the number of documents that contributed to a topic, using probability analysis in LDA. Furthermore, we calculated the co-occurrence of topics in the publications, and visualized the outcome of this network analysis using VOSViewer ( www.vosviewer.com ). 12

Document clustering

For this second approach, the dataset was reduced to two periods, and we analysed the cardiovascular publications from 2006 to 2008 and those from 2011 to 2013, separately. For each time period, we then calculated the similarities between documents based on the noun phrase text fragments from the titles and abstracts of all publications and based on the references in these publications, using adapted cosine calculations and a hybrid document clustering algorithm, as previously described. 13 We then applied the Louvain 14 community detection algorithm to identify clusters of similar documents. For this method, each document is only located in one cluster. Subsequently, we applied the DrL/OpenOrd algorithm 15 to map and visualize the documents and clusters. We used R 16 in a high-powered cloud-based parallelized computing environment for all operations.

We identified and described the core documents, 13 the most common text fragments, as well as, the most highly cited documents and the most productive authors in each cluster, to name the clusters. For each document cluster, we identified the most highly representative topics from the LDA topic model.

Evolution of cardiovascular topics—trends and ‘hot’ topics

We identified 175 topics, listed alphabetically in Supplementary material online , Table S2 . This list groups specific topics within areas such as atherosclerosis, coronary artery disease, arrhythmias, heart failure, and their evolution over time.

For a visual and comprehensive overview, we prepared a map of the topics and their interrelation, based on co-occurrence within publications using a network analysis ( Figure 1 A ). This map identifies different categories of research: population (at the top, blue), clinical (left, green/yellow), and basic research (right, red). Large topics in each category define overarching interests such as Evidence-guided-treatment and Outcomes and prognosis in clinical research, and Epidemiology of CVD and risk factors in population research, topics that have seen large growth in numbers of publications since 2004 ( Figure 1 B ). Cell signalling and gene transcription is a central topic for basic research, with modest growth ( Figure 1 B ).

Main areas and organization of research focus. ( A ) Visual presentation of the topics in 2013 and how they relate to each other, based on how often the topics are included in the same publication. Each circle represents one topic and each group of topics is highlighted in a separate colour; the most similar documents and clusters are located closer to each other based on VOSviewer mapping. ( B ) Evolution of overarching topics.

More focused ‘hot’ topics that experienced a large growth in number of publications are presented in Figure  2 .

Topics with large growth. For population research, the eight topics that increased more than two-fold in volume are shown; for clinical research, 27 topics increased more than two-fold and 10 of these are presented; for basic research only two topics had more than a two-fold increase, and the top 8 growers are presented. Overarching topics are shown in Figure 1 B .

In population research, risk factors with research on metabolic syndrome, lipids, diabetes, physical activity, and mental health are prominent. In clinical research, patient management after myocardial infarction (MI) and outside the hospital are leading topics, but the true ‘hot’ topic was aortic valve disease that saw a surge of interest, related to transaortic valve repair, starting 2008. Though still small in numbers, heart failure research and stem cells saw substantial growth. This last clinical topic complements the major hot topics in basic research, on stem cells and cardiac repair and tissue engineering. In basic research, increasing translational output in metabolic syndrome and diabetes use mostly mouse models. Focused topics are organelle studies on mitochondria and endoplasmic reticulum.

Table  1 complements the fast growing topics of Figure  2 with additional leading 2013 topics. Most of these also have grown since 2004, but two topics, even if large, seem to have lost momentum, i.e. longitudinal studies on blood pressure, and basic research in cardiac electrophysiology.

Large topics in 2013

PCOS, polycystic ovary syndrome.

Only four topics in clinical, and none in population research, saw a decrease, whereas seven topics in basic research saw a decline in output ( Figure 3 A ). Across all topics, the growth in publication output, measured as the number of documents in 2013 divided by the number of documents in 2004, was significantly larger in clinical and population research topics than in basic research topics ( Figure 3 B ).

Unequal growth of research output across categories. ( A ) Topics that saw a decrease of >5%, i.e. 4/102 clinical and 7/50 basic research topics. ( B ) Average growth in each category. Each dot presents a topic; the values are the fractional growth, i.e. the number of documents in 2013 divided by the number of documents in 2004. Kruskal–Wallis followed by Dunn’s test for multiple comparisons; *** P < 0.0001 basic vs. clinical and vs. population.

When considering the overall output and growth of publications across the categories of population, clinical and basic research, the data suggest that the share of basic research publications is declining.

Document clusters define large research areas and trends

The size of topics represents the activity within each of these—documents contribute to more than one topic. In a complementary approach, we examined how documents group together based on the similarity of their text and of their references, whereby each document can belong to one cluster only, effectively dividing the total publication output into different areas. The hybrid clustering algorithm was applied to two datasets, i.e. the publications from 2006 to 2008 and 2011 to 2013.

In each period, 10 large clusters emerged, accounting for >90% of all documents.

To identify trends, we compare the two periods and examine the evolution over time ( Figure  4 ). In the graph legends, emerging areas are marked by green triangle, decreasing ones with a red triangle. Risk scoring in the population and related patient management are the leading areas, growing over time (top position). In 2011–13, a large cluster emerges that relates to gene and stem-cell therapy, including research on inducible pluripotent stem cells. Documents within this cluster include research on ischaemic heart disease and arrhythmias. Haemodynamics and biomechanics are another emerging area that includes documents on atherosclerosis and vascular diseases such as aneurysms, but also heart failure and assist devices. Aortic valve disease is a newly defined area in 2011–13. Imaging also becomes very prominent as an area in its own right. Whereas in 2006–08, hypertension was a defined area, this is no longer identifiable in 2011–13.

Distribution of document clusters in 2006–08 and in 2011–13. ( A ) In 2006–08, the 10 largest clusters represent 93% of the total publication output in this period. ( B ) In 2011–13, the 10 largest clusters represent 92% of the total publication output in this period. The colour codes for similar clusters are maintained across the periods. However, some clusters are present in only one period. The clusters are arranged by size, reading clockwise from the top, and the legends arranged accordingly. Red triangles mark clusters that disappeared and green triangles emerging clusters.

For the last period, we also examined the structure and interrelation of clusters, using a graphical rendering, giving insight in the size, composition, and presence of subclusters ( Figure  5 ).

Document clusters’ map 2011–13. A visual presentation of documents in clusters and subclusters: the most similar documents and clusters are located closer to each other, based on the DrL two-dimensional mapping layout technique.

In this force-directed DrL graph layout, the documents and clusters are mapped to minimize the distance between the most similar documents and maximize the distance between non-linked documents. This produces a two-dimensional co-ordinate layout where the documents closest to each other share the most similarities since they share common text fragments and references. Conversely, documents and clusters on the edges of the graph have the least similarity to other documents or clusters.

Cluster 2 on gene and stem cells is dense and separate, yet touches and interacts with Cluster 5 [acute coronary syndrome (ACS) and MI]. Cluster 9 on imaging is spread out in subclusters at different locations, including one near Cluster 5 (ACS and MI), and one near Cluster 4 (heart failure). Cluster 8 (arrhythmias) is also split with one part closer to heart failure, another to anticoagulation and atrial fibrillation.

Further naming the subclusters is presently beyond reach, as it would require a lot of expert input and resources. However, linking the clusters and the topics adds granularity to the larger research areas and provides internal methodological validation of the cluster naming.

Table  2 presents the most highly associated topics in the ten largest document clusters in each period. Overall, agreement with the LDA topics is high and provides more detail on the research contained in the clusters. E.g., the cluster ‘Haemodynamics’ is now showing different areas of focus, i.e. in congenital disease, aortic, and valvular diseases; the topic ‘Arrhythmias’ is more populated with device research in the second time period compared to the first.

Cluster names and topics present within clusters

AF, atrial fibrillation; ANS, autonomic nervous system; BP, blood pressure; CABG, coronary artery bypass grafting; CRT, cardiac resynchronization therapy; CT, computed tomography; CV, cardiovascular; DES, drug-eluting stent; ECG, electrocardiogram; HF, heart failure; LV, left ventricular; NOAC, new oral anticoagulant; PTCI, percutaneous transluminal coronary intervention; RV, right ventricle; STEMI, ST elevated myocardial infarction.

The method for identification of topics in cardiovascular publication output allowed the visualization and evaluation of trends in cardiovascular research. Over a 10-year period significant shifts occur.

Identification of cardiovascular research topics through natural language processing

In cardiovascular research, topics are generally predefined in a taxonomy that can be hierarchical and/or matrix structured. The CardioScape project approach (see Introduction section) was well suited to its purpose of the analysis of 2476 project abstracts in a single time period and using an existing taxonomy has the advantage of recognizable areas of research. The bottom-up approach used here lent itself well to analysis of much larger numbers of documents and generated a topic list that represents the interests from the community during the period under study.

A recent study by the WHO working to identify cardiovascular disease research output from random sets of publications from PubMed required a significant amount of expert-based review of only a small proportion of the published articles. 17 The current approach was more comprehensive in coverage of the field, but despite reliance on advanced automated analysis, experts still had an important role in interpreting and linking concepts to validate the results.

In the current naming of topics and clusters, experts frequently used terms that connect to a classic hierarchical list in the field, including major diseases, and recognizing clinical, population, and basic discovery research. Nevertheless, the approach uncovered specific emerging areas of research such as transcatheter aortic valve implantation (TAVI), topics consistent with broad trends, such as risk stratification and evidence-based guidance, and innovation (gene and stem cell research). Some of these terms would not appear in a classic taxonomy and thus the NLP approach offers novel insights.

The present study was not attempting to classify all research but to capture and identify the most common and evolving topics over time in the cardiovascular field by using a comprehensive set of cardiovascular publications across some 5000 journals.

Emphasis on improving clinical care and risk assessment

The most represented and fast growing topics across the documents are evidence-based guidance for treatment and research on outcomes and prognosis. These result underscore the attention given to guidelines and evidence based medicine (EBM). 18–23 Part of this research is likely to represent the large number of clinical trials taking place in the cardiovascular field, 24 which over time have had a significant effect on the reduction of mortality from CVD due to establishing the effectiveness and safety of a number of drugs and medical interventions in cardiovascular disease. 25 The presence of policy related topics, such as the topics on quality of care and health economics likewise supports the focus on implementation research and a shift of focus from reducing acute mortality to care in chronic disease.

Growth of research on risk factors emphasizes the importance of preventative medicine, evident in both the topics analysis and the document cluster analysis. However, some specific blood pressure studies declined over time, perhaps reflecting the change in focus on the single risk factor of ‘blood pressure’ to a multivariable spectrum and newly identified risk factors. We have also previously shown that hypertension has moved more closely to clinical cardiovascular research over time. 26

Smaller topics illustrate crosstalk with non-cardiovascular diseases, because of shared risk factors or common methods used in research or occurrence of cardiovascular complications. The latter is particularly evident in two topics that focus on cardiovascular complications in pregnancy and in cancer.

Innovation and translation in clinical and basic science

Major diseases such as ischaemic heart disease and arrhythmias, remain present over time but shifts can be seen. There is for example, a larger focus on atrial fibrillation, in particular embolic risk, on novel treatments, such as stem cells in heart failure, and transcatheter aortic valve interventions as a dominant element within the topic of valvular heart disease. 19 Imaging is present in several topics but emerges as a cluster in its own right in the document analysis. Many of these changes are driven by technological innovation and translation.

Basic research as a whole saw its share decline, but with interesting shifts in content. Although the topic analysis and mapping identifies basic research topics as a category, there are complementarities across categories. Stem cell research, tissue engineering, and biomechanical factors saw rapid growth and are also present in clinical topics. This also applies to inflammation and diabetes. Animal models for disease are rapidly growing topics consistent with growth of translational research.

An analysis of the countries of authorship of the publications in the emerging clusters of discovery research shows that the USA leads in the number and share of publications (30%+), followed mostly by Germany, or the UK or Italy. However, for the large document cluster on genes and stem cells in 2011–13, the second most productive country is China, contributing 17.5% of the publications in this cluster (Supplementary material online, Figure S3 ).

Interestingly, inflammation, biomarkers, metabolic syndrome, obesity, and lipids are hot topics with growing research output in population, clinical and basic research, indicating integration and crosstalk across the spectrum of cardiovascular research.

Drivers of change

Technology and opportunity-driven scientific interest, but also strategic choices and funding policies are likely to influence trends in research. CardioScape studied public and charity funding in the years 2010–12 and describes major investments in clinical research. Yet the share of publication output globally for clinical research appears to be substantially larger than the share of funding for clinical research reported in CardioScape. This could be explained by clinical research funded by other sources, such as industry or local funding, which are not included in the CardioScape analysis. Also, the present data represent global output. Major research investments in China, and the emphasis on clinical research in the USA, can contribute to some of the global trends.

The slower growth in basic science could reflect a slower growth in investment. This can be absolute or relative towards the increasing costs of advanced research methodology. Another reason could be editorial pressure for more comprehensive papers that may reduce quantity to the benefit of rich content in individual papers.

Finally, growing translational research may blur the boundaries between basic and clinical research and lead to an apparent slower growth in discovery research.

Policy perspectives

Policy development is a forward looking exercise. In health research, medical needs identified by health data and expert opinion, are an important consideration. 27 Past research output helps to identify areas that may need more investment. Research funders also use input from society. 28 When assessing current priorities in cardiovascular research for the Dutch 28 and British 29 Heart Foundations we can see that research into heart failure and arrhythmias are common across their top priorities. Focus on healthy lifestyles is a top priority in the Dutch Heart Foundation as well as in the US vision and strategic agenda. 4   ,   5 At the macro-level, the data presented here indicate that some of the main issues presented in these research agendas are actively pursued but others less so.

Study limitations

Limitations of studying research topics have been addressed in the bibliometric field. 8 The reliance of expert input is a limitation and potential source of bias that we tried to minimize by using mixed panels.

The current approach was not sufficiently granular to extract recent emerging topics that contain a limited number of documents. In addition, publication output is somewhat delayed vs. actual research and experts may be aware of ongoing research with still limited output. In this case, the method and dataset can be used to interrogate about specific developments (see Supplementary material online , Table S3 for data on micro-RNA and personalized medicine).

As the data set ends in 2013, very recent developments are not covered. This relates to the methodological complexity. Web of Science data including 2014 references were available mid-2015, the cardiovascular publications dataset was complete in 2016 and algorithms for analysis including re-iterative expert review required another 18 months. A similar time lag is seen in other studies that rely on data mining and processing. 9 Congress abstracts could be considered as a source to identify emerging topics but have several limitations. They are of a different nature than papers and the scope of a congress shapes content of selected abstracts. We provide a complementary survey of 3000 abstracts from the 2018 congress of the European Society of Cardiology, illustrating the strong presence of clinical research at this event, within the topics of Clusters 1 and 3–7 of Table  2 ( Supplementary material online , Figure S4 ). Two emerging topics were cardio-oncology and digital health, each representing however <25 abstracts.

In the present analysis, quality and impact of studies in a particular domain were not evaluated, though highly cited papers were part of the cluster identification. In their analysis of poorly cited papers covering 165 000 papers in 1997–2008, Ranasinghe et al . 30 noted the highest percentage of poorly cited papers in the clinical and population research category. Nevertheless, as they and others 31 have noted, citations are not the only parameter to assess impact, in particular in clinical medicine.

Identification of leading research topics and trends illustrates the emphasis on improving clinical medicine, and the growing interest in risk stratification and preventive medicine. Translation and innovation redefine cardiovascular research. Linking the present data with the insights of the professional community and of funders and society, may contribute to the building of a future research roadmap.

The authors thank to the following experts for their review of the text fragments and input into the names of the topics: Dr Matthew Amoni, Dr Peter Haemers, Prof Sian Harding, Dr Frederik Helsen, Prof Gerd Heusch, Prof Tatiana Kuznetsova, Prof Tobias Op‘t Hof, Prof Frank Rademakers, Dr Sander Trenson, Dr Bert Vandenberk, and Dr Maarten Vanhaverbeke.

D.G. had a PhD Fellowship through KU Leuven.

Conflict of interest: K.R.S. is Past Editor-in-Chief of Cardiovascular Research (2013–17). W.G. is Editor-in-Chief of Scientometrics .

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Could Your Cellphone Be Harming Your Heart?

Key Takeaways

Cellphone use can increase risk of heart disease

The more a person uses their phone, the greater the risk

Poor sleep, stress and the radio waves emitted by the phones are potential causes

WEDNESDAY, Sept. 4, 2024 (HealthDay News) -- Your cellphone might be harming your heart, a new study warns.

People who regularly use a cellphone have a higher risk of heart disease , researchers found in a large-scale study.

And the more time someone spends on their phone, the greater their risk for heart problems, researchers report.

Risk of heart disease was about 21% higher in people who use their phone six hours or more a week, compared with 15% higher for those on the phone four to six hours and 13% for one to three hours, results show.

“We found that compared with non-regular mobile phone users, regular mobile phone users had a significantly higher risk of incident cardiovascular diseases,” said researcher Dr. Ziliang Ye , with Southern Medical University in China.

For the study, researchers analyzed data from more than 444,000 participants in the long-term UK Biobank research project.

The participants all reported the frequency of their cellphone use. Researchers defined regular mobile phone use as at least one call per week.

The research team tracked the participants for about 12 years, looking to see if they’d been diagnosed with stroke, heart disease, heart rhythm problems or heart failure.

It turned out that regular phone users did have a higher risk of heart problems, compared to non-users, and that more cellphone use increased that risk.

Cellphone use can impact a person’s sleep patterns and stress levels, which in turn affects heart health, researchers said.

Study results showed that people who used a mobile phone more than an hour a week had poorer sleep patterns than those who used their phone less.

Overall, poor sleep explains about 5% of the link between cellphone use and heart problems, researchers said. Psychological distress caused by cellphone use explained another 11% of the increased risk.

“A poor sleep pattern and poor mental health may adversely affect the development of cardiovascular diseases,” said researcher Dr. Xianhui Qin with Southern Medical University in China said.

It’s also possible that the radio waves emitted from the phones could be causing inflammation in the body that affects the heart and blood vessels, researchers added.

The new study was published Sept. 4 in the Canadian Journal of Cardiology .

“Chronic exposure to RF-EMF radiation emitted from mobile phones could lead to oxidative stress and inflammatory response,” Qin said in a journal news release. “Therefore, RF-EMF radiation exposure from mobile phones in combination with smoking and diabetes may have a synergistic effect in increasing cardiovascular diseases risk.”

The results indicate that people would do well to put down their smartphones, according to an accompanying editorial written by epidemiologists at the University of Toronto.

“Maintaining responsible mobile phone habits should be a valuable component of an all-encompassing approach to supporting cardiovascular health,” said lead editorial writer Nicolas Grubic , a doctoral student with the University of Toronto. “Before diving into hours of mindless ’doom-scrolling’ on your smartphone today, consider redirecting this time toward a more heart-healthy activity."

More information

The U.S. Centers for Disease Control and Prevention has more on cellphones and health .

SOURCE: Elsevier, news release, Sept. 4, 2024

What This Means For You

Too much cellphone use could be increasing your risk of heart problems.

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Updates in the Management of Coronary Artery Disease: A Review Article

Archit bansal.

1 Pathology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND

Kishore Hiwale

Coronary artery disease (CAD) remains a significant health challenge, imposing substantial burdens on individuals and healthcare systems worldwide. CAD's impact stems from artery narrowing and blockage, leading to severe complications like heart attacks and heart failure. Collaborative efforts by researchers, professionals, and governments have fostered advancements in comprehending and managing this cardiovascular ailment. Evolving CAD management embraces modern diagnostics, cutting-edge pharmaceuticals, invasive procedures, lifestyle modifications, and cardiac rehabilitation. This comprehensive approach aims to amplify outcomes and elevate the quality of life for CAD-affected individuals. This review delves into innovative treatments, pivotal breakthroughs, and recent trends in clinical practices that collectively shape CAD management. The exploration encompasses novel diagnostic technologies enabling early detection and risk assessment. Moreover, it investigates recent breakthroughs in medications that profoundly impact platelet disorders, lipid reduction, and angina. Precision medicine's role in tailoring treatment strategies based on patient characteristics is thoroughly examined. Advances in invasive procedures, like percutaneous coronary intervention (PCI) and coronary artery bypass grafting (CABG), have revolutionised coronary revascularisation, substantially improved long-term outcomes, and reduced restenosis rates. The increasing significance of lifestyle changes and cardiac rehabilitation in CAD management, augmenting treatment options and patient recovery, are meticulously scrutinized. While these strides are pivotal, research continues to chart new paths in CAD management, from innovative drugs to collaborative multidisciplinary care models. Staying attuned to the latest advancements and embracing a patient-centric approach can collectively reduce CAD's impact and facilitate the lives of those grappling with this chronic cardiovascular disorder.

Introduction and background

Coronary artery disease (CAD) continues to pose a health challenge, impacting numerous individuals and placing strain on healthcare systems worldwide. The narrowing and blockage of arteries due to CAD can lead to complications like heart attacks and heart failure. Over time, dedicated efforts from researchers, professionals, and governments have contributed to advancements in understanding and managing this cardiovascular condition [ 1 ]. The management of CAD has changed, adopting an approach that incorporates modern diagnostic technologies, cutting-edge pharmaceutical medications, invasive procedures, advances in prevention, emerging therapies, collaborative models, long-term outcomes with follow-ups, and increased emphasis on lifestyle modifications and cardiac rehabilitation (Figure ​ (Figure1). 1 ). This comprehensive approach aims to enhance CAD patients' outcomes and overall quality of life. This review will explore the treatment innovations for artery disease while delving into recent breakthroughs that have influenced clinical practices and improved patient care [ 2 ].

Image Credit: Author

Additionally, we will examine advancements in techniques that provide healthcare providers with tools for early detection and risk assessment. Moreover, we explore the range of treatments available today, including innovative medications that have significantly transformed the management of platelet disorders, lipid reduction, and angina. Our discussion primarily focuses on comprehending the role played by precision medicine in tailoring treatment approaches based on patient characteristics [ 3 ]. The advancements in procedures, like percutaneous coronary intervention (PCI) and coronary artery bypass grafting (CABG), have revolutionised coronary revascularisation and resulted in better long-term outcomes and decreased restenosis rates. Furthermore, we analyze the growing importance of lifestyle changes and cardiac rehab centres in treating CAD, showing their ability to supplement treatment options and improve patient recovery and well-being. While these developments are significant, ongoing research opens new vistas in CAD management, from new medicines to multidisciplinary collaborative care models promoting teamwork [ 4 ]. We can work together to reduce the impact of CAD and improve the future for those affected by this chronic cardiovascular disease by staying updated on the latest advancements and using a patient-centred approach.

Methodology

For gathering all the required papers for this review article, a thorough literature search technique was needed in the methodology section of this narrative review. Google Scholar and PubMed were the two electronic databases accessed. The following keywords were used: "therapies," "treatment," "advancements," " management," and "coronary artery disease." All the authors unanimously carried out a thorough search.

The inclusion of both original research articles and review papers was taken into consideration. To ensure that the inclusion of studies satisfies the established criteria, the selection procedure comprised of evaluating article titles, abstracts, and full texts. Conflicts over the choice of studies were settled by author consensus. The method for choosing the articles we utilized in our study is shown in Figure ​ Figure2 2 .

PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses

Diagnostic advancements

CAD continues to be a leading cause of morbidity and death worldwide, with such proliferation calling for the ongoing advancement of analysis techniques to enhance early discovery and threat stratification. These testimonials highlight current breakthroughs in the management of CAD, with a concentration on non-invasive imaging methods, biomarkers, and customised threat accounts.

Non-invasive imaging methods have considerably advanced the medical diagnosis of coronary artery conditions and have enabled the exact visualisation of the coronary composition and myocardial features [ 5 ]. Specifically, coronary computed tomography angiography (CCTA) has become an effective device for the non-invasive examination of coronary arteries because it provides high-resolution photos and enables the precise discovery of coronary constriction and plaque. Cardiac magnetic resonance (CMR) imaging is likewise valued for its ability to evaluate myocardial feasibility perfusion and other features that assist in danger stratification and therapy preparation.

Biomarkers have emerged as promising tools for early CAD diagnosis and risk assessment. Their presence serves as a guarantee in very early CAD medical diagnoses and danger analyses. Highly sensitive troponins, for instance, have transformed the detection of myocardial damage by facilitating the early identification of patients with acute coronary syndromes. Additionally, researchers have explored new inflammatory markers, such as C-reactive protein and interleukins, to predict CAD progression and adverse cardiovascular events [ 6 ].

Personalised risk profiling, which encompasses genetic testing and advanced risk assessment tools, has ushered in an era of precision medicine in CAD management. Uncovering the genetic variants associated with susceptibility and response to CAD allows for tailored treatment strategies that cater to individual patient needs. Moreover, risk prediction models that integrate clinical, genetic, and lifestyle factors have been found to assist in identifying individuals at high risk of CAD, thereby enabling timely intervention and preventive measures. 

Despite these remarkable advances, challenges still exist in promoting the widespread adoption of these diagnostic strategies. Access to advanced imaging techniques, associated costs, and required operating expertise must be addressed. Further studies assessing these diagnostic tools' clinical utility and cost-effectiveness in routine CAD management are warranted [ 7 ].

Pharmacological interventions

In the last few years, brand-new antiplatelet strategies have reinvented how CAD is addressed and treated. Dual antiplatelet treatment (DAPT) with painkillers and P2Y12 receptor agents, such as clopidogrel, prasugrel, and ticagrelor, have become key approaches to caring for individuals with PCI [ 8 ]. These recently discovered mechanisms, specifically ticagrelor, have demonstrated greater effectiveness in treating major adverse cardiovascular events (MACE) and stent apoplexy, providing substantial benefits over traditional treatments. Breakthroughs in lipid-lowering treatments have also revealed considerable success. The development of proprotein convertase subtilisin/kexin kind 9 (PCSK9), an innovative medication course, has offered a reliable method for dealing with hypercholesterolemia, specifically in domestic cases or statin intolerance. When contributing to statin treatment, PCSK9 treatments have been associated with substantial decreases in low-density lipoprotein (LDL) cholesterol and cardiac events [ 9 ].

There have also been advancements in angina medications, with the development of newer agents, such as ranolazine, offering alternative treatment options. Due to its unique mechanism of action, which involves the inhibition of late sodium currents, ranolazine has shown efficacy in reducing tension-related chest pain and improving exercise tolerance, making it a valuable addition to traditional angina therapies. In addition, precision medicine has gained momentum in managing CAD, enabling individualised drug therapy based on genetic testing and risk profiling. Tailoring pharmacological interventions to a patient's genetic makeup and associated risk factors can improve treatment efficacy and minimise side effects, and the success of such approaches has launched a new era in personalised CAD treatment [ 10 ].

Invasive procedures

PCIs have seen substantial development in recent years. The introduction of next-generation drug-eluting stents has transformed worldwide strategies for coronary revascularisation. These stents are coated with medications that prevent restenosis, which significantly reduces the incidence of stent restenosis and the need for repeat interventions [ 11 ]. Additionally, bioresorbable scaffolds have emerged as a preferred option to typical steel stents, as they progressively liquify with time and are thus likely to bring back vascular features. Intravascular imaging strategies have become essential to PCI treatments. Intravascular ultrasound (IVUS) and optical comprehensibility tomography (OCT) offer high-resolution, real-time imaging of coronary arteries, thereby helping with more precise strengths, opportunities, aspirations, and result (SOAR) analysis and ideal stent positioning. The ability to image plaque attributes and stent growth has enhanced treatment success and led to long-lasting results. Minimally intrusive methods have likewise received more attention in CAD monitoring. A transradial approach, in which the procedure is performed through the carpal artery, has led to fewer bleeding complications and better patient comfort than the traditional femoral approach [ 12 ].

On top of these developments, robot-assisted PCI has demonstrated the capacity to boost driver accuracy and minimise exposure to direct radiation during treatment. Although PCI remains the mainstay of CAD treatment, CABG can be an essential option for select patients. Advances in surgical techniques, such as off-pump CABG and total arterial revascularisation, have reduced perioperative complications and improved graft capacity [ 13 ].

Lifestyle modifications and cardiac rehabilitation

A comprehensive modification of one's lifestyle is critical to avoid further complications due to CAD. Current research has highlighted the value of a heart-healthy diet regimen, routine workouts, smoking cessation, and general decreases in tension [ 14 ]. Integrating these changes with the use of drug therapy can minimise certain cardiac risks, such as high blood pressure, dyslipidaemia, and weight problems. Heart rehabilitation programmes have made considerable improvements in providing customised and structured treatments for individuals with CAD. These programmes include exercise, education, counselling to improve physical fitness, adherence to prescribed protocols, and psychosocial support. Technological developments have made remote monitoring and rehabilitation even more straightforward, allowing rehabilitation services to be available, even in remote or underserved areas [ 15 ].

Generally, studies have highlighted the long-term benefits of cardiac rehabilitation in reducing recurrent cardiovascular events and all-cause mortality. Individualised and patient-centred approaches have improved patient compliance with cardiac rehabilitation programmes, ensuring the protocol is tailored to individual needs and preferences. However, there are still challenges in adopting significant lifestyle changes and fully engaging in cardiac rehabilitation. Limited access to rehabilitation programmes, especially in low-income areas, and a lack of patient commitment to long-term lifestyle changes are ongoing challenges [ 16 ]. Addressing these challenges requires collaboration between healthcare providers, policymakers, and communities to create more affordable and accessible rehabilitation services.

Advances in secondary prevention

A crucial aspect of the secondary prevention of CAD involves effectively managing the modifiable risk factors that influence the progression and recurrence of the condition. In this context, evidence-based guidelines and treatment protocols have been established to provide healthcare professionals with more precise targets and therapeutic options [ 17 ]. Pharmaceutical developments have led to the introduction of novel lipid-lowering treatments, such as PCSK9 approaches and mixed treatments, which have revealed considerable efficiency in accomplishing reduced LDL cholesterol levels alongside conventional statin treatment. New antiplatelet agents and antithrombotic drugs have been developed to improve secondary prevention strategies in patients with a history of acute coronary syndrome. In addition to pharmacological measures, lifestyle changes play a crucial role in secondary prevention. Innovative digital health solutions and telemedicine have been helpful in this respect by facilitating remote monitoring and individualised interventions, allowing patients to adopt healthier habits and follow treatment plans more effectively [ 18 ]. Additionally, cardiac rehabilitation programmes have evolved to incorporate comprehensive lifestyle interventions, including exercise, nutritional counselling, and stress management. They have shown significant benefits in improving patient outcomes and reducing future cardiovascular events. Another significant advancement in secondary prevention is the emphasis on individualised risk assessment and treatment plans. Genetic testing and biomarker assessments have allowed doctors to identify high-risk patients and tailor their treatments accordingly. This approach optimises pharmacotherapy, invasive procedures, and lifestyle interventions based on a patient's genetic susceptibility and underlying pathophysiology [ 19 ].

Emerging therapies and research

Genetic treatments and gene-modifying strategies have become prospective game-changers in CAD therapy. These treatments target certain hereditary aspects that add to the advancement of CAD, ensuring customised and accurate treatments. Early studies have shown encouraging results, indicating the potential to reverse the progression of the disease and reduce the burden of atherosclerotic plaques [ 20 ]. Although these approaches are still in their infancy and require further investigation, they offer hope for people with a genetic predisposition to CAD and treatable cases that have not responded adequately to conventional therapies. Targeted therapies tailored to specific CAD subtypes are another exciting research avenue, wherein treatments addressing the unique pathophysiological mechanisms underlying the different subtypes are developed, recognising the heterogeneity of CAD presentations. This approach can improve treatment efficacy and reduce side effects by better tailoring interventions to each patient's disease characteristics. Regenerative medicine and stem cell therapy are gaining attention as potential ways to repair and regenerate damaged heart tissue in patients with CAD [ 21 ]. Preclinical studies and early clinical trials have demonstrated promising results in improving heart muscle function and reducing scar tissue formation. As these therapies progress in more extensive trials, they may revolutionise the management of CAD by providing restorative and regenerative options for patients with extensive CAD. Among these promising advances, however, are challenges. New therapies' long-term safety, cost-effectiveness, and scalability require thorough evaluation before clinical introduction. In addition, ensuring equal access to these innovative treatments for all patients is essential to avoid widening health inequalities [ 22 ].

Collaborative care models

Collaborative care models incorporate a selection of medical care experts consisting of cardiologists, medical care doctors, registered nurses, pharmacologists, nutritional experts, and rehab professionals who collaborate as a natural group to attend to the varied requirements of people with CAD. These models promise smooth interactions, shared decision-making, and collaborative activity, ensuring people get thorough and personalised treatment. One significant benefit of joint treatment designs is the capability to apply a multidisciplinary method to monitoring CAD [ 23 ]. Each team member brings specialised knowledge that enables a comprehensive assessment of the patient's condition. For example, cardiologists can focus on the medical management of CAD, nutritionists can help patients implement heart-related eating habits, and rehabilitation specialists can create customised exercise plans. This comprehensive method enhances therapy results and assists in addressing threats and way-of-life elements.

Furthermore, collaborative models enhance a person's education, learning, and self-care. Through regular interaction with different medical care specialists, clients get a much deeper understanding of their problem, therapy alternatives, and the way-of-life modifications required to deal with CAD effectively [ 24 ]. With knowledge and support, patients are more likely to adhere to prescribed medications, lifestyle changes, and follow-ups, leading to better long-term compliance and overall prognosis. In addition, collaborative care models promote early detection and timely intervention. Regular communication between team members allows for the quick identification of potential complications or changes in the patient's condition, which allows for quick changes in treatment plans. This proactive approach can prevent the progression of the disease and reduce the risk of side effects. However, successfully implementing collaborative care models requires effective communication, a streamlined workflow, and a commitment to professional collaboration. Barriers such as time constraints, resource allocation, and organisational culture can be complex but can be overcome with strong leadership, training, and support [ 25 ].

Addressing disparities in CAD management

Initiatives to eliminate disparities in CAD management have progressed over the last few years. Raised understanding projects, as well as targeted education and learning programmes, have aided in boosting public awareness about CAD threat variables and preventive measures. Furthermore, efforts to increase accessibility to healthcare and cardio testing in underserved locations have contributed to increased detection and prompt treatment. However, there are still significant gaps in resolving disparities in CAD management [ 26 ]. Socioeconomic factors critically determine access to quality healthcare, and marginalised communities often face barriers to seeking appropriate medical care. Limited access to preventive measures such as healthy food and recreational activities may exacerbate coronary heart disease (CHD) risks in vulnerable populations. Cultural competence and sensitivity in CAD management are important aspects that require more attention. Healthcare providers must have the necessary skills to understand and respond to the unique needs of different patient populations [ 27 ]. Adapting management strategies by considering cultural beliefs and practices can improve patient engagement and adherence. In addition, differences between CAD studies and clinical trials must be recognised and addressed. Minorities are historically underrepresented in medical research, and this can limit the generalisability of research findings. Encouraging participation in clinical trials can provide valuable information about the efficacy and safety of CAD interventions in different populations. Collaboration between healthcare providers, policymakers, and community organisations is essential to address disparities in CAD management. Implementing targeted interventions that address socioeconomic factors, cultural competence, and research engagement can significantly reduce disparities in CAD management [ 28 ].

Long-term outcomes and follow-up

Developments in CAD monitoring have led to enhanced temporary results, such as fewer step-by-step difficulties and medical facility readmissions. Nevertheless, comprehending the treatment's toughness and adherence efficiency is essential to enhancing a person's treatment [ 29 ]. Comprehensive research involving significant professional tests and real-world computer registries is needed to assess the performance of brand-new treatments and enhance existing techniques [ 30 ]. They provide valuable information about the risk of recurrent events, the need for secondary prevention, and the impact of lifestyle changes on long-term prognosis. Follow-up care is central to managing CAD, allowing healthcare providers to monitor patient progress, address ongoing risk factors, and assess follow-ups. Routine follow-ups promote timely changes in the therapy routine and the execution of safety nets, which can gradually impact personal diagnostic results [ 31 ].

Moreover, independent learning of the follow-up procedures equips individuals to handle their problems and employ lasting way-of-life adjustments productively. Telemedicine and digital health solutions have transformed follow-up care by providing remote monitoring and increasing patient engagement [ 32 ]. Virtual consultations allow healthcare providers to reach patients with barriers to an in-person visit, facilitating ongoing care and quick resolution of any issues. Despite significant advances in managing CAD, ensuring equitable access to follow-up care remains a challenge, particularly for underserved populations. Hence, addressing disparities in follow-up care is vital to improving long-term outcomes for all patients with CAD [ 33 ].

CAD: coronary artery disease; CVD: cardiovascular disease; LDL: low-density lipoprotein; CKD: chronic kidney disease; PCSK9: proprotein convertase subtilisin/kexin kind 9; CHD: coronary heart disease; CABG: coronary artery bypass grafting; PCI: percutaneous coronary intervention; NNCAs: non-atherosclerotic coronary arteries; MI: myocardial infarction; CCCS: Collaborative Cardiac Care Service; OPCAB: off-pump coronary artery bypass; ICA: invasive coronary angiography; COD: coronary obstructive disease; EF: ejection fraction

Conclusions

Continued innovations in managing CAD have led to significant advances that promise to improve patient outcomes and reduce the global burden of this common cardiovascular disease. With CAD's profound global impact, researchers, practitioners, and policymakers have collaborated to revolutionise its diagnosis, treatment, and prevention. The diagnostic landscape has been redefined as non-invasive imaging techniques like CCTA and CMR grant clinicians unprecedented insight into coronary anatomy and function. Biomarkers and personalised risk profiling enhance early detection and tailored intervention strategies, ushering in an era of precision medicine. Pharmacological interventions have also seen remarkable strides, boasting potent antiplatelet agents, lipid-lowering therapies, and angina treatments that have rewritten the trajectory of CAD management. These breakthroughs and precision medicine empower practitioners to craft personalised therapeutic regimens that optimise efficacy and minimise adverse effects. Invasive procedures have been revitalised, exemplified by PCI advancements like drug-eluting stents and bioresorbable scaffolds. Intravascular imaging techniques amplify the precision of interventions, while minimally invasive methods reduce procedural risks. Emphasising holistic well-being, lifestyle changes, and cardiac rehabilitation have emerged as formidable allies against CAD. Integrating telemedicine and digital health solutions further augments care, empowering remote monitoring, early detection, and patient engagement. Secondary prevention strategies underscore a nuanced understanding of patient risks, integrating genetic insights and targeted therapies into the management approach. Collaborative care models unite diverse experts, providing comprehensive, patient-centric strategies. As disparities persist, strides have been made to address them, promoting inclusivity and equitable access to cutting-edge care. The commitment to research, evidenced by genetic therapies, targeted interventions, and regenerative medicine, underscores an unwavering dedication to CAD's conquest. In summation, the mosaic of CAD management encapsulates innovation, collaboration, and patient centricity. With each stride forward, the goal of minimising CAD's global impact inches closer, reinvigorating the medical community's resolve to craft a healthier future for all affected by chronic cardiovascular diseases or syndromes.

The authors have declared that no competing interests exist.

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MHRA approves GLP –1 receptor agonist semaglutide to reduce risk of serious heart problems in obese or overweight adults

Semaglutide is the first weight loss drug approved in the UK as a preventative treatment for those with established cardiovascular disease

The Medicines and Healthcare products Regulatory Agency (MHRA) has today, 23 July 2024, approved a new indication for semaglutide (Wegovy) to reduce the risk of overweight and obese adults suffering serious heart problems or strokes.

This medicine, a GLP-1 receptor agonist, was already approved for use in the treatment of obesity and for weight management, to be used alongside diet, physical activity and behavioural support.

The approval means that semaglutide is the first weight loss drug to be prescribed to prevent cardiovascular events, such as cardiovascular death, non-fatal heart attack and non-fatal stroke, in people with established cardiovascular disease and a Body Mass Index (BMI) higher or equal to 27 kg/m2.

Cardiovascular disease (CVD) is a general term for conditions affecting the heart or blood vessels. It is usually associated with a build-up of fatty deposits inside the arteries ( atherosclerosis ) and an increased risk of blood clots .

CVD is one of the main causes of death and disability in the UK, but it can often be prevented by leading a healthy lifestyle.

The approval is based on new data from a post-approval clinical study which demonstrated that semaglutide (2.4 mg once weekly by subcutaneous injection, for up to five years) lowers the incidence of major adverse cardiovascular events (MACE) vs placebo.

In a multi-national, multi-centre, placebo-controlled double-blind trial that randomly assigned over 17,600 participants to receive either Wegovy or a placebo, Wegovy significantly reduced the risk of major adverse cardiovascular events by 20%, such as cardiovascular death, heart attack and stroke, which occurred in 6.5% of participants who received Wegovy compared to 8% of participants who received placebo.

Shirley Hopper, MHRA Deputy Director of Innovative Medicines, said:

Our key priority  is enabling access to high quality, safe and effective medical products. We’re assured that the appropriate regulatory standards of safety, quality and effectiveness for the approval of this medicine have been met. This treatment option that prevents heart disease and strokes is an important step forward in tackling the serious health consequences of obesity. As with all medicines, we will keep its safety under close review.

Professor Bryan Williams, Chief Scientific and Medical Officer at the British Heart Foundation, said:

Nearly two thirds of adults in England are living with excess weight or obesity. Those that also have an established cardiovascular disease live with a very high risk that a serious event like a heart attack or stroke could happen. Several recent studies have shown us that semaglutide is an effective tool that can improve the quality of life for those with cardiovascular disease, including by lowering the risk of serious cardiac events. It is important that people using the drug to lose weight and improve their health are given the support they need from healthcare professionals to maintain these improvements long into the future. This means appropriate training and healthcare workforce development, along with policies to create a wider environment that supports everyone to stay as healthy as possible. Altogether, this can help save lives from the devastating impact of heart attacks and strokes.

The treatment is taken as a solution for injection in a pre-filled pen.

The active ingredient, semaglutide, is a GLP-1 receptor agonist. This mimics the action of the GLP-1 hormone, which is involved in regulating blood sugar levels. Semaglutide binds to GLP-1 receptors on pancreatic cells, enhancing the insulin secretion in response to meals, reducing glucagon release and slowing the gastric emptying process. This helps to promote weight loss.

The most common side effects of the medicine are gastrointestinal disorders including nausea, diarrhoea, constipation and vomiting.

As with any medicine, the MHRA keeps the safety and effectiveness of semaglutide under close review. Anyone who suspects they are having a side effect from this medicine is encouraged to talk to their doctor, pharmacist or nurse and report it directly to the Yellow Card scheme, either through the website or by searching the Google Play or Apple App stores for MHRA Yellow Card.

Notes to editors

• Authorisation for this new indication for semaglutide (Wegovy) was granted on 23 July 2024 to Novo Nordisk.
• The authorisation was granted as part of the International Recognition Procedure (IRP), via the reference regulator, the Food and Drug Administration in the USA. Launched in January this year, the IRP allows the MHRA to accelerate the assessment of new medicines by taking into account the expertise and decision-making of trusted regulatory partners in the authorisation process.
• More information can be found in the Summary of Product Characteristics and Patient Information leaflets which will be published on the MHRA Products website within 7 days of approval.
• The Medicines and Healthcare products Regulatory Agency (MHRA) is responsible for regulating all medicines and medical devices in the UK by ensuring they work and are acceptably safe.  All our work is underpinned by robust and fact-based judgements to ensure that the benefits justify any risks.
• The MHRA is an executive agency of the Department of Health and Social Care.
• For media enquiries, please contact the news centre on 020 3080 7651 or [email protected]

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EXCLUSIVE: Girl, 5, receives double-lung transplant after rare condition turned her skin blue

When Harper Ryals turned 2, she hit a growth spurt. But her mom, Rebecca Newman, noticed something unusual with her daughter’s growth — Harper’s fingertips had turned blue.

“She got really big really fast,” Newman, 26, of Long Beach, Mississippi, tells TODAY.com. “Once she hit that growth spurt, we started noticing subtle changes in her, such as she was having trouble breathing.”

After a doctor’s appointment led an emergency room visit, testing found numerous arteriovenous malformations (AVM) in Harper’s lungs. An AVM is a jumble of incorrectly connected blood vessels that are weak and can easily rupture and bleed. Having just one is considered dangerous.

Eventually, doctors diagnosed Harper, now 5, with a rare disease called hereditary hemorrhagic telangiectasia (HHT), which causes AVMs, according to the U.S. Centers for Disease Control and Prevention .

The family soon learned that she had a genetic mutation that made her HHT unique. Her family felt stunned.

“This is already a rare disease but ... she has a very rare genetic mutation that has never been seen before,” Newman says. “It is something that actually happens by a crazy astronomical chance in the conception process.” 

‘Struggling to breath’

When Harper was born, she appeared to be completely healthy. It wasn’t until she was 2 that her parents noticed anything wrong with her. After her fingers turned blue, she underwent a battery of tests that revealed that Harper had thousands of tiny AVMs in her lungs. This impacted her ability to receive properly oxygenated blood, and her lower blood oxygen levels caused a blue tint to her skin.

“She was struggling to breathe,” Newman says. “She was struggling with her strength.” 

Doctors soon placed Harper on oxygen, and the little girl lived with thin tube called a cannula in her nose and an oxygen tank nearby. During the testing, doctors also discovered Harper also had a large AVM in her brain. If a brain AVM bleeds, it can cause cognitive and sometimes physical impairment, according to past TODAY.com reporting .

While Harper had a lot of scary health concerns, doctors worried most about her lungs.

“They were trying to come up with treatment options for the lungs because that was a more pressing matter,” Newman says. “Unfortunately, there was nothing that the doctors could do.”

Doctors couldn’t embolize the AVMs — a procedure that stops blood flow to a specific blood vessel to prevent it from bleeding — because there were too many. They also couldn’t remove a part of the toddler’s lung because the AVMs were spread throughout both of her lungs. Doctors felt flummoxed by Harper’s health.

“One doctor said, ‘Harper is an extraordinary, one-of-a-kind child,’” Neman recalls. “She also let us know that she has never in her career met a child as sick as Harper. That was very heartbreaking.”

The family was devastated by this assessment. The doctor mentioned a double-lung transplant as a possible solution, but that, too, sounded frightening.

“A double-lung transplant is a very rare, very risky surgery that is not all the time very successful," Newman says.

Even with all the health challenges she faced, Harper “pushed herself past her limits.” She enjoys playing video games and excels at Animal Crossing, Kirby and the Mario Kart.

“She has always been very ahead of her age as far as her intelligence goes, and she’s very intuitive,” Newman says. “She really likes to play with other kids and especially kids that are different, kind of like her.”

Children often asked Harper about her cannula, and Newman would try to explain it as best she could.

“We would have to tell them she has a very, very rare disease that nobody has ever heard about and it affects her lungs,” Newman says. 

As time passed, Harper’s health deteriorated. 

“She was suffering from very, very severe migraines and headaches because of the lack of oxygen,” Newman says.

In the fall of 2023, Newman and Harper traveled to Texas Children’s Hospital in Houston so Harper could be assessed for a double-lung transplant. A team of doctors decided she would be a candidate for the procedure. The family planned to relocate to Houston in 2024 so that Harper could be close by when lungs became available. During her Make-A-Wish trip in March 2024, though, Harper became very ill with a respiratory infection.

“She was receiving treatment until she got well enough to be air flighted directly to Texas Children’s,” Newman says. “On April 2, 2024, (she was listed) for a double lung transplant.”

After Harper spent 74 days in the intensive care unit waiting, the family learned that there were lungs available. Soon after surgery, Newman noticed a difference in her daughter.

“Now that her body is finally receiving the amount of oxygen that it needs, our baby is pink again,” Newman says. “It’s amazing to see her natural complexion.”

Pediatric lung transplants

Harper’s condition HHT causes AVMs, where “the arteries and the veins are connected in an abnormal way,” Dr. Tina Melicoff, medical director, lung transplant program, Texas Children’s Hospital, tells TODAY.com. “It can affect different organs.”

Many children with HHT experience recurrent nosebleeds due to broken capillaries in the nose. But they often have larger AVMs that can develop anywhere, including the skin, stomach, brain, liver or lungs, she says. AVMs in the lungs prevent oxygen from traveling to the bloodstream and the heart to be pumped through the body.

“The abnormal connection of those vessels do not allow the blood to be oxygenated,” Melicoff says. “It bypasses that gas exchange.”

While doctors can treat large AVMs in the lungs with surgery, Harper had micro-AVMs distributed throughout her lungs, likely due to her genetic mutation. A double-lung transplant was her only option.

“She was getting too exhausted because of her oxygen being very low,” Melicoff says. “It was very scary for the family. It was scary for us.”

Overall, lung transplants remain uncommon, Melicoff says, and she estimates that between 1,500 to 2,000 adults receive a lung transplant annually in the United States. Children account for about 20 to 50 lung transplants a year.

“Even with those low numbers, their wait times can be very long — and longer than for adults,” she says. “The reason is, thankfully, children are usually healthy.”

Texas Children’s Hospital is one of the few pediatric transplant centers, so patients need to be in Houston while they wait for organs.

“We don’t have that many donors, but the average wait time for somebody her age and size is around three to four months,” Melicoff says. “Harper waited a little bit longer.” 

In mid-June, the family learned that lungs were available, and on June 15, Harper underwent a successful double-lung transplant. Doctors do not anticipate Harper developing any more AVMs in her transplanted lungs.

“The mutation is at the level of the blood vessels,” Melicoff says. “The healthy lungs that don’t have the mutation, so the expectation is ... she will continue to have normal oxygen saturation from now on.”

Still, Harper has the AVM in her brain, which needs to be monitored. And she takes anti-rejection medications "to convince her body that the lungs belong," which suppress her immune system and increase her risk of infection, Melicoff explains.

While lung transplants allow many children to live close to a normal life, the transplanted organs are unlikely to last their whole lives for a variety of reasons.

“We concentrate on the day-to-day, and we’re hopeful that she’s doing great right now," Melicoff says. "We’ll maintain her health for as long as we can."

'Overall wonderful'

Harper is still in Houston for follow-up visits with her doctors, and she’s thriving. Newman can see a difference not just in how she looks but by how much energy Harper now possesses. Today, Harper does physical therapy and walks without breathing heavily or being tethered to oxygen.

“Despite not having enough oxygen before the transplant, she was still an intelligent little girl, and now she’s just exceeding and excelling in everything,” Newman says. “She’s learning to read. She’s learning to write. She’s learning to spell. She’s learning to count. … She’s doing absolutely overall wonderful.”

Her mood has improved, too.

“She’s very sweet, very lovable, very happy,” Newman says. “She’s never complained of pain since her transplant, and she’s very, very strong.”

The family feels grateful for the donor’s family.  

“Some child’s family decided to make a generous donation of their loved one’s organs to save the lives of other children, one of them being Harper,” Newman says. “Sometimes the things that may seem impossible can really happen if you just believe.” 

Meghan Holohan is a digital health reporter for TODAY.com and covers patient-centered stories, women’s health, disability and rare diseases.

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Man, 27, had recurring sinus infections. It was sign of rare cancer in his sinuses and mouth

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He thought the blurriness in the corner of his eye was part of aging. It was a rare uveal melanoma

Ryan Reynolds discusses ‘complicated relationship’ with late father who died of Parkinson’s disease

You might be missing the signs of asthma and depression in kids, says research

Nurse, 22, on uncovering the source of the chest pains she felt on duty

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