Cardiovascular Disease Risk Assessment Research Articles

Artificial Intelligence in Cardiovascular Disease Prevention: Is it Ready for Prime Time?

This review evaluates how Artificial Intelligence (AI) enhances atherosclerotic cardiovascular disease (ASCVD) risk assessment, allows for opportunistic screening, and improves adherence to guidelines through the analysis of unstructured clinical data and patient-generated data. Additionally, it discusses strategies for integrating AI into clinical practice in preventive cardiology. AI models have shown superior performance in personalized ASCVD risk evaluations compared to traditional risk scores. These models now support automated detection of ASCVD risk markers, including coronary artery calcium (CAC), across various imaging modalities such as dedicated ECG-gated CT scans, chest X-rays, mammograms, coronary angiography, and non-gated chest CT scans. Moreover, large language model (LLM) pipelines are effective in identifying and addressing gaps and disparities in ASCVD preventive care, and can also enhance patient education. AI applications are proving invaluable in preventing and managing ASCVD and are primed for clinical use, provided they are implemented within well-regulated, iterative clinical pathways.

UltraAIGenomics: Artificial Intelligence-Based Cardiovascular Disease Risk Assessment by Fusion of Ultrasound-Based Radiomics and Genomics Features for Preventive, Personalized and Precision Medicine: A Narrative Review.

Cardiovascular disease (CVD) diagnosis and treatment are challenging since symptoms appear late in the disease's progression. Despite clinical risk scores, cardiac event prediction is inadequate, and many at-risk patients are not adequately categorised by conventional risk factors alone. Integrating genomic-based biomarkers (GBBM), specifically those found in plasma and/or serum samples, along with novel non-invasive radiomic-based biomarkers (RBBM) such as plaque area and plaque burden can improve the overall specificity of CVD risk. This review proposes two hypotheses: (i) RBBM and GBBM biomarkers have a strong correlation and can be used to detect the severity of CVD and stroke precisely, and (ii) introduces a proposed artificial intelligence (AI)-based preventive, precision, and personalized ( ) CVD/Stroke risk model. The PRISMA search selected 246 studies for the CVD/Stroke risk. It showed that using the RBBM and GBBM biomarkers, deep learning (DL) modelscould be used for CVD/Stroke risk stratification in the framework. Furthermore, we present a concise overview of platelet function, complete blood count (CBC), and diagnostic methods. As part of the AI paradigm, we discuss explainability, pruning, bias, and benchmarking against previous studies and their potential impacts. The review proposes the integration of RBBM and GBBM, an innovative solution streamlined in the DL paradigm for predicting CVD/Stroke risk in the framework. The combination of RBBM and GBBM introduces a powerful CVD/Stroke risk assessment paradigm. model signifies a promising advancement in CVD/Stroke risk assessment.

Māori and Pacific families’ experiences and perspectives of cardiovascular care; A qualitative study

ObjectiveThis study aimed to understand the reasons behind evidence-practice gaps and inequities in cardiovascular care for Māori and Pacific people, as evidenced by the experiences and perspectives of patients and their families. MethodsThe research was guided by Māori and Pacific worldviews, incorporating Kaupapa Māori Theory and Pacific conceptual frameworks and research methodologies. Template analysis was used to analyse interview data from 61 Māori and Pacific people who had experienced a cardiovascular disease (CVD) risk assessment, acute coronary syndrome, and/or heart failure. ResultsThe range of experiences relating to participants’ heart health journeys are presented in five main themes: Context, Mana (maintaining control and dignity), Condition, People and Journey. ConclusionsMāori and Pacific people want to take charge of their heart health but face challenges. Participants described important obligations to family, community and tikanga (the culturally correct way of doing things). Participants described times when health care undermined existing responsibilities, their dignity and/or their mana, and they felt excluded from treatment as a result. Implications for Public HealthGood reciprocal communication, stemming from a high-quality relationship is essential for successful outcomes. A workforce that is representative of the population it serves and is culturally safe lays the foundation for excellence in care.

A community health worker led approach to cardiovascular disease prevention in the UK-SPICES-Sussex (scaling-up packages of interventions for cardiovascular disease prevention in selected sites in Europe and Sub-saharan Africa): an implementation research project.

This paper describes a UK-based study, SPICES-Sussex, which aimed to co-produce and implement a community-based cardiovascular disease (CVD) risk assessment and reduction intervention to support under-served populations at moderate risk of CVD. The objectives were to enhance stakeholder engagement; to implement the intervention in four research sites and to evaluate the use of Voluntary and Community and Social Enterprises (VCSE) and Community Health Worker (CHW) partnerships in health interventions. A type three hybrid implementation study design was used with mixed methods data. This paper represents the process evaluation of the implementation of the SPICES-Sussex Project. The evaluation was conducted using the RE-AIM framework. Reach: 381 individuals took part in the risk profiling questionnaire and forty-one women, and five men participated in the coaching intervention. Effectiveness: quantitative results from intervention participants showed significant improvements in CVD behavioural risk factors across several measures. Qualitative data indicated high acceptability, with the holistic, personalised, and person-centred approach being valued by participants. Adoption: 50% of VCSEs approached took part in the SPICES programme, The CHWs felt empowered to deliver high-quality and mutually beneficial coaching within a strong project infrastructure that made use of VCSE partnerships. Implementation: Co-design meetings resulted in local adaptations being made to the intervention. 29 (63%) of participants completed the intervention. Practical issues concerned how to embed CHWs in a health service context, how to keep engaging participants, and tensions between research integrity and the needs and expectations of those in the voluntary sector. Maintenance: Several VCSEs expressed an interest in continuing the intervention after the end of the SPICES programme. Community-engagement approaches have the potential to have positively impact the health and wellbeing of certain groups. Furthermore, VCSEs and CHWs represent a significant untapped resource in the UK. However, more work needs to be done to understand how links between the sectors can be bridged to deliver evidence-based effective alternative preventative healthcare. Reaching vulnerable populations remains a challenge despite partnerships with VCSEs which are embedded in the community. By showing what went well and what did not, this project can guide future work in community engagement for health.

Cardiovascular Risk Assessment in Pregnancy: Findings From a Large Urban Academic Medical Center [ID 2683572

INTRODUCTION: The goal of this study is to describe patients who screened positive and had true cardiovascular disease (CVD) identified using a risk assessment tool in pregnancy or postpartum. METHODS: This is a prospective validation study of the CVD screening algorithm proposed by the California Maternal Quality Care Collaborative (CMQCC). The CVD risk assessment tool was embedded in the electronic medical record for use by all prenatal care providers with a goal of at least one CVD risk assessment during pregnancy and/or postpartum period. Patients identified with increased risk of CVD received an echocardiogram, electrocardiogram, and cardiology consultation. We describe the assessment tool responses, timing of assessment, cardiac findings, and subsequent interventions. This study was approved by the IRB at Albert Einstein College of Medicine. RESULTS: A total of 1,877 patients were screened as of September 1, 2023, with 148 screening positive for at risk of CVD. Thirty-two patients were found to be true positives including 2 (6.25%) with systolic and/or diastolic dysfunction, 10 (31.3%) with ventricular dilation or hypertrophy, and 23 (71.9%) with a valvular abnormality. CONCLUSION: All 32 true-positive cases were seen by a multidisciplinary cardio-obstetrics team at least once. Ten patients had multiple visits with the cardio-obstetrics team. Three patients were deemed to be of higher risk for decompensation during labor by modified World Health Organization classification and required detailed plans for intrapartum and postpartum management. Seven patients were started on cardiovascular medications. The cardiovascular risk assessment tool proved valuable for identifying and providing individualized care for cardio-obstetric patients.

Cardiovascular Considerations and Implications for Treatment in Psoriasis: An Updated Review.

Psoriasis, a prevalent chronic inflammatory skin disorder affecting 2-3% of the global population, has transcended its dermatological confines, revealing a profound association with cardiovascular diseases (CVD). This comprehensive review explores the intricate interplay between psoriasis and cardiovascular system, delving into genetic links, immune pathways, and adipose tissue dysfunction beyond conventional CVD risk factors. The pathophysiological connections unveil unique signatures, distinct from other inflammatory skin conditions, in particular psoriasis-specific genetic polymorphisms in IL-23 and TNF-α have consistently been linked to CVD. The review navigates the complex landscape of psoriasis treatments, addressing challenges and future directions in particular relevance to CVDs in psoriasis. Therapeutic interventions, including TNF inhibitors (TNFi), present promise in reducing cardiovascular risks, and methotrexate could constitute a favourable choice. Conversely, the relationship between IL-12/23 inhibitors and cardiovascular risk remains uncertain, while recent evidence indicates that Janus kinase inhibitors may not carry CVD risks. Emerging evidence supports the safety and efficacy of IL-17 and IL-23 inhibitors in patients with CVDs, hinting at evolving therapeutic paradigms. Lifestyle modifications, statins, and emerging therapies offer preventive strategies. Dedicated screening guidelines for CVD risk assessment in psoriasis are however lacking. Further, the impact of different disease phenotypes and treatment hierarchies in cardiovascular outcomes remains elusive, demanding ongoing research at the intersection of dermatology, rheumatology, and cardiology. In conclusion, unraveling the intricate connections between psoriasis and CVD provides a foundation for a holistic approach to patient care. Collaboration between specialties, advancements in screening methodologies, and a nuanced understanding of treatment impacts are essential for comprehensive cardiovascular risk management in individuals with psoriasis.

Effects of At-Risk Drinking on Ambulatory Blood Pressure in Young Adults

Introduction: At-risk drinking is associated with an increased risk of cardiovascular diseases (CVD) even in young adults. However, this association is mainly based on offce blood pressure (BP) measurement. Ambulatory BP monitoring captures diurnal variation in BP and is a more sensitive CVD predictor than offce BP. Therefore, the purpose of this study is to investigate the effects of at-risk drinking on ambulatory BP in young healthy adults with normal offce BP. Method: A total of 20 healthy men and women (21-35 years), non-smokers, were included in this cross-sectional study. All participants had an offce BP lower than 130/80 mmHg. Using the US Alcohol Use Disorders Identification Test (USAUDIT), at-risk drinkers were defined as those having a total USAUDIT score higher than 7 for men (6 for women). All participants underwent 24-hour ambulatory BP monitoring (Oscar 2™, SunTech Medical®). To investigate the vascular mechanisms underlying the effects of at-risk drinking on ambulatory blood pressure, aortic arterial stiffness and wave reflection were measured as pulse wave velocity (PWV) and augmentation index (AIx) by using SphygmoCor XCEL system (AtCor Medical). Results: No differences were found in age and BMI between at-risk drinkers (n=10) vs. low-risk drinkers (n=10; mean ± SD for age: 26.7 ± 3.7 vs. 26.6 ± 4.3 years, P>0.99 and BMI: 24.5 ± 2.4 vs. 23.8 ± 2.9 kg/m2, P=0.6). At-risk drinkers had a higher total USAUDIT score (10.6 ± 2.7 vs. 1.4 ± 1.2, p<0.001). While there was no difference between at-risk drinkers and low-risk drinkers in 24-hour, daytime, and nighttime systolic and diastolic BP (P≥0.5), at-risk drinkers had a higher blood pressure morning surge compared to low-risk drinkers (systolic: 34 ± 10 vs. 22 ± 6 mmHg, P=0.014 and diastolic: 29 ± 9 vs. 15 ± 4 mmHg, P=0.002). No significant difference was found in PWV (5.7 ± 0.6 Vs. 5.5 ± 0.7 m/sec, P=0.4) and AIx (8 ± 8 Vs. 15 ± 11, P=0.1). Conclusion: Our results indicate that despite similar offce BP, young adult at-risk drinkers had higher BP morning surge, an independent risk factor for cardiovascular events, than low-risk drinkers. These findings provide implications in using USAUDIT and out-of-offce BP measurements for cardiovascular disease risk assessment. This work was supported by NIAAA AA028537 to CLH. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

A Multi-Stage Approach for Cardiovascular Risk Assessment from Retinal Images Using an Amalgamation of Deep Learning and Computer Vision Techniques.

Cardiovascular diseases (CVDs) are a leading cause of mortality worldwide. Early detection and effective risk assessment are crucial for implementing preventive measures and improving patient outcomes for CVDs. This work presents a novel approach to CVD risk assessment using fundus images, leveraging the inherent connection between retinal microvascular changes and systemic vascular health. This study aims to develop a predictive model for the early detection of CVDs by evaluating retinal vascular parameters. This methodology integrates both handcrafted features derived through mathematical computation and retinal vascular patterns extracted by artificial intelligence (AI) models. By combining these approaches, we seek to enhance the accuracy and reliability of CVD risk prediction in individuals. The methodology integrates state-of-the-art computer vision algorithms and AI techniques in a multi-stage architecture to extract relevant features from retinal fundus images. These features encompass a range of vascular parameters, including vessel caliber, tortuosity, and branching patterns. Additionally, a deep learning (DL)-based binary classification model is incorporated to enhance predictive accuracy. A dataset comprising fundus images and comprehensive metadata from the clinical trials conducted is utilized for training and validation. The proposed approach demonstrates promising results in the early prediction of CVD risk factors. The interpretability of the approach is enhanced through visualization techniques that highlight the regions of interest within the fundus images that are contributing to the risk predictions. Furthermore, the validation conducted in the clinical trials and the performance analysis of the proposed approach shows the potential to provide early and accurate predictions. The proposed system not only aids in risk stratification but also serves as a valuable tool for identifying vascular abnormalities that may precede overt cardiovascular events. The approach has achieved an accuracy of 85% and the findings of this study underscore the feasibility and efficacy of leveraging fundus images for cardiovascular risk assessment. As a non-invasive and cost-effective modality, fundus image analysis presents a scalable solution for population-wide screening programs. This research contributes to the evolving landscape of precision medicine by providing an innovative tool for proactive cardiovascular health management. Future work will focus on refining the solution's robustness, exploring additional risk factors, and validating its performance in additional and diverse clinical settings.

Prevalence of Cardiovascular Disease Risk Factors And Assessment of 10-Year Risk of Developing Cardiovascular Diseases in Premenopausal and Postmenopausal Women with Type 2 Diabetes: A Comparative Analysis

Prevalence of Cardiovascular Disease Risk Factors And Assessment of 10-Year Risk of Developing Cardiovascular Diseases in Premenopausal and Postmenopausal Women with Type 2 Diabetes: A Comparative Analysis

Assessing, communicating and managing cardiovascular disease risk: a practical summary of the 2023 guideline.

The outdated cardiovascular disease risk calculator has been reported to overestimate cardiovascular disease risk for a contemporary Australian population, and does not include relevant variables, such as socioeconomic disadvantage, which has been shown to increase the incidence of both heart attack and stroke. The 2023 Australian Guideline for Assessing and Managing Cardiovascular Disease Risk marks a major milestone as the first update to Australia's cardiovascular disease prevention guideline in over a decade. The new guideline may help to refine and recategorise risk estimates, hence improving the discriminatory and predictive value of the new calculator. The new Australian Cardiovascular Disease Risk Calculator expresses risk scores as a percentage estimate of a person's probability of dying or being hospitalised due to cardiovascular disease within the next 5 years. The new calculator expresses risk scores as low (less than 5%), intermediate (5% to less than 10%), or high (10% or higher) risk over 5 years. Reclassification factors built into the new calculator are designed to help clinicians individualise risk estimates. These factors include ethnicity (e.g. First Nations status), family history of premature cardiovascular disease, severe mental illness, kidney disease and coronary artery calcium score. The new calculator also uses optional diabetes-specific variables (supporting a more granular cardiovascular disease risk assessment of people with type 2 diabetes). People who meet the clinically determined high-risk criteria (chronic kidney disease, familial hypercholesterolaemia) should not progress through the Australian Cardiovascular Disease risk calculator, but move straight to management. For a person with a cardiovascular disease risk score recorded from the outdated calculator, clinicians may want to reassess their risk using the new calculator the next time the person attends.

How do general practitioners manage patient health literacy differences in cardiovascular disease prevention consultations? An interview study

ObjectivesLow health literacy is associated with worse health outcomes, including for cardiovascular disease (CVD). However, general practitioners (GPs) have limited support to identify and address patient health literacy needs in CVD prevention consultations. This study explored GPs’ experiences of patient health literacy needs during CVD risk assessment and management consultations. MethodsSemi-structured interviews with 18 GPs in Tasmania, Australia in 2021. A Framework Analysis approach was used to code transcripts to a thematic framework. ResultsGPs perceptions on patient health literacy informed three themes: 1. Methods of estimating health literacy; 2. GPs’ perceptions about the impact of health literacy on CVD prevention including risk factor knowledge and behaviours; and 3. Strategies for communicating with patients experiencing health literacy challenges. The findings show that while no formal tools were used to assess health literacy in this sample, perceived health literacy can change GPs’ communication and prevention strategies. ConclusionThe findings raise concerns about the equity of choices made available to patients, based on subjective perceptions of their health literacy level. Practice implicationGPs could be better supported to assess and address patient health literacy needs in CVD prevention consultations.

2023 Australian guideline for assessing and managing cardiovascular disease risk.

The 2023 Australian guideline for assessing and managing cardiovascular disease risk provides updated evidence-based recommendations for the clinical assessment and management of cardiovascular disease (CVD) risk for primary prevention. It includes the new Australian CVD risk calculator (Aus CVD Risk Calculator), based on an equation developed from a large New Zealand cohort study, customised and recalibrated for the Australian population. The new guideline replaces the 2012 guideline that recommended CVD risk assessment using the Framingham risk equation. The new guideline recommends CVD risk assessment in people without known CVD: all people aged 45-79 years, people with diabetes from 35 years, and First Nations people from 30 years. The new Aus CVD Risk Calculator should be used to estimate and categorise CVD risk into low (< 5% risk over five years), intermediate (5% to < 10% risk over five years) or high risk (≥ 10% over five years). The following reclassification factors may be applied to recategorise calculated risk to improve accuracy of risk prediction, particularly in individuals close to a risk threshold: Indigenous status/ethnicity, estimated glomerular filtration rate, urine albumin to creatinine ratio measurements, severe mental illness, coronary artery calcium score and family history of premature CVD. A variety of communication formats is available to communicateCVD risk to help enable shared decision making. Healthy lifestylemodification, including smoking cessation, nutrition, physical activity and limiting alcohol, is encouraged for all individuals. Blood pressure-lowering and lipid-modifying pharmacotherapies should be prescribed for high risk and considered for intermediate risk individuals, unless contraindicated or clinically inappropriate. Reassessment of CVD risk should be considered within five years for individuals at low risk and within two years for those with intermediate risk. Reassessment of CVD risk is not recommended for individuals at high risk. CHANGES IN ASSESSMENT AND MANAGEMENT AS A RESULT OF THE GUIDELINE: The updated guideline recommends assessment over a broader age range and uses the Aus CVD Risk Calculator, which replaces the previous Framingham-based equation. It incorporates new variables: social disadvantage, diabetes-specific risk markers, diagnosis of atrial fibrillation and use of blood pressure-lowering and lipid-modifying therapies. Reclassification factors are also a new addition. Updated risk categories and thresholds are based on the new AusCVD Risk Calculator. The proportion of the population in the high risk category (≥ 10% over five years) is likely to be broadly comparable to more than 15% risk from the Framingham-based equation. The full guideline and Aus CVD Risk Calculator can be accessed at www.cvdcheck.org.au.

Risk Factors and Modifiers for Cardiovascular Disease Assessment of Patients with Heterozygous Familial Hypercholesterolaemia.

Background: The assessment of the risk of cardiovascular disease (CVD) in patients with heterozygous familial hypercholesterolemia (HeFH) is determined by conventional risk factors. However, factors modifying CVD, or risk modifiers, beyond conventional risk factors may inform their CVD risk assessment and the subsequent use of new therapies. This work identifies and characterises patients within a lipid clinic cohort with regards to conventional CVD risk factors and risk modifiers with a focus on those with HeFH. Methods: A study of consecutive adult patients attending our specialist lipid clinic was performed over a six-month period. The patient data recorded included demographics, clinical characteristics, risk factors and risk modifiers, biochemical profiles and genetic testing results. Risk modifiers were identified based on ESC/EAS guidance, and those with HeFH were compared to those without. Results: A total of 370 patients were included. Of these, 98 HeFH patients were identified (26%). Then, 52% of HeFH patients were stratified into the very-high risk category due to the presence of CVD risk factors. Risk modifiers were present in 73%. These included a family history of premature CVD (56%), obesity (28%), a sedentary lifestyle (13%) and a major psychiatric disorder (12%). Compared to the rest of the cohort, those with HeFH were less likely to have hypertension and more likely to have a family history of premature CVD. Conclusions: Half of patients with HeFH are categorised as having very high CV risk. Consideration of risk modifiers, particularly a family history of premature CV disease, increases this very-high-risk category further. This may have implications for the clinical application and access to novel treatments.

Predictive Modeling for Cardiovascular Disease in Patients Based on Demographic and Biometric Data

Cardiovascular disease (CVD) remains the leading global cause of death, highlighting the urgent need for accurate risk assessment and prediction tools. Machine learning (ML) has emerged as a promising approach for CVD risk prediction, offering the potential to capture complex relationships between clinical and biometric data and patient outcomes. This study explores the application of support vector machines (SVMs), ensemble learning, and artificial neural networks (NNs) for predictive modeling of CVD in patients. The study utilizes a comprehensive dataset comprising demographic and biometric data of patients, including age, gender, blood pressure, cholesterol levels, and body mass index, features. SVMs, ensemble learning, and NNs are employed to construct predictive models based on these data. The performance of each model is evaluated using metrics such as accuracy, sensitivity, specificity, and the area under the receiver operating characteristic (ROC) curve (AUC). The results demonstrate that all three models achieve accuracy performance in predicting CVD events, with AUC values ranging from 0.85 to 0.92. Ensemble learning exhibits the highest overall accuracy, while SVM and ANN demonstrate strengths in specific aspects of prediction. The study concludes that Machine learning algorithms, particularly ensemble learning, hold significant promise for improving CVD risk assessment. The integration of ML-based predictive models into demographic practice can facilitate early intervention, personalized treatment strategies, and improved patient outcomes.

Cardiovascular disease risk assessment through sensing the circulating microbiome with perovskite quantum dots leveraging deep learning models for bacterial species selection.

Perovskite quantum dots (PQDs) are novel nanomaterials wherein perovskites are used to formulate quantum dots (QDs). The present study utilizes the excellent fluorescence quantum yields of these nanomaterials to detect 16S rRNA of circulating microbiome for risk assessment of cardiovascular diseases (CVDs). A long short-term memory (LSTM) deep learning model was used to find the association of the circulating bacterial species with CVD risk, which showed the abundance of three different bacterial species (Bauldia litoralis (BL), Hymenobacter properus (HYM), and Virgisporangium myanmarense (VIG)). The observations suggested that the developed nano-sensor provides high sensitivity, selectivity, and applicability. The observed sensitivities for Bauldia litoralis, Hymenobacter properus, and Virgisporangium myanmarense were 0.606, 0.300, and 0.281fg, respectively. The developed sensor eliminates the need for labelling, amplification, quantification, and biochemical assessments, which are more labour-intensive, time-consuming, and less reliable. Due to the rapid detection time, user-friendly nature, and stability, the proposed method has a significant advantage in facilitating point-of-care testing of CVDs in the future. This may also facilitate easy integration of the approach into various healthcare settings, making it accessible and valuable for resource-constrained environments.

A population-based prospective study on obesity-related non-communicable diseases in northern Iran: rationale, study design, and baseline analysis.

Iran is facing an epidemiological transition with the increasing burden of non-communicable diseases, such as obesity-related disorders and cardiovascular diseases (CVDs). We conducted a population-based prospective study to assess the prevalence and incidence rates of CVDs and obesity-related metabolic disorders and to evaluate the predictive ability of various CVD risk assessment tools in an Iranian population. We enrolled 5,799 participants in Amol, a city in northern Iran, in 2009-2010 and carried out the first repeated measurement (RM) after seven years (2016-2017). For all participants, demographic, anthropometric, laboratory, hepatobiliary imaging, and electrocardiography data have been collected in the enrollment and the RM. After enrollment, all participants have been and will be followed up annually for 20 years, both actively and passively. We adopted a multidisciplinary approach to overcome barriers to participation and achieved a 7-year follow-up success rate of 93.0% with an active follow-up of 5,394 participants aged 18-90 years. In the RM, about 64.0% of men and 81.2% of women were obese or overweight. In 2017, about 16.2% and 5.2% of men had moderate or severe non-alcoholic fatty liver disease, while women had a significantly higher prevalence of metabolic syndrome (35.9%), and type 2 diabetes mellitus (20.9%) than men. Of 160 deceased participants, 69 cases (43.1%) died due to CVDs over seven years. The most prevalent obesity-related chronic disease in the study was metabolic syndrome. Across the enrollment and RM phases, women exhibited a higher prevalence of obesity-related metabolic disorders. Focusing on obesity-related metabolic disorders in a population not represented previously and a multidisciplinary approach for enrolling and following up were the strengths of this study. The study outcomes offer an evidence base for future research and inform policies regarding non-communicable diseases in northern Iran.

Development and validation of risk prediction model for recurrent cardiovascular events among Chinese: the Personalized CARdiovascular DIsease risk Assessment for Chinese model.

Cardiovascular disease (CVD) is a leading cause of mortality, especially in developing countries. This study aimed to develop and validate a CVD risk prediction model, Personalized CARdiovascular DIsease risk Assessment for Chinese (P-CARDIAC), for recurrent cardiovascular events using machine learning technique. Three cohorts of Chinese patients with established CVD were included if they had used any of the public healthcare services provided by the Hong Kong Hospital Authority (HA) since 2004 and categorized by their geographical locations. The 10-year CVD outcome was a composite of diagnostic or procedure codes with specific International Classification of Diseases, Ninth Revision, Clinical Modification. Multivariate imputation with chained equations and XGBoost were applied for the model development. The comparison with Thrombolysis in Myocardial Infarction Risk Score for Secondary Prevention (TRS-2°P) and Secondary Manifestations of ARTerial disease (SMART2) used the validation cohorts with 1000 bootstrap replicates. A total of 48 799, 119 672 and 140 533 patients were included in the derivation and validation cohorts, respectively. A list of 125 risk variables were used to make predictions on CVD risk, of which 8 classes of CVD-related drugs were considered interactive covariates. Model performance in the derivation cohort showed satisfying discrimination and calibration with a C statistic of 0.69. Internal validation showed good discrimination and calibration performance with C statistic over 0.6. The P-CARDIAC also showed better performance than TRS-2°P and SMART2. Compared with other risk scores, the P-CARDIAC enables to identify unique patterns of Chinese patients with established CVD. We anticipate that the P-CARDIAC can be applied in various settings to prevent recurrent CVD events, thus reducing the related healthcare burden.

Comparison of American College of Cardiology/American Heart Association Cardiovascular Risk Score, Framingham Risk Scores, European Society of Cardiology Cardiovascular Disease Risk Calculator, QRISK3-2018 Risk Calculator in Patients Presenting with First Time with Myocardial Infarction: A Step toward the Development of Indian Cardiac Risk Score

Introduction: One of the primary reasons for fatalities and impairments on a global scale is cardiovascular disease (CVD), encompassing a cluster of illnesses impacting the heart and blood vessels. A CVD risk assessment is a screening tool that measures an individual’s risk of developing CVD. Objective: The objective of this study was to compare and evaluate cardiovascular risk using internationally recognized CVD risk scoring systems. Methodology: A prospective, cross-sectional study was carried out for 2 months after the approval of the ethics committee. Naive myocardial infarction (MI) patients presenting to the cardiology department and conforming to the inclusion criteria were enrolled in the study. Patients who are taking statins or any lipid-lowering drugs, as well as patients who are on low-dose aspirin and antidiabetic medications. Using the aforementioned gathered data, the cardiovascular risk scores were computed employing the American College of Cardiology/American Heart Association (ACC/AHA), Framingham risk scores (FRS), European Society of Cardiology (ESC) – CVD risk calculator, and QRISK→3-2018, and subsequently categorized as low risk, intermediate, or high risk based on the individual risk scores. Results: We enrolled 42 patients, with a mean age of 60 years with male dominance. The mean body mass index was 25.5. With a cholesterol/high-density lipoprotein (HDL) ratio of 4.9, the mean systolic blood pressure/diastolic blood pressure was 126/77 mm of Hg. The mean ACC/AHA cardiovascular risk score for 10-year atherosclerotic cardiovascular disease (ASCVD) risk was 11.34%, FRS mean score was 12%, ESC CVD risk calculator mean score was 15.45%, and QRISK3-2018 risk calculator mean score was 20.87% in patients presenting with first time with MI. Conclusion: QRISK3-2018 risk calculator is the most effective out of all the risk calculators with a maximum detection rate of MI in first time presented patients with Naïve MI patients.

Low income and education are associated with greater ASCVD risk scores among adults in the US

IntroductionSocial determinants of health (SDOH) are fundamental causes of poor cardiovascular health, yet cardiovascular disease (CVD) risk assessment tools exclude SDOH. Our objective was to determine whether SDOH are independently associated with CVD risk in US adults. MethodsUtilizing the National Health and Nutrition Examination Survey (NHANES), we combined years 1999–2018 and included participants aged 40–79 without history of CVD and with information to calculate CVD risk (n = 21,694). Ten-year risk of atherosclerotic CVD (ASCVD) was calculated using the American Heart Association/American College of Cardiology (AHA/ACC) pooled cohort equations. We used linear regression models to estimate the association between SDOH and ASCVD risk, after adjusting for demographic factors. All analyses accounted for the complex survey design. ResultsMean age was 54.7 years, with 52.7 % female, 73.8 % non-Hispanic White, 9.4 % non-Hispanic Black, and 10.7 % Hispanic. From adjusted models, compared with an income of ≥ $75 K, ASCVD risk was greater by 3.06 (95 % CI: 2.65, 3.47) among those with income < $25 K, by 1.55 (95 % CI: 1.21, 1.89) among those with income $25 K–<$55 K, and by 1.20 (95 % CI: 0.84, 1.56) among those with income $55 K–<$75 K. Compared to college graduates, ASCVD risk was greater by 3.09 (95 % CI: 2.56, 3.62) among those with less than a high school education, by 1.65 (95 % CI: 1.31, 200) among those who were high school graduates, and by 1.41 (95 % CI: 1.11, 1.72) among those with some college education. ConclusionWe found strong graded associations between lower income and lower educational attainment with greater CVD risk.

Random Forest-Based Prediction Models for Assessing Cardiovascular Disease Risk: Integrating Clinical and Genetic Factors

This research investigates the adequacy of Irregular Forest-based expectation models in evaluating cardiovascular disease (CVD) hazards by joining clinical and hereditary variables. Leveraging a dataset comprising comprehensive clinical profiles and hereditary data, we prepared and assessed Arbitrary Woodland models near other machine learning calculations. Ours comes about illustrates that the Random Forest show outperformed Logistic Relapse, Support Vector Machine, and Slope Boosting in precision (85%), affectability (82%), specificity (88%), and zone beneath the recipient working characteristic bend (AUC-ROC) (0.92). Furthermore, the consideration of hereditary highlights altogether progressed the prescient execution, with the show accomplishing a precision of 88%, affectability of 86%, specificity of 90%, and AUC-ROC of 0.94. This study highlights the significance of coordination hereditary data for personalized hazard evaluation in CVD. The comparison with related works underscores the progressions made in leveraging machine learning for cardiovascular hazard forecast and conclusion. Our discoveries recommend that Irregular Forest-based models offer a promising approach for upgrading quiet results through precise chance evaluation, early location, and focused on preventive intercessions. Moving forward, encourage inquiry about is justified to illustrate fundamental pathophysiological components and direct accuracy medication approaches in cardiovascular wellbeing.