Prediction models for cardiovascular disease risk in the general population: systematic review

Computed tomography and coronary artery calcium score for screening of coronary artery disease and cardiovascular risk management in asymptomatic individuals

Several risk prediction models exist to predict atherosclerotic cardiovascular disease in asymptomatic individuals, but systematic reviews have generally found these models to be of limited utility. The coronary artery calcium score (CACS) offers an improvement in risk prediction, yet its role remains contentious. Notably, its negative predictive value has a high ability to rule out clinically relevant atherosclerotic cardiovascular disease. Nonetheless, CACS 0 does not permanently reclassify to a lower cardiovascular risk and periodic reassessment every 5 to 10 years remains necessary. Conversely, elevated CACS (> 100 or > 75th percentile adjusted for age, sex and ethnicity) can reclassify intermediate-risk individuals to a high risk, benefiting from preventive medication. The forthcoming update to the Dutch cardiovascular risk management guideline intends to re-position CACS for cardiovascular risk assessment as such in asymptomatic individuals. Beyond CACS as a single number, several guidelines recommend coronary CT angiography (CCTA), which provides additional information about luminal stenosis and (high-risk) plaque composition, as the first choice of test in symptomatic patients and high-risk patients. Ongoing randomised studies will have to determine the value of atherosclerosis evaluation with CCTA for primary prevention in asymptomatic individuals.

Predicting the resolution of hypertension following adrenalectomy in primary aldosteronism: Controversies and unresolved issues a narrative review

BACKGROUND

Hypertension resolution following adrenalectomy in patients with primary aldosteronism (PA) remains a critical clinical challenge. Identifying preoperatively which patients will become normotensive is both a priority and a point of contention. In this narrative review, we explore the controversies and unresolved issues surrounding the prediction of hypertension resolution after adrenalectomy in PA.

METHODS

A comprehensive literature review was conducted, focusing on studies published between 1954 and 2024 that evaluated all studies that discussed predictive models for hypertension resolution post-adrenalectomy in PA patients. Databases searched included MEDLINE®, Ovid Embase, and Web of Science databases.

RESULTS

The review identified several predictors and predictive models of hypertension resolution, including female sex, duration of hypertension, antihypertensive medication, and BMI. However, inconsistencies in study designs and patient populations led to varied conclusions.

CONCLUSIONS

Although certain predictors and predictive models of hypertension resolution post-adrenalectomy in PA patients are supported by evidence, significant controversies and unresolved issues remain. While the current predictive models provide valuable insights, there is a clear need for further research in this area. Future studies should focus on validating and refining these models.

A Comprehensive Examination of ChatGPT's Contribution to the Healthcare Sector and Hepatology

Artificial Intelligence and Natural Language Processing technology have demonstrated significant promise across several domains within the medical and healthcare sectors. This technique has numerous uses in the field of healthcare. One of the primary challenges in implementing ChatGPT in healthcare is the requirement for precise and up-to-date data. In the case of the involvement of sensitive medical information, it is imperative to carefully address concerns regarding privacy and security when using GPT in the healthcare sector. This paper outlines ChatGPT and its relevance in the healthcare industry. It discusses the important aspects of ChatGPT's workflow and highlights the usual features of ChatGPT specifically designed for the healthcare domain. The present review uses the ChatGPT model within the research domain to investigate disorders associated with the hepatic system. This review demonstrates the possible use of ChatGPT in supporting researchers and clinicians in analyzing and interpreting liver-related data, thereby improving disease diagnosis, prognosis, and patient care.

Association between functional dependence and cardiovascular disease among middle-aged and older adults: Findings from the China health and retirement longitudinal study

Background

The effect of different functional dependency types on cardiovascular disease (CVD) is largely unknown. Here, we aimed to investigate the association between functional dependence and CVD among middle-aged and older adults by conducting a cross-sectional and longitudinal study.

Methods

The study sample comprised 16,459 individuals of ≥40 years (including 10,438 without CVD) who had participated in the 2011 China Health and Retirement Longitudinal Study (CHARLS). Functional dependence was categorized based on the "interval-of-need" method, while CVD was defined as physician-diagnosed heart disease or stroke. Cox proportional hazard regression was employed to assess the effects of functional dependence on CVD. Moreover, patients were grouped according to the functional status changes, and the impact of these changes on CVD was observed. Heterogeneity, subgroup, and interaction analyses were used to evaluate the consistency of the study findings. Finally, a mediation analysis was performed to estimate the potential mediation effects on the relationship between functional dependence and CVD risk.

Results

CVD prevalence in the overall study population was 13.73 % (2260/16,459), while its prevalence among individuals with functional independence, low dependency, medium dependency, and high dependency was 9.60 % (1085/11,302), 14.25 % (119/835), 17.72 % (115/649), and 25.01 % (941/3763), respectively. Additionally, medium (odds ratio: 1.33, 95 % confidence interval: 1.06-1.68) and high functional dependency (1.55, 95 % CI: 1.38-1.75) were associated with CVD. A total of 2987 (28.62 %) participants with CVD were identified during the 9-year follow-up, with 4.85 % (145/2987) of the CVD cases being attributed to functional dependence. The individuals with medium (HR: 1.20, 95 % CI: 1.01-1.44) and high functional dependency (1.25, 95 % CI: 1.14-1.37) were more likely to develop CVD than their peers with functional independence. Furthermore, persistent functional dependence (HR: 1.72, 95 % CI: 1.52-1.94) and transition from functional independence to dependence (1.79, 95 % CI: 1.61-1.98) were associated with a higher CVD risk than continuous functional independence. Hypertension and diabetes may partially mediate CVD caused by functional dependence.

Conclusion

Functional dependence is associated with high CVD risk. Therefore, appropriate healthcare attention must be directed towards functionally dependent populations to protect their cardiovascular health.

Sex inequalities in cardiovascular risk prediction

AIMS

Evaluate sex differences in cardiovascular disease (CVD) risk prediction, including use of (i) optimal sex-specific risk predictors and (ii) sex-specific risk thresholds.

METHODS AND RESULTS

Prospective cohort study using UK Biobank, including 121 724 and 182 632 healthy men and women, respectively, aged 38-73 years at baseline. There were 11 899 (men) and 9110 (women) incident CVD cases (hospitalization or mortality) with a median of 12.1 years of follow-up. We used recalibrated pooled cohort equations (PCEs; 7.5% 10-year risk threshold as per US guidelines), QRISK3 (10% 10-year risk threshold as per UK guidelines), and Cox survival models using sparse sex-specific variable sets (via LASSO stability selection) to predict CVD risk separately in men and women. LASSO stability selection included 12 variables in common between men and women, with 3 additional variables selected for men and 1 for women. C-statistics were slightly lower for PCE than QRISK3 and models using stably selected variables, but were similar between men and women: 0.67 (0.66-0.68), 0.70 (0.69-0.71), and 0.71 (0.70-0.72) in men and 0.69 (0.68-0.70), 0.72 (0.71-0.73), and 0.72 (0.71-0.73) in women for PCE, QRISK3, and models using stably selected variables, respectively. At current clinically implemented risk thresholds, test sensitivity was markedly lower in women than men for all models: at 7.5% 10-year risk, sensitivity was 65.1 and 68.2% in men and 24.0 and 33.4% in women for PCE and models using stably selected variables, respectively; at 10% 10-year risk, sensitivity was 53.7 and 52.3% in men and 16.8 and 20.2% in women for QRISK3 and models using stably selected variables, respectively. Specificity was correspondingly higher in women than men. However, the sensitivity in women at 5% 10-year risk threshold increased to 50.1, 58.5, and 55.7% for PCE, QRISK3, and models using stably selected variables, respectively.

CONCLUSION

Use of sparse sex-specific variables improved CVD risk prediction compared with PCE but not QRISK3. At current risk thresholds, PCE and QRISK3 work less well for women than men, but sensitivity was improved in women using a 5% 10-year risk threshold. Use of sex-specific risk thresholds should be considered in any re-evaluation of CVD risk calculators.

PREVENT Equation: The Black Sheep among Cardiovascular Risk Scores? A Comparative Agreement Analysis of Nine Prediction Models in High-Risk Lithuanian Women

Background and Objectives: In the context of female cardiovascular risk categorization, we aimed to assess the inter-model agreement between nine risk prediction models (RPM): the novel Predicting Risk of cardiovascular disease EVENTs (PREVENT) equation, assessing cardiovascular risk using SIGN, the Australian CVD risk score, the Framingham Risk Score for Hard Coronary Heart Disease (FRS-hCHD), the Multi-Ethnic Study of Atherosclerosis risk score, the Pooled Cohort Equation (PCE), the QRISK3 cardiovascular risk calculator, the Reynolds Risk Score, and Systematic Coronary Risk Evaluation-2 (SCORE2). Materials and Methods: A cross-sectional study was conducted on 6527 40-65-year-old women with diagnosed metabolic syndrome from a single tertiary university hospital in Lithuania. Cardiovascular risk was calculated using the nine RPMs, and the results were categorized into high-, intermediate-, and low-risk groups. Inter-model agreement was quantified using Cohen's Kappa coefficients. Results: The study uncovered a significant diversity in risk categorization, with agreement on risk category by all models in only 1.98% of cases. The SCORE2 model primarily classified subjects as high-risk (68.15%), whereas the FRS-hCHD designated the majority as low-risk (94.42%). The range of Cohen's Kappa coefficients (-0.09-0.64) reflects the spectrum of agreement between models. Notably, the PREVENT model demonstrated significant agreement with QRISK3 (κ = 0.55) and PCE (κ = 0.52) but was completely at odds with the SCORE2 (κ = -0.09). Conclusions: Cardiovascular RPM selection plays a pivotal role in influencing clinical decisions and managing patient care. The PREVENT model revealed balanced results, steering clear of the extremes seen in both SCORE2 and FRS-hCHD. The highest concordance was observed between the PREVENT model and both PCE and QRISK3 RPMs. Conversely, the SCORE2 model demonstrated consistently low or negative agreement with other models, highlighting its unique approach to risk categorization. These findings accentuate the need for additional research to assess the predictive accuracy of these models specifically among the Lithuanian female population.

Lifestyle Habits and Risk of Cardiovascular Mortality in Menopausal Women with Cardiovascular Risk Factors: A Retrospective Cohort Study

Current cardiovascular prevention guidelines emphasise considering sex, gender, and gender identity in risk assessment. This study evaluated the impact of lifestyle habits and chronic diseases on cardiovascular mortality risk in women over 50 with high vascular risk and developed a predictive model for menopausal women with cardiovascular risk factors. A retrospective cohort study used data from the 2011 Spanish National Health Survey and the national death register, focusing on menopausal and postmenopausal women without prior cardiovascular events but with at least one major risk factor. Participants were followed for up to 10 years, assessing mortality from circulatory system diseases and other causes. Exposure variables included socio-demographics, lifestyle habits, health status, self-perceived health, health service use, and pharmacological treatments. Of the 21,007 respondents, 3057 women met the inclusion criteria. The 10-year cumulative incidence of mortality from circulatory causes was 5.9%, and from other causes, 12.7%. Independent predictors of cardiovascular mortality were never consuming legumes, poor self-perceived health, diabetes treatment, lack of physical activity, and older age. Lipid-lowering treatment was protective. The model demonstrated good fit and predictive capacity (C-index = 0.773). This study highlights the significant influence of physical activity, legume consumption, self-perceived health, and specific treatments on cardiovascular mortality risk in menopausal women.

Research inefficiencies in external validation studies of the Framingham Wilson coronary heart disease risk rule: A systematic review

BACKGROUND

External validation studies create evidence about a clinical prediction rule's (CPR's) generalizability by evaluating and updating the CPR in populations different from those used in the derivation, and also by contributing to estimating its overall performance when meta-analysed in a systematic review. While most cardiovascular CPRs do not have any external validation, some CPRs have been externally validated repeatedly. Hence, we examined whether external validation studies of the Framingham Wilson coronary heart disease (CHD) risk rule contributed to generating evidence to their full potential.

METHODS

A forward citation search of the Framingham Wilson CHD risk rule's derivation study was conducted to identify studies that evaluated the Framingham Wilson CHD risk rule in different populations. For external validation studies of the Framingham Wilson CHD risk rule, we examined whether authors updated the Framingham Wilson CHD risk rule when it performed poorly. We also assessed the contribution of external validation studies to understanding the Predicted/Observed (P/O) event ratio and c statistic of the Framingham Wilson CHD risk rule.

RESULTS

We identified 98 studies that evaluated the Framingham Wilson CHD risk rule; 40 of which were external validation studies. Of these 40 studies, 27 (67.5%) concluded the Framingham Wilson CHD risk rule performed poorly but did not update it. Of 23 external validation studies conducted with data that could be included in meta-analyses, 13 (56.5%) could not fully contribute to the meta-analyses of P/O ratio and/or c statistic because these performance measures were neither reported nor could be calculated from provided data.

DISCUSSION

Most external validation studies failed to generate evidence about the Framingham Wilson CHD risk rule's generalizability to their full potential. Researchers might increase the value of external validation studies by presenting all relevant performance measures and by updating the CPR when it performs poorly.

The distribution of cardiovascular diseases in Tanzania: a spatio-temporal investigation

Cardiovascular Disease (CVD) is currently the major challenge to people's health and the world's top cause of death. In Tanzania, deaths due to CVD account for about 13% of the total deaths caused by the non-communicable diseases. This study examined the spatio-temporal clustering of CVDs from 2010 to 2019 in Tanzania for retrospective spatio-temporal analysis using the Bernoulli probability model on data sampled from four selected hospitals. Spatial scan statistics was performed to identify CVD clusters and the effect of covariates on the CVD incidences was examined using multiple logistic regression. It was found that there was a comparatively high risk of CVD during 2011-2015 followed by a decline during 2015-2019. The spatio-temporal analysis detected two high-risk disease clusters in the coastal and lake zones from 2012 to 2016 (p<0.001), with similar results produced by purely spatial analysis. The multiple logistic model showed that sex, age, blood pressure, body mass index (BMI), alcohol intake and smoking were significant predictors of CVD incidence.

Future AI Will Most Likely Predict Antibody-Drug Conjugate Response in Oncology: A Review and Expert Opinion

The medical research field has been tremendously galvanized to improve the prediction of therapy efficacy by the revolution in artificial intelligence (AI). An earnest desire to find better ways to predict the effectiveness of therapy with the use of AI has propelled the evolution of new models in which it can become more applicable in clinical settings such as breast cancer detection. However, in some instances, the U.S. Food and Drug Administration was obliged to back some previously approved inaccurate models for AI-based prognostic models because they eventually produce inaccurate prognoses for specific patients who might be at risk of heart failure. In light of instances in which the medical research community has often evolved some unrealistic expectations regarding the advances in AI and its potential use for medical purposes, implementing standard procedures for AI-based cancer models is critical. Specifically, models would have to meet some general parameters for standardization, transparency of their logistic modules, and avoidance of algorithm biases. In this review, we summarize the current knowledge about AI-based prognostic methods and describe how they may be used in the future for predicting antibody-drug conjugate efficacy in cancer patients. We also summarize the findings of recent late-phase clinical trials using these conjugates for cancer therapy.

Debate: CAMHS will be at the forefront of the next generation of psychosis risk models, but further integration with early intervention psychosis services is needed to realise this potential: Re Debate: Prevention of psychosis in adolescents - does CAMHS have a role?

The detection of psychosis and its prodrome have unique considerations in a child and adolescent population. Young people attending CAMHS are already a high-risk group, which confers significant limitations in applying the current clinical high-risk (CHR) model. This has catalysed calls for a transdiagnostic approach to psychosis risk prediction, but without a clear pathway forward. We contribute to the debate opened by Salazar de Pablo and Arango (2023, Child and Adolescent Mental Health) on the role of CAMHS in this initiative. CAMHS have a key role in developing comprehensive longitudinal datasets to inform risk models. Closer integration with early intervention in psychosis (EIP) services will be needed to realise this potential. This integration is also required to reliably detect prodromes and emerging psychosis in young people. Where there is robust evidence to support prevention initiatives, we should proceed with their implementation, even in the absence of enhanced risk models.

Analysis of hyperlipidemia risk factors among pilots based on physical examination data: A study using a multilevel propensity score models

Pilot tends to have a high prevalence of dyslipidemia. The present study aimed to identify key factors of pilot hyperlipidemia through thorough analysis of physical examination data, and to provide pilot-targeted health guidance to manage hyperlipidemia risks. The physical examination data of 1,253 pilot inpatients from January 2019 to June 2022, were evaluated and divided into two groups based on whether or not the pilot had hyperlipidemia. A total of three multivariate analysis models including logistic model, multilevel model and boosting propensity score were applied to find the risk factors of pilot hyperlipidemia. In the group of pilots with hyperlipidemia, four risk factors, including thrombin time, carbohydrate antigen 199, lymphocyte count and rheumatoid factor, were significantly different from pilots without hyperlipidemia, which might be positively associated with the incidence of hyperlipidemia. In future studies regarding pilots, whether hyperlipidemia is connected to abnormalities in thrombin time, carbohydrate antigen 199 and rheumatoid factor should be further explored. Based on the findings of the present study, pilot health management should be more refined and personalized, and attention should be paid to the risk factors of hyperlipidemia including diet and lifestyle.

Predicted Heart Age and Life's Essential 8 Among U.S. Adults: NHANES 2015-March 2020

INTRODUCTION

This study examined the association between American Heart Association's (AHA) cardiovascular health (CVH) metrics, Life's Essential 8 (LE8), and predicted heart age among U.S. adults.

METHODS

The sample comprised 7,075 participants aged 30-74 years without CVD and/or stroke from the National Health and Nutrition Examination Survey (NHANES) 2015-March 2020. LE8 was measured according to AHA's metrics (overall score ranging from 0 to 100 points), and nonlaboratory-based Framingham Risk Score was used to estimate predicted heart age. Analyses were completed in June 2024.

RESULTS

Median LE8 scores were 62.8 for men and 66.0 for women. Over 80% of participants had less than optimal CVH scores, affecting 141.5 million people and 1-in-6 participants had a low CVH score, impacting 30.0 million people. Mean predicted heart age and excess heart age (EHA, difference between actual and predicted heart age) were 56.6 (95% CI 56.1-57.1) and 8.6 (8.1-9.1) years for men and 54.0 (53.4-54.7) and 5.9 (5.2-6.5) years for women. Participants in the low CVH group (scores<50), had an EHA that was 20.7 years higher than those in the high CVH group (score 80-100). Compared to the high CVH group, participants in low CVH group had 15 times (for men) and 44 times (for women) higher risk of having EHA ≥10 years. The pattern of differences in predicted heart age, EHA, and prevalence of EHA ≥10 years by LE8 groups remained largely consistent across subpopulations.

CONCLUSIONS

These findings highlight the importance of maintaining a healthy lifestyle to improve cardiovascular health and reduce excess heart age.

Artificial intelligence in cardiovascular medicine: clinical applications

Clinical medicine requires the integration of various forms of patient data including demographics, symptom characteristics, electrocardiogram findings, laboratory values, biomarker levels, and imaging studies. Decision-making on the optimal management should be based on a high probability that the envisaged treatment is appropriate, provides benefit, and bears no or little potential harm. To that end, personalized risk-benefit considerations should guide the management of individual patients to achieve optimal results. These basic clinical tasks have become more and more challenging with the massively growing data now available; artificial intelligence and machine learning (AI/ML) can provide assistance for clinicians by obtaining and comprehensively preparing the history of patients, analysing face and voice and other clinical features, by integrating laboratory results, biomarkers, and imaging. Furthermore, AI/ML can provide a comprehensive risk assessment as a basis of optimal acute and chronic care. The clinical usefulness of AI/ML algorithms should be carefully assessed, validated with confirmation datasets before clinical use, and repeatedly re-evaluated as patient phenotypes change. This review provides an overview of the current data revolution that has changed and will continue to change the face of clinical medicine radically, if properly used, to the benefit of physicians and patients alike.

Comparison of Patients Classified as High-Risk between International Cardiovascular Disease Primary Prevention Guidelines

Background: Cardiovascular disease (CVD) primary prevention guidelines classify people at high risk and recommended for pharmacological treatment based on clinical criteria and absolute CVD risk estimation. Despite relying on similar evidence, recommendations vary between international guidelines, which may impact who is recommended to receive treatment for CVD prevention. Objective: To determine the agreement in treatment recommendations according to guidelines from Australia, England and the United States. Methods: Cross-sectional analysis of the National Health and Nutrition Examination Survey (n = 2647). Adults ≥ 40 years were classified as high-risk and recommended for treatment according to Australia, England and United States CVD prevention guidelines. Agreement in high-risk classification and recommendation for treatment was assessed by Kappa statistic. Results: Participants were middle aged, 49% were male and 38% were white. The proportion recommended for treatment was highest using the United States guidelines (n = 1318, 49.8%) followed by the English guidelines (n = 1276, 48.2%). In comparison, only 26.6% (n = 705) of participants were classified as recommended for treatment according to the Australian guidelines. There was moderate agreement in the recommendation for treatment between the English and United States guidelines (κ = 0.69 [0.64-0.74]). In comparison, agreement in recommendation for treatment was minimal between the Australian and United States guidelines (κ = 0.47 [0.43-0.52]) and weak between the Australian and English guidelines (κ = 0.50 [0.45-0.55]). Conclusions: Despite similar evidence underpinning guidelines, there is little agreement between guidelines regarding the people recommended to receive treatment for CVD prevention. These findings suggest greater consistency in high-risk classification between CVD prevention guidelines may be required.

External validation and comparison of six cardiovascular risk prediction models in the Prospective Urban Rural Epidemiology (PURE)-Colombia study

AIMS

To external validate the SCORE2, AHA/ACC Pooled Cohort Equation (PCE), Framingham Risk Score (FRS), Non-Laboratory INTERHEART Risk Score (NL-IHRS), Globorisk-LAC, and WHO prediction models and compare their discrimination and calibration capacity.

METHODS

Validation in individuals aged 40-69 years with at least 10 years follow-up and without baseline use of statins or cardiovascular diseases from the Prospective Urban Rural Epidemiology prospective cohort study (PURE)-Colombia. For discrimination, the C-statistic, and Receiver Operating Characteristic curves with the integrated area under the curve (AUCi) were used and compared. For calibration, the smoothed time-to-event method was used, choosing a recalibration factor based on the integrated calibration index (ICI). In the NL-IHRS, linear regressions were used.

RESULTS

In 3,802 participants (59.1% women), baseline risk ranged from 4.8% (SCORE2 women) to 55.7% (NL-IHRS). After a mean follow-up of 13.2 years, 234 events were reported (4.8 cases per 1000 person-years). The C-statistic ranged between 0.637 (0.601-0.672) in NL-IHRS and 0.767 (0.657-0.877) in AHA/ACC PCE. Discrimination was similar between AUCi. In women, higher overprediction was observed in the Globorisk-LAC (61%) and WHO (59%). In men, higher overprediction was observed in FRS (72%) and AHA/ACC PCE (71%). Overestimations were corrected after multiplying by a factor derived from the ICI.

CONCLUSIONS

Six prediction models had a similar discrimination capacity, supporting their use after multiplying by a correction factor. If blood tests are unavailable, NL-IHRS is a reasonable option. Our results suggest that these models could be used in other countries of Latin America after correcting the overestimations with a multiplying factor.

Cost-effectiveness of applying high-sensitivity troponin I to a score for cardiovascular risk prediction in asymptomatic population

INTRODUCTION

Risk stratification scores such as the European Systematic COronary Risk Evaluation (SCORE) are used to guide individuals on cardiovascular disease (CVD) prevention. Adding high-sensitivity troponin I (hsTnI) to such risk scores has the potential to improve accuracy of CVD prediction. We investigated how applying hsTnI in addition to SCORE may impact management, outcome, and cost-effectiveness.

METHODS

Characteristics of 72,190 apparently healthy individuals from the Biomarker for Cardiovascular Risk Assessment in Europe (BiomarCaRE) project were included into a discrete-event simulation comparing two strategies for assessing CVD risk. The standard strategy reflecting current practice employed SCORE (SCORE); the alternative strategy involved adding hsTnI information for further stratifying SCORE risk categories (S-SCORE). Individuals were followed over ten years from baseline examination to CVD event, death or end of follow-up. The model tracked the occurrence of events and calculated direct costs of screening, prevention, and treatment from a European health system perspective. Cost-effectiveness was expressed as incremental cost-effectiveness ratio (ICER) in € per quality-adjusted life year (QALYs) gained during 10 years of follow-up. Outputs were validated against observed rates, and results were tested in deterministic and probabilistic sensitivity analyses.

RESULTS

S-SCORE yielded a change in management for 10.0% of individuals, and a reduction in CVD events (4.85% vs. 5.38%, p<0.001) and mortality (6.80% vs. 7.04%, p<0.001). S-SCORE led to 23 (95%CI: 20-26) additional event-free years and 7 (95%CI: 5-9) additional QALYs per 1,000 subjects screened, and resulted in a relative risk reduction for CVD of 9.9% (95%CI: 7.3-13.5%) with a number needed to screen to prevent one event of 183 (95%CI: 172 to 203). S-SCORE increased costs per subject by 187€ (95%CI: 177 € to 196 €), leading to an ICER of 27,440€/QALY gained. Sensitivity analysis was performed with eligibility for treatment being the most sensitive.

CONCLUSION

Adding a person's hsTnI value to SCORE can impact clinical decision making and eventually improves QALYs and is cost-effective compared to CVD prevention strategies using SCORE alone. Stratifying SCORE risk classes for hsTnI would likely offer cost-effective alternatives, particularly when targeting higher risk groups.

Construction and Validation of a Predictive Model for Coronary Artery Disease Using Extreme Gradient Boosting

Purpose

Early recognition of coronary artery disease (CAD) could delay its progress and significantly reduce mortality. Sensitive, specific, cost-efficient and non-invasive indicators for assessing individual CAD risk in community population screening are urgently needed.

Patients and Methods

3112 patients with CAD and 3182 controls were recruited from three clinical centers in China, and differences in baseline and clinical characteristics were compared. For the discovery cohort, the least absolute shrinkage and selection operator (LASSO) regression was used to identify significant features and four machine learning algorithms (logistic regression, support vector machine (SVM), random forest (RF) and extreme gradient boosting (XGBoost)) were applied to construct models for CAD risk assessment, the receiver operating characteristics (ROC) curve and precision-recall (PR) curve were conducted to evaluate their predictive accuracy. The optimal model was interpreted by Shapley additive explanations (SHAP) analysis and assessed by the ROC curve, calibration curve, and decision curve analysis (DCA) and validated by two external cohorts.

Results

Using LASSO filtration, all included variables were considered to be statistically significant. Four machine learning models were constructed based on these features and the results of ROC and PR curve implied that the XGBoost model exhibited the highest predictive performance, which yielded a high area of ROC curve (AUC) of 0.988 (95% CI: 0.986-0.991) to distinguish CAD patients from controls with a sensitivity of 94.6% and a specificity of 94.6%. The calibration curve showed that the predicted results were in good agreement with actual observations, and DCA exhibited a better net benefit across a wide range of threshold probabilities. External validation of the model also exhibited favorable discriminatory performance, with an AUC, sensitivity, and specificity of 0.953 (95% CI: 0.945-0.960), 89.9%, and 87.1% in the validation cohort, and 0.935 (95% CI: 0.915-0.955), 82.0%, and 90.3% in the replication cohort.

Conclusion

Our model is highly informative for clinical practice and will be conducive to primary prevention and tailoring the precise management for CAD patients.

Prediction model for cardiovascular disease in patients with diabetes using machine learning derived and validated in two independent Korean cohorts

This study aimed to develop and validate a machine learning (ML) model tailored to the Korean population with type 2 diabetes mellitus (T2DM) to provide a superior method for predicting the development of cardiovascular disease (CVD), a major chronic complication in these patients. We used data from two cohorts, namely the discovery (one hospital; n = 12,809) and validation (two hospitals; n = 2019) cohorts, recruited between 2008 and 2022. The outcome of interest was the presence or absence of CVD at 3 years. We selected various ML-based models with hyperparameter tuning in the discovery cohort and performed area under the receiver operating characteristic curve (AUROC) analysis in the validation cohort. CVD was observed in 1238 (10.2%) patients in the discovery cohort. The random forest (RF) model exhibited the best overall performance among the models, with an AUROC of 0.830 (95% confidence interval [CI] 0.818-0.842) in the discovery dataset and 0.722 (95% CI 0.660-0.783) in the validation dataset. Creatinine and glycated hemoglobin levels were the most influential factors in the RF model. This study introduces a pioneering ML-based model for predicting CVD in Korean patients with T2DM, outperforming existing prediction tools and providing a groundbreaking approach for early personalized preventive medicine.

Association between extremely high prognostic nutritional index and all-cause mortality in patients with coronary artery disease: secondary analysis of a prospective cohort study in China

OBJECTIVES

Decreased prognostic nutritional index (PNI) was associated with adverse outcomes in many clinical diseases. This study aimed to evaluate the relationship between baseline PNI value and adverse clinical outcomes in patients with coronary artery disease (CAD).

DESIGN

The Personalized Antiplatelet Therapy According to CYP2C19 Genotype in Coronary Artery Disease (PRACTICE) study, a prospective cohort study of 15 250 patients with CAD, was performed from December 2016 to October 2021. The longest follow-up period was 5 years. This study was a secondary analysis of the PRACTICE study.

SETTING

The study setting was Xinjiang Medical University Affiliated First Hospital in China.

PARTICIPANTS

Using the 50th and 90th percentiles of the PNI in the total cohort as two cut-off limits, we divided all participants into three groups: Q1 (PNI <51.35, n = 7515), Q2 (51.35 ≤ PNI < 59.80, n = 5958) and Q3 (PNI ≥ 59.80, n = 1510). The PNI value was calculated as 10 × serum albumin (g/dL) + 0.005 × total lymphocyte count (per mm3).

PRIMARY OUTCOME

The primary outcome measure was mortality, including all-cause mortality (ACM) and cardiac mortality (CM).

RESULTS

In 14 983 participants followed for a median of 24 months, a total of 448 ACM, 333 CM, 1162 major adverse cardiovascular events (MACE) and 1276 major adverse cardiovascular and cerebrovascular events (MACCE) were recorded. The incidence of adverse outcomes was significantly different among the three groups (p <0.001). There were 338 (4.5%), 77 (1.3%) and 33 (2.2%) ACM events in the three groups, respectively. A restricted cubic spline displayed a J-shaped relationship between the PNI and worse 5-year outcomes, including ACM, CM, MACE and MACCE. After adjusting for traditional cardiovascular risk factors, we found that only patients with extremely high PNI values in the Q3 subgroup or low PNI values in the Q1 subgroup had a greater risk of ACM (Q3 vs Q2, HR: 1.617, 95% CI 1.012 to 2.585, p=0.045; Q1 vs Q2, HR=1.995, 95% CI 1.532 to 2.598, p <0.001).

CONCLUSION

This study revealed a J-shaped relationship between the baseline PNI and ACM in patients with CAD, with a greater risk of ACM at extremely high PNI values.

TRIAL REGISTRATION NUMBER

NCT05174143.

Changes in muscle strength and risk of cardiovascular disease among middle-aged and older adults in China: Evidence from a prospective cohort study

BACKGROUND

Evidence indicates that low muscle strength is associated with an increased cardiovascular diseases (CVDs) risk. However, the association between muscle strength changes based on repeated measurements and CVD incidence remains unclear.

METHODS

The study used data from the China Health and Retirement Longitudinal Study in 2011 (Wave 1), 2013 (Wave 2), 2015 (Wave 3), and 2018 (Wave 4). Low muscle strength was defined as handgrip strength <28 kg for men or <18 kg for women, or chair-rising time ≥12 s. Based on changes in muscle strength from Waves 1 to 2, participants were categorized into four groups of Normal-Normal, Low-Normal, Normal-Low, and Low-Low. CVD events, including heart disease and stroke, were recorded using a self-reported questionnaire during Waves 3 and 4 visits. Cox proportional hazards models were used to investigate the association between muscle strength changes and CVD incidence after multivariable adjustments. Hazard ratios (HRs) and 95% confidence intervals (95% CIs) were estimated with the Normal-Normal group as the reference.

RESULTS

A total of 1164 CVD cases were identified among 6608 participants. Compared to participants with sustained normal muscle strength, the CVD risks increased progressively across groups of the Low-Normal (HR = 1.20, 95% CI: 1.01-1.43), the Normal-Low (HR = 1.35, 95% CI: 1.14-1.60), and the Low-Low (HR = 1.76, 95% CI: 1.49-2.07). Similar patterns were observed for the significant associations between muscle strength status and the incidence risks of heart disease and stroke. Subgroup analyses showed that the significant associations between CVD and muscle strength changes were consistent across age, sex, and body mass index (BMI) categories.

CONCLUSIONS

The study found that muscle strength changes were associated with CVD risk. This suggests that continuous tracking of muscle status may be helpful in screening cardiovascular risk.

The potential benefit of statin prescription based on prediction of treatment responsiveness in older individuals: an application to the PROSPER randomized controlled trial

AIMS

Clinical guidelines often recommend treating individuals based on their cardiovascular risk. We revisit this paradigm and quantify the efficacy of three treatment strategies: (i) overall prescription, i.e. treatment to all individuals sharing the eligibility criteria of a trial; (ii) risk-stratified prescription, i.e. treatment only to those at an elevated outcome risk; and (iii) prescription based on predicted treatment responsiveness.

METHODS AND RESULTS

We reanalysed the PROSPER randomized controlled trial, which included individuals aged 70-82 years with a history of, or risk factors for, vascular diseases. We conducted the derivation and internal-external validation of a model predicting treatment responsiveness. We compared with placebo (n = 2913): (i) pravastatin (n = 2891); (ii) pravastatin in the presence of previous vascular diseases and placebo in the absence thereof (n = 2925); and (iii) pravastatin in the presence of a favourable prediction of treatment response and placebo in the absence thereof (n = 2890). We found an absolute difference in primary outcome events composed of coronary death, non-fatal myocardial infarction, and fatal or non-fatal stroke, per 10 000 person-years equal to: -78 events (95% CI, -144 to -12) when prescribing pravastatin to all participants; -66 events (95% CI, -114 to -18) when treating only individuals with an elevated vascular risk; and -103 events (95% CI, -162 to -44) when restricting pravastatin to individuals with a favourable prediction of treatment response.

CONCLUSION

Pravastatin prescription based on predicted responsiveness may have an encouraging potential for cardiovascular prevention. Further external validation of our results and clinical experiments are needed.

TRIAL REGISTRATION

ISRCTN40976937.

Establishment and validation of prediction model for atherosclerotic cardiovascular disease in patients with hyperuricemia

OBJECTIVE

To construct a risk prediction model for atherosclerotic cardiovascular disease (ASCVD) in patients with hyperuricemia.

METHODS

Data in this study were obtained from the National Health and Nutrition Examination Survey (NHANES) (2007-2010). Participants from Huashan Hospital were included as an external validation. Logistic regression analysis was used to explore the relevant factors of ASCVD in patients with hyperuricemia. The discriminability of the model was evaluated using the area under the curve (AUC) statistic of the receiver operating characteristic curve. Hosmer-Lemeshow test, correction curve and decision curve analysis (DCA) were used to evaluate the model.

RESULTS

A total of 389 patients collected from the NHANES were included in the final analysis. Logistic regression analysis showed that age, creatinine (Cr), glucose (Glu), serum uric acid (SUA), and history of gout were predictive factors for ASCVD in hyperuricemia (HUA) patients. These predictive factors were used to construct a nomogram. And 157 patients from NHANES were in the internal validation group and 136 patients from Huashan Hospital were in the external validation group. The AUC values of the three groups were 0.943, 0.735, and 0.664. The p values of the Hosmer-Lemeshow test were .568, .600, and .763. The calibration curve showed consistency between the nomogram and the actual observed values. The DCA curve indicated that the model has good clinical practicality.

CONCLUSION

This study constructed the ASCVD risk prediction model for HUA patients, which is beneficial for medical staff to detect high-risk populations of ASCVD in the early stage.

Association between atherosclerotic cardiovascular disease score and skin carotenoid levels estimated via refraction spectroscopy in the Japanese population: a cross-sectional study

Carotenoids play a role in preventing and impeding the progression of atherosclerotic cardiovascular diseases (ASCVDs) through their anti-oxidative effects. This study evaluated associations between ASCVD risk and skin carotenoid (SC) levels, reflecting dietary carotenoid intake. Participants' ASCVD risk was assessed using the Hisayama ASCVD risk prediction model, and SC levels were measured through a reflection spectroscope (Veggie Meter). The associations between high ASCVD risk and SC levels were analyzed using logistic regression analysis and a restricted cubic spline (RCS) model. A total of 1130 men and women (mean age: 56 years) from participants who underwent a health examination in Seirei Center for Health Promotion and Prevention Medicine in 2019 and 2022 were analyzed. Of these, 4.6% had moderate or high ASCVD risk. Mean SC values were 236, 315, 376, 447, and 606 in quintile Q1 to Q5, respectively. The adjusted odds ratios (95% confidence intervals) of SC quintile for moderate- or high-risk ASCVD was 0.24 (0.12-0.51) in Q5 (495 ≤), 0.42 (0.23-0.77) in Q4, 0.50 (0.29-0.88) in Q3, and 0.68 (0.41-1.12) in Q2 compared to Q1 (< 281). High SC values continuously showed non-linear inverse association with moderate- or high-risk for ASCVD in Japanese adults. Non-invasive SC measurements may be a good indicator for recommending carotenoids to prevent cardiovascular disease.

A dynamic probabilistic model of the onset and interaction of cardio-metabolic comorbidities on an ageing adult population

Comorbidity is widespread in the ageing population, implying multiple and complex medical needs for individuals and a public health burden. Determining risk factors and predicting comorbidity development can help identify at-risk subjects and design prevention strategies. Using socio-demographic and clinical data from approximately 11,000 subjects monitored over 11 years in the English Longitudinal Study of Ageing, we develop a dynamic Bayesian network (DBN) to model the onset and interaction of three cardio-metabolic comorbidities, namely type 2 diabetes (T2D), hypertension, and heart problems. The DBN allows us to identify risk factors for developing each morbidity, simulate ageing progression over time, and stratify the population based on the risk of outcome occurrence. By applying hierarchical agglomerative clustering to the simulated, dynamic risk of experiencing morbidities, we identified patients with similar risk patterns and the variables contributing to their discrimination. The network reveals a direct joint effect of biomarkers and lifestyle on outcomes over time, such as the impact of fasting glucose, HbA1c, and BMI on T2D development. Mediated cross-relationships between comorbidities also emerge, showcasing the interconnected nature of these health issues. The model presents good calibration and discrimination ability, particularly in predicting the onset of T2D (iAUC-ROC = 0.828, iAUC-PR = 0.294) and survival (iAUC-ROC = 0.827, iAUC-PR = 0.311). Stratification analysis unveils two distinct clusters for all comorbidities, effectively discriminated by variables like HbA1c for T2D and age at baseline for heart problems. The developed DBN constitutes an effective, highly-explainable predictive risk tool for simulating and stratifying the dynamic risk of developing cardio-metabolic comorbidities. Its use could help identify the effects of risk factors and develop health policies that prevent the occurrence of comorbidities.

A meta-analysis of diabetes risk prediction models applied to prediabetes screening

AIM

To provide a systematic overview of diabetes risk prediction models used for prediabetes screening to promote primary prevention of diabetes.

METHODS

The Cochrane, PubMed, Embase, Web of Science and China National Knowledge Infrastructure (CNKI) databases were searched for a comprehensive search period of 30 August 30, 2023, and studies involving diabetes prediction models for screening prediabetes risk were included in the search. The Quality Assessment Checklist for Diagnostic Studies (QUADAS-2) tool was used for risk of bias assessment and Stata and R software were used to pool model effect sizes.

RESULTS

A total of 29 375 articles were screened, and finally 20 models from 24 studies were included in the systematic review. The most common predictors were age, body mass index, family history of diabetes, history of hypertension, and physical activity. Regarding the indicators of model prediction performance, discrimination and calibration were only reported in 79.2% and 4.2% of studies, respectively, resulting in significant heterogeneity in model prediction results, which may be related to differences between model predictor combinations and lack of important methodological information.

CONCLUSIONS

Numerous models are used to predict diabetes, and as there is an association between prediabetes and diabetes, researchers have also used such models for screening the prediabetic population. Although it is a new clinical practice to explore, differences in glycaemic metabolic profiles, potential complications, and methods of intervention between the two populations cannot be ignored, and such differences have led to poor validity and accuracy of the models. Therefore, there is no recommended optimal model, and it is not recommended to use existing models for risk identification in alternative populations; future studies should focus on improving the clinical relevance and predictive performance of existing models.

Comparison of prediction models for cardiovascular and mortality risk in people with type 2 diabetes: An external validation in 23 685 adults included in the UK Biobank

AIMS

To validate cardiovascular risk prediction models for individuals with diabetes using the UK Biobank in order to assess their applicability.

METHODS

We externally validated 19 cardiovascular risk scores from seven risk prediction models (Chang et al., Framingham, University of Hong Kong-Singapore [HKU-SG], Li et al, RECODe [risk equations for complications of type 2 diabetes], SCORE [Systematic Coronary Risk Evaluation] and the UK Prospective Diabetes Study Outcomes Model 2 [UKPDS OM2]), identified from systematic reviews, using UK Biobank data from 2006 to 2021 (n = 23 685; participant age 40-71 years, 63.5% male). We evaluated performance by assessing the discrimination and calibration of the models for the endpoints of mortality, cardiovascular mortality, congestive heart failure, myocardial infarction, stroke, and ischaemic heart disease.

RESULTS

Over a total of 269 430 person-years of follow-up (median 11.89 years), the models showed low-to-moderate discrimination performance on external validation (concordance indices [c-indices] 0.50-0.71). Most models had low calibration with overprediction of the observed risk. RECODe outperformed other models across four comparable endpoints for discrimination: all-cause mortality (c-index 0.67, 95% confidence interval [CI] 0.65-0.69), congestive heart failure (c-index 0.71, 95% CI 0.69-0.72), myocardial infarction (c-index 0.67, 95% CI 0.65-0.68); and stroke (c-index 0.65, 95% CI 0.62-0.68), and for calibration (except for all-cause mortality). The UKPDS OM2 had comparable performance to RECODe for all-cause mortality (c-index 0.67, 95% CI 0.66-0.69) and cardiovascular mortality (c-index 0.71, 95% CI 0.70-0.73), but worse performance for other outcomes. The models performed better for younger participants and somewhat better for non-White ethnicities. Models developed from non-Western datasets showed worse performance in our UK-based validation set.

CONCLUSIONS

The RECODe model led to better risk estimations in this predominantly White European population. Further validation is needed in non-Western populations to assess generalizability to other populations.

Age-dependent association of central blood pressure with cardiovascular outcomes: a cohort study involving 34 289 participants using the UK biobank

BACKGROUND

It remained unclear whether central blood pressures (BP) was more closely associated with cardiovascular disease (CVD) than brachial BP in different age groups.

OBJECTIVES

To investigate the age-stratified association of CVD with brachial and central BPs, and to evaluate corresponding improvement in model performance.

METHODS

This cohort study included 34 289 adults without baseline CVD from the UK Biobank dataset. Participants were categorized into middle-aged and older aged groups using the cut-off of age 65 years. The primary endpoint was a composite cardiovascular outcome consisting of cardiovascular mortality combined with nonfatal coronary events, heart failure and stroke. Multivariable-adjusted hazard ratios expressed CVD risks associated with BP increments of 10 mmHg. Akaike Information Criteria (AIC) was used for model comparisons.

RESULTS

In both groups, CVD events were associated with brachial or central SBP ( P  ≤ 0.002). Model fit was better for central SBP in middle-aged adults (AIC 4427.2 vs. 4429.5), but model fit was better for brachial SBP in older adults (AIC 10 246.7 vs. 10 247.1). Central SBP remained significantly associated to CVD events [hazard ratio = 1.05; 95% confidence interval (CI) 1.0-1.1] and improved model fit (AIC = 4426.6) after adjustment of brachial SBP only in the middle-aged adults. These results were consistent for pulse pressure (PP).

CONCLUSION

In middle-aged adults, higher central BPs were associated with greater risks of CVD events, even after adjusting for brachial BP indexes. For older adults, the superiority of central BP was not observed. Additional trials with adequate follow-up time will confirm the role of central BP in estimating CVD risk for middle-aged individuals.

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

Aims

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.

Methods and results

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.

Conclusion

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.

Validation of the European SCORE2 models in a Canadian primary care cohort

AIMS

Systematic Coronary Risk Evaluation Model 2 (SCORE2) was recently developed to predict atherosclerotic cardiovascular disease (ASCVD) in Europe. Whether these models could be used outside of Europe is not known. The objective of this study was to test the validity of SCORE2 in a large Canadian cohort.

METHODS AND RESULTS

A primary care cohort of persons with routinely collected electronic medical record data from 1 January 2010 to 31 December 2014, in Ontario, Canada, was used for validation. The SCORE2 models for younger persons (YP) were applied to 57 409 individuals aged 40-69 while the models for older persons (OPs) were applied to 9885 individuals 70-89 years of age. Five-year ASCVD predictions from both the uncalibrated and low-risk region recalibrated SCORE2 models were evaluated. The C-statistic for SCORE2-YP was 0.74 in women and 0.69 in men. The uncalibrated SCORE2-YP overestimated risk by 17% in women and underestimated by 2% in men. In contrast, the low-risk region recalibrated model demonstrated worse calibration, overestimating risk by 100% in women and 36% in men. The C-statistic for SCORE2-OP was 0.64 and 0.62 in older women and men, respectively. The uncalibrated SCORE2-OP overestimated risk by more than 100% in both sexes. The low-risk region recalibrated model demonstrated improved calibration but still overestimated risk by 60% in women and 13% in men.

CONCLUSION

The performance of SCORE2 to predict ASCVD risk in Canada varied by age group and depended on whether regional calibration was applied. This underscores the necessity for validation assessment of SCORE2 prior to implementation in new jurisdictions.

Comparison of the performance of cardiovascular risk prediction tools in rural India: the Rishi Valley Prospective Cohort Study

AIMS

We compared the performance of cardiovascular risk prediction tools in rural India.

METHODS AND RESULTS

We applied the World Health Organization Risk Score (WHO-RS) tools, Australian Risk Score (ARS), and Global risk (Globorisk) prediction tools to participants aged 40-74 years, without prior cardiovascular disease, in the Rishi Valley Prospective Cohort Study, Andhra Pradesh, India. Cardiovascular events during the 5-year follow-up period were identified by verbal autopsy (fatal events) or self-report (non-fatal events). The predictive performance of each tool was assessed by discrimination and calibration. Sensitivity and specificity of each tool for identifying high-risk individuals were assessed using a risk score cut-off of 10% alone or this 10% cut-off plus clinical risk criteria of diabetes in those aged >60 years, high blood pressure, or high cholesterol. Among 2333 participants (10 731 person-years of follow-up), 102 participants developed a cardiovascular event. The 5-year observed risk was 4.4% (95% confidence interval: 3.6-5.3). The WHO-RS tools underestimated cardiovascular risk but the ARS overestimated risk, particularly in men. Both the laboratory-based (C-statistic: 0.68 and χ2: 26.5, P = 0.003) and non-laboratory-based (C-statistic: 0.69 and χ2: 20.29, P = 0.003) Globorisk tools showed relatively good discrimination and agreement. Addition of clinical criteria to a 10% risk score cut-off improved the diagnostic accuracy of all tools.

CONCLUSION

Cardiovascular risk prediction tools performed disparately in a setting of disadvantage in rural India, with the Globorisk performing best. Addition of clinical criteria to a 10% risk score cut-off aids assessment of risk of a cardiovascular event in rural India.

LAY SUMMARY

In a cohort of people without prior cardiovascular disease, tools used to predict the risk of cardiovascular events varied widely in their ability to accurately predict who would develop a cardiovascular event.The Globorisk, and to a lesser extent the ARS, tools could be appropriate for this setting in rural India.Adding clinical criteria, such as sustained high blood pressure, to a cut-off of 10% risk of a cardiovascular event within 5 years could improve identification of individuals who should be monitored closely and provided with appropriate preventive medications.

The global burden and risk factors of cardiovascular diseases in adolescent and young adults, 1990-2019

BACKGROUND

To provide details of the burden and the trend of the cardiovascular disease (CVD) and its risk factors in adolescent and young adults.

METHODS

Age-standardized rates (ASRs) of incidence, mortality and Disability-Adjusted Life Years (DALYs) were used to describe the burden of CVD in adolescents and young adults. Estimated Annual Percentage Changes (EAPCs) of ASRs were used to describe the trend from 1990 to 2019. Risk factors were calculated by Population Attributable Fractions (PAFs).

RESULTS

In 2019, the age-standardized incidence rate (ASIR), age-standardized mortality rate (ASMR) and age-standardized DALYs rate (ASDR) of CVD were 129.85 per 100 000 (95% Confidence interval (CI): 102.60, 160.31), 15.12 per 100 000 (95% CI: 13.89, 16.48) and 990.64 per 100 000 (95% CI: 911.06, 1076.46). The highest ASRs were seen in low sociodemographic index (SDI) and low-middle SDI regions. The burden was heavier in male and individuals aged 35-39. From 1990 to 2019, 72 (35.29%) countries showed an increasing trend of ASIR and more than 80% countries showed a downward trend in ASMR and ASDR. Rheumatic heart disease had the highest ASIR and Ischemic Heart Disease was the highest in both ASMR and ASDR. The main attributable risk factor for death and DALYs were high systolic blood pressure, high body-mass index and high LDL cholesterol.

CONCLUSIONS

The burden of CVD in adolescent and young adults is a significant global health challenge. It is crucial to take into account the disparities in SDI levels among countries, gender and age characteristics of the population, primary types of CVD, and the attributable risk factors when formulating and implementing prevention strategies.

Preeclampsia-Associated Cardiovascular Risk Factors 6 Months and 2 Years After Pregnancy: The P4 Study

BACKGROUND

Increased cardiovascular risk following preeclampsia is well established and there are signs of early cardiovascular aging 6 months postpartum. This study assessed whether blood pressure (BP) and other cardiovascular measures are abnormal 2 years postpartum in the same cohort to determine ongoing risk markers.

METHODS

Six months and 2 years postpartum, BP was measured using sphygmomanometry, 24-hour ambulatory BP monitoring, and noninvasive central BP. Anthropometric measures, blood, and urine biochemistry were performed. Cross-sectional comparisons between preeclampsia and normotensive pregnancy (NP) groups and longitudinal comparisons within each group were made at 6 months and 2 years.

RESULTS

Two years postpartum, 129 NP, and 52 preeclampsia women were studied who also had 6 months measures. At both time points, preeclampsia group had significantly higher BP (office BP 2 years, 112±12/72±8 versus 104±9/67±7 mm Hg NP; [P<0.001]; mean ambulatory BP monitoring 116±9/73±8 versus 106±8/67±6 mm Hg NP; [P<0.001]). No significant BP changes noted 6 months to 2 years within either group. Office BP thresholds of 140 mm Hg systolic and 90 mm Hg diastolic classified 2% preeclampsia and 0% NP at 2 years. American Heart Association 2017 criteria (above normal, >120/80 mm Hg) classified 25% versus 8% (P<0.002), as did our reference range threshold of 122/79 mm Hg. American Heart Association criteria classified 60% post-preeclampsia versus 16% after NP with above-normal ambulatory BP monitoring (P<0.001). Other cardiovascular risk markers more common 2 years post-preeclampsia included higher body mass index (median 26.6 versus 23.1, P=0.003) and insulin resistance.

CONCLUSIONS

After preeclampsia, women have significantly higher BP 6 months and 2 years postpartum, and have higher body mass index and insulin-resistance scores, increasing their future cardiovascular risk. Regular cardiovascular risk screening should be implemented for all who have experienced preeclampsia.

Navigating the Landscape of Cardiovascular Risk Scores: A Comparative Analysis of Eight Risk Prediction Models in a High-Risk Cohort in Lithuania

Background: Numerous cardiovascular risk prediction models (RPM) have been developed, however, agreement studies between these models are scarce. We aimed to assess the inter-model agreement between eight RPMs: assessing cardiovascular risk using SIGN, the Australian CVD risk score (AusCVDRisk), the Framingham Risk Score for Hard Coronary Heart Disease, the Multi-Ethnic Study of Atherosclerosis risk score, the Pooled Cohort Equation (PCE), the QRISK3 cardiovascular risk calculator, the Reynolds Risk Score, and Systematic Coronary Risk Evaluation-2 (SCORE2). Methods: A cross-sectional study was conducted on 11,174 40-65-year-old individuals with diagnosed metabolic syndrome from a single tertiary university hospital in Lithuania. Cardiovascular risk was calculated using the eight RPMs, and the results were categorized into high, intermediate, and low-risk groups. Inter-model agreement was quantified using Cohen's Kappa coefficients. Results: The study revealed significant heterogeneity in risk categorizations with only 1.49% of cases where all models agree on the risk category. SCORE2 predominantly categorized participants as high-risk (67.39%), while the PCE identified the majority as low-risk (62.03%). Cohen's Kappa coefficients ranged from -0.09 to 0.64, indicating varying degrees of inter-model agreement. Conclusions: The choice of RPM can substantially influence clinical decision-making and patient management. The PCE and AusCVDRisk models exhibited the highest degree of agreement while the SCORE2 model consistently exhibited low agreement with other models.

Risk of cardiovascular death in patients with hepatocellular carcinoma based on the Fine-Gray model

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the most common types of cancers worldwide, ranking fifth among men and seventh among women, resulting in more than 7 million deaths annually. With the development of medical technology, the 5-year survival rate of HCC patients can be increased to 70%. However, HCC patients are often at increased risk of cardiovascular disease (CVD) death due to exposure to potentially cardiotoxic treatments compared with non-HCC patients. Moreover, CVD and cancer have become major disease burdens worldwide. Thus, further research is needed to lessen the risk of CVD death in HCC patient survivors.

AIM

To determine the independent risk factors for CVD death in HCC patients and predict cardiovascular mortality (CVM) in HCC patients.

METHODS

This study was conducted on the basis of the Surveillance, Epidemiology, and End Results database and included HCC patients with a diagnosis period from 2010 to 2015. The independent risk factors were identified using the Fine-Gray model. A nomograph was constructed to predict the CVM in HCC patients. The nomograph performance was measured using Harrell's concordance index (C-index), calibration curve, receiver operating characteristic (ROC) curve, and area under the ROC curve (AUC) value. Moreover, the net benefit was estimated via decision curve analysis (DCA).

RESULTS

The study included 21545 HCC patients, of whom 619 died of CVD. Age (< 60) [1.981 (1.573-2.496), P < 0.001], marital status (married) [unmarried: 1.370 (1.076-1.745), P = 0.011], alpha fetoprotein (normal) [0.778 (0.640-0.946), P = 0.012], tumor size (≤ 2 cm) [(2, 5] cm: 1.420 (1.060-1.903), P = 0.019; > 5 cm: 2.090 (1.543-2.830), P < 0.001], surgery (no) [0.376 (0.297-0.476), P < 0.001], and chemotherapy(none/unknown) [0.578 (0.472-0.709), P < 0.001] were independent risk factors for CVD death in HCC patients. The discrimination and calibration of the nomograph were better. The C-index values for the training and validation sets were 0.736 and 0.665, respectively. The AUC values of the ROC curves at 2, 4, and 6 years were 0.702, 0.725, 0.740 in the training set and 0.697, 0.710, 0.744 in the validation set, respectively. The calibration curves showed that the predicted probabilities of the CVM prediction model in the training set vs the validation set were largely consistent with the actual probabilities. DCA demonstrated that the prediction model has a high net benefit.

CONCLUSION

Risk factors for CVD death in HCC patients were investigated for the first time. The nomograph served as an important reference tool for relevant clinical management decisions.

Effects of message framing and risk perception on health communication for optimum cardiovascular disease primary prevention: a protocol for a multicenter randomized controlled study

Background

Although several guidelines for cardiovascular disease (CVD) management have highlighted the significance of primary prevention, the execution and adherence to lifestyle modifications and preventive medication interventions are insufficient in everyday clinical practice. The utilization of effective risk communication can assist individuals in shaping their perception of CVD risk, motivating them to make lifestyle changes, and increasing their willingness to engage with preventive medication, ultimately reducing their CVD risks and potential future events. However, there is limited evidence available regarding the optimal format and content of CVD risk communication.

Objective

The pilot study aims to elucidate the most effective risk communication strategy, utilizing message framing (gain-framed, loss-framed, or no-framed), for distinct subgroups of risk perception (under-perceived, over-perceived, and correctly-perceived CVD risk) through a multi-center randomized controlled trial design.

Methods

A multi-center 3 × 3 factorial, observer-blinded experimental design was conducted. The participants will be assigned into three message-framing arms randomly in a 1:1:1 ratio and will receive an 8-week intervention online. Participants are aged 20-80 years old and have a 10-year risk of absolute CVD risk of at least 5% (moderate risk or above). We plan to enroll 240 participants based on the sample calculation. The primary outcome is the CVD prevention behaviors and CVD absolute risk value. Data collection will occur at baseline, post-intervention, and 3-month follow-up.

Discussion

This experimental study will expect to determine the optimal matching strategy between risk perception subgroups and risk information format, and it has the potential to offer health providers in community or clinic settings a dependable and efficient health communication information template for conducting CVD risk management.Clinical trial registration: https://www.chictr.org.cn/bin/project/edit?pid=207811, ChiCTR2300076337.

Seasonally adjusted laboratory reference intervals to improve the performance of machine learning models for classification of cardiovascular diseases

BACKGROUND

Variation in laboratory healthcare data due to seasonal changes is a widely accepted phenomenon. Seasonal variation is generally not systematically accounted for in healthcare settings. This study applies a newly developed adjustment method for seasonal variation to analyze the effect seasonality has on machine learning model classification of diagnoses.

METHODS

Machine learning methods were trained and tested on ~ 22 million unique records from ~ 575,000 unique patients admitted to Danish hospitals. Four machine learning models (adaBoost, decision tree, neural net, and random forest) classifying 35 diseases of the circulatory system (ICD-10 diagnosis codes, chapter IX) were run before and after seasonal adjustment of 23 laboratory reference intervals (RIs). The effect of the adjustment was benchmarked via its contribution to machine learning models trained using hyperparameter optimization and assessed quantitatively using performance metrics (AUROC and AUPRC).

RESULTS

Seasonally adjusted RIs significantly improved cardiovascular disease classification in 24 of the 35 tested cases when using neural net models. Features with the highest average feature importance (via SHAP explainability) across all disease models were sex, C- reactive protein, and estimated glomerular filtration. Classification of diseases of the vessels, such as thrombotic diseases and other atherosclerotic diseases consistently improved after seasonal adjustment.

CONCLUSIONS

As data volumes increase and data-driven methods are becoming more advanced, it is essential to improve data quality at the pre-processing level. This study presents a method that makes it feasible to introduce seasonally adjusted RIs into the clinical research space in any disease domain. Seasonally adjusted RIs generally improve diagnoses classification and thus, ought to be considered and adjusted for in clinical decision support methods.

FRAX predicts cardiovascular risk in women undergoing osteoporosis screening: the Manitoba bone mineral density registry

Osteoporosis and cardiovascular disease (CVD) are highly prevalent in older women, with increasing evidence for shared risk factors and pathogenesis. Although FRAX was developed for the assessment of fracture risk, we hypothesized that it might also provide information on CVD risk. To test the ability of the FRAX tool and FRAX-defined risk factors to predict incident CVD in women undergoing osteoporosis screening with DXA, we performed a retrospective prognostic cohort study which included women aged 50 yr or older with a baseline DXA scan in the Manitoba Bone Mineral Density Registry between March 31, 1999 and March 31, 2018. FRAX scores for major osteoporotic fracture (MOF) were calculated on all participants. Incident MOF and major adverse CV events (MACE; hospitalized acute myocardial infarction [AMI], hospitalized non-hemorrhagic cerebrovascular disease [CVA], or all-cause death) were ascertained from linkage to population-based healthcare data. The study population comprised 59 696 women (mean age 65.7 ± 9.4 yr). Over mean 8.7 yr of observation, 6021 (10.1%) had MOF, 12 277 women (20.6%) had MACE, 2274 (3.8%) had AMI, 2061 (3.5%) had CVA, and 10 253 (17.2%) died. MACE rates per 1000 person-years by FRAX risk categories low (10-yr predicted MOF <10%), moderate (10%-19.9%) and high (≥20%) were 13.5, 34.0, and 64.6, respectively. Although weaker than the association with incident MOF, increasing FRAX quintile was associated with increasing risk for MACE (all P-trend <.001), even after excluding prior CVD and adjusting for age. HR for MACE per SD increase in FRAX was 1.99 (95%CI, 1.96-2.02). All FRAX-defined risk factors (except parental hip fracture and lower BMI) were independently associated with higher non-death CV events. Although FRAX is intended for fracture risk prediction, it has predictive value for cardiovascular risk.

Risk prediction models for cardiovascular events in hemodialysis patients: A systematic review

OBJECTIVE

To perform a systematic review of risk prediction models for cardiovascular (CV) events in hemodialysis (HD) patients, and provide a reference for the application and optimization of related prediction models.

METHODS

PubMed, The Cochrane Library, Web of Science, and Embase databases were searched from inception to 1 February 2023. Two authors independently conducted the literature search, selection, and screening. The Prediction model Risk Of Bias Assessment Tool (PROBAST) was applied to evaluate the risk of bias and applicability of the included literature.

RESULTS

A total of nine studies containing 12 models were included, with performance measured by the area under the receiver operating characteristic curve (AUC) lying between 0.70 and 0.88. Age, diabetes mellitus (DM), C-reactive protein (CRP), and albumin (ALB) were the most commonly identified predictors of CV events in HD patients. While the included models demonstrated good applicability, there were still certain risks of bias, primarily related to inadequate handling of missing data and transformation of continuous variables, as well as a lack of model performance validation.

CONCLUSION

The included models showed good overall predictive performance and can assist healthcare professionals in the early identification of high-risk individuals for CV events in HD patients. In the future, the modeling methods should be improved, or the existing models should undergo external validation to provide better guidance for clinical practice.