Adding ethnicity to cardiovascular risk prediction: External validation and model updating of SCORE2 using data from the HELIUS population cohort.

BACKGROUND: Current prediction models for mainland Europe do not include ethnicity, despite ethnic disparities in cardiovascular disease (CVD) risk. SCORE2 performance was evaluated across the largest ethnic groups in the Netherlands and ethnic backgrounds were added to the model. METHODS: 11,614 participants, aged between 40 and 70 years without CVD, from the population-based multi-ethnic HELIUS study were included. Fine and Gray models were used to calculate sub-distribution hazard ratios (SHR) for South-Asian Surinamese, African Surinamese, Ghanaian, Turkish and Moroccan origin groups, representing their CVD risk relative to the Dutch group, on top of individual SCORE2 risk predictions. Model performance was evaluated by discrimination, calibration and net reclassification index (NRI). RESULTS: Overall, 274 fatal and non-fatal CVD events, and 146 non-cardiovascular deaths were observed during a median of 7.8 years follow-up (IQR 6.8-8.8). SHRs for CVD events were 1.86 (95 % CI 1.31-2.65) for the South-Asian Surinamese, 1.09 (95 % CI 0.76-1.56) for the African-Surinamese, 1.48 (95 % CI 0.94-2.31) for the Ghanaian, 1.63 (95 % CI 1.09-2.44) for the Turkish, and 0.67 (95 % CI 0.39-1.18) for the Moroccan origin groups. Adding ethnicity to SCORE2 yielded comparable calibration and discrimination [0.764 (95 % CI 0.735-0.792) vs. 0.769 (95 % CI 0.740-0.797)]. The NRI for adding ethnicity to SCORE2 was 0.24 (95 % CI 0.18-0.31) for events and - 0.12 (95 % CI -0.13-0.12) for non-events. CONCLUSIONS: Adding ethnicity to the SCORE2 risk prediction model in a middle-aged, multi-ethnic Dutch population did not improve overall discrimination but improved risk classification, potentially helping to address CVD disparities through timely treatment.

Prediction of Severe Baseline Asymptomatic Carotid Stenosis and Subsequent Risk of Stroke and Cardiovascular Disease.

BACKGROUND: Risk models to identify patients at high risk of asymptomatic carotid artery stenosis (ACAS) can help in selecting patients for screening, but long-term outcomes in these patients are unknown. We assessed the diagnostic and prognostic value of the previously published Prevalence of ACAS (PACAS) risk model to detect ACAS at baseline and to predict subsequent risk of stroke and cardiovascular disease (CVD) during follow-up. METHODS: We validated the discrimination and calibration of the PACAS risk model to detect severe (≥70% narrowing) ACAS with patients from the Reduction of Atherothrombosis for Continued Health registry. We subsequently calculated the incidence rates of stroke and CVD (fatal and nonfatal stroke or myocardial infarction or vascular death) during follow-up in 4 risk groups (low, medium, high, and very high, corresponding to sum scores of ≤9, 10-13, 14-17, and ≥18, respectively). RESULTS: Among 26 384 patients, aged between 45 and 80 years, without prior carotid procedures, 1662 (6.3%) had severe baseline ACAS. During ≈70 000 patient-years of follow-up, 1124 strokes and 2484 CVD events occurred. Discrimination of the PACAS model was 0.67 (95% CI, 0.65-0.68), and calibration showed adequate concordance between predicted and observed risks of severe baseline ACAS after recalibration. Significantly higher incidence rates of stroke (Ptrend<0.011) and CVD (Ptrend<0.0001) during follow-up were found with increasing PACAS risk groups. Among patients with high PACAS sum score of ≥14 (corresponding to 27.7% of all patients), severe baseline ACAS prevalence was 11.4%. In addition, 56.6% of incident strokes and 64.9% of incident CVD events occurred in this group. CONCLUSIONS: The PACAS risk model can reliably identify patients at high risk of severe baseline ACAS. Incidence rates of stroke and CVD during follow-up were significantly higher in patients with high PACAS sum scores. Selective screening of patients with high PACAS sum scores may help to prevent future stroke or CVD.

Estimating uncertainty when providing individual cardiovascular risk predictions: a Bayesian survival analysis.

BACKGROUND: Cardiovascular disease (CVD) risk scores provide point estimates of individual risk without uncertainty quantification. The objective of the current study was to demonstrate the feasibility and clinical utility of calculating uncertainty surrounding individual CVD-risk predictions using Bayesian methods. STUDY DESIGN AND SETTING: Individuals with established atherosclerotic CVD were included from the Utrecht Cardiovascular Cohort-Secondary Manifestations of ARTerial disease (UCC-SMART). In 8,355 individuals, followed for median of 8.2 years (IQR 4.2-12.5), a Bayesian Weibull model was derived to predict the 10-year risk of recurrent CVD events. RESULTS: Model coefficients and individual predictions from the Bayesian model were very similar to that of a traditional ('frequentist') model but the Bayesian model also predicted 95% credible intervals (CIs) surrounding individual risk estimates. The median width of the individual 95%CrI was 5.3% (IQR 3.6-6.5) and 17% of the population had a 95%CrI width of 10% or greater. The uncertainty decreased with increasing sample size used for derivation of the model. Combining the Bayesian Weibull model with sampled hazard ratios based on trial reports may be used to estimate individual estimates of absolute risk reduction with uncertainty measures and the probability that a treatment option will result in a clinically relevant risk reduction. CONCLUSION: Estimating uncertainty surrounding individual CVD risk predictions using Bayesian methods is feasible. The uncertainty regarding individual risk predictions could have several applications in clinical practice, like the comparison of different treatment options or by calculating the probability of the individual risk being below a certain treatment threshold. However, as the individual uncertainty measures only reflect sampling error and no biases in risk prediction, physicians should be familiar with the interpretation before widespread clinical adaption.

Prediction of individual lifetime cardiovascular risk and potential treatment benefit: development and recalibration of the LIFE-CVD2 model to four European risk regions.

AIMS: The 2021 European Society of Cardiology prevention guidelines recommend the use of (lifetime) risk prediction models to aid decisions regarding initiation of prevention. We aimed to update and systematically recalibrate the LIFEtime-perspective CardioVascular Disease (LIFE-CVD) model to four European risk regions for the estimation of lifetime CVD risk for apparently healthy individuals. METHODS AND RESULTS: The updated LIFE-CVD (i.e. LIFE-CVD2) models were derived using individual participant data from 44 cohorts in 13 countries (687 135 individuals without established CVD, 30 939 CVD events in median 10.7 years of follow-up). LIFE-CVD2 uses sex-specific functions to estimate the lifetime risk of fatal and non-fatal CVD events with adjustment for the competing risk of non-CVD death and is systematically recalibrated to four distinct European risk regions. The updated models showed good discrimination in external validation among 1 657 707 individuals (61 311 CVD events) from eight additional European cohorts in seven countries, with a pooled C-index of 0.795 (95% confidence interval 0.767-0.822). Predicted and observed CVD event risks were well calibrated in population-wide electronic health records data in the UK (Clinical Practice Research Datalink) and the Netherlands (Extramural LUMC Academic Network). When using LIFE-CVD2 to estimate potential gain in CVD-free life expectancy from preventive therapy, projections varied by risk region reflecting important regional differences in absolute lifetime risk. For example, a 50-year-old smoking woman with a systolic blood pressure (SBP) of 140 mmHg was estimated to gain 0.9 years in the low-risk region vs. 1.6 years in the very high-risk region from lifelong 10 mmHg SBP reduction. The benefit of smoking cessation for this individual ranged from 3.6 years in the low-risk region to 4.8 years in the very high-risk region. CONCLUSION: By taking into account geographical differences in CVD incidence using contemporary representative data sources, the recalibrated LIFE-CVD2 model provides a more accurate tool for the prediction of lifetime risk and CVD-free life expectancy for individuals without previous CVD, facilitating shared decision-making for cardiovascular prevention as recommended by 2021 European guidelines.

The study introduces LIFE-CVD2, a new tool that helps predict the risk of heart disease over a person's lifetime, and highlights how where you live in Europe can affect this risk.Using health information from over 687 000 people, LIFE-CVD2 looks at things like blood pressure and whether someone smokes to figure out their chance of having heart problems later in life. Health information from another 1.6 million people in seven different European countries was used to show that it did a good job of predicting who might develop heart disease.Knowing your heart disease risk over your whole life helps doctors give you the best advice to keep your heart healthy. Let us say there is a 50-year-old woman who smokes and has a bit high blood pressure. Right now, she might not look like she is in danger. But with the LIFE-CVD2 tool, doctors can show her how making changes today, like lowering her blood pressure or stopping smoking, could mean many more years without heart problems. These healthy changes can make a big difference over many years.

Development and validation of a lifetime prediction model for incident type 2 diabetes in patients with established cardiovascular disease: the CVD2DM model.

AIMS: Identifying patients with established cardiovascular disease (CVD) who are at high risk of type 2 diabetes (T2D) may allow for early interventions, reducing the development of T2D and associated morbidity. The aim of this study was to develop and externally validate the CVD2DM model to estimate the 10-year and lifetime risks of T2D in patients with established CVD. METHODS AND RESULTS: Sex-specific, competing risk-adjusted Cox proportional hazard models were derived in 19 281 participants with established CVD and without diabetes at baseline from the UK Biobank. The core model's pre-specified predictors were age, current smoking, family history of diabetes mellitus, body mass index, systolic blood pressure, fasting plasma glucose, and HDL cholesterol. The extended model also included HbA1c. The model was externally validated in 3481 patients from the UCC-SMART study. During a median follow-up of 12.2 years (interquartile interval 11.3-13.1), 1628 participants with established CVD were diagnosed with T2D in the UK Biobank. External validation c-statistics were 0.79 [95% confidence interval (CI) 0.76-0.82] for the core model and 0.81 (95% CI 0.78-0.84) for the extended model. Calibration plots showed agreement between predicted and observed 10-year risk of T2D. CONCLUSION: The 10-year and lifetime risks of T2D can be estimated with the CVD2DM model in patients with established CVD, using readily available clinical predictors. The model would benefit from further validation across diverse ethnic groups to enhance its applicability. Informing patients about their T2D risk could motivate them further to adhere to a healthy lifestyle.

In this study, we developed and externally validated the CVD2DM model, which predicts the 10-year and lifetime risk of type 2 diabetes (T2D) in individuals who already have cardiovascular disease (CVD). The key findings are as follows:The CVD2DM model is the first model to estimate the risk of developing T2D applicable in all patients with atherosclerotic CVD. The model is based on several factors available in clinical practice, such as age, fasting plasma glucose, family history of diabetes, and body mass index. It was developed in 19 281 patients from the UK Biobank. The model performed well in 3481 patients from the UCC-SMART study.Informing patients about their T2D risk could motivate them further to adhere to a healthy lifestyle.

Lifetime and 10-year cardiovascular risk prediction in individuals with type 1 diabetes: The LIFE-T1D model.

AIMS: To develop and externally validate the LIFE-T1D model for the estimation of lifetime and 10-year risk of cardiovascular disease (CVD) in individuals with type 1 diabetes. MATERIALS AND METHODS: A sex-specific competing risk-adjusted Cox proportional hazards model was derived in individuals with type 1 diabetes without prior CVD from the Swedish National Diabetes Register (NDR), using age as the time axis. Predictors included age at diabetes onset, smoking status, body mass index, systolic blood pressure, glycated haemoglobin level, estimated glomerular filtration rate, non-high-density lipoprotein cholesterol, albuminuria and retinopathy. The model was externally validated in the Danish Funen Diabetes Database (FDDB) and the UK Biobank. RESULTS: During a median follow-up of 11.8 years (interquartile interval 6.1-17.1 years), 4608 CVD events and 1316 non-CVD deaths were observed in the NDR (n = 39 756). The internal validation c-statistic was 0.85 (95% confidence interval [CI] 0.84-0.85) and the external validation c-statistics were 0.77 (95% CI 0.74-0.81) for the FDDB (n = 2709) and 0.73 (95% CI 0.70-0.77) for the UK Biobank (n = 1022). Predicted risks were consistent with the observed incidence in the derivation and both validation cohorts. CONCLUSIONS: The LIFE-T1D model can estimate lifetime risk of CVD and CVD-free life expectancy in individuals with type 1 diabetes without previous CVD. This model can facilitate individualized CVD prevention among individuals with type 1 diabetes. Validation in additional cohorts will improve future clinical implementation.

Balancing risks of recurrent venous thromboembolism and bleeding with extended anticoagulation: a decision analysis.

Background: A decision to stop or continue anticoagulation after 3 months of anticoagulation for venous thromboembolism (VTE) should be made by weighing individual risks of recurrence and bleeding. Objectives: To determine the optimal ratio of recurrence risk reduction to increase the risk of bleeding in terms of maximizing quality-adjusted life years (QALYs) gained. Methods: Using a microsimulation model, outcomes within 5 years were simulated after assigning extended treatment if absolute recurrence risk reduction outweighed absolute increase in clinically relevant bleeding risk (International Society on Thrombosis and Haemostasis definition), weighted by a certain ratio. Data were simulated based on the Bleeding Risk Study, a prospective cohort including patients after ≥3 months of anticoagulation for unprovoked VTE or provoked VTE with history of VTE. The VTE-PREDICT risk score was used to estimate 5-year risks of recurrent VTE and clinically relevant bleeding. Results: Among 10,000 individuals (mean age, 60.2 years, 36% female), the ratio of 0.90 (95% CI, 0.51-3.40; ie, bleeding is considered 0.90 the severity of recurrent VTE), with 99% of patients assigned extended anticoagulation, was considered optimal and resulted in 93 (95% CI, -23 to 203) additional QALYs compared with the least favorable ratio (5.10, 0% extended anticoagulation). At the optimal ratio, treatment based on VTE-PREDICT yielded 44 (95% CI, -69 to 157) additional QALYs versus standard of care. Conclusion: With the current evidence, the optimal ratio between relevant bleeding risk and absolute recurrence risk reduction remains uncertain. Our results confirm that clinical equipoise exists regarding the decision to stop or continue anticoagulation after initial VTE treatment, emphasizing the importance of shared decision-making.

Generalisability of trials on antithrombotic treatment intensification in patients with cardiovascular disease.

OBJECTIVE: Assessment of generalisability of guideline-informing trials on antithrombotic treatment intensification to real-world patients with cardiovascular disease (CVD). METHODS: Inclusion and exclusion criteria of the Cardiovascular Outcomes for People Using Anticoagulation Strategies (COMPASS), Clopidogrel for High Atherothrombotic Risk and Ischemic Stabilization, Management and Avoidance (CHARISMA), Prevention of Cardiovascular events in Patients with Prior Heart Attack Using Ticagrelor Compared to Placebo on a Background of Aspirin-Thrombolysis in Myocardial Infarction (PEGASUS-TIMI) and Dual Antiplatelet Therapy (DAPT) study were applied to coronary artery disease (CAD) and/or peripheral artery disease (PAD) patients from Utrecht Cardiovascular Cohort-Second Manifestations of Arterial Disease (UCC-SMART) to determine real-world eligibility. Eligible and ineligible patients were compared on baseline characteristics, cardiovascular events, major bleeding and mortality. RESULTS: Eligibility ranged from 11%-94% for CAD to 75%-90% for patients with PAD. Cardiovascular, bleeding and mortality risks were higher in COMPASS-eligible patients with CAD (rate ratios (RR) 1.98 (95% CI 1.74 to 2.26), 2.02 (95% CI 1.47 to 2.78) and 3.11 (95% CI 2.71 to 3.57), respectively) and CHARISMA-eligible patients (RR 1.51 (95% CI 1.12 to 2.06), 2.25 (95% CI 1.01 to 6.21) and 4.43 (95% CI 2.79 to 7.51), respectively), and lower in COMPASS-eligible patients with PAD (RR 0.45 (95% CI 0.36 to 0.56), 0.29 (95% CI 0.18 to 0.46) and 0.45 (95% CI 0.38 to 0.54), respectively) and DAPT-eligible patients with CAD (RR CVD 0.49 (95% CI 0.34 to 0.69) and mortality 0.67 (95% CI 0.48 to 0.94)) than ineligible patients. After adjustment for trial eligibility criteria, only higher cardiovascular and mortality risks in COMPASS-eligible patients with CAD and lower cardiovascular risks in CHARISMA-eligible and DAPT-eligible patients persisted with CAD. CONCLUSION: A large proportion of contemporary CVD patients would be eligible for intensified antithrombotic treatment trials, with mostly similar adjusted event risks to ineligible patients. Trial-based guideline recommendations are largely applicable to real-world patients.

Validation of Systematic Coronary Risk Evaluation 2 (SCORE2) and SCORE2-Older Persons in the EPIC-Norfolk prospective population cohort.

AIMS: The European Systematic Coronary Risk Evaluation 2 (SCORE2) and SCORE2-Older Persons (OP) models are recommended to identify individuals at high 10-year risk for cardiovascular disease (CVD). Independent validation and assessment of clinical utility is needed. This study aims to assess discrimination, calibration, and clinical utility of low-risk SCORE2 and SCORE2-OP. METHODS AND RESULTS: Validation in individuals aged 40-69 years (SCORE2) and 70-79 years (SCORE2-OP) without baseline CVD or diabetes from the European Prospective Investigation of Cancer (EPIC) Norfolk prospective population study. We compared 10-year CVD risk estimates with observed outcomes (cardiovascular mortality, non-fatal myocardial infarction, and stroke). For SCORE2, 19 560 individuals (57% women) had 10-year CVD risk estimates of 3.7% [95% confidence interval (CI) 3.6-3.7] vs. observed 3.8% (95% CI 3.6-4.1) [observed (O)/expected (E) ratio 1.0 (95% CI 1.0-1.1)]. The area under the curve (AUC) was 0.75 (95% CI 0.74-0.77), with underestimation of risk in men [O/E 1.4 (95% CI 1.3-1.6)] and overestimation in women [O/E 0.7 (95% CI 0.6-0.8)]. Decision curve analysis (DCA) showed clinical benefit. Systematic Coronary Risk Evaluation 2-Older Persons in 3113 individuals (58% women) predicted 10-year CVD events in 10.2% (95% CI 10.1-10.3) vs. observed 15.3% (95% CI 14.0-16.5) [O/E ratio 1.6 (95% CI 1.5-1.7)]. The AUC was 0.63 (95% CI 0.60-0.65) with underestimation of risk across sex and risk ranges. Decision curve analysis showed limited clinical benefit. CONCLUSION: In a UK population cohort, the SCORE2 low-risk model showed fair discrimination and calibration, with clinical benefit for preventive treatment initiation decisions. In contrast, in individuals aged 70-79 years, SCORE2-OP demonstrated poor discrimination, underestimated risk in both sexes, and limited clinical utility.

To effectively prevent heart disease, it is important to identify individuals who are at a higher risk of developing it. Researchers have developed models that can estimate the likelihood of a healthy person developing heart disease within the next 10 years. This study, involving 22 673 healthy individuals in the UK, aimed to determine if these risk estimation models are accurate and can guide decisions about who should receive preventive treatment.

Effect of adipose tissue quantity and dysfunction on the risk of cancer in individuals with and without type 2 diabetes.

OBJECTIVE: To determine the role of waist circumference and metabolic dysfunction in the risk of cancer in individuals with type 2 diabetes (T2D) and to compare this to individuals without T2D. METHODS: Individuals with (n = 1925) and without T2D (n = 10,204) were included from the UCC-SMART cohort. Incident cancer diagnoses were obtained by linkage with the Netherlands Cancer Registry. Metabolic dysfunction was defined as ≥ 3 adapted NCEP ATP-III metabolic syndrome criteria. The effects of waist circumference and metabolic dysfunction on cancer were assessed using Cox proportional hazards models, adjusted for confounders. RESULTS: During a median follow-up of 8.3 years (IQR 4.2-13.1), 1740 individuals were diagnosed with cancer. Incidence rates of total cancer were 19.3 and 15.5/1000 person-years for individuals with and without T2D, respectively. In individuals without T2D, a higher waist circumference was associated with an increased risk of colorectal (per standard deviation: HR 1.23; 95%CI 1.03-1.46), urinary tract (HR 1.28; 95%CI 1.05-1.56) and total cancer (HR 1.06; 95%CI 1.02-1.13). Metabolic dysfunction was related to an increased risk of colorectal (HR 1.35; 95%CI 1.01-1.82), lung (HR 1.37; 95%CI 1.07-1.75) and total cancer (HR 1.13; 95%CI 1.01-1.25) in individuals without T2D. In individuals with T2D, no significant associations were found. CONCLUSION: Incidence rates of cancer are higher among individuals with T2D. However, higher waist circumference and metabolic dysfunction are only associated with an increased cancer risk in patients without T2D. These findings provide novel insights into the role of metabolic dysfunction in the occurrence of cancer.

Variability in benefit from intensive insulin therapy on cardiovascular events in individuals with type 1 diabetes: A post hoc analysis of the DCCT/EDIC study.

AIM: To evaluate presence of treatment effect heterogeneity of intensive insulin therapy (INT) on occurrence of major adverse cardiovascular events (MACE) in individuals with type 1 diabetes. METHODS: In participants from the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) study, individual treatment effect of INT (≥3 daily insulin injections/insulin pump therapy) versus conventional therapy (once/twice daily insulin) on the risk of MACE was estimated using a penalized Cox regression model including treatment-by-covariate interaction terms. RESULTS: In 1441 participants, 120 first MACE events were observed and 1279 individuals (89%) were predicted to benefit from INT with regard to MACE risk reduction. The study population was divided into four groups based on predicted treatment effect: one group with no predicted benefit and three tertiles with predicted treatment benefit. The median absolute reduction in 30-year risk of MACE across groups of predicted treatment effect ranged from -0.2% (i.e. risk increase; interquartile range [IQR] -0.1% to -0.3%) in the group with no predicted benefit to 6.6% (i.e. risk reduction; IQR 3.8%-10.9%; number needed to treat 15) in the highest tertile of predicted benefit. The observed benefit of preventing microvascular complications was stable across all subgroups of predicted MACE benefit. CONCLUSIONS: Although INT reduces the risk of MACE in the majority of individuals with type 1 diabetes, benefit varies substantially. These individual differences in the effect of INT underline the necessity for a better understanding of the individual response to intensive treatment.

The relevance of competing risk adjustment in cardiovascular risk prediction models for clinical practice.

BACKGROUND: Many models developed for predicting the risk of cardiovascular disease (CVD) are adjusted for the competing risk of non-CVD mortality, which has been suggested to reduce potential overestimation of cumulative incidence in populations where the risk of competing events is high. The objective was to evaluate and illustrate the clinical impact of competing risk adjustment when deriving a CVD prediction model in a high-risk population. METHODS AND RESULTS: Individuals with established atherosclerotic CVD were included from the Utrecht Cardiovascular Cohort-Secondary Manifestations of ARTerial disease (UCC-SMART). In 8355 individuals, followed for a median of 8.2 years (IQR 4.2-12.5), two similar prediction models for the estimation of 10-year residual CVD risk were derived: with competing risk adjustment using a Fine and Gray model and without competing risk adjustment using a Cox proportional hazards model. On average, predictions were higher from the Cox model. The Cox model predictions overestimated the cumulative incidence [predicted-observed ratio 1.14 (95% CI 1.09-1.20)], which was most apparent in the highest risk quartiles and in older persons. Discrimination of both models was similar. When determining treatment eligibility on thresholds of predicted risks, more individuals would be treated based on the Cox model predictions. If, for example, individuals with a predicted risk > 20% were considered eligible for treatment, 34% of the population would be treated according to the Fine and Gray model predictions and 44% according to the Cox model predictions. INTERPRETATION: Individual predictions from the model unadjusted for competing risks were higher, reflecting the different interpretations of both models. For models aiming to accurately predict absolute risks, especially in high-risk populations, competing risk adjustment must be considered.

Improving 10-year cardiovascular risk prediction in apparently healthy people: flexible addition of risk modifiers on top of SCORE2.

AIMS: In clinical practice, factors associated with cardiovascular disease (CVD) like albuminuria, education level, or coronary artery calcium (CAC) are often known, but not incorporated in cardiovascular risk prediction models. The aims of the current study were to evaluate a methodology for the flexible addition of risk modifying characteristics on top of SCORE2 and to quantify the added value of several clinically relevant risk modifying characteristics. METHODS AND RESULTS: Individuals without previous CVD or DM were included from the UK Biobank; Atherosclerosis Risk in Communities (ARIC); Multi-Ethnic Study of Atherosclerosis (MESA); European Prospective Investigation into Cancer, The Netherlands (EPIC-NL); and Heinz Nixdorf Recall (HNR) studies (n = 409 757) in whom 16 166 CVD events and 19 149 non-cardiovascular deaths were observed over exactly 10.0 years of follow-up. The effect of each possible risk modifying characteristic was derived using competing risk-adjusted Fine and Gray models. The risk modifying characteristics were applied to individual predictions with a flexible method using the population prevalence and the subdistribution hazard ratio (SHR) of the relevant predictor. Risk modifying characteristics that increased discrimination most were CAC percentile with 0.0198 [95% confidence interval (CI) 0.0115; 0.0281] and hs-Troponin-T with 0.0100 (95% CI 0.0063; 0.0137). External validation was performed in the Clinical Practice Research Datalink (CPRD) cohort (UK, n = 518 015, 12 675 CVD events). Adjustment of SCORE2-predicted risks with both single and multiple risk modifiers did not negatively affect calibration and led to a modest increase in discrimination [0.740 (95% CI 0.736-0.745) vs. unimproved SCORE2 risk C-index 0.737 (95% CI 0.732-0.741)]. CONCLUSION: The current paper presents a method on how to integrate possible risk modifying characteristics that are not included in existing CVD risk models for the prediction of CVD event risk in apparently healthy people. This flexible methodology improves the accuracy of predicted risks and increases applicability of prediction models for individuals with additional risk known modifiers.

Heart disease is a major health concern worldwide, and predicting an individual's risk for developing heart disease is an important tool for prevention. Current risk prediction models often use factors such as age, gender, smoking, and blood pressure, but other factors like education level, albuminuria (protein in the urine), and coronary artery calcium (CAC) may also affect an individual's risk. The aim of this study was to develop a new method for using these additional risk factors for predicting risk even more accurately. The researchers used data from several large studies that included over 400 000 apparently healthy individuals who were followed for 10 years. They examined the effect of various risk factors on cardiovascular disease (CVD) risk using a statistical model. They found that adding coronary scan ('CAC score'); NT-proBNP, a biomarker of heart strain; and hs-Troponin-T, a marker of heart damage, to the existing risk prediction model (SCORE2) improved the accuracy of predicted CVD risk. The key findings are: The methods presented in the current study can help to add additional risk factors to predictions of existing models, such as SCORE2. This flexible method may help identify individuals who are at higher risk for CVD and guide prevention strategies.

External validation of bleeding risk models for the prediction of long-term bleeding risk in patients with established cardiovascular disease.

OBJECTIVE: The long-term predictive performance of existing bleeding risk models in patients with various manifestations of cardiovascular disease (CVD) is not well known. This study aims to assess and compare the performance of relevant existing bleeding risk models in estimating the long-term risk of major bleeding in a cohort of patients with established CVD. METHODS: Seven existing bleeding risk models (PRECISE-DAPT, DAPT, Ducrocq et al, de Vries et al, S2TOP-BLEED, Intracranial B2LEED3S and HAS-BLED) were identified and externally validated in 7,249 patients with established CVD included in the Utrecht Cardiovascular Cohort-second manifestations of arterial disease study. Predictive performance was assessed in terms of discrimination and calibration, both at 10 years and the original prediction horizon of the models. Major bleeding was defined as Bleeding Academic Research Consortium type 3 or 5. RESULTS: After a median follow-up of 8.4 years (interquartile range 4.5-12.5), a total of 233 (3.2%) major bleeding events occurred. C-statistics for discrimination at 10 years ranged from 0.53 (95%CI 0.49-0.57) to 0.64 (95%CI 0.60-0.68). Calibration plots after recalibration to 10 years showed best agreement between predicted and observed bleeding risk for De Vries et al, S2TOP-BLEED, DAPT and PRECISE-DAPT. CONCLUSIONS: The performance of existing bleeding risk models to predict long-term bleeding in patients with CVD varied. Discrimination and calibration were best for the models of de Vries et al, S2TOP-BLEED, DAPT and PRECISE-DAPT. Of these, recalibrated models requiring the least predictors may be preferred for use to personalize prevention with antithrombotic therapy.

Unexploited potential of risk factor treatment in patients with atherosclerotic cardiovascular disease.

BACKGROUND: Most patients with atherosclerotic cardiovascular disease remain at (very) high risk for recurrent events due to suboptimal risk factor control. AIMS: This study aimed to quantify the potential of maximal risk factor treatment on 10-year and lifetime risk of recurrent atherosclerotic cardiovascular events in patients 1 year after a coronary event. METHODS AND RESULTS: Pooled data from six studies are as follows: RESPONSE 1, RESPONSE 2, OPTICARE, EUROASPIRE IV, EUROASPIRE V, and HELIUS. Patients aged ≥45 years at ≥6 months after coronary event were included. The SMART-REACH score was used to estimate 10-year and lifetime risk of recurrent atherosclerotic cardiovascular events with current treatment and potential risk reduction and gains in event-free years with maximal treatment (lifestyle and pharmacological). In 3230 atherosclerotic cardiovascular disease patients (24% women), at median interquartile range (IQR) 1.1 years (1.0-1.8) after index event, 10-year risk was median (IQR) 20% (15-27%) and lifetime risk 54% (47-63%). Whereas 70% used conventional medication, 82% had ≥1 drug-modifiable risk factor not on target. Furthermore, 91% had ≥1 lifestyle-related risk factor not on target. Maximizing therapy was associated with a potential reduction of median (IQR) 10-year risk to 6% (4-8%) and of lifetime risk to 20% (15-27%) and a median (IQR) gain of 7.3 (5.4-10.4) atherosclerotic cardiovascular disease event-free years. CONCLUSIONS: Amongst patients with atherosclerotic cardiovascular disease, maximizing current, guideline-based preventive therapy has the potential to mitigate a large part of their risk of recurrent events and to add a clinically important number of event-free years to their lifetime.

Patients with heart disease are at high risk of new cardiac events. This study amongst 3230 patients who had a heart attack or received a stent or bypass surgery shows missed potential for healthy life after a heart attack. The average age of study patients was 61 years, and 24% were women. At 1 year after the cardiac event, nearly one in three (30%) continued smoking, 79% were overweight, 45% reported insufficient physical activity, 40% had high blood pressure, and 65% had a too high LDL ('bad') cholesterol. We calculated that adherence to lifestyle advice and medications could on average halve the risk for another heart attack and add over 7 healthy years of life after a heart attack. This highlights the importance of healthy lifestyle and medication adherence after a heart attack. Key finding:• adherence to lifestyle advice and medications could add over 7 healthy years of life after a heart attack.

Cohort profile: the Utrecht Cardiovascular Cohort-Second Manifestations of Arterial Disease (UCC-SMART) Study-an ongoing prospective cohort study of patients at high cardiovascular risk in the Netherlands.

PURPOSE: The Utrecht Cardiovascular Cohort-Second Manifestations of Arterial Disease (UCC-SMART) Study is an ongoing prospective single-centre cohort study with the aim to assess important determinants and the prognosis of cardiovascular disease progression. This article provides an update of the rationale, design, included patients, measurements and findings from the start in 1996 to date. PARTICIPANTS: The UCC-SMART Study includes patients aged 18-90 years referred to the University Medical Center Utrecht, the Netherlands, for management of cardiovascular disease (CVD) or severe cardiovascular risk factors. Since September 1996, a total of 14 830 patients have been included. Upon inclusion, patients undergo a standardised screening programme, including questionnaires, vital signs, laboratory measurements, an ECG, vascular ultrasound of carotid arteries and aorta, ankle-brachial index and ultrasound measurements of adipose tissue, kidney size and intima-media thickness. Outcomes of interest are collected through annual questionnaires and adjudicated by an endpoint committee. FINDINGS TO DATE: By May 2022, the included patients contributed to a total follow-up time of over 134 000 person-years. During follow-up, 2259 patients suffered a vascular endpoint (including non-fatal myocardial infarction, non-fatal stroke and vascular death) and 2794 all-cause deaths, 943 incident cases of diabetes and 2139 incident cases of cancer were observed up until January 2020. The UCC-SMART cohort contributed to over 350 articles published in peer-reviewed journals, including prediction models recommended by the 2021 European Society of Cardiology CVD prevention guidelines. FUTURE PLANS: The UCC-SMART Study guarantees an infrastructure for research in patients at high cardiovascular risk. The cohort will continue to include about 600 patients yearly and follow-up will be ongoing to ensure an up-to-date cohort in accordance with current healthcare and scientific knowledge. In the near future, UCC-SMART will be enriched by echocardiography, and a food frequency questionnaire at baseline enabling the assessment of associations between nutrition and CVD and diabetes.

Visceral adipose tissue quantity and dysfunction and the occurrence of major bleeding in patients with established cardiovascular disease.

OBJECTIVES: To determine the association between both visceral fat quantity and adipose tissue dysfunction, and major bleeding in patients with established cardiovascular disease. METHODS: Patients from the Second Manifestations of ARTerial disease study with established cardiovascular disease were included. Visceral fat was measured using ultrasound and adipose tissue dysfunction was depicted using metabolic syndrome criteria (revised National Cholesterol Education Program). Cox regression models were fitted to study the relation with major bleeding defined as Bleeding Academic Research Consortium (BARC) type 3 or 5, or International Society on Thrombosis and Haemostasis (ISTH) major bleeding. Sensitivity analyses were performed using C-reactive protein levels to reflect adipose tissue dysfunction. RESULTS: In 6927 patients during a median follow up of 9.2 years, a total of 237 BARC type 3 or 5 bleedings and 224 ISTH major bleedings were observed. Visceral fat quantity was not related to major bleeding (HR 1.01, 95%CI 0.88-1.16 for BARC type 3 or 5 bleeding and HR 1.00, 95%CI 0.87-1.15 for ISTH major bleeding), nor was metabolic syndrome (HR 0.97, 95%CI 0.75-1.26 for BARC type 3 or 5 bleeding and HR 0.98, 95%CI 0.75-1.28 for ISTH major bleeding). Sensitivity analyses using C-reactive protein levels showed similar results. No effect modification was observed by sex, antithrombotic therapy, presence of metabolic syndrome or diabetes. CONCLUSION: In patients with cardiovascular disease, no association was found between visceral fat quantity measured with ultrasound or measures of adipose tissue dysfunction and the risk of major bleeding, irrespective of antithrombotic agent use.

Including measures of chronic kidney disease to improve cardiovascular risk prediction by SCORE2 and SCORE2-OP.

AIMS: The 2021 European Society of Cardiology (ESC) guideline on cardiovascular disease (CVD) prevention categorizes moderate and severe chronic kidney disease (CKD) as high and very-high CVD risk status regardless of other factors like age and does not include estimated glomerular filtration rate (eGFR) and albuminuria in its algorithms, systemic coronary risk estimation 2 (SCORE2) and systemic coronary risk estimation 2 in older persons (SCORE2-OP), to predict CVD risk. We developed and validated an 'Add-on' to incorporate CKD measures into these algorithms, using a validated approach. METHODS: In 3,054 840 participants from 34 datasets, we developed three Add-ons [eGFR only, eGFR + urinary albumin-to-creatinine ratio (ACR) (the primary Add-on), and eGFR + dipstick proteinuria] for SCORE2 and SCORE2-OP. We validated C-statistics and net reclassification improvement (NRI), accounting for competing risk of non-CVD death, in 5,997 719 participants from 34 different datasets. RESULTS: In the target population of SCORE2 and SCORE2-OP without diabetes, the CKD Add-on (eGFR only) and CKD Add-on (eGFR + ACR) improved C-statistic by 0.006 (95%CI 0.004-0.008) and 0.016 (0.010-0.023), respectively, for SCORE2 and 0.012 (0.009-0.015) and 0.024 (0.014-0.035), respectively, for SCORE2-OP. Similar results were seen when we included individuals with diabetes and tested the CKD Add-on (eGFR + dipstick). In 57 485 European participants with CKD, SCORE2 or SCORE2-OP with a CKD Add-on showed a significant NRI [e.g. 0.100 (0.062-0.138) for SCORE2] compared to the qualitative approach in the ESC guideline. CONCLUSION: Our Add-ons with CKD measures improved CVD risk prediction beyond SCORE2 and SCORE2-OP. This approach will help clinicians and patients with CKD refine risk prediction and further personalize preventive therapies for CVD.

Estimating individual lifetime risk of incident cardiovascular events in adults with Type 2 diabetes: an update and geographical calibration of the DIAbetes Lifetime perspective model (DIAL2).

AIMS: The 2021 European Society of Cardiology cardiovascular disease (CVD) prevention guidelines recommend the use of (lifetime) risk prediction models to aid decisions regarding intensified preventive treatment options in adults with Type 2 diabetes, e.g. the DIAbetes Lifetime perspective model (DIAL model). The aim of this study was to update the DIAL model using contemporary and representative registry data (DIAL2) and to systematically calibrate the model for use in other European countries. METHODS AND RESULTS: The DIAL2 model was derived in 467 856 people with Type 2 diabetes without a history of CVD from the Swedish National Diabetes Register, with a median follow-up of 7.3 years (interquartile range: 4.0-10.6 years) and comprising 63 824 CVD (including fatal CVD, non-fatal stroke and non-fatal myocardial infarction) events and 66 048 non-CVD mortality events. The model was systematically recalibrated to Europe's low- and moderate-risk regions using contemporary incidence data and mean risk factor distributions. The recalibrated DIAL2 model was externally validated in 218 267 individuals with Type 2 diabetes from the Scottish Care Information-Diabetes (SCID) and Clinical Practice Research Datalink (CPRD). In these individuals, 43 074 CVD events and 27 115 non-CVD fatal events were observed. The DIAL2 model discriminated well, with C-indices of 0.732 [95% confidence interval (CI) 0.726-0.739] in CPRD and 0.700 (95% CI 0.691-0.709) in SCID. CONCLUSION: The recalibrated DIAL2 model provides a useful tool for the prediction of CVD-free life expectancy and lifetime CVD risk for people with Type 2 diabetes without previous CVD in the European low- and moderate-risk regions. These long-term individualized measures of CVD risk are well suited for shared decision-making in clinical practice as recommended by the 2021 CVD ESC prevention guidelines.