This study aimed to evaluate the stepwise approach for cardiovascular (CV) risk factor treatment as outlined by the European Society for Cardiology 2021 guidelines on CV disease (CVD) prevention in ...patients with established atherosclerotic CVD (ASCVD).
In patients with ASCVD, included in UCC-SMART (n = 8730) and European parts of the REACH registry (n = 18 364), the 10-year CV risk was estimated using SMART2. Treatment effects were derived from meta-analyses and trials. Step 1 recommendations were LDL cholesterol (LDLc) < 1.8 mmol/L, systolic blood pressure (SBP) < 140 mmHg, using any antithrombotic medication, sodium-glucose co-transporter 2 (SGLT2) inhibition, and smoking cessation. Step 2 recommendations were LDLc < 1.4 mmol/L, SBP < 130 mmHg, dual-pathway inhibition (DPI, aspirin plus low-dose rivaroxaban), colchicine, glucagon-like peptide (GLP)-1 receptor agonists, and eicosapentaenoic acid. Step 2 was modelled accounting for Step 1 non-attainment. With current treatment, residual CV risk was 22%, 32%, and 60% in the low, moderate, and pooled (very) high European risk regions, respectively. Step 2 could prevent up to 198, 223 and 245 events per 1000 patients treated, respectively. Intensified LDLc reduction, colchicine, and DPI could be applied to most patients, preventing up to 57, 74, and 59 events per 1000 patients treated, respectively. Following Step 2, the number of patients with a CV risk of <10% could increase from 20%, 6.4%, and 0.5%, following Step 1, to 63%, 48%, and 12%, in the respective risk regions.
With current treatment, residual CV risk in patients with ASCVD remains high across all European risk regions. The intensified Step 2 treatment options result in marked further reduction of residual CV risk in patients with established ASCVD.
Guideline-recommended intensive treatment of patients with cardiovascular disease could prevent additional 198-245 new cardiovascular events for every 1000 patients treated.
Abstract
Aims
To quantify the relationship between self-reported, long-term lifestyle changes (smoking, waist circumference, physical activity, and alcohol consumption) and clinical outcomes in ...patients with established cardiovascular disease (CVD).
Methods and results
Data were used from 2011 participants (78% male, age 57 ± 9 years) from the Utrecht Cardiovascular Cohort—Second Manifestations of ARTerial disease cohort who returned for a re-assessment visit (SMART2) after ∼10 years. Self-reported lifestyle change was classified as persistently healthy, improved, worsened, or persistently unhealthy. Cox proportional hazard models were used to quantify the relationship between lifestyle changes and the risk of (cardiovascular) mortality and incident Type 2 diabetes (T2D). Fifty-seven per cent of participants was persistently healthy, 17% improved their lifestyle, 8% worsened, and 17% was persistently unhealthy. During a median follow-up time of 6.1 (inter-quartile range 3.6–9.6) years after the SMART2 visit, 285 deaths occurred, and 99 new T2D diagnoses were made. Compared with a persistently unhealthy lifestyle, individuals who maintained a healthy lifestyle had a lower risk of all-cause mortality hazard ratio (HR) 0.48, 95% confidence interval (CI) 0.36–0.63, cardiovascular mortality (HR 0.57, 95% CI 0.38–0.87), and incident T2D (HR 0.46, 95% CI 0.28–0.73). Similarly, those who improved their lifestyle had a lower risk of all-cause mortality (HR 0.52, 95% CI 0.37–0.74), cardiovascular mortality (HR 0.46, 95% CI 0.26–0.81), and incident T2D (HR 0.50, 95% CI 0.27–0.92).
Conclusion
These findings suggest that maintaining or adopting a healthy lifestyle can significantly lower mortality and incident T2D risk in CVD patients. This study emphasizes the importance of ongoing lifestyle optimization in CVD patients, highlighting the potential for positive change regardless of previous lifestyle habits.
Lay Summary
In this study, we investigated whether lifestyle changes were related to improved health outcomes in individuals with cardiovascular disease (CVD). We assessed self-reported lifestyle behaviours (smoking, waist circumference, alcohol consumption, and physical activity), at inclusion in the cohort and again ∼10 years later. The results emphasize the importance of making healthy lifestyle choices, even for individuals already diagnosed with CVD, and suggest that it is never too late to improve one’s lifestyle.
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.
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.
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.
Abstract
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.
Lay Summary
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.
ObjectiveTo determine whether communicating personalised statin therapy-effects obtained by prognostic algorithm leads to lower decisional conflict associated with statin use in patients with stable ...cardiovascular disease (CVD) compared with standard (non-personalised) therapy-effects.DesignHypothesis-blinded, three-armed randomised controlled trialSetting and participants303 statin users with stable CVD enrolled in a cohortInterventionParticipants were randomised in a 1:1:1 ratio to standard practice (control-group) or one of two intervention arms. Intervention arms received standard practice plus (1) a personalised health profile, (2) educational videos and (3) a structured telephone consultation. Intervention arms received personalised estimates of prognostic changes associated with both discontinuation of current statin and intensification to the most potent statin type and dose (ie, atorvastatin 80 mg). Intervention arms differed in how these changes were expressed: either change in individual 10-year absolute CVD risk (iAR-group) or CVD-free life-expectancy (iLE-group) calculated with the SMART-REACH model (http://U-Prevent.com).OutcomePrimary outcome was patient decisional conflict score (DCS) after 1 month. The score varies from 0 (no conflict) to 100 (high conflict). Secondary outcomes were collected at 1 or 6 months: DCS, quality of life, illness perception, patient activation, patient perception of statin efficacy and shared decision-making, self-reported statin adherence, understanding of statin-therapy, post-randomisation low-density lipoprotein cholesterol level and physician opinion of the intervention. Outcomes are reported as median (25th– 75th percentile).ResultsDecisional conflict differed between the intervention arms: median control 27 (20–43), iAR-group 22 (11–30; p-value vs control 0.001) and iLE-group 25 (10–31; p-value vs control 0.021). No differences in secondary outcomes were observed.ConclusionIn patients with clinically manifest CVD, providing personalised estimations of treatment-effects resulted in a small but significant decrease in decisional conflict after 1 month. The results support the use of personalised predictions for supporting decision-making.Trial registrationNTR6227/NL6080.
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.
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.
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.
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.
Treatment thresholds based on risk predictions can be optimized by considering various health (economic) outcomes and performing marginal analyses, but this is rarely performed. We demonstrate a ...general approach to identify treatment thresholds optimizing individual health (economic) outcomes, illustrated for statin treatment based on 10-year coronary heart disease (CHD) risk predicted by the Framingham risk score.
Creating a health economic model for a risk-based prevention strategy, risk thresholds can be evaluated on several outcomes of interest. Selecting an appropriate threshold range and decrement size for the thresholds and adapting the health economic model accordingly, outcomes can be calculated for each risk threshold. A stepwise, or marginal, comparison of clinical as well as health economic outcomes, that is, comparing outcomes using a specific threshold to outcomes of the former threshold while gradually lowering the threshold, then takes into account the balance between additional numbers of individuals treated and their outcomes (additional health effects and costs). In our illustration, using a Markov model for CHD, we evaluated risk thresholds by gradually lowering thresholds from 20% to 0%.
This approach can be applied to identify optimal risk thresholds on any outcome, such as to limit complications, maximize health outcomes, or optimize cost-effectiveness. In our illustration, keeping the population-level fraction of statin-induced complications <10% resulted in thresholds of T = 6% (men) and T = 2% (women). Lowering the threshold and comparing quality-adjusted life-years (QALYs) after each 1% decrease, QALYs were gained down to T = 1% (men) and T = 0% (women). Also accounting for costs, net health benefits were favorable down to T = 3% (men) and T = 6% (women).
Using a stepwise risk-based approach to threshold optimization allows for preventive strategies that optimize outcomes. Presenting this comprehensive overview of outcomes will better inform decision makers when defining a treatment threshold.
Abstract
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.
Lay Summary
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.
Risk assessment and risk prediction have become essential in the prevention of cardiovascular disease. Even though risk prediction tools are recommended in the European guidelines, they are not ...adequately implemented in clinical practice. Risk prediction tools are meant to estimate prognosis in an unbiased and reliable way and to provide objective information on outcome probabilities. They support informed treatment decisions about the initiation or adjustment of preventive medication. Risk prediction tools facilitate risk communication to the patient and their family, and this may increase commitment and motivation to improve their health. Over the years many risk algorithms have been developed to predict 10-year cardiovascular mortality or lifetime risk in different populations, such as in healthy individuals, patients with established cardiovascular disease and patients with diabetes mellitus. Each risk algorithm has its own limitations, so different algorithms should be used in different patient populations. Risk algorithms are made available for use in clinical practice by means of – usually interactive and online available – tools. To help the clinician to choose the right tool for the right patient, a summary of available tools is provided. When choosing a tool, physicians should consider medical history, geographical region, clinical guidelines and additional risk measures among other things. Currently, the U-prevent.com website is the only risk prediction tool providing prediction algorithms for all patient categories, and its implementation in clinical practice is suggested/advised by the European Association of Preventive Cardiology.
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