Prediction of Cardiovascular Disease Risk Accounting for Future Initiation of Statin Treatment

Treatment drop-in in a contemporary cohort used to derive cardiovascular risk prediction equations

BACKGROUND

No routinely recommended cardiovascular disease (CVD) risk prediction equations have adjusted for CVD preventive medications initiated during follow-up (treatment drop-in) in their derivation cohorts. This will lead to underestimation of risk when equations are applied in clinical practice if treatment drop-in is common. We aimed to quantify the treatment drop-in in a large contemporary national cohort to determine whether equations are likely to require adjustment.

METHODS

Eight de-identified individual-level national health administrative datasets in Aotearoa New Zealand were linked to establish a cohort of almost all New Zealanders without CVD and aged 30-74 years in 2006. Individuals dispensing blood-pressure-lowering and/or lipid-lowering medications between 1 July 2006 and 31 December 2006 (baseline dispensing), and in each 6-month period during 12 years' follow-up to 31 December 2018 (follow-up dispensing), were identified. Person-years of treatment drop-in were determined.

RESULTS

A total of 1 399 348 (80%) out of the 1 746 695 individuals in the cohort were not dispensed CVD medications at baseline. Blood-pressure-lowering and/or lipid-lowering treatment drop-in accounted for 14% of follow-up time in the group untreated at baseline and increased significantly with increasing predicted baseline 5-year CVD risk (12%, 31%, 34% and 37% in <5%, 5-9%, 10-14% and ≥15% risk groups, respectively) and with increasing age (8% in 30-44 year-olds to 30% in 60-74 year-olds).

CONCLUSIONS

CVD preventive treatment drop-in accounted for approximately one-third of follow-up time among participants typically eligible for preventive treatment (≥5% 5-year predicted risk). Equations derived from cohorts with long-term follow-up that do not adjust for treatment drop-in effect will underestimate CVD risk in higher risk individuals and lead to undertreatment. Future CVD risk prediction studies need to address this potential flaw.

Age and sex specific thresholds for risk stratification of cardiovascular disease and clinical decision making: prospective open cohort study

Objective

To quantify the potential advantages of using 10 year risk prediction models for cardiovascular disease, in combination with risk thresholds specific to both age and sex, to identify individuals at high risk of cardiovascular disease for allocation of statin treatment.

Design

Prospective open cohort study.

Setting

Primary care data from the UK Clinical Practice Research Datalink GOLD, linked with hospital admissions from Hospital Episode Statistics and national mortality records from the Office for National Statistics in England, 1 January 2006 to 31 May 2019.

Participants

1 046 736 individuals (aged 40-85 years) with no cardiovascular disease, diabetes, or a history of statin treatment at baseline using data from electronic health records.

Main outcome measures

10 year risk of cardiovascular disease, calculated with version 2 of the QRISK cardiovascular disease risk algorithm (QRISK2), with two main strategies to identify individuals at high risk: in strategy A, estimated risk was a fixed cut-off value of ≥10% (ie, as per the UK National Institute for Health and Care Excellence guidelines); in strategy B, estimated risk was ≥10% or ≥90th centile of age and sex specific risk distributions.

Results

Compared with strategy A, strategy B stratified 20 241 (149.8%) more women aged ≤53 years and 9832 (150.2%) more men aged ≤47 years as having a high risk of cardiovascular disease; for all other ages the strategies were the same. Assuming that treatment with statins would be initiated in those identified as high risk, differences in the estimated gain in cardiovascular disease-free life years from statin treatment for strategy B versus strategy A were 0.14 and 0.16 years for women and men aged 40 years, respectively; among individuals aged 40-49 years, the numbers needed to treat to prevent one cardiovascular disease event for strategy B versus strategy A were 39 versus 21 in women and 19 versus 15 in men, respectively.

Conclusions

This study quantified the potential gains in cardiovascular disease-free life years when implementing prevention strategies based on age and sex specific risk thresholds instead of a fixed risk threshold for allocation of statin treatment. Such gains should be weighed against the costs of treating more younger people with statins for longer.

Individual treatment effects of sodium-glucose co-transporter-2 inhibitors on the risk of chronic kidney disease in patients with type 2 diabetes: A counterfactual prediction model based on real-world data

AIM

To estimate individual treatment effects (ITEs) of sodium-glucose co-transporter-2 inhibitors (SGLT2is) on lowering the risk of developing chronic kidney disease (CKD) in patients with type 2 diabetes (T2D) and to identify those most probable to benefit from treatment.

METHODS

This study followed a T2D cohort from Ramathibodi Hospital, Thailand, from 2015 to 2022. A counterfactual model was constructed to predict factual and counterfactual risks of CKD if patients did/did not receive SGLT2is. ITEs were estimated by subtracting the factual risk from the counterfactual risk of CKD.

RESULTS

There were 1619 and 15 879 patients included in the SGLT2i and non-SGLT2i groups, respectively. The estimated ITEs varied from -1.19% to -17.51% with a median of -4.49%, that is, 50% of patients had a 4.49% or greater lower CKD risk if they received an SGLT2i. Patients who gained the greatest benefit from SGLT2is were more probable to be male, aged at least 60 years, with a history of diabetes duration of at least 3 months, hypertension, peripheral arterial disease, diabetic retinopathy and low high-density lipoprotein cholesterol.

CONCLUSIONS

Our prediction model provides individualized information that helps target T2D patients who may benefit more from SGLT2is. This could help clinical decision making and implementation of personalized medicine in clinical practice, especially in resource-limited settings.

Survival prediction landscape: an in-depth systematic literature review on activities, methods, tools, diseases, and databases

Survival prediction integrates patient-specific molecular information and clinical signatures to forecast the anticipated time of an event, such as recurrence, death, or disease progression. Survival prediction proves valuable in guiding treatment decisions, optimizing resource allocation, and interventions of precision medicine. The wide range of diseases, the existence of various variants within the same disease, and the reliance on available data necessitate disease-specific computational survival predictors. The widespread adoption of artificial intelligence (AI) methods in crafting survival predictors has undoubtedly revolutionized this field. However, the ever-increasing demand for more sophisticated and effective prediction models necessitates the continued creation of innovative advancements. To catalyze these advancements, it is crucial to bring existing survival predictors knowledge and insights into a centralized platform. The paper in hand thoroughly examines 23 existing review studies and provides a concise overview of their scope and limitations. Focusing on a comprehensive set of 90 most recent survival predictors across 44 diverse diseases, it delves into insights of diverse types of methods that are used in the development of disease-specific predictors. This exhaustive analysis encompasses the utilized data modalities along with a detailed analysis of subsets of clinical features, feature engineering methods, and the specific statistical, machine or deep learning approaches that have been employed. It also provides insights about survival prediction data sources, open-source predictors, and survival prediction frameworks.

Prediction Under Interventions: Evaluation of Counterfactual Performance Using Longitudinal Observational Data

Predictions under interventions are estimates of what a person's risk of an outcome would be if they were to follow a particular treatment strategy, given their individual characteristics. Such predictions can give important input to medical decision-making. However, evaluating the predictive performance of interventional predictions is challenging. Standard ways of evaluating predictive performance do not apply when using observational data, because prediction under interventions involves obtaining predictions of the outcome under conditions that are different from those that are observed for a subset of individuals in the validation dataset. This work describes methods for evaluating counterfactual performance of predictions under interventions for time-to-event outcomes. This means we aim to assess how well predictions would match the validation data if all individuals had followed the treatment strategy under which predictions are made. We focus on counterfactual performance evaluation using longitudinal observational data, and under treatment strategies that involve sustaining a particular treatment regime over time. We introduce an estimation approach using artificial censoring and inverse probability weighting that involves creating a validation dataset mimicking the treatment strategy under which predictions are made. We extend measures of calibration, discrimination (c-index and cumulative/dynamic AUCt) and overall prediction error (Brier score) to allow assessment of counterfactual performance. The methods are evaluated using a simulation study, including scenarios in which the methods should detect poor performance. Applying our methods in the context of liver transplantation shows that our procedure allows quantification of the performance of predictions supporting crucial decisions on organ allocation.

Making predictions under interventions: a case study from the PREDICT-CVD cohort in New Zealand primary care

Background

Most existing clinical prediction models do not allow predictions under interventions. Such predictions allow predicted risk under different proposed strategies to be compared and are therefore useful to support clinical decision making. We aimed to compare methodological approaches for predicting individual level cardiovascular risk under three interventions: smoking cessation, reducing blood pressure, and reducing cholesterol.

Methods

We used data from the PREDICT prospective cohort study in New Zealand to calculate cardiovascular risk in a primary care setting. We compared three strategies to estimate absolute risk under intervention: (a) conditioning on hypothetical interventions in non-causal models; (b) combining existing prediction models with causal effects estimated using observational causal inference methods; and (c) combining existing prediction models with causal effects reported in published literature.

Results

The median absolute cardiovascular risk among smokers was 3.9%; our approaches predicted that smoking cessation reduced this to a median between a non-causal estimate of 2.5% and a causal estimate of 2.8%, depending on estimation methods. For reducing blood pressure, the proposed approaches estimated a reduction of absolute risk from a median of 4.9% to a median between 3.2% and 4.5% (both derived from causal estimation). Reducing cholesterol was estimated to reduce median absolute risk from 3.1% to between 2.2% (non-causal estimate) and 2.8% (causal estimate).

Conclusions

Estimated absolute risk reductions based on non-causal methods were different to those based on causal methods, and there was substantial variation in estimates within the causal methods. Researchers wishing to estimate risk under intervention should be explicit about their causal modelling assumptions and conduct sensitivity analysis by considering a range of possible approaches.

Delayed versus primary closure to minimize risk of surgical-site infection for complicated appendicitis: A secondary analysis of a randomized trial using counterfactual prediction modeling

OBJECTIVE

To evaluate the risk of surgical site infection (SSI) following complicated appendectomy in individual patients receiving delayed primary closure (DPC) versus primary closure (PC) after adjustment for individual risk factors.

DESIGN

Secondary analysis of randomized controlled trial (RCT) with prediction model.

SETTING

Referral centers across Thailand.

PARTICIPANTS

Adult patients who underwent appendectomy via a lower-right-quadrant abdominal incision due to complicated appendicitis.

METHODS

A secondary analysis of a published RCT was performed applying a counterfactual prediction model considering interventions (PC vs DPC) and other significant predictors. A multivariable logistic regression was applied, and a likelihood-ratio test was used to select significant predictors to retain in a final model. Factual versus counterfactual SSI risks for individual patients along with individual treatment effect (iTE) were estimated.

RESULTS

In total, 546 patients (271 PC vs 275 DPC) were included in the analysis. The individualized prediction model consisted of allocated intervention, diabetes, type of complicated appendicitis, fecal contamination, and incision length. The iTE varied between 0.4% and 7% for PC compared to DPC; ∼38.1% of patients would have ≥2.1% lower SSI risk following PC compared to DPC. The greatest risk reduction was identified in diabetes with ruptured appendicitis, fecal contamination, and incision length of 10 cm, where SSI risks were 47.1% and 54.1% for PC and DPC, respectively.

CONCLUSIONS

In this secondary analysis, we found that most patients benefited from early PC versus DPC. Findings may be used to inform SSI prevention strategies for patients with complicated appendicitis.

Dose-Response Associations of Lipid Traits With Coronary Artery Disease and Mortality

Importance

Apolipoprotein B (apoB), low-density lipoprotein cholesterol (LDL-C), and triglycerides (TG) are associated with coronary artery disease (CAD). However, trial evidence for the association of intensive LDL-C lowering and TG lowering with mortality is less definitive.

Objectives

To investigate the associations of apoB, LDL-C, and TG with CAD and mortality, both overall and by sex and age, and to characterize the shapes of these associations.

Design, Setting, and Participants

This genetic association study used linear and nonlinear mendelian randomization (MR) to analyze a population-based cohort of individuals of European ancestry from the UK Biobank, which recruited participants from 2006 to 2010 with follow-up information updated until September 2021. Data analysis occurred from December 2022 to November 2023.

Exposures

Genetically predicted apoB, LDL-C, and TG.

Main Outcomes and Measures

The primary outcomes were CAD, all-cause mortality, and cause-specific mortality. Genetic associations with CAD were calculated using logistic regression, associations with all-cause mortality using Cox proportional hazards regression, and associations with cause-specific mortality using cause-specific Cox proportional hazards regression with censoring for other causes of mortality.

Results

This study included 347 797 participants (mean [SD] age, 57.2 [8.0] years; 188 330 female [54.1%]). There were 23 818 people who developed CAD and 23 848 people who died. Genetically predicted apoB was positively associated with risk of CAD (odds ratio [OR], 1.65 per SD increase; 95% CI 1.57-1.73), all-cause mortality (hazard ratio [HR], 1.11; 95% CI, 1.06-1.16), and cardiovascular mortality (HR, 1.36; 95% CI, 1.24-1.50), with some evidence for larger associations in male participants than female participants. Findings were similar for LDL-C. Genetically predicted TG was positively associated with CAD (OR, 1.60; 95% CI 1.52-1.69), all-cause mortality (HR, 1.08; 95% CI, 1.03-1.13), and cardiovascular mortality (HR, 1.21; 95% CI, 1.09-1.34); however, sensitivity analyses suggested evidence of pleiotropy. The association of genetically predicted TG with CAD persisted but it was no longer associated with mortality outcomes after controlling for apoB. Nonlinear MR suggested that all these associations were monotonically increasing across the whole observed distribution of each lipid trait, with no diminution at low lipid levels. Such patterns were observed irrespective of sex or age.

Conclusions and relevance

In this genetic association study, apoB (or, equivalently, LDL-C) was associated with increased CAD risk, all-cause mortality, and cardiovascular mortality, all in a dose-dependent way. TG may increase CAD risk independent of apoB, although the possible presence of pleiotropy is a limitation. These insights highlight the importance of apoB (or, equivalently, LDL-C) lowering for reducing cardiovascular morbidity and mortality across its whole distribution.

Comparison of the European and US guidelines for lipid-lowering therapy in primary prevention of cardiovascular disease

AIMS

Population-wide impacts of new guidelines in the primary prevention of atherosclerotic cardiovascular disease (ASCVD) should be explored in independent cohorts. Assess and compare the lipid-lowering therapy eligibility and predictive classification performance of 2016 and 2021 European Society of Cardiology (ESC), 2019 American Heart Association/American College of Cardiology (AHA/ACC), and 2022 US Preventive Services Task Force (USPSTF) guidelines.

METHODS AND RESULTS

Participants from the CoLaus|PsyCoLaus study, without ASCVD and not taking lipid-lowering therapy at baseline. Derivation of 10-year risk for ASCVD using Systematic COronary Risk Evaluation (SCORE1), SCORE2 [including SCORE2-Older Persons (SCORE2-OP)], and pooled cohort equation. Computation of the number of people eligible for lipid-lowering therapy based on each guideline and assessment of discrimination and calibration metrics of the risk models using first incident ASCVD as an outcome. Among 4,092 individuals, 158 (3.9%) experienced an incident ASCVD during a median follow-up of 9 years (interquartile range, 1.1). Lipid-lowering therapy was recommended or considered in 40.2% (95% confidence interval, 38.2-42.2), 26.4% (24.6-28.2), 28.6% (26.7-30.5), and 22.6% (20.9-24.4) of women and in 62.1% (59.8-64.3), 58.7% (56.4-61.0), 52.6% (50.3-54.9), and 48.4% (46.1-50.7) of men according to the 2016 ESC, 2021 ESC, 2019 AHA/ACC, and 2022 USPSTF guidelines, respectively. 43.3 and 46.7% of women facing an incident ASCVD were not eligible for lipid-lowering therapy at baseline according to the 2021 ESC and 2022 USPSTF, compared with 21.7 and 38.3% using the 2016 ESC and 2019 AHA/ACC, respectively.

CONCLUSION

Both the 2022 USPSTF and 2021 ESC guidelines particularly reduced lipid-lowering therapy eligibility in women. Nearly half of women who faced an incident ASCVD were not eligible for lipid-lowering therapy.

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.

SCORE2-Diabetes: 10-year cardiovascular risk estimation in type 2 diabetes in Europe

AIMS

To develop and validate a recalibrated prediction model (SCORE2-Diabetes) to estimate the 10-year risk of cardiovascular disease (CVD) in individuals with type 2 diabetes in Europe.

METHODS AND RESULTS

SCORE2-Diabetes was developed by extending SCORE2 algorithms using individual-participant data from four large-scale datasets comprising 229 460 participants (43 706 CVD events) with type 2 diabetes and without previous CVD. Sex-specific competing risk-adjusted models were used including conventional risk factors (i.e. age, smoking, systolic blood pressure, total, and HDL-cholesterol), as well as diabetes-related variables (i.e. age at diabetes diagnosis, glycated haemoglobin [HbA1c] and creatinine-based estimated glomerular filtration rate [eGFR]). Models were recalibrated to CVD incidence in four European risk regions. External validation included 217 036 further individuals (38 602 CVD events), and showed good discrimination, and improvement over SCORE2 (C-index change from 0.009 to 0.031). Regional calibration was satisfactory. SCORE2-Diabetes risk predictions varied several-fold, depending on individuals' levels of diabetes-related factors. For example, in the moderate-risk region, the estimated 10-year CVD risk was 11% for a 60-year-old man, non-smoker, with type 2 diabetes, average conventional risk factors, HbA1c of 50 mmol/mol, eGFR of 90 mL/min/1.73 m2, and age at diabetes diagnosis of 60 years. By contrast, the estimated risk was 17% in a similar man, with HbA1c of 70 mmol/mol, eGFR of 60 mL/min/1.73 m2, and age at diabetes diagnosis of 50 years. For a woman with the same characteristics, the risk was 8% and 13%, respectively.

CONCLUSION

SCORE2-Diabetes, a new algorithm developed, calibrated, and validated to predict 10-year risk of CVD in individuals with type 2 diabetes, enhances identification of individuals at higher risk of developing CVD across Europe.

Recurrent venous thromboembolism and bleeding with extended anticoagulation: the VTE-PREDICT risk score

AIMS

Deciding to stop or continue anticoagulation for venous thromboembolism (VTE) after initial treatment is challenging, as individual risks of recurrence and bleeding are heterogeneous. The present study aimed to develop and externally validate models for predicting 5-year risks of recurrence and bleeding in patients with VTE without cancer who completed at least 3 months of initial treatment, which can be used to estimate individual absolute benefits and harms of extended anticoagulation.

METHODS AND RESULTS

Competing risk-adjusted models were derived to predict recurrent VTE and clinically relevant bleeding (non-major and major) using 14 readily available patient characteristics. The models were derived from combined individual patient data from the Bleeding Risk Study, Hokusai-VTE, PREFER-VTE, RE-MEDY, and RE-SONATE (n = 15,141, 220 recurrences, 189 bleeding events). External validity was assessed in the Danish VTE cohort, EINSTEIN-CHOICE, GARFIELD-VTE, MEGA, and Tromsø studies (n = 59 257, 2283 recurrences, 3335 bleeding events). Absolute treatment effects were estimated by combining the models with hazard ratios from trials and meta-analyses. External validation in different settings showed agreement between predicted and observed risks up to 5 years, with C-statistics ranging from 0.48-0.71 (recurrence) and 0.61-0.68 (bleeding). In the Danish VTE cohort, 5-year risks ranged from 4% to 19% for recurrent VTE and 1% -19% for bleeding.

CONCLUSION

The VTE-PREDICT risk score can be applied to estimate the effect of extended anticoagulant treatment for individual patients with VTE and to support shared decision-making.

Educational Technology on Urinary Incontinence during Pregnancy: Development and Validation of an Online Course for the Brazilian Population

Objective: To describe the development and validation process of an online course on urinary incontinence during pregnancy in Brazil. Materials and methods: This methodological study followed an online course’s literature search, development, and validation steps. A total of 22 specialists participated in the validation step, and the content validity index (CVI) was used. Fifty-one Physical Therapy students (target audience) also participated in the Suitability Assessment of Materials. Results: The synthesis reached in the integrative review provided the basis for the course’s theoretical content, which was regarded as suitable by the specialists regarding its content, language, presentation, stimulation/motivation, and cultural adequacy (CVI = 0.99). The target audience considered the course organized, easily understandable, engaging, and motivational, with a positive response index ranging from 84.3 % to 100 %. Conclusions: The Brazilian version of the online course was considered sufficiently adequate in content and interface quality by both specialists and the target audience.

Estimation of recurrent atherosclerotic cardiovascular event risk in patients with established cardiovascular disease: the updated SMART2 algorithm

AIMS

The 10-year risk of recurrent atherosclerotic cardiovascular disease (ASCVD) events in patients with established ASCVD can be estimated with the Secondary Manifestations of ARTerial disease (SMART) risk score, and may help refine clinical management. To broaden generalizability across regions, we updated the existing tool (SMART2 risk score) and recalibrated it with regional incidence rates and assessed its performance in external populations.

METHODS AND RESULTS

Individuals with coronary artery disease, cerebrovascular disease, peripheral artery disease, or abdominal aortic aneurysms were included from the Utrecht Cardiovascular Cohort-SMART cohort [n = 8355; 1706 ASCVD events during a median follow-up of 8.2 years (interquartile range 4.2-12.5)] to derive a 10-year risk prediction model for recurrent ASCVD events (non-fatal myocardial infarction, non-fatal stroke, or cardiovascular mortality) using a Fine and Gray competing risk-adjusted model. The model was recalibrated to four regions across Europe, and to Asia (excluding Japan), Japan, Australia, North America, and Latin America using contemporary cohort data from each target region. External validation used data from seven cohorts [Clinical Practice Research Datalink, SWEDEHEART, the international REduction of Atherothrombosis for Continued Health (REACH) Registry, Estonian Biobank, Spanish Biomarkers in Acute Coronary Syndrome and Biomarkers in Acute Myocardial Infarction (BACS/BAMI), the Norwegian COgnitive Impairment After STroke, and Bialystok PLUS/Polaspire] and included 369 044 individuals with established ASCVD of whom 62 807 experienced an ASCVD event. C-statistics ranged from 0.605 [95% confidence interval (CI) 0.547-0.664] in BACS/BAMI to 0.772 (95% CI 0.659-0.886) in REACH Europe high-risk region. The clinical utility of the model was demonstrated across a range of clinically relevant treatment thresholds for intensified treatment options.

CONCLUSION

The SMART2 risk score provides an updated, validated tool for the prediction of recurrent ASCVD events in patients with established ASCVD across European and non-European populations. The use of this tool could allow for a more personalized approach to secondary prevention based upon quantitative rather than qualitative estimates of residual risk.

Risk Stratification in Patients with Ischemic Stroke and Residual Cardiovascular Risk with Current Secondary Prevention

PURPOSE

Suboptimal secondary prevention in patients with stroke causes a remaining cardiovascular risk desirable to reduce. We have validated a prognostic model for secondary preventive settings and estimated future cardiovascular risk and theoretical benefit of reaching guideline recommended risk factor targets.

PATIENTS AND METHODS

The SMART-REACH (Secondary Manifestations of Arterial Disease-Reduction of Atherothrombosis for Continued Health) model for 10-year and lifetime risk of cardiovascular events was applied to 465 patients in the Norwegian Cognitive Impairment After Stroke (Nor-COAST) study, a multicenter observational study with two-year follow-up by linkage to national registries for cardiovascular disease and mortality. The residual risk when reaching recommended targets for blood pressure, low-density lipoprotein cholesterol, smoking cessation and antithrombotics was estimated.

RESULTS

In total, 11.2% had a new event. Calibration plots showed adequate agreement between estimated and observed 2-year prognosis (C-statistics 0.63, 95% confidence interval 0.55-0.71). Median estimated 10-year risk of recurrent cardiovascular events was 42% (Interquartile range (IQR) 32-54%) and could be reduced to 32% by optimal guideline-based therapy. The corresponding numbers for lifetime risk were 70% (IQR 63-76%) and 61%. We estimated an overall median gain of 1.4 (IQR 0.2-3.4) event-free life years if guideline targets were met.

CONCLUSION

Secondary prevention was suboptimal and residual risk remains elevated even after optimization according to current guidelines. Considerable interindividual variation in risk exists, with a corresponding variation in benefit from intensification of treatment. The SMART-REACH model can be used to identify patients with the largest benefit from more intensive treatment and follow-up.

SCORE2-OP risk prediction algorithms: estimating incident cardiovascular event risk in older persons in four geographical risk regions

AIMS

The aim of this study was to derive and validate the SCORE2-Older Persons (SCORE2-OP) risk model to estimate 5- and 10-year risk of cardiovascular disease (CVD) in individuals aged over 70 years in four geographical risk regions.

METHODS AND RESULTS

Sex-specific competing risk-adjusted models for estimating CVD risk (CVD mortality, myocardial infarction, or stroke) were derived in individuals aged over 65 without pre-existing atherosclerotic CVD from the Cohort of Norway (28 503 individuals, 10 089 CVD events). Models included age, smoking status, diabetes, systolic blood pressure, and total- and high-density lipoprotein cholesterol. Four geographical risk regions were defined based on country-specific CVD mortality rates. Models were recalibrated to each region using region-specific estimated CVD incidence rates and risk factor distributions. For external validation, we analysed data from 6 additional study populations {338 615 individuals, 33 219 CVD validation cohorts, C-indices ranged between 0.63 [95% confidence interval (CI) 0.61-0.65] and 0.67 (0.64-0.69)}. Regional calibration of expected-vs.-observed risks was satisfactory. For given risk factor profiles, there was substantial variation across the four risk regions in the estimated 10-year CVD event risk.

CONCLUSIONS

The competing risk-adjusted SCORE2-OP model was derived, recalibrated, and externally validated to estimate 5- and 10-year CVD risk in older adults (aged 70 years or older) in four geographical risk regions. These models can be used for communicating the risk of CVD and potential benefit from risk factor treatment and may facilitate shared decision-making between clinicians and patients in CVD risk management in older persons.

SCORE2 risk prediction algorithms: new models to estimate 10-year risk of cardiovascular disease in Europe

AIMS

The aim of this study was to develop, validate, and illustrate an updated prediction model (SCORE2) to estimate 10-year fatal and non-fatal cardiovascular disease (CVD) risk in individuals without previous CVD or diabetes aged 40-69 years in Europe.

METHODS AND RESULTS

We derived risk prediction models using individual-participant data from 45 cohorts in 13 countries (677 684 individuals, 30 121 CVD events). We used sex-specific and competing risk-adjusted models, including age, smoking status, systolic blood pressure, and total- and HDL-cholesterol. We defined four risk regions in Europe according to country-specific CVD mortality, recalibrating models to each region using expected incidences and risk factor distributions. Region-specific incidence was estimated using CVD mortality and incidence data on 10 776 466 individuals. For external validation, we analysed data from 25 additional cohorts in 15 European countries (1 133 181 individuals, 43 492 CVD events). After applying the derived risk prediction models to external validation cohorts, C-indices ranged from 0.67 (0.65-0.68) to 0.81 (0.76-0.86). Predicted CVD risk varied several-fold across European regions. For example, the estimated 10-year CVD risk for a 50-year-old smoker, with a systolic blood pressure of 140 mmHg, total cholesterol of 5.5 mmol/L, and HDL-cholesterol of 1.3 mmol/L, ranged from 5.9% for men in low-risk countries to 14.0% for men in very high-risk countries, and from 4.2% for women in low-risk countries to 13.7% for women in very high-risk countries.

CONCLUSION

SCORE2-a new algorithm derived, calibrated, and validated to predict 10-year risk of first-onset CVD in European populations-enhances the identification of individuals at higher risk of developing CVD across Europe.