Personalized absolute benefit of statin treatment for primary or secondary prevention of vascular disease in individual elderly patients

Statin treatment for primary and secondary prevention in elderly patients-a cross-sectional study in Stockholm, Sweden

BACKGROUND

Age is a major risk factor for atherosclerotic cardiovascular disease (CVD) and death, but there has been a debate about benefit-risk of statin treatment in the elderly with limited evidence on benefits for primary prevention, while there is strong evidence for its use in secondary prevention.

AIM

The aim of this study was to provide an overview of statin utilization in primary and secondary prevention for patients 75-84 years and ≥ 85 years in the Swedish capital Region Stockholm in 2019.

METHODS

This is a cross-sectional study based on the regional healthcare database VAL containing all diagnoses and dispensed prescription drugs for all 174,950 inhabitants ≥ 75 years old in the Stockholm Region. Prevalence and incidence were analyzed by sex, age, cardiovascular risk, substance, and the intensity of treatment.

RESULTS

A total of 35% of all individuals above the age of 75 in the region were treated with statins in 2019. The overall incidence in this age group was 31 patients per 1000 inhabitants. Men, individuals 75-84 compared to ≥ 85 years of age, and those with higher cardiovascular risk were treated to a greater extent. Simvastatin was used primarily by prevalent users and atorvastatin by incident users. The majority was treated with moderate-intensity dosages and fewer women received high intensity treatment.

CONCLUSIONS

Statins are widely prescribed in the elderly. Physicians seem to consider individual cardiovascular risk when deciding to initiate statin treatment for elderly patients, but here may still be some undertreatment among high-risk patients (especially women and elderly 85 + years) and some overtreatment among patients with low-risk for CVD.

Statins under-treatment and mortality in patients with atrial fibrillation. Insights from the nationwide START registry

BACKGROUND AND AIMS

Statins are mainstream drugs for cardiovascular (CV) prevention, but under-prescription is an important clinical challenge. Data on the use of single statins and on the rate of under-prescription in atrial fibrillation (AF) are lacking. We evaluated the association of statin underuse with mortality risk in a large AF cohort.

METHODS AND RESULTS

As many as 5477 patients from the Italian nationwide START registry were included. The prevalence of different statins was reported and the association of under prescription with all-cause and CV mortality investigated. Mean age was 80.2 years, and 46.4% were women. Among 2899 patients with a clinical indication to statin, only 1578 (54.4%) were on treatment. In a mean follow-up of 22.5 ± 17.1 months, 491 (4.7%/year) deaths occurred (106 CV deaths, 1.0%/year). Atorvastatin and Simvastatin were inversely associated with all-cause (HR 0.692, 95% CI 0.519-0.923, p = 0.012 and HR 0.598, 95% CI 0.428-0.836, p = 0.003, respectively) and CV death (HR 0.372, 95% CI 0.178-0.776, p = 0.008 and HR 0.306, 95% CI 0.123-0.758, p = 0.010, respectively). The 1321 untreated patients were older, more frequently women and with a higher prevalence of diabetes, previous cerebrovascular disease, peripheral artery disease compared to those on treatment. Statin undertreatment was associated with higher risk of all-cause (HR 1.400, 95% CI 1.078-1.819, p = 0.012) and CV death (HR 2.057, 95% CI 1.188-3.561, p = 0.010).

CONCLUSIONS

AF patients with an indication to statins but left untreated show a high risk of all-cause and CV mortality. Implementation of statin prescription in the AF population can help reducing the residual mortality risk.

[General Estimation of Cardiovascular Risk]

The assessment of individual cardiovascular disease risk (CVD) is essential for a cost-effective prevention of cardiovascular morbidity and mortality. While almost half of the population have a very low CVD risk approximately 1/5th of the population have a CVD risk >20% over the next 10 years.Modern risk scores like the ESC-SCORE2 help to identify those in need of intensified preventive efforts based on basic risk factors like smoking, systolic blood pressure, total cholesterol, age, and sex. According to current ESC-guidelines all men >45 years and all women >55 years should be assessed with SCORE2, which is the best calibrated and validated scoring system for Europe.The calculation of total cardiovascular risk also permits to calculate an individual heart-age, which makes it easier for the individual patient to understand his own risk profile. Most current risk estimation systems can be accessed online under u-prevent.com/calculators.

Cardiovascular risk prediction in healthy older people

Identification of individuals with increased risk of major adverse cardiovascular events (MACE) is important. However, algorithms specific to the elderly are lacking. Data were analysed from a randomised trial involving 18,548 participants ≥ 70 years old (mean age 75.4 years), without prior cardiovascular disease events, dementia or physical disability. MACE included coronary heart disease death, fatal or nonfatal ischaemic stroke or myocardial infarction. Potential predictors tested were based on prior evidence and using a machine-learning approach. Cox regression analyses were used to calculate 5-year predicted risk, and discrimination evaluated from receiver operating characteristic curves. Calibration was also assessed, and the findings internally validated using bootstrapping. External validation was performed in 25,138 healthy, elderly individuals in the primary care environment. During median follow-up of 4.7 years, 594 MACE occurred. Predictors in the final model included age, sex, smoking, systolic blood pressure, high-density lipoprotein cholesterol (HDL-c), non-HDL-c, serum creatinine, diabetes and intake of antihypertensive agents. With variable selection based on machine-learning, age, sex and creatinine were the most important predictors. The final model resulted in an area under the curve (AUC) of 68.1 (95% confidence intervals 65.9; 70.4). The model had an AUC of 67.5 in internal and 64.2 in external validation. The model rank-ordered risk well but underestimated absolute risk in the external validation cohort. A model predicting incident MACE in healthy, elderly individuals includes well-recognised, potentially reversible risk factors and notably, renal function. Calibration would be necessary when used in other populations.

Reduced cardiovascular morbidity in patients with hemophilia: results of a 5-year multinational prospective study

In this prospective study, PWH have a lower-than-predicted incidence of CVD.

The QRISK-2011 risk predictor is not valid for PWH.

Hemophilia is a congenital bleeding disorder caused by low levels of clotting factor VIII or IX. The life expectancy of people with hemophilia (PWH) has increased with the availability of clotting factor concentrates. At the same time, the incidence of cardiovascular disease (CVD) has increased; in retrospective studies, there are conflicting data regarding if, despite this increase, the incidence is still lower than in the general population. We prospectively compared the incidence of CVD in PWH vs the predicted incidence. This prospective, multicenter, observational study included adult PWH (aged >30 years) from The Netherlands and United Kingdom. They were followed up for a 5-year period, and CVD incidence was compared with a predicted event rate based on the QRISK2-2011 CVD risk model. The primary end point was the observed fatal and nonfatal CVD incidence after 5 years compared with the estimated events and in relation to severity of hemophilia. The study included 709 patients, of whom 687 (96.9%) completed 5 years’ follow-up or reached an end point. For 108 patients, the QRISK score could not be calculated at inclusion. For the remaining 579, fewer CVD events were observed than predicted: 9 vs 24 (relative risk, 0.38; 95% confidence interval, 0.18-0.80; P = .01), corresponding with an absolute risk reduction of 2.4%. Severe hemophilia treated on demand had the highest risk reduction. There was no statistically significant relation between severity of hemophilia and incidence of CVD. In hemophilia, a lower-than-predicted CVD incidence was found, supporting the theory that hemophilia protects against CVD. The study is registered at www.clinicaltrials.gov as #NCT01303900.

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.

Real-time imputation of missing predictor values in clinical practice

Aims

Use of prediction models is widely recommended by clinical guidelines, but usually requires complete information on all predictors, which is not always available in daily practice. We aim to describe two methods for real-time handling of missing predictor values when using prediction models in practice.

Methods and results

We compare the widely used method of mean imputation (M-imp) to a method that personalizes the imputations by taking advantage of the observed patient characteristics. These characteristics may include both prediction model variables and other characteristics (auxiliary variables). The method was implemented using imputation from a joint multivariate normal model of the patient characteristics (joint modelling imputation; JMI). Data from two different cardiovascular cohorts with cardiovascular predictors and outcome were used to evaluate the real-time imputation methods. We quantified the prediction model's overall performance [mean squared error (MSE) of linear predictor], discrimination (c-index), calibration (intercept and slope), and net benefit (decision curve analysis). When compared with mean imputation, JMI substantially improved the MSE (0.10 vs. 0.13), c-index (0.70 vs. 0.68), and calibration (calibration-in-the-large: 0.04 vs. 0.06; calibration slope: 1.01 vs. 0.92), especially when incorporating auxiliary variables. When the imputation method was based on an external cohort, calibration deteriorated, but discrimination remained similar.

Conclusions

We recommend JMI with auxiliary variables for real-time imputation of missing values, and to update imputation models when implementing them in new settings or (sub)populations.

The scientific basis for secondary prevention of coronary artery disease: recent contributions from the Netherlands

While the beneficial effects of secondary prevention of cardiovascular disease are undisputed, implementation remains challenging. A gap between guideline-mandated risk factor targets and clinical reality was documented as early as the 1990s. To address this issue, research groups in the Netherlands have performed several major projects. These projects address innovative, multidisciplinary strategies to improve medication adherence and to stimulate healthy lifestyles, both in the setting of cardiac rehabilitation and at dedicated outpatient clinics. The findings of these projects have led to changes in prevention and rehabilitation guidelines.

Primary prevention of cardiovascular disease: Updated review of contemporary guidance and literature

Cardiovascular disease remains a substantial concern in terms of global mortality and morbidity, while prevalence of cardiovascular disease is increasing as treatment modalities improve survival. With an ageing population and increasing costs of chronic medical care, primary prevention of cardiovascular disease is an important target for healthcare providers. Since the previous iteration of this paper, new international guidelines have been produced regarding hypertension and lipid lowering therapies, whilst there is a growing body of evidence and new therapies emerging in other areas of lifestyle and pharmacotherapeutic intervention. This review outlines emerging evidence in the field and compares and contrasts contemporary recommendations from European and American guidelines.

Treatment of hypercholesterolaemia in older adults calls for a patient-centred approach

Due to an increasing number of older adults with (risk factors for) cardiovascular disease (CVD), the sum of older adults eligible for lipid-lowering drugs will increase. This has risen questions about benefits and harms of lipid-lowering therapy in older adults with a varying number of (cardiovascular) comorbidities and functional status. The heterogeneity in physical and functional health increases with age, leading to a much wider variety in cardiovascular risk and life expectancy than in younger adults. We suggest treatment decisions on hypercholesterolaemia in adults aged ≥75 years should shift from a strictly 10-year cardiovascular risk-driven approach to a patient-centred and lifetime benefit-based approach. With this, estimated 10-year risk of CVD should be placed into the perspective of life expectancy. Moreover, frailty and safety concerns must be taken into account for a risk–benefit discussion between clinician and patient. Based on the Dutch addendum ‘Cardiovascular Risk Management in (frail) older adults’, our approach offers more detailed information on when not to initiate or deprescribe therapy than standard guidelines. Instead of using traditional risk estimating tools which tend to overestimate risk of CVD in older adults, use a competing risk adjusted, older adults-specific risk score (available at https://u-prevent.com). By filling in a patient’s (cardiovascular) health profile (eg, cholesterol, renal function), the tool estimates risk of CVD and models the effect of medication in terms of absolute risk reduction for an individual patient. Using this tool can guide doctors and patients in making shared decisions on initiating, continuing or deprescribing lipid-lowering therapy.

A computerised decision support system for cardiovascular risk management 'live' in the electronic health record environment: development, validation and implementation-the Utrecht Cardiovascular Cohort Initiative

Purpose

We set out to develop a real-time computerised decision support system (CDSS) embedded in the electronic health record (EHR) with information on risk factors, estimated risk, and guideline-based advice on treatment strategy in order to improve adherence to cardiovascular risk management (CVRM) guidelines with the ultimate aim of improving patient healthcare.

Methods

We defined a project plan including the scope and requirements, infrastructure and interface, data quality and study population, validation and evaluation of the CDSS.

Results

In collaboration with clinicians, data scientists, epidemiologists, ICT architects, and user experience and interface designers we developed a CDSS that provides ‘live’ information on CVRM within the environment of the EHR. The CDSS provides information on cardiovascular risk factors (age, sex, medical and family history, smoking, blood pressure, lipids, kidney function, and glucose intolerance measurements), estimated 10-year cardiovascular risk, guideline-compliant suggestions for both pharmacological and non-pharmacological treatment to optimise risk factors, and an estimate on the change in 10-year risk of cardiovascular disease if treatment goals are adhered to. Our pilot study identified a number of issues that needed to be addressed, such as missing data, rules and regulations, privacy, and patient participation.

Conclusion

Development of a CDSS is complex and requires a multidisciplinary approach. We identified opportunities and challenges in our project developing a CDSS aimed at improving adherence to CVRM guidelines. The regulatory environment, including guidance on scientific evaluation, legislation, and privacy issues needs to evolve within this emerging field of eHealth.

Risk prediction tools in cardiovascular disease prevention: A report from the ESC Prevention of CVD Programme led by the European Association of Preventive Cardiology (EAPC) in collaboration with the Acute Cardiovascular Care Association (ACCA) and the Association of Cardiovascular Nursing and Allied Professions (ACNAP)

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.

Risk prediction tools in cardiovascular disease prevention: A report from the ESC Prevention of CVD Programme led by the European Association of Preventive Cardiology (EAPC) in collaboration with the Acute Cardiovascular Care Association (ACCA) and the Association of Cardiovascular Nursing and Allied Professions (ACNAP)

Risk assessment 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.

Risk prediction tools in cardiovascular disease prevention: A report from the ESC Prevention of CVD Programme led by the European Association of Preventive Cardiology (EAPC) in collaboration with the Acute Cardiovascular Care Association (ACCA) and the Association of Cardiovascular Nursing and Allied Professions (ACNAP)

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.

A Patient-Centered Approach to Comparative Effectiveness Research Focused on Older Adults: Lessons From the Patient-Centered Outcomes Research Institute

The mission of the Patient-Centered Outcomes Research Institute (PCORI) is to fund the production of high-quality evidence that will enable patients and clinicians to make informed, personalized healthcare decisions. Since 2012, the PCORI has invested $177 million in patient-centered comparative effectiveness research (CER) that specifically targets the health needs of older adults, with additional relevant studies in its broader portfolio. Developing the PCORI's research portfolio has provided us with significant insights into what factors to consider when conducting CER in older adult populations. When comparing the net benefit of two or more interventions for older adults, investigators should consider the following: absolute risk difference, competing risks, life expectancy, the difference between chronologic and physiologic age, the importance of patient preferences, and other potential drivers of variable treatment effects. Investigators should also engage older adults and their caregivers as partners throughout the research process. Their input helps to identify key outcomes of interest and insights about the conduct of the research. As the PCORI continues to support research that addresses the healthcare decisions of the rapidly growing older adult population, it needs to partner with patients and researchers to identify the most important questions to address. J Am Geriatr Soc 67:21-28, 2019.

The prediction of therapy-benefit for individual cardiovascular disease prevention: rationale, implications, and implementation

PURPOSE OF REVIEW

We aim to outline the importance and the clinical implications of using predicted individual therapy-benefit in making patient-centered treatment decisions in cardiovascular disease (CVD) prevention. Therapy-benefit concepts will be illustrated with examples of patients undergoing lipid management.

RECENT FINDINGS

In both primary and secondary CVD prevention, the degree of variation in individual therapy-benefit is large. An individual's therapy-benefit can be estimated by combining prediction algorithms and clinical trial data. Measures of therapy-benefit can be easily integrated into clinical practice via a variety of online calculators. Lifetime estimates (e.g., gain in healthy life expectancy) look at therapy-benefit over the course of an individual's life, and are less influenced by age than short-term estimates (e.g., 10-year absolute risk reduction). Lifetime estimates can thus identify people who could substantially benefit from early initiation of CVD prevention. Compared with current guidelines, treatment based on predicted therapy-benefit would increase eligibility for therapy among young people with a moderate risk-factor burden and individuals with a high residual risk.

SUMMARY

The estimation of individual therapy-benefit is an important part of individualized medicine. Implementation tools allow for clinicians to readily estimate both short-term and lifetime therapy-benefit.

Comparison of Lipoprotein(a)-Levels in Patients ≥70 Years of Age With Versus Without Aortic Valve Stenosis

Although lipoprotein(a) (Lp[a]) is linked with aortic valve calcification and clinical aortic valve stenosis (AVS) in middle-aged cohorts, patients aged ≥70 years represent a majority of patients with AVS, in which mechanisms leading to AVS may differ. We sought to determine whether Lp(a) distinguishes patients ≥70 years with and without AVS. We matched 484 patients ≥70 years with AVS, scheduled for transcatheter aortic valve implantation with 484 patients without AVS by age group and gender. Lp(a) levels were compared in patients with and without AVS and stratified by presence and absence of clinical coronary artery disease (CAD) manifestation. A total of 968 patients (mean age 80 ± 5 years, 48% women) were included. When comparing patients with and without AVS, no difference in Lp(a) was observed (AVS: 17 [8; 56] mg/dl, no AVS: 18.5 [8.5; 57] mg/dl, p = 0.56). In contrast, patients with clinical CAD manifestation had higher Lp(a) levels than those without clinical CAD manifestation (coronary artery disease: 19 [9; 60] mg/dl, no coronary artery disease 15 [7; 44] mg/dl, p = 0.0006). In regression analysis, no significant association of Lp(a) with AVS was observed in unadjusted (OR [95% CI]: 0.98 [0.91 to 1.06], p = 0.59) and risk factor-adjusted models (0.98 [0.90 to 1.06], p = 0.57). However, Lp(a) was independently associated with clinical CAD manifestation (unadjusted: 1.14 [1.04 to 1.24], p = 0.003, risk factor adjusted: 1.17 [1.07 to 1.27], p = 0.0006). In conclusion, in a large cohort of patients ≥70 years, Lp(a) was associated with clinical CAD manifesation, but not with AVS. Our results suggest that in patients over 70 years, the development of AVS is not influenced by Lp(a).

Personalized Statin Therapy and Coronary Atherosclerotic Plaque Burden in Asymptomatic Low/Intermediate-Risk Individuals

BACKGROUND

Current guidelines for the primary prevention of atherosclerotic cardiovascular disease are based on the estimation of a predicted 10-year cardiovascular disease risk and the average relative risk reduction estimates from statin trials. In the clinical setting, however, decision-making is better informed by the expected benefit for the individual patient, which is typically lacking. Consequently, a personalized statin benefit approach based on absolute risk reduction over 10 years (ARR10 benefit threshold ≥2.3%) has been proposed as a novel approach. However, how this benefit threshold relates with coronary plaque burden in asymptomatic individuals with low/intermediate cardiovascular disease risk is unknown.

AIMS

In this study, we compared the predicted ARR10 obtained in each individual with plaque burden detected by coronary computed tomography angiography.

METHODS AND RESULTS

Plaque burden (segment volume score, segment stenosis score, and segment involvement score) was assessed in prospectively recruited asymptomatic subjects (n = 70; 52% male; median age 56 years [interquartile range 51-64 years]) with low/intermediate Framingham risk score (< 20%). The expected ARR10 with statin in the entire cohort was 2.7% (1.5-4.6%) with a corresponding number needed to treat over 10 years of 36 (22-63). In subjects with an ARR10 benefit threshold ≥2.3% (vs. < 2.3%), plaque burden was significantly higher (p = 0.02).

CONCLUSION

These findings suggest that individuals with higher coronary plaque burden are more likely to get greater benefit from statin therapy even among asymptomatic individuals with low cardiovascular risk.

Targeting LDL Cholesterol: Beyond Absolute Goals Toward Personalized Risk

PURPOSE OF REVIEW

The aim of this study was to review and assess the evidence for low-density lipoprotein cholesterol (LDL-C) treatment goals as presented in current guidelines for primary and secondary prevention of cardiovascular disease.

RECENT FINDINGS

Different sets of guidelines and clinical studies for secondary prevention have centered on lower absolute LDL-C targets [<70 mg/dL (<1.8 mmol/L)], greater percent reductions of LDL-C (≥50%), or more intense treatment to achieve greater reductions in cardiovascular risk. Population-based risk models serve as the basis for statin initiation in primary prevention. Reviews of current population risk models for primary prevention show moderate ability to discriminate [with c-statistics ranging from 0.67 to 0.77 (95% CIs from 0.62 to 0.83) for men and women] with poor calibration and overestimation of risk. Individual clinical trial data are not compelling to support specific LDL-C targets and percent reductions in secondary prevention. Increasing utilization of electronic health records and data analytics will enable the development of individualized treatment goals in both primary and secondary prevention.