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Published in final edited form as: Am J Cardiol. 2025 Jan 3;241:43–51. doi: 10.1016/j.amjcard.2024.12.034 Search in PMC Search in PubMed View in NLM Catalog Add to search Differences in Statin Eligibility with the Use of Predicting Risk of Cardiovascular Disease EVENTs Versus Pooled Cohort Equations in the UK Biobank Jasninder S Dhaliwal Jasninder S Dhaliwal , MD a. Division of Cardiovascular Disease, University of Alabama at Birmingham, Birmingham, AL 35233 Find articles by Jasninder S Dhaliwal a , Mokshad Gaonkar Mokshad Gaonkar , MS a. Division of Cardiovascular Disease, University of Alabama at Birmingham, Birmingham, AL 35233 Find articles by Mokshad Gaonkar a , Nirav Patel Nirav Patel , MD, MSPH a. Division of Cardiovascular Disease, University of Alabama at Birmingham, Birmingham, AL 35233 Find articles by Nirav Patel a , Naman S Shetty Naman S Shetty , MD b. Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA c. Harvard Medical School, Boston, MA Find articles by Naman S Shetty b, c , Peng Li Peng Li , PhD d. School of Nursing, University of Alabama at Birmingham, Birmingham, AL 35233 Find articles by Peng Li d , Nehal Vekariya Nehal Vekariya , MS a. Division of Cardiovascular Disease, University of Alabama at Birmingham, Birmingham, AL 35233 Find articles by Nehal Vekariya a , Rajat Kalra Rajat Kalra , MBChB e. Cardiovascular Division, University of Minnesota, Minneapolis, MN, United States. Find articles by Rajat Kalra e , Garima Arora Garima Arora , MD a. Division of Cardiovascular Disease, University of Alabama at Birmingham, Birmingham, AL 35233 Find articles by Garima Arora a , Pankaj Arora Pankaj Arora , MD a. Division of Cardiovascular Disease, University of Alabama at Birmingham, Birmingham, AL 35233 f. Section of Cardiology, Birmingham Veterans Affairs Medical Center, Birmingham, AL 35233 Find articles by Pankaj Arora a, f Author information Article notes Copyright and License information a. Division of Cardiovascular Disease, University of Alabama at Birmingham, Birmingham, AL 35233 b. Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA c. Harvard Medical School, Boston, MA d. School of Nursing, University of Alabama at Birmingham, Birmingham, AL 35233 e. Cardiovascular Division, University of Minnesota, Minneapolis, MN, United States. f. Section of Cardiology, Birmingham Veterans Affairs Medical Center, Birmingham, AL 35233 ✉ Corresponding Author: Pankaj Arora, MD, FAHA, FASE, Division of Cardiovascular Disease, 1670 University Boulevard, Volker Hall B140 The University of Alabama at Birmingham Birmingham, AL 35294-0019, United States of America, [email protected] Issue date 2025 Apr 15. PMC Copyright notice PMCID: PMC11913567 NIHMSID: NIHMS2045767 PMID: 39756506 The publisher's version of this article is available at Am J Cardiol Abstract The Pooled-Cohort Equations (PCEs), developed by the American Heart Association (AHA) and American College of Cardiology (ACC), have been widely used since 2013 to estimate 10-year atherosclerotic cardiovascular disease (ASCVD) risk and guide statin therapy. Recently, the AHA introduced the Predicting Risk of CVD EVENTs (PREVENT) equations to improve ASCVD risk estimation. However, the effect of using PREVENT instead of PCEs on risk classification and statin eligibility remains unclear. This retrospective cohort study analyzed 261,303 UK Biobank participants, aged 40 to 69 years, who were free from cardiovascular disease and not on statin therapy. The PCEs and the base PREVENT equations were used to estimate 10-year ASCVD risk, categorize risk levels, and determine statin eligibility based on a common risk threshold of 7.5%. The median 10-year ASCVD risk was 5.2% (2.2%, 10.6%) using the PCEs and 3.5% (1.8%, 5.8%) with the PREVENT equations. The PREVENT equations classified 14.0% of participants as high-risk (ASCVD risk >7.5%), compared to 36.9% classified by PCEs. Among participants classified as intermediate-risk by PCEs, 75.3% were reclassified as low-risk by PREVENT. The proportion of individuals eligible for statin use by the PREVENT equation was 19.9%, and by the PCEs was 40.7%. The corresponding difference was 20.8% (95% CI: 20.6%−20.9%). More men (33.0% [95% CI: 32.7%−33.3%]) than women (11.5% [95% CI: 11.3%−11.7%]) and more individuals in the older age group (60–69 years: 34.0% [95% CI: 33.7%–34.3%]) than in the younger age group (40–49 years: 3.5% [95% CI: 3.3%−3.6%]) would not be recommended for statin consideration with the PREVENT equations. In conclusion, based on the common risk threshold of 7.5%, replacing the PCEs with the base PREVENT equation would reduce statin eligibility in the UK Biobank participants by ~20%, especially among men and older adults. Keywords: Statin eligibility, PREVENT, PCE Introduction The American Heart Association (AHA)/American College of Cardiology (ACC) developed the pooled cohort equations (PCEs) to estimate the 10-year ASCVD risk and to provide guidance on preventive strategies and therapeutic decisions. 1 , 2 The PCEs were sex- and race-specific, extensively validated, and developed from five population cohort studies. 2 Several concerns were raised about PCEs, including the varying accuracy of risk estimation across different samples, development based on outdated data, and the exclusion of individuals below 40 years of age. 3 – 5 To address the shortcomings of PCEs, the AHA-led cardiovascular-kidney-metabolic (CKM) Health Science Advisory Group (SAG) developed the Predicting Risk of Cardiovascular Disease EVENTs (PREVENT) equations. 5 The PREVENT equations were designed to provide more accurate risk estimation across diverse populations for 10-year and 30-year risk of total cardiovascular disease, including heart failure. The current guidelines recommend a multifaceted approach for consideration of statin therapy based on 10-year ASCVD risk scores along with consideration of other factors such as coronary artery calcium (CAC) score, risk-enhancing factors, and patient-clinician shared decision-making. 1 However, the impact of using the PREVENT equations instead of PCEs on ASCVD risk-based classification and statin consideration remains limited in longitudinal cohorts with adjudicated ASCVD events. This study hypothesized that using the PREVENT equations would alter statin eligibility for the primary prevention of ASCVD in the general population as compared to PCEs. In this study, the implications of replacing the PCEs with the PREVENT equations were explored on 1) 10-year ASCVD risk estimation and 2) determination of statin eligibility in UK Biobank participants aged 40–69 years. Methods Study Population The UK Biobank is a prospective population cohort started in 2006 aimed at elucidating genetic and non-genetic contributions to diseases of people aged 40–69 years. 6 The assessment was carried out on 502,493 individuals from 22 centers around the United Kingdom. Extensive phenotyping at these centers involved comprehensive health and lifestyle surveys, anthropometric measurements, and biological sample collection, including blood samples for genotyping. Furthermore, the UK Biobank integrates electronic health record data for participants who provided consent. Written informed consent was obtained from all participants at the assessment centers. Participants having prevalent cardiovascular disease, pregnant or breastfeeding females, and those with incomplete data required to compute the PCEs and PREVENT equations were excluded from the study. As the PCEs were developed for individuals not on statin therapy, participants on lipid-lowering therapy were also excluded from the study. Estimation of ASCVD Risk The AHA/ACC developed PCEs to predict the 10-year risk of ASCVD, which was defined as a composite of coronary heart disease, death, nonfatal myocardial infarction, and stroke. The PREVENT equations were developed to predict 10-year and 30-year risk of ASCVD, heart failure, and CVD (composite of ASCVD and heart failure). To allow a more direct comparison with the PCEs, the study employed the 10-year PREVENT equations for ASCVD. We used the base model PREVENT equations in our study, which does not consider urine albumin-to-creatinine ratio (uACR), hemoglobin A1c (HbA1c), and the social deprivation index (SDI). The phenotypic variables included in the base PREVENT equations and PCEs have been previously published. 2 , 7 In summary, the PCEs included variables such as age, diabetes, sex, smoking, total cholesterol, high-density lipoprotein (HDL) cholesterol, systolic blood pressure, the use of antihypertensive medications, and race. The base PREVENT equations for ASCVD incorporated sex, age, total cholesterol, HDL cholesterol, systolic blood pressure, diabetes, smoking, estimated glomerular filtration rate (eGFR), use of antihypertensive medications, use of statin, and body mass index (BMI). 2 , 7 For all of the analyses, we used baseline measures of the phenotypes. The PREVENT and PCEs were developed and validated for parameters within a specific range. 2 , 7 Participants who had parameters outside the specified range of the PREVENT and PCEs were excluded, as outlined previously. 2 , 7 Self-reported measures of age, sex, race, smoking status, and antihypertensive medication were used in our analysis. Hypertension was defined as systolic blood pressure ≥130 mm Hg, diastolic blood pressure ≥80 mm Hg, or the use of antihypertensive medication. 8 Diabetes was defined as a fasting blood glucose level of ≥126 mg/dL, random blood glucose level of ≥200 mg/dL, HbA1c levels ≥6.5%, or insulin use. 9 The methodology for BP, glucose level, glycated hemoglobin, and cholesterol measurement has been published previously. 10 Events Incident events of ASCVD, which is a composite of nonfatal myocardial infarction, coronary heart disease (CHD) death, and fatal or nonfatal stroke, were identified using the International Classification of Diseases (ICD), 9th and 10th revision codes and Operative Procedure Code (OPCS-4). ( Supplementary Table 1 ) All events were censored at 10 years since the PCEs are specifically designed for predicting 10-year ASCVD risk. Statistical Analysis Continuous variables were summarized as median with interquartile ranges and categorical data as counts with percentages. The sample was further stratified based on sex and age. The estimates of the PREVENT and PCEs for each individual were calculated and graphically presented using histograms. The 10-year estimated ASCVD risk was used to categorize the cohort into low (<7.5%), intermediate (7.5%−20.0%), and high (>20.0%) cardiovascular risk using both the PREVENT and the PCEs. Risk reclassification across these groups was examined by replacing the PCEs with the PREVENT equations and is represented using the Sankey plot. 11 Harrell’s C-statistic was used to examine the risk discrimination of models using the PREVENT and PCEs to predict 10-year ASCVD risk. Because of modest number of ASCVD events we employed 10-fold cross-validation to assess calibration. Calibration slopes were calculated for each fold, and the median along with the interquartile range (IQR) was reported, providing a robust summary of calibration performance across iterations. This study estimated eligibility for statin therapy based on the Class I recommendations outlined in the 2019 ACC/AHA guidelines on primary prevention. 1 For the comparison of PREVENT with PCEs and statin eligibility analysis, we further performed sex- and age-stratified analyses, in addition to analyzing the overall sample. We have additionally used a differential threshold of 5% for PREVENT and 7.5% for PCE to suggest future updates in threshold when moving from PCEs to PREVENT equations. We conducted two sensitivity analyses. In the first analysis, we included participants currently taking statin medication, and in the second analysis, we lowered the ASCVD risk threshold from 7.5% to 5% to accommodate low borderline-risk individuals. All 95% Confidence Intervals (CI) were calculated using the Wald method with 1000 bootstrap resamples. A two-tailed p-value of <0.05 was considered statistically significant. All analysis was conducted on SAS 9.4 (Cary, NC) and R version 4.0.2 (R Core Team, Vienna, Austria). Results In this study, 502,493 participants were enrolled in the UK Biobank cohort. After excluding 241,190 participants (i.e., lack of complete data, prevalent cardiovascular events, use of lipid-lowering medication use, and extreme values), the final cohort consisted of 261,303 individuals. ( Supplementary Figure 1 ) The median age was 57 (49, 62) years, with 43.2% males and 95.3% non-Hispanic White. The median 10-year ASCVD risk estimated by the PCEs and PREVENT equations was 5.2% (2.2%, 10.6%) and 3.5% (1.8%, 5.8%), respectively. ( Table 1 ) Compared with the PCEs, the PREVENT equations estimated a lower 10-year ASCVD risk for 216,613 (82.3%) of participants. ( Figure 1 & Supplementary Figure 2 ) Among our study participants, a total of 8,128 individuals reported statin use during subsequent visits. Table 1: Baseline Characteristics in the Overall Population and Stratified by Sex Characteristics Overall (n=261,303) Males (n=112,776) Females (n=148,527) Age (Years) 57 (49, 62) 57 (49, 63) 57 (49, 62) White race 249,090 (95%) 107,606 (95%) 141,484 (95%) Body Mass Index (kg/m 2 ) 26.4 (23.9, 29.3) 27.0 (24.8, 29.6) 25.8 (23.3, 29.1) Townsend Deprivation Index -2.2 (−3.7, 0.3) -2.2 (−3.7, 0.4) -2.2 (−3.7, 0.3) Systolic Blood Pressure (mm Hg) 136 (124, 149) 139 (129, 151) 132 (121, 146) Diastolic Blood Pressure (mm Hg) 82 (75, 89) 84 (78, 91) 80 (74, 87) Total Cholesterol (mg/dL) 225 (200, 252) 221 (197, 247) 229 (203, 256) HbA1c (%) 5.3 (5.1, 5.6) 5.3 (5.1, 5.6) 5.3 (5.1, 5.6) Blood Pressure Medication 32,385 (12%) 14,867 (13%) 17,518 (12%) Diabetes 7,581 (3%) 3,448 (3%) 4,133 (3%) Current Smoker 27,462 (11%) 14,324 (13%) 13,138 (9%) PCE ASCVD Risk (%) 5.2 (2.2, 10.6) 9.8 (4.9, 16.0) 3.2 (1.3, 6.2) PREVENT ASCVD Risk (%) 3.5 (1.8, 5.8) 4.9 (2.8, 7.6) 2.7 (1.31, 4.5) Open in a new tab Continuous variables are represented as median (interquartile range). Categorical variables are represented as frequencies (percent). Abbreviations: HbA1c: Hemoglobin A1c; ASCVD: Atherosclerotic Cardiovascular Disease; PCEs: Pooled Cohort Equations; PREVENT: Predicting Risk of CVD Event Figure 1: 10-Year Atherosclerotic Cardiovascular Disease Risk Determined Using the PCEs and the PREVENT Equations. Open in a new tab The 10-year atherosclerotic cardiovascular disease risk estimated by the PREVENT equations and the Pooled Cohort Equations (PCEs) has been depicted in the overall population and further stratified by sex and age. The 10-year ASCVD risk estimated by the PREVENT equations and the PCEs have been depicted in red and blue, respectively. The x-axis represents the proportion of individuals, and the y-axis represents the ASCVD risk. The sample populations depicted in each panel are as follows: A. Overall, B. Males, C. Females, D. Age group 40–49 years, E. Age group 50–59 years, F. Age group 60–69 years. Abbreviations: PCEs: Pooled Cohort Equations; ASCVD: Atherosclerotic Cardiovascular Disease; PREVENT: Predicting Risk of CVD Events Overall Cohort Using the PCEs, 164,832 (63.1%), 79,665 (30.5%), and 16,806 (6.4%) were classified as low, intermediate, and high-risk, respectively. Utilizing the PREVENT equations, 224,670 (86.0%), 36,451 (13.9%), and 182 (0.1%) were categorized as low, intermediate, and high-risk, respectively. Using the ASCVD risk threshold for statin eligibility, 96,471 (36.9%) using PCEs and 36,633 (14.0%) using PREVENT equations of the study population were classified as having a 10-year ASCVD risk of > 7.5%.( Figure 2 ) Figure 2: Sankey Diagram Showing Reclassification of 10-Year Atherosclerotic Cardiovascular Disease Risk Categories Using the PCEs and PREVENT Equations. Open in a new tab This figure depicts the reclassification of 10-year ASCVD risk using the PCEs and PREVENT equations. The low, intermediate, and high-risk groups have been defined as <7.5%, 7.5%−20%, and >20% risk of ASCVD. The low, intermediate, and high-risk groups are depicted in green, yellow, and red, respectively. Abbreviations: PCEs: Pooled Cohort Equations; ASCVD: Atherosclerotic Cardiovascular Disease; PREVENT: Predicting Risk of CVD Events Among the participants classified as high-risk by the PCEs, the PREVENT equations classified 16,592 (98.7%) as intermediate risk. Within participants with intermediate ASCVD risk by the PCEs, the PREVENT equations classified 1 (0.0009%) and 59,974 (75.3%) as high and low risk, respectively. Among the participants with low ASCVD risk determined by the PCEs, the PREVENT equations classified 169 (0.1%) individuals as having intermediate ASCVD risk. ( Figure 2 ) Overall, statin eligibility rates differed significantly between the PREVENT equations and PCEs. Using PREVENT equations, 19.9% (95% CI: 19.7%−20.0%) of participants were statin eligible, compared to 40.7% (95% CI: 40.5%−40.9%) with the PCEs method. The absolute difference in rates between PREVENT and PCEs was 20.8% (95% CI: 20.6% to 20.9%), indicating lower statin eligibility rates with PREVENT equations. ( Figure 3 ) Figure 3: Comparison of Statin Eligibility in Overall and Stratified by Sex and Age. Open in a new tab This bar graph compares statin therapy eligibility based on the PCEs and the PREVENT equations model. The data is stratified by the overall population, sex (males and females), and age groups (40–49 years, 50–59 years, 60–69 years). Each bar is segmented into three categories: LDL > 189 mg/dL (blue), Diabetes with LDL between 70–189 mg/dL (orange), and Predicted ASCVD Risk ≥7.5% (maroon). The graph highlights significant differences in statin therapy eligibility across different demographics, showing that the PREVENT equations model generally identifies fewer participants as statin eligible compared to the PCEs. Abbreviations: PCEs: Pooled Cohort Equations; LDL: Low Density Lipoprotein; ASCVD: Atherosclerotic Cardiovascular Disease; PREVENT: Predicting Risk of CVD Events. For 10-year ASCVD censored events, Harrell’s C-statistics for models with the PREVENT equations was 0.730 (SE=0.002), and the PCEs were 0.733 (SE=0.002). Based on the C-statistics, the PREVENT and the PCEs both demonstrated good risk discrimination for ASCVD events. Based on differential C-statistics, the PREVENT and PCEs had similar risk discrimination of 0.003 (95% CI: −0.004 to 0.010). ( Table 2 ) Table 2: Risk Discrimination Value of the PREVENT Equations and PCEs for Predicting the Risk of 10-Year Atherosclerotic Cardiovascular Disease PREVENT PCE C-Statistics * 0.730 (0.002) 0.733 (0.002) Differential C-Statistics (95% CI.) Reference 0.003 (−0.004,0.010) Open in a new tab * Point estimates of Harrell’s C-Statistics along with Standard Error are reported in parenthesis. Abbreviations: PREVENT: Predicting Risk of CVD Events; PCEs: Pooled Cohort Equations. For uncensored ASCVD events, Harrell’s C-statistics for models with the PREVENT equations was 0.719 (SE=0.002), and the PCEs were 0.733 (SE=0.002). Based on differential C-statistics of 0.014 (95% CI: 0.009, 0.019), the predictive performance of PCE is slightly higher compared to PREVENT equations while assessing the risk of uncensored ASCVD events. ( Supplementary Table 2 ) The 10-year crude event rates for ASCVD, stratified by risk groups, revealed notable differences between the PREVENT and PCE models. Specifically, the crude event rates for the PCE model were 2.1% for low risk, 7.0% for intermediate risk, and 13.6% for high risk. In contrast, the crude event rates for the PREVENT model were 3.2% for low risk, 11.2% for intermediate risk, and 17.0% for high risk. ( Table 3 ) The calibration slopes of PCE and PREVENT equations were similar in this analysis. ( Supplementary Table 3 ) Table 3: 10-Year Event Rates for Atherosclerotic Cardiovascular Disease According to PREVENT and PCE Stratified by Risk Groups Low Risk Intermediate Risk High Risk PCE 3,374 (2%) 5,566 (7%) 2,284 (13%) PREVENT 7,099 (3%) 4,094 (11%) 31 (17%) Open in a new tab The numbers in each cell represent the total number of events, followed by the event rate in parentheses (%). Low risk is defined as a 10-year ASCVD risk of < 7.5%, intermediate risk as 7.5–20%, and high risk as > 20%. Abbreviations: PCEs: Pooled Cohort Equations; PREVENT: Predicting Risk of CVD Events; ASCVD: Atherosclerotic Cardiovascular Disease The 10-year ASCVD event rates stratified by risk groups using differential risk thresholds according to the PREVENT and PCE risk scores are presented in Supplementary Table 4 . Sex Stratified Analyses As outlined in Figure 1 & Supplementary Figure 2 , more males (98.4%) than females (71.1%) had lower 10-year ASCVD risk estimates using the PREVENT equations instead of the PCEs. The statin eligibility rate was 29.9% (95% CI: 29.7%−30.2%) with PREVENT equations and 62.9% (95% CI: 62.6%−63.2%) with PCEs, the absolute difference of 33.0% (95% CI: 32.7%−33.3%) in males. In female participants, the statin eligibility rates were 12.3% (95% CI: 12.1%−12.4%) with PREVENT equations and 23.8% (95% CI: 23.6% to 24.0%) with PCEs, showing absolute difference of 11.5% (95% CI: 11.3%−11.7%). ( Figure 3 ) Age-Stratified Analyses The proportion of individuals with a reduction in 10-year ASCVD risk estimates using the PREVENT equations versus the PCEs was highest among participants aged 60–69 years, with 102,874 participants (99.7%), followed by participants aged 50–59 years, with 74,162 participants (82.8%), and then participants aged 40–49 years, with 39,572 participants (57.8%). ( Figure 1 & Supplementary Figure 2 ) Among participants in the age group of 40–49 years, the statin eligibility rate was 5.9% (95% CI: 5.7%−6.1%) using the PREVENT equations and 9.4% (95% CI: 9.1%−9.6%) using the PCEs, resulting in an absolute difference of 3.5% (95% CI: 3.3%−3.6%). For those aged 50–59 years, the PREVENT equations method identified 11.7% (95% CI: 11.5%−12.0%) of participants eligible for statin therapy, while the PCEs did so for 30.5% (95% CI: 30.2%−30.8%), with an absolute difference of 18.8% (95% CI: 18.5%−19.0%). Among participants aged 60–69 years, the PREVENT equations method resulted in a statin eligibility rate of 36.2% (95% CI: 35.9%−36.5%), compared to 70.2% (95% CI: 70.0%−70.5%) using the PCEs method, showing an absolute difference of 34.0% (95% CI: 33.7%−34.3%). ( Figure 3 ) Risk Reclassification based on Differential Risk Thresholds (PCEs 7.5% and PREVENT 5%) Using the 7.5% risk threshold for PCEs, 164,832 (63.1%), 79,665 (30.5%), and 16,806 (6.4%) were classified as low, intermediate, and high-risk, respectively. Utilizing the 5.0% risk threshold PREVENT equations, 176,270 (67.4%), 84,851 (32.5%), and 182 (0.1%) were categorized as low, intermediate, and high-risk, respectively. ( Figure 4 ) Figure 4: Sankey Diagram Showing Reclassification of 10-Year Atherosclerotic Cardiovascular Disease Risk Categories Using the PCEs and PREVENT Equations Using Differential Risk Thresholds. Open in a new tab This figure depicts the reclassification of 10-year ASCVD risk using the PCEs and PREVENT equations. The low, intermediate, and high-risk groups for PCEs have been defined as <7.5%, 7.5%−20%, and >20% risk of ASCVD. The low, intermediate, and high-risk groups for and PREVENT equations have been defined as <5.0%, 5.0%−20%, and >20% risk of ASCVD The low, intermediate, and high-risk groups are depicted in green, yellow, and red, respectively. Abbreviations: PCEs: Pooled Cohort Equations; LDL: Low Density Lipoprotein; ASCVD: Atherosclerotic Cardiovascular Disease; PREVENT: Predicting Risk of CVD Events Among the participants classified as high-risk by the PCEs (Predicted risk >20%), the PREVENT equations classified 16,625 (98.9%) as intermediate risk (Predicted risk between 5%−20%). Within participants with intermediate ASCVD risk by the PCEs (Predicted risk between 7.5%−20%), the PREVENT equations classified 1 (0.0009%) and 16,723 (21.0%) as high (Predicted risk >20%) and low risk (Predicted risk<5.0%), respectively. Among the participants with low ASCVD risk determined by the PCEs (Predicted risk<7.5%), the PREVENT equations classified 5,285 (3.2%) individuals as having intermediate ASCVD risk (Predicted risk between 5%−20%). ( Figure 4 ) Differences in Statin Eligibility based on Differential Risk Thresholds (PCEs 7.5% and PREVENT 5%) The absolute difference in rates between PREVENT and PCEs was 4.5% (95% CI: 4.2%−4.7%) ( Figure 5 ) Figure 5: Comparison of Statin Therapy Recommendations in Overall and Stratified by Sex and Age Using Differential Risk Thresholds for PCEs and PREVENT Equations. Open in a new tab This bar graph compares statin therapy eligibility based on the PCEs and the PREVENT equations model. The data is stratified by the overall population, sex (males and females), and age groups (40–49 years, 50–59 years, 60–69 years). Each bar is segmented into three categories: LDL > 189 mg/dL (blue), Diabetes with LDL between 70–189 mg/dL (orange), Predicted PCE Risk ≥7.5% (dark maroon) and Predicted PREVENT Risk ≥5.0% (light maroon). The graph highlights significant differences in statin therapy eligibility across different demographics, showing that the PREVENT equations model generally identifies fewer participants as statin eligible compared to the PCEs. Abbreviations: PCEs: Pooled Cohort Equations; LDL: Low Density Lipoprotein; PREVENT: Predicting Risk of CVD Events. In male participants, the absolute difference in rates between PREVENT and PCEs was 11.4% (95% CI: 11.0%−11.8). In female participants, the absolute difference in rates between PREVENT and PCEs was 0.8% (95% CI: 0.6%−1.0%) ( Figure 5 ) Among participants in the age group of 40–49 years, the absolute difference in rates between PREVENT and PCEs was 2.3% (95% CI: 2.0%−2.6%). For those aged 50–59 years, the absolute difference in rates between PREVENT and PCEs was 7.5% (95% CI: 7.1%−7.9%). Among participants aged 60–69 years, the absolute difference in rates between PREVENT and PCEs was 3.3% (95% CI: 2.8%−3.7%) ( Figure 5 ) Sensitivity Analyses The results of lowering the ASCVD risk threshold from 7.5% to 5% are presented in Supplementary Table 5 and Supplementary Figure 5 . The sensitivity analysis results, which include individuals currently taking statins, are consistent with our main findings ( Supplementary Tables 6 and 7 , Supplementary Figures 3 and 4 ). Discussion In the current study involving approximately ~250,000 UK individuals, the newly introduced PREVENT equations estimate a lower 10-year ASCVD risk compared to the PCEs. This lower estimation of ASCVD risk by the PREVENT equations as compared with the PCEs disproportionately affected males and older individuals aged 60–69 years. Compared with the ~6 in 100 individuals classified as high risk by PCEs, the PREVENT equations categorized only 1 in 1,000 individuals as high-risk. Among the individuals classified as intermediate risk by the PCEs, the PREVENT equations reclassified 75.3% of individuals as low risk. Using the ASCVD risk threshold for statin eligibility, the PREVENT equations resulted in substantially lower statin eligibility rates compared to the PCEs. A differential risk threshold analysis between PREVENT (5%) and the PCEs (7.5%) demonstrated lower rates of group reclassification, leading to more consistent statin eligibility rates across cohorts. The variation in crude event rates between PREVENT and the PCE provides valuable insights for refining future risk stratification and optimizing treatment strategies in clinical decision-making. If we extrapolate our estimates to the population of England and Wales based on the 2021 census, around 4.6 million people will not be recommended for statin consideration if guidelines-based risk calculation is changed to PREVENT from PCEs. The findings from our study may have several underlying explanations. While PCEs are known to overestimate the risk, 3 , 4 this is unlikely to account for the substantial differences found in our study results. This may be attributed to a multitude of differences between the PCEs and the PREVENT equations. While the PCE was developed using data from large prospective cohort studies, the PREVENT equations integrate more recent and extensive EMR data, capturing contemporary risk factor profiles and treatment patterns. Additionally, the PREVENT equations use competing risk models rather than traditional proportional hazard survival analyses used in PCEs, to predict ASCVD events. The PREVENT equations were specifically designed to provide accurate risk estimates across diverse populations for 10-year and 30-year risk of cardiovascular disease, including heart failure. Further, the use of electronic health records poses a risk of underreporting ASCVD risk factors, especially among males and old individuals. 12 , 13 Moreover, the PREVENT equations have certain modifications in terms of ASCVD risk calculations, such as the removal of race and the addition of a younger population, eGFR, use of statins, and BMI. 5 The changes likely contribute to different risk estimates. Further, the PREVENT equations incorporated the contemporary population (1992–2022) as compared to the antiquated population in PCEs (1968–1995), resulting in around 50% lower ASCVD risk estimates. 14 Since the publication of the PREVENT equations, interest in their clinical implications has grown. Two recent studies utilizing National Health and Nutrition Examination Survey (NHANES) data have investigated these implications. 15 , 16 Both studies found a significant reduction in ASCVD risk estimation using the PREVENT equations compared to the PCEs. 15 , 16 Anderson et al . estimated a 50% lower mean ASCVD risk was about using PREVENT equations and found that ~40% of adults were reclassified into a lower-risk category based on the PREVENT equations score. 15 Diao et al . similarly reported a ~50% reduction in mean ASCVD risk and noted larger decreases in risk for males and older individuals. 16 Anderson et al. also estimated that using PCEs would result in the recommendation of statins for primary ASCVD prevention in approximately 45.4 million individuals, whereas this number was reduced to 28.3 million with the application of the PREVENT equations. 15 Diao et al . showed that the recommendation of statins would decrease from 81.8 million to 67.5 million if the PCEs were replaced with the PREVENT equations. 16 While prior research showed that the PREVENT equations had marginally better risk discrimination compared with the PCEs for the outcome of ASCVD, 5 , 7 our study showed similar risk discrimination for the PREVENT equations and the PCEs. The widespread clinical adoption of the PREVENT equations would need to consider their lower risk estimation compared to the PCEs. The transition from PCEs to PREVENT equations has the potential to bring both benefits and risks. The benefit could be reduced polypharmacy and overtreatment in low-risk populations, leading to fewer statin-related adverse events, albeit the latter are rare. 17 – 19 However, the risk lies in reducing eligibility for statin therapy in a population where statins are already under-prescribed. 20 , 21 This could potentially reverse the long-term trend of decreasing LDL-cholesterol and total cholesterol levels that were enabled by an increase in guideline-recommended statin therapy, particularly for high-risk subgroups, e.g., Asian-Americans/other ethnic minorities. 11 , 22 Additionally, loss of statin eligibility could further worsen the recent reversal of the declining trend in cardiovascular mortality. 23 , 24 It may eventually lead to an elevated ASCVD burden in the overall population. Guidelines advocate for a comprehensive approach to statin therapy, which includes not only 10-year ASCVD risk scores but also considers additional factors such as coronary artery calcium (CAC) scores, risk-enhancing factors, and shared decision-making between patients and clinicians. Therefore, the findings from the study recommend the cautious application of the PREVENT equations in clinical practice. Further, there have been ongoing discussions about adjusting the ASCVD risk threshold for considering statin initiation. 14 The raw differences in event rates between the PCE and PREVENT highlight the potential impact of each model’s risk threshold on observed outcomes. These variations may provide insights that inform future risk stratification and treatment approaches, emphasizing the need for careful consideration in clinical decision-making.” Our study has some limitations. First, there is a lack of generalizability, as the study population is predominantly non-Hispanic Whites. Second, the study is limited by a lack of integration of longitudinal changes in parameters used to estimate risk. The ASCVD risk calculations were done at a single time point, while ASCVD risk is dynamic and changes over time. There is a lack of integration of longitudinal changes in parameters used to estimate risk. Third, the PREVENT equations can estimate risk among individuals aged 30–79 years and account for lipid-lowering medication use, whereas the PCEs are limited to individuals aged 40–79 years and not on statin therapy. In this study, the base PREVENT equations was used, which doesn’t consider UACR, HbA1c levels, and SDI. The risk calculation focused on patients aged 40 to 69 years to align with both the PREVENT equations and PCEs. Fourth, while the statin eligibility threshold was estimated at 7.5% of 10-year ASCVD risk per the 2019 ACC/AHA guidelines, other groups have recommended different thresholds. 17 Although the PREVENT equations are not currently recommended to replace the PCEs, they may be considered in future guidelines with potentially revised risk thresholds. Fifth, the statin eligibility criteria in the UK population are based on the QRISK2 score. 25 Therefore, using the PCEs or PREVENT equations may not accurately represent the risk for the UK population. 25 Sixth, in the UK Biobank, events were identified using ICD codes from hospital records and death registries, without independent adjudication. This lack of external validation may introduce event misclassification, a limitation common in large observational studies. Seventh, the absence of coronary artery calcium (CAC) score data in the UK Biobank limits our ability to fully assess certain risk stratification measures. Eight, both the PREVENT and PCE risk functions were developed using U.S. populations. Applying these risk functions to other populations without recalibration may lead to inaccuracies in risk prediction. Recalibration involves (i) utilizing the incidence of ASCVD events in the assessed population and (ii) incorporating the prevalence of risk factors specific to the assessed population. Without these adjustments, the baseline assumptions in the original risk functions may not accurately reflect the risk distribution in external populations. This limitation underscores the need for recalibration when transporting risk models across diverse populations to ensure their validity and applicability. Lastly, the exclusion of individuals with prevalent cardiovascular disease, those on statins, and pregnant or breastfeeding females from the study may increase the variance in the estimates. Despite these limitations, this analysis is the first to explore the impact of using the PREVENT equations instead of PCEs on ASCVD risk-based classification and statin consideration in longitudinal cohorts with adjudicated ASCVD events. Also, UK Biobank is a non-U.S. cohort, which enables testing the generalizability of both equations outside of the US population. Conclusions This study showed that the PREVENT equations estimate a lower 10-year ASCVD risk compared with PCEs. Males and older individuals were substantially affected by relatively lower risk estimates of the PREVENT equations compared to the PCEs. In conclusion, based on the common risk threshold of 7.5%, replacing the PCEs with the base PREVENT equation would reduce statin eligibility in the UK Biobank by ~20%. The transition from PCEs to the PREVENT equations for calculating ASCVD risk in current cholesterol guidelines presents both opportunities and challenges. Supplementary Material Supplementary Material NIHMS2045767-supplement-Supplementary_Material.docx (1.2MB, docx) Sources of Funding: Dr. Pankaj Arora is supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health (NIH) awards (R01HL160982, R01HL163852, and R01HL163081). Dr. Nirav Patel is supported by the National Institutes of Health grant T32HL007457. Disclosures: Dr. Pankaj Arora reports grant support from Merck Sharp & Dohme LLC and Bristol-Myers Squibb, which are all unrelated to this work. Pankaj Arora reports financial support was provided by National Heart, Lung, and Blood Institute of the National Institutes of Health. Pankaj Arora reports a relationship with Merck Sharp & Dohme LLC and Bristol-Myers Squibb that includes: funding grants. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Footnotes Declaration of interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. References 1. 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