S-EPMC9627894 - Evaluation of Atherosclerotic Cardiovascular Risk Prediction Models in China: Results From the CHERRY Study. - OmicsDI

Unknown

Dataset Information

Evaluation of Atherosclerotic Cardiovascular Risk Prediction Models in China: Results From the CHERRY Study.

ABSTRACT:

Background

Updated American or Chinese guidelines recommended calculating atherosclerotic cardiovascular disease (ASCVD) risk using the Pooled Cohort Equations (PCE) or Prediction for Atherosclerotic Cardiovascular Disease Risk in China (China-PAR) models; however, evidence on performance of both models in Asian populations is limited.

Objectives

The authors aimed to evaluate the accuracy of the PCE or China-PAR models in a Chinese contemporary cohort.

Methods

Data were extracted from the CHERRY (CHinese Electronic health Records Research in Yinzhou) study. Participants aged 40 to 79 years without prior ASCVD at baseline from 2010 to 2016 were included. ASCVD was defined as nonfatal or fatal stroke, nonfatal myocardial infarction, and cardiovascular death. Models were assessed for discrimination and calibration.

Results

Among 226,406 participants, 5362 (2.37%) adults developed a first ASCVD event during a median of 4.60 years of follow-up. Both models had good discrimination: C-statistics in men were 0.763 (95% confidence interval [CI]: 0.754-0.773) for PCE and 0.758 (95% CI: 0.749-0.767) for China-PAR; C-statistics in women were 0.820 (95% CI: 0.812-0.829) for PCE and 0.811 (95% CI: 0.802-0.819) for China-PAR. The China-PAR model underpredicted risk by 20% in men and by 40% in women, especially in the highest-risk groups. However, PCE overestimated by 63% in men and inversely underestimated the risk by 34% in women with poor calibration (both P < 0.001). After recalibration, observed and predicted risks by recalibrated PCE were better aligned.

Conclusions

In this large-scale population-based study, both PCE and China-PAR had good discrimination in 5-year ASCVD risk prediction. China-PAR outperformed PCE in calibration, whereas recalibration equalized the performance of PCE and China-PAR. Further specific models are needed to improve accuracy in the highest-risk groups.

SUBMITTER: Liu X

PROVIDER: S-EPMC9627894 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature

altmetric image

Publications

Evaluation of Atherosclerotic Cardiovascular Risk Prediction Models in China: Results From the CHERRY Study.

Liu Xiaofei X Shen Peng P Zhang Dudan D Sun Yexiang Y Chen Yi Y Liang Jingyuan J Wu Jinguo J Zhang Jingyi J Lu Ping P Lin Hongbo H Tang Xun X Gao Pei P

JACC. Asia 20220104 1

<h4>Background</h4>Updated American or Chinese guidelines recommended calculating atherosclerotic cardiovascular disease (ASCVD) risk using the Pooled Cohort Equations (PCE) or Prediction for Atherosclerotic Cardiovascular Disease Risk in China (China-PAR) models; however, evidence on performance of both models in Asian populations is limited.<h4>Objectives</h4>The authors aimed to evaluate the accuracy of the PCE or China-PAR models in a Chinese contemporary cohort.<h4>Methods</h4>Data were ext ...[more]

Similar Datasets

Project description:ImportanceUse of race-specific risk prediction in clinical medicine is being questioned. Yet, the most commonly used prediction tool for atherosclerotic cardiovascular disease (ASCVD)-pooled cohort risk equations (PCEs)-uses race stratification.ObjectiveTo quantify the incremental value of race-specific PCEs and determine whether adding social determinants of health (SDOH) instead of race improves model performance.Design, setting, and participantsIncluded in this analysis were participants from the biracial Reasons for Geographic and Racial Differences in Stroke (REGARDS) prospective cohort study. Participants were aged 45 to 79 years, without ASCVD, and with low-density lipoprotein cholesterol level of 70 to 189 mg/dL or non-high-density lipoprotein cholesterol level of 100 to 219 mg/dL at baseline during the period of 2003 to 2007. Participants were followed up to 10 years for incident ASCVD, including myocardial infarction, coronary heart disease death, and fatal and nonfatal stroke. Study data were analyzed from July 2022 to February 2023.Main outcome/measuresDiscrimination (C statistic, Net Reclassification Index [NRI]), and calibration (plots, Nam D'Agostino test statistic comparing observed to predicted events) were assessed for the original PCE, then for a set of best-fit, race-stratified equations including the same variables as in the PCE (model C), best-fit equations without race stratification (model D), and best-fit equations without race stratification but including SDOH as covariates (model E).ResultsThis study included 11 638 participants (mean [SD] age, 61.8 [8.3] years; 6764 female [58.1%]) from the REGARDS cohort. Across all strata (Black female, Black male, White female, and White male participants), C statistics did not change substantively compared with model C (Black female, 0.71; 95% CI, 0.68-0.75; Black male, 0.68; 95% CI, 0.64-0.73; White female, 0.77; 95% CI, 0.74-0.81; White male, 0.68; 95% CI, 0.64-0.71), in model D (Black female, 0.71; 95% CI, 0.67-0.75; Black male, 0.68; 95% CI, 0.63-0.72; White female, 0.76; 95% CI, 0.73-0.80; White male, 0.68; 95% CI, 0.65-0.71), or in model E (Black female, 0.72; 95% CI, 0.68-0.76; Black male, 0.68; 95% CI, 0.64-0.72; White female, 0.77; 95% CI, 0.74-0.80; White male, 0.68; 95% CI, 0.65-0.71). Comparing model D with E using the NRI showed a net percentage decline in the correct assignment to higher risk for male but not female individuals. The Nam D'Agostino test was not significant for all race-sex strata in each model series, indicating good calibration in all groups.ConclusionsResults of this cohort study suggest that PCE performed well overall but had poorer performance in both BM and WM participants compared with female participants regardless of race in the REGARDS cohort. Removal of race or the addition of SDOH did not improve model performance in any subgroup.

Project description:BackgroundAmerican College of Cardiology/American Heart Association guidelines recommend distinct risk classification systems for primary and secondary cardiovascular disease prevention. However, both systems rely on similar predictors (eg, age and diabetes), indicating the possibility of a universal risk prediction approach for major adverse cardiovascular events (MACEs).ObjectivesThe authors examined the performance of predictors in persons with and without atherosclerotic cardiovascular disease (ASCVD) and developed and validated a universal risk prediction model.MethodsAmong 9,138 ARIC (Atherosclerosis Risk In Communities) participants with (n = 609) and without (n = 8,529) ASCVD at baseline (1996-1998), we examined established predictors in the risk classification systems and other predictors, such as body mass index and cardiac biomarkers (troponin and natriuretic peptide), using Cox models with MACEs (myocardial infarction, stroke, and heart failure). We also evaluated model performance.ResultsOver a follow-up of approximately 20 years, there were 3,209 MACEs (2,797 for no prior ASCVD). Most predictors showed similar associations with MACE regardless of baseline ASCVD status. A universal risk prediction model with the predictors (eg, established predictors, cardiac biomarkers) identified by least absolute shrinkage and selection operator regression and bootstrapping showed good discrimination for both groups (c-statistics of 0.747 and 0.691, respectively), and risk classification and showed excellent calibration, irrespective of ASCVD status. This universal prediction approach identified individuals without ASCVD who had a higher risk than some individuals with ASCVD and was validated externally in 5,322 participants in the MESA (Multi-Ethnic Study of Atherosclerosis).ConclusionsA universal risk prediction approach performed well in persons with and without ASCVD. This approach could facilitate the transition from primary to secondary prevention by streamlining risk classification and discussion between clinicians and patients.

Project description:BackgroundLipid-lowering therapy (LLT) is one of the key strategies for reducing the atherosclerotic cardiovascular disease (ASCVD) burden. However, little is known about the percentage of people in need of different LLT regimens to achieve optimal targets of low-density lipoprotein cholesterol (LDL-C), and the corresponding cost and benefit.MethodsWe conducted a simulation study based on the data from the nationwide China PEACE MPP population cohort (2015-2020), from which we included 2,904,914 participants aged 35-75 years from all the 31 provinces in mainland China. Participants were grouped based on their 10-year ASCVD risks, then entered into a Monte Carlo model which was used to perform LLT intensification simulation scenarios to achieve corresponding LDL-C goals in each risk stratification.ResultsAfter standardizing age and sex, the proportions of participants included at low, moderate, high, and very-high risk were 70.8%, 15.6%, 11.5%, and 2.1%, respectively. People who failed to achieve the corresponding LDL-C goals -8.1% at low risk, 19.6% at moderate risk, 53.2% at high risk, and 93.6% at very-high risk (either not achieving the goal or not receiving LLT)-would be in need of the LLT intensification simulation. After the use of atorvastatin 20 mg was simulated, over 99% of the population at low or moderate risk could achieve the LDL-C goals; while 11.3% at high and 24.5% at very-high risk would still require additional non-statin therapy. After the additional use of ezetimibe, there were still 4.8% at high risk and 11.3% at very-high risk in need of evolocumab; and 99% of these two groups could achieve the LDL-C goals after the use of evolocumab. Such LLT intensification with statin, ezetimibe, and evolocumab would annually cost $2.4 billion, $4.2 billion, and $24.5 billion, respectively, and prevent 264,170, 18,390, and 17,045 cardiovascular events, respectively.ConclusionsModerate-intensity statin therapy is pivotal for the attainment of optimal LDL-C goals in China, and around 10-25% of high- or very-high-risk patients would require additional non-statin agents. There is an opportunity to reduce the rising ASCVD burden in China by optimizing LLT.

Project description:ImportancePeople with severe mental illness (SMI), including schizophrenia and bipolar disorder, have excess rates of cardiovascular disease (CVD). Risk prediction models validated for the general population may not accurately estimate cardiovascular risk in this group.ObjectiveTo develop and validate a risk model exclusive to predicting CVD events in people with SMI incorporating established cardiovascular risk factors and additional variables.Design, setting, and participantsWe used anonymous/deidentified data collected between January 1, 1995, and December 31, 2010, from the Health Improvement Network (THIN) to conduct a primary care, prospective cohort and risk score development study in the United Kingdom. Participants included 38,824 people with a diagnosis of SMI (schizophrenia, bipolar disorder, or other nonorganic psychosis) aged 30 to 90 years. During a median follow-up of 5.6 years, 2324 CVD events (6.0%) occurred.Main outcomes and measuresTen-year risk of the first cardiovascular event (myocardial infarction, angina pectoris, cerebrovascular accidents, or major coronary surgery). Predictors included age, sex, height, weight, systolic blood pressure, diabetes mellitus, smoking, body mass index (BMI), lipid profile, social deprivation, SMI diagnosis, prescriptions for antidepressants and antipsychotics, and reports of heavy alcohol use.ResultsWe developed 2 CVD risk prediction models for people with SMI: the PRIMROSE BMI model and the PRIMROSE lipid model. These models mutually excluded lipids and BMI. In terms of discrimination, from cross-validations for men, the PRIMROSE lipid model D statistic was 1.92 (95% CI, 1.80-2.03) and C statistic was 0.80 (95% CI, 0.76-0.83) compared with 1.74 (95% CI, 1.63-1.86) and 0.78 (95% CI, 0.75-0.82) for published Cox Framingham risk scores. The corresponding results in women were 1.87 (95% CI, 1.76-1.98) and 0.79 (95% CI, 0.76-0.82) for the PRIMROSE lipid model and 1.58 (95% CI, 1.48-1.68) and 0.77 (95% CI, 0.73-0.81) for the Cox Framingham model. Discrimination statistics for the PRIMROSE BMI model were comparable to those for the PRIMROSE lipid model. Calibration plots suggested that both PRIMROSE models were superior to the Cox Framingham models.Conclusions and relevanceThe PRIMROSE BMI and lipid CVD risk prediction models performed better in SMI compared with models that include only established CVD risk factors. Further work on the clinical effectiveness and cost-effectiveness of the PRIMROSE models is needed to ascertain the best thresholds for offering CVD interventions.