SCORE2 cardiovascular risk prediction models in an ethnic and socioeconomic diverse population in the Netherlands: an external validation study

Antihypertensive medication classes and risk of incident dementia in primary care patients: a longitudinal cohort study in the Netherlands

Background

Hypertension is a modifiable risk factor for dementia affecting over 70% of individuals older than 60. Lowering dementia risk through preferential treatment with antihypertensive medication (AHM) classes that are otherwise equivalent in indication could offer a cost-effective, safe, and accessible approach to reducing dementia incidence globally. Certain AHM-classes have been associated with lower dementia risk, potentially attributable to angiotensin-II-receptor (Ang-II) stimulating properties. Previous study results have been inconclusive, possibly due to heterogeneous methodology and limited power. We aimed to comprehensively investigate associations between AHM (sub-)classes and dementia risk using large-scale continuous, real-world prescription and outcome data from primary care.

Methods

We used data from three Dutch General Practice Registration Networks. Primary endpoints were clinical diagnosis of incident all-cause dementia and mortality. Using Cox regression analysis with time-dependent covariates, we compared the use of angiotensin-converting enzyme inhibitors (ACEi) to angiotensin receptor blockers (ARBs), beta blockers, calcium channel blockers (CCBs), and diuretics; and Ang-II-stimulating- to Ang-II-inhibiting AHM.

Findings

Of 133,355 AHM-using participants, 5877 (4.4%) developed dementia, and 14,079 (10.6%) died during a median follow-up of 7.6 [interquartile range = 4.1-11.0] years. Compared to ACEi, ARBs [HR = 0.86 (95% CI = 0.80-0.92)], beta blockers [HR = 0.81 (95% CI = 0.75-0.87)], CCBs [HR = 0.77 (95% CI = 0.71-0.84)], and diuretics [HR = 0.65 (95% CI = 0.61-0.70)] were associated with significantly lower dementia risks. Regarding competing risk of death, beta blockers [HR = 1.21 (95% CI = 1.15-1.27)] and diuretics [HR = 1.69 (95% CI = 1.60-1.78)] were associated with higher, CCBs with similar, and ARBs with lower [HR = 0.83 (95% CI = 0.80-0.87)] mortality risk. Dementia [HR = 0.88 (95% CI = 0.82-0.95)] and mortality risk [HR = 0.86 (95% CI = 0.82-0.91)] were lower for Ang-II-stimulating versus Ang-II-inhibiting AHM. There were no interactions with sex, diabetes, cardiovascular disease, and number of AHM used.

Interpretation

Among patients receiving AHM, ARBs, CCBs, and Ang-II-stimulating AHM were associated with lower dementia risk, without excess mortality explaining these results. Extensive subgroup and sensitivity analyses suggested that confounding by indication did not importantly influence our findings. Dementia risk may be influenced by AHM-classes' angiotensin-II-receptor stimulating properties. An RCT comparing BP treatment with different AHM classes with dementia as outcome is warranted.

Funding

Netherlands Organisation for Health, Research and Development (ZonMw); Stoffels-Hornstra Foundation.

Validation of Systematic Coronary Risk Evaluation 2 (SCORE2) and SCORE2-Older Persons in the EPIC-Norfolk prospective population cohort

AIMS

The European Systematic Coronary Risk Evaluation 2 (SCORE2) and SCORE2-Older Persons (OP) models are recommended to identify individuals at high 10-year risk for cardiovascular disease (CVD). Independent validation and assessment of clinical utility is needed. This study aims to assess discrimination, calibration, and clinical utility of low-risk SCORE2 and SCORE2-OP.

METHODS AND RESULTS

Validation in individuals aged 40-69 years (SCORE2) and 70-79 years (SCORE2-OP) without baseline CVD or diabetes from the European Prospective Investigation of Cancer (EPIC) Norfolk prospective population study. We compared 10-year CVD risk estimates with observed outcomes (cardiovascular mortality, non-fatal myocardial infarction, and stroke). For SCORE2, 19 560 individuals (57% women) had 10-year CVD risk estimates of 3.7% [95% confidence interval (CI) 3.6-3.7] vs. observed 3.8% (95% CI 3.6-4.1) [observed (O)/expected (E) ratio 1.0 (95% CI 1.0-1.1)]. The area under the curve (AUC) was 0.75 (95% CI 0.74-0.77), with underestimation of risk in men [O/E 1.4 (95% CI 1.3-1.6)] and overestimation in women [O/E 0.7 (95% CI 0.6-0.8)]. Decision curve analysis (DCA) showed clinical benefit. Systematic Coronary Risk Evaluation 2-Older Persons in 3113 individuals (58% women) predicted 10-year CVD events in 10.2% (95% CI 10.1-10.3) vs. observed 15.3% (95% CI 14.0-16.5) [O/E ratio 1.6 (95% CI 1.5-1.7)]. The AUC was 0.63 (95% CI 0.60-0.65) with underestimation of risk across sex and risk ranges. Decision curve analysis showed limited clinical benefit.

CONCLUSION

In a UK population cohort, the SCORE2 low-risk model showed fair discrimination and calibration, with clinical benefit for preventive treatment initiation decisions. In contrast, in individuals aged 70-79 years, SCORE2-OP demonstrated poor discrimination, underestimated risk in both sexes, and limited clinical utility.

Improving cardiovascular risk prediction through machine learning modelling of irregularly repeated electronic health records

Aims

Existing electronic health records (EHRs) often consist of abundant but irregular longitudinal measurements of risk factors. In this study, we aim to leverage such data to improve the risk prediction of atherosclerotic cardiovascular disease (ASCVD) by applying machine learning (ML) algorithms, which can allow automatic screening of the population.

Methods and results

A total of 215 744 Chinese adults aged between 40 and 79 without a history of cardiovascular disease were included (6081 cases) from an EHR-based longitudinal cohort study. To allow interpretability of the model, the predictors of demographic characteristics, medication treatment, and repeatedly measured records of lipids, glycaemia, obesity, blood pressure, and renal function were used. The primary outcome was ASCVD, defined as non-fatal acute myocardial infarction, coronary heart disease death, or fatal and non-fatal stroke. The eXtreme Gradient boosting (XGBoost) algorithm and Least Absolute Shrinkage and Selection Operator (LASSO) regression models were derived to predict the 5-year ASCVD risk. In the validation set, compared with the refitted Chinese guideline-recommended Cox model (i.e. the China-PAR), the XGBoost model had a significantly higher C-statistic of 0.792, (the differences in the C-statistics: 0.011, 0.006-0.017, P < 0.001), with similar results reported for LASSO regression (the differences in the C-statistics: 0.008, 0.005-0.011, P < 0.001). The XGBoost model demonstrated the best calibration performance (men: Dx = 0.598, P = 0.75; women: Dx = 1.867, P = 0.08). Moreover, the risk distribution of the ML algorithms differed from that of the conventional model. The net reclassification improvement rates of XGBoost and LASSO over the Cox model were 3.9% (1.4-6.4%) and 2.8% (0.7-4.9%), respectively.

Conclusion

Machine learning algorithms with irregular, repeated real-world data could improve cardiovascular risk prediction. They demonstrated significantly better performance for reclassification to identify the high-risk population correctly.

Algorithmic fairness in cardiovascular disease risk prediction: overcoming inequalities

The main purpose of prognostic risk prediction models is to identify individuals who are at risk of disease, to enable early intervention. Current prognostic cardiovascular risk prediction models, such as the Systematic COronary Risk Evaluation (SCORE2) and the SCORE2-Older Persons (SCORE2-OP) models, which represent the clinically used gold standard in assessing patient risk for major cardiovascular events in the European Union (EU), generally overlook socioeconomic determinants, leading to disparities in risk prediction and resource allocation. A central recommendation of this article is the explicit inclusion of individual-level socioeconomic determinants of cardiovascular disease in risk prediction models. The question of whether prognostic risk prediction models can promote health equity remains to be answered through experimental research, potential clinical implementation and public health analysis. This paper introduces four distinct fairness concepts in cardiovascular disease prediction and their potential to narrow existing disparities in cardiometabolic health.