SynthesisAmerican journal of epidemiology2017
Use of Repeated Blood Pressure and Cholesterol Measurements to Improve Cardiovascular Disease Risk Prediction: An Individual-Participant-Data Meta-Analysis.
Synthesis in American journal of epidemiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
33 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Progression of conventional cardiovascular risk factors and vascular disease risk in individuals: insights from the PROG-IMT consortium.European journal of preventive cardiology · 2020Pooled it
- [The Development of Automated Personalized Self-Care (APSC) Program for Patients with Type 2 Diabetes Mellitus].Journal of Korean Academy of Nursing · 2022Trial
- A Taxonomy of Data Synthesis.Personality & social psychology bulletin · 2026Article
- Mitigating bias in the analysis and inferences from using longitudinal EHR data in disease outcomes research.BMC medical research methodology · 2026Article
- Predictive value of baseline, cumulative exposure and annual mean changes in TyG-related indices for cardiovascular events in older rural adults with cardiovascular-kidney-metabolic syndrome stages 0-3: a cohort study.Frontiers in endocrinology · 2026Article
- Development of a dynamic prediction model with the inclusion of time-dependent inflammatory biomarker enhances recurrence prediction after curative surgery for stage II or III gastric cancer.Japanese journal of clinical oncology · 2025Article
- LDL cholesterol burden in elderly patients with familial hypercholesterolemia: Insights from real-world data.American journal of preventive cardiology · 2025Article
- Plasma Ceramide C24:0/C16:0 Ratio is Associated with Improved Survival in Patients with Pancreatic Ductal Adenocarcinoma.Annals of surgical oncology · 2024Article
- Pitfalls in Developing Machine Learning Models for Predicting Cardiovascular Diseases: Challenge and Solutions.Journal of medical Internet research · 2024Article
- Association between gait video information and general cardiovascular diseases: a prospective cross-sectional study.European heart journal. Digital health · 2024Article
- The association between metabolic syndrome and insulin resistance with risk of cardiovascular events in different states of cardiovascular health status.Journal of diabetes investigation · 2024Article
- Improving cardiovascular risk prediction through machine learning modelling of irregularly repeated electronic health records.European heart journal. Digital health · 2024Article
- Residual risks and evolving atherosclerotic plaques.Molecular and cellular biochemistry · 2023Review
- Using Polygenic Risk Scores for Prioritizing Individuals at Greatest Need of a Cardiovascular Disease Risk Assessment.Journal of the American Heart Association · 2023Article
- Trajectories of cardiac troponin in the decades before cardiovascular death: a longitudinal cohort study.BMC medicine · 2023Article
- Multivariate longitudinal data for survival analysis of cardiovascular event prediction in young adults: insights from a comparative explainable study.BMC medical research methodology · 2023Observational
- Incremental value of risk factor variability for cardiovascular risk prediction in individuals with type 2 diabetes: results from UK primary care electronic health records.International journal of epidemiology · 2022Article
- Landmarking 2.0: Bridging the gap between joint models and landmarking.Statistics in medicine · 2022Article
- Cholesterol and Hypertension Treatment Improve Coronary Risk Prediction but Not Time-Dependent Covariates or Competing Risks.Clinical epidemiology · 2022Article
- Multi-morbidity and blood pressure trajectories in hypertensive patients: A multiple landmark cohort study.PLoS medicine · 2021Observational
Corrections and comments
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Authors and funding
47 authors.
Funding
Abstract
The added value of incorporating information from repeated blood pressure and cholesterol measurements to predict cardiovascular disease (CVD) risk has not been rigorously assessed. We used data on 191,445 adults from the Emerging Risk Factors Collaboration (38 cohorts from 17 countries with data encompassing 1962-2014) with more than 1 million measurements of systolic blood pressure, total cholesterol, and high-density lipoprotein cholesterol. Over a median 12 years of follow-up, 21,170 CVD events occurred. Risk prediction models using cumulative mean values of repeated measurements and summary measures from longitudinal modeling of the repeated measurements were compared with models using measurements from a single time point. Risk discrimination (C-index) and net reclassification were calculated, and changes in C-indices were meta-analyzed across studies. Compared with the single-time-point model, the cumulative means and longitudinal models increased the C-index by 0.0040 (95% confidence interval (CI): 0.0023, 0.0057) and 0.0023 (95% CI: 0.0005, 0.0042), respectively. Reclassification was also improved in both models; compared with the single-time-point model, overall net reclassification improvements were 0.0369 (95% CI: 0.0303, 0.0436) for the cumulative-means model and 0.0177 (95% CI: 0.0110, 0.0243) for the longitudinal model. In conclusion, incorporating repeated measurements of blood pressure and cholesterol into CVD risk prediction models slightly improves risk prediction.
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