ArticleJournal of the American College of Cardiology2024
Universal Risk Prediction for Individuals With and Without Atherosclerotic Cardiovascular Disease.
Article in Journal of the American College of Cardiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed, 9 citations in OpenAlex.
- Race and Ethnicity in Cardiovascular Disease Risk Prediction for Multiethnic Populations: Insights From Global Guidelines.JACC. Asia · 2026Review
- Article
- Dynamic and Static Effects of the Systemic Inflammatory Response Index on All-Cause Mortality in Individuals With Atherosclerotic Cardiovascular Disease: Evidence From National Health and Nutrition Examination Survey.Mediators of inflammation · 2025Article
- Prediction of major adverse cardiovascular events following acute myocardial infarction using electrocardiogram DETERMINE score.BMC cardiovascular disorders · 2024Article
- When Does Primary Prevention Encroach on Secondary Prevention?Current atherosclerosis reports · 2024Review
- Article
Corrections and comments
- Commented on by
Authors and funding
14 authors at 4 institutions in 2 countries.
Funding
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.