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Data on coronary artery calcium score performance and cardiovascular risk reclassification across gender and ethnicities.

ABSTRACT: The current guidelines recommend the new risk score, Atherosclerotic Cardiovascular Disease score (ASCVD), to assess an individual?s risk of future cardiovascular disease (CVD) events. No data exist on the predictive utility of ASCVD score with the incremental value of coronary artery calcium scoring (CACS) across ethnicities and gender. Multi-Ethnic Study of Atherosclerosis (MESA) is a population based study (n=6814) of White (38%), Black (28%), Chinese (22%) and Hispanic (12%) subjects, aged 45-84 years, free from clinical cardiovascular disease. We performed a post-hoc analysis of 6742 participants (mean age 62, 53% female) from the MESA cohort. We evaluated the predictive accuracy for the ASCVD score for each participant in accord with the American College of Cardiology/American Heart Association guidelines using pooled cohort equations. Similar to the publication by Fudim et al. "The Metabolic Syndrome, Coronary Artery Calcium Score and Cardiovascular Risk Reclassification" [1] the analytic properties of models incorporating the ASCVD score with and without CACS were compared for cardiovascular disease CVD prediction. Here the analysis focused on ASCVD score (with and without CACS) performance across gender and ethnicities.

SUBMITTER: Fudim M

PROVIDER: S-EPMC4731459 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature

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Data on coronary artery calcium score performance and cardiovascular risk reclassification across gender and ethnicities.

Fudim Marat M Zalawadiya Sandip S Patel Devin K DK Egolum Ugochukwu O UO Afonso Luis L

Data in brief 20160113

The current guidelines recommend the new risk score, Atherosclerotic Cardiovascular Disease score (ASCVD), to assess an individual׳s risk of future cardiovascular disease (CVD) events. No data exist on the predictive utility of ASCVD score with the incremental value of coronary artery calcium scoring (CACS) across ethnicities and gender. Multi-Ethnic Study of Atherosclerosis (MESA) is a population based study (n=6814) of White (38%), Black (28%), Chinese (22%) and Hispanic (12%) subjects, aged 4 ...[more]

PMID: 26909370

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Project description:BackgroundPrecision estimation of cardiovascular risk remains the cornerstone of atherosclerotic cardiovascular disease (ASCVD) prevention. While coronary artery calcium (CAC) scoring is the best available non-invasive quantitative modality to evaluate risk of ASCVD, it excludes risk related to prior myocardial infarction, cardiomyopathy, and arrhythmia which are implicated in ASCVD. The high-dimensional and inter-correlated nature of ECG data makes it a good candidate for analysis using machine learning techniques and may provide additional prognostic information not captured by CAC. In this study, we aimed to develop a quantitative ECG risk score (eRiS) to predict major adverse cardiovascular events (MACE) alone, or when added to CAC. Further, we aimed to construct and validate a novel nomogram incorporating ECG, CAC and clinical factors for ASCVD.MethodsWe analyzed 5,864 patients with at least 1 cardiovascular risk factor who underwent CAC scoring and a standard ECG as part of the CLARIFY study (ClinicalTrials.gov Identifier: NCT04075162). Events were defined as myocardial infarction, coronary revascularization, stroke or death. A total of 649 ECG features, consisting of measurements such as amplitude and interval measurements from all deflections in the ECG waveform (53 per lead and 13 overall) were automatically extracted using a clinical software (GE Muse™ Cardiology Information System, GE Healthcare). The data was split into 4 training (Str) and internal validation (Sv) sets [Str (1): Sv (1): 50:50; Str (2): Sv (2): 60:40; Str (3): Sv (3): 70:30; Str (4): Sv (4): 80:20], and the results were compared across all the subsets. We used the ECG features derived from Str to develop eRiS. A least absolute shrinkage and selection operator-Cox (LASSO-Cox) regularization model was used for data dimension reduction, feature selection, and eRiS construction. A Cox-proportional hazards model was used to assess the benefit of using an eRiS alone (Mecg), CAC alone (Mcac) and a combination of eRiS and CAC (Mecg+cac) for MACE prediction. A nomogram (Mnom) was further constructed by integrating eRiS with CAC and demographics (age and sex). The primary endpoint of the study was the assessment of the performance of Mecg, Mcac, Mecg+cac and Mnom in predicting CV disease-free survival in ASCVD.FindingsOver a median follow-up of 14 months, 494 patients had MACE. The feature selection strategy preserved only about 18% of the features that were consistent across the various strata (Str). The Mecg model, comprising of eRiS alone was found to be significantly associated with MACE and had good discrimination of MACE (C-Index: 0.7, p = <2e-16). eRiS could predict time-to MACE (C-Index: 0.6, p = <2e-16 across all Sv). The Mecg+cac model was associated with MACE (C-index: 0.71). Model comparison showed that Mecg+cac was superior to Mecg (p = 1.8e-10) or Mcac (p < 2.2e-16) alone. The Mnom, comprising of eRiS, CAC, age and sex was associated with MACE (C-index 0.71). eRiS had the most significant contribution, followed by CAC score and other clinical variables. Further, Mnom was able to identify unique patient risk-groups based on eRiS, CAC and clinical variables.ConclusionThe use of ECG features in conjunction with CAC may allow for improved prognostication and identification of populations at risk. Future directions will involve prospective validation of the risk score and the nomogram across diverse populations with a heterogeneity of treatment effects.

Dataset Information

Data on coronary artery calcium score performance and cardiovascular risk reclassification across gender and ethnicities.

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Data on coronary artery calcium score performance and cardiovascular risk reclassification across gender and ethnicities.

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