Dataset Information

ECG morphological variability in beat space for risk stratification after acute coronary syndrome.

ABSTRACT:

Background

Identification of patients who are at high risk of adverse cardiovascular events after an acute coronary syndrome (ACS) remains a major challenge in clinical cardiology. We hypothesized that quantifying variability in electrocardiogram (ECG) morphology may improve risk stratification post-ACS.

Methods and results

We developed a new metric to quantify beat-to-beat morphologic changes in the ECG: morphologic variability in beat space (MVB), and compared our metric to published ECG metrics (heart rate variability [HRV], deceleration capacity [DC], T-wave alternans, heart rate turbulence, and severe autonomic failure). We tested the ability of these metrics to identify patients at high risk of cardiovascular death (CVD) using 1082 patients (1-year CVD rate, 4.5%) from the MERLIN-TIMI 36 (Metabolic Efficiency with Ranolazine for Less Ischemia in Non-ST-Elevation Acute Coronary Syndrome-Thrombolysis in Myocardial Infarction 36) clinical trial. DC, HRV/low frequency-high frequency, and MVB were all associated with CVD (hazard ratios [HRs] from 2.1 to 2.3 [P<0.05 for all] after adjusting for the TIMI risk score [TRS], left ventricular ejection fraction [LVEF], and B-type natriuretic peptide [BNP]). In a cohort with low-to-moderate TRS (N=864; 1-year CVD rate, 2.7%), only MVB was significantly associated with CVD (HR, 3.0; P=0.01, after adjusting for LVEF and BNP).

Conclusions

ECG morphological variability in beat space contains prognostic information complementary to the clinical variables, LVEF and BNP, in patients with low-to-moderate TRS. ECG metrics could help to risk stratify patients who might not otherwise be considered at high risk of CVD post-ACS.

SUBMITTER: Liu Y

PROVIDER: S-EPMC4309066 | biostudies-literature | 2014 Jun

REPOSITORIES: biostudies-literature

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Publications

ECG morphological variability in beat space for risk stratification after acute coronary syndrome.

Liu Yun Y Syed Zeeshan Z Scirica Benjamin M BM Morrow David A DA Guttag John V JV Stultz Collin M CM

Journal of the American Heart Association 20140624 3

<h4>Background</h4>Identification of patients who are at high risk of adverse cardiovascular events after an acute coronary syndrome (ACS) remains a major challenge in clinical cardiology. We hypothesized that quantifying variability in electrocardiogram (ECG) morphology may improve risk stratification post-ACS.<h4>Methods and results</h4>We developed a new metric to quantify beat-to-beat morphologic changes in the ECG: morphologic variability in beat space (MVB), and compared our metric to publ ...[more]

PMID: 24963105

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Project description:Background Cystatin C (Cys-C) is a marker of renal function that has shown prognostic value for cardiovascular risk stratification across different patient populations. The incremental value of Cys-C beyond established cardiac and renal biomarkers remains incompletely explored. Methods and Results SOLID - TIMI 52 (Stabilization of Plaques Using Darapladib-Thrombolysis in Myocardial Infarction 52; www.clinicaltrials.gov , NCT01000727) randomized patients ≤30 days post-acute coronary syndrome were treated with darapladib or placebo. The association between Cys-C and long-term risk (median follow-up 2.5 years) was assessed in 4965 individuals with adjustments made for clinical variables and other risk markers (eg, estimated glomerular filtration rate, high-sensitivity troponin I, brain-type natriuretic peptide, and fibroblast growth factor-23). The prespecified outcome of interest was cardiovascular death (CVD) or heart failure hospitalization. Cys-C was strongly correlated with creatinine ( r=0.60) and estimated glomerular filtration rate ( r=-0.68), moderately correlated with fibroblast growth factor-23 ( r=0.39), and weakly correlated with brain-type natriuretic peptide ( r=0.28) and high-sensitivity troponin I ( r=0.06) (all P<0.0001). After multivariate adjustment, increasing concentration of Cys-C (per SD of log-transformed Cys-C) was significantly associated with a 28% higher hazard of CVD or heart failure hospitalization (hazard ratio [ HR ] 1.28, 95% confidence interval [ CI ] 1.12-1.46, P<0.001), including CVD ( HR 1.24, 95% CI 1.04-1.47, P=0.01) and heart failure hospitalization ( HR 1.42, 95% CI 1.19-1.69, P<0.001). Cys-C was also associated with a higher hazard of CVD, myocardial infarction, or stroke ( HR 1.15, 95% CI 1.04-1.28, P<0.01), including myocardial infarction ( HR 1.17, 95% CI 1.02-1.33, P=0.02). The addition of Cys-C to a fully adjusted model without estimated glomerular filtration rate improved the C-statistic from 0.80 to 0.81 ( P=0.03) for CVD or heart failure hospitalization. In contrast, the addition of estimated glomerular filtration rate to a fully adjusted model without Cys-C failed to improve model discrimination ( P=0.17). Conclusions Cys-C is associated with the risk of adverse outcomes in patients after acute coronary syndrome. This relationship is independent of established and novel biomarkers of the cardiorenal axis.

Dataset Information

ECG morphological variability in beat space for risk stratification after acute coronary syndrome.

Background

Methods and results

Conclusions

Publications

ECG morphological variability in beat space for risk stratification after acute coronary syndrome.

Similar Datasets

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