Literature DB >> 19166680

Relation of death within 90 days of non-ST-elevation acute coronary syndromes to variability in electrocardiographic morphology.

Zeeshan Syed1, Benjamin M Scirica, Satishkumar Mohanavelu, Phil Sung, Eric L Michelson, Christopher P Cannon, Peter H Stone, Collin M Stultz, John V Guttag.   

Abstract

Electrocardiographic measures can facilitate the identification of patients at risk of death after acute coronary syndromes. This study evaluates a new risk metric, morphologic variability (MV), which measures beat-to-beat variability in the shape of the entire heart beat signal. This metric is analogous to heart rate variability (HRV) approaches, which focus on beat-to-beat changes in the heart rate. MV was calculated using a dynamic time-warping technique in 764 patients from the DISPERSE2 (TIMI 33) trial for whom 24-hour continuous electrocardiograph was recorded within 48 hours of non-ST-elevation acute coronary syndrome. The patients were evaluated during a 90-day follow-up for the end point of death. Patients with high MV showed an increased risk of death during follow-up (hazard ratio 8.46; p <0.001). The relationship between high MV and death could be observed even after adjusting for baseline clinical characteristics and HRV measures (adjusted hazard ratio 6.91; p = 0.001). Moreover, the correlation between MV and HRV was low (R < or =0.25). These findings were consistent among several subgroups, including patients under the age of 65 and those with no history of diabetes or hyperlipidemia. In conclusion, our results suggest that increased variation in the entire heart beat morphology is associated with a considerably elevated risk of death and may provide information complementary to the analysis of heart rate.

Entities:  

Mesh:

Year:  2008        PMID: 19166680     DOI: 10.1016/j.amjcard.2008.09.099

Source DB:  PubMed          Journal:  Am J Cardiol        ISSN: 0002-9149            Impact factor:   2.778


  6 in total

Review 1.  The clinical significance of continuous ECG (ambulatory ECG or Holter) monitoring of the ST-segment to evaluate ischemia: a review.

Authors:  Neil J Wimmer; Benjamin M Scirica; Peter H Stone
Journal:  Prog Cardiovasc Dis       Date:  2013-08-16       Impact factor: 8.194

2.  Breathing rate and heart rate as confounding factors in measuring T wave alternans and morphological variability in ECG.

Authors:  Ismail Sadiq; Erick A Perez-Alday; Amit J Shah; Gari D Clifford
Journal:  Physiol Meas       Date:  2021-02-06       Impact factor: 2.688

3.  Beatquency domain and machine learning improve prediction of cardiovascular death after acute coronary syndrome.

Authors:  Yun Liu; Benjamin M Scirica; Collin M Stultz; John V Guttag
Journal:  Sci Rep       Date:  2016-10-06       Impact factor: 4.379

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

Authors:  Yun Liu; Zeeshan Syed; Benjamin M Scirica; David A Morrow; John V Guttag; Collin M Stultz
Journal:  J Am Heart Assoc       Date:  2014-06-24       Impact factor: 5.501

5.  Effect of trimetazidine on heart rate variability in elderly patients with acute coronary syndrome.

Authors:  Jing Zhang; Shenghu He; Xuefei Wang; Daxin Wang
Journal:  Pak J Med Sci       Date:  2016 Jan-Feb       Impact factor: 1.088

6.  Machine Learning Improves Risk Stratification After Acute Coronary Syndrome.

Authors:  Paul D Myers; Benjamin M Scirica; Collin M Stultz
Journal:  Sci Rep       Date:  2017-10-04       Impact factor: 4.379

  6 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.