Associations of T Wave Morphology and Heart Rate Variability with Major Cardiac Events and Mortality in Hemodialysis patients

2019 ◽  
Vol 57 ◽  
pp. S125-S126
Author(s):  
Dimitrios Poulikakos ◽  
Marek Malik ◽  
Debasish Banerjee
2015 ◽  
pp. 487-493 ◽  
Author(s):  
D. POULIKAKOS ◽  
D. BANERJEE ◽  
M. MALIK

T wave morphology (TWM) descriptors derived from Holter electrocardiograms during hemodialysis (HD) are of potential value for cardiac risk assessment in HD patients. Our knowledge on autonomic regulation of TWM descriptors is limited. The purpose of this study was to investigate the association between TWM parameters and heart rate variability (HRV) during intradialytic monitoring. In each of 81 patients on maintenance HD, continuous electrocardiograms were recorded 5 times during HD on alternate weeks. TWM descriptors were calculated every 5 s in overlapping 10-s ECG segments and Low Frequency (LF) (0.04 Hz to 0.15 Hz), High Frequency (HF) (0.15 Hz to 0.40 Hz) powers of the spectrum of HRV were calculated every five min. The calculated values of TWM and HRV were averaged during the first hour of the recordings and subsequently over all recordings in each subject. Analyzable data for HRV and TWM were available in 71 HD patients (aged 61±15, 36 % diabetics, 32 % females). LF in normalized units correlated positively with Total Cosine R to T (r=0.374, p=0.001) and negatively with T wave morphology dispersion (r=−0.253, p=0.033) after adjusting for heart rate. A heart rate independent association between repolarisation descriptors and HRV exists in HD patients. Autonomic modulation needs to be considered when using TWM characteristics for risk profiling of HD patients.


2018 ◽  
Vol 23 (6) ◽  
pp. e12570 ◽  
Author(s):  
Dimitrios Poulikakos ◽  
Katerina Hnatkova ◽  
Debasish Banerjee ◽  
Marek Malik

2021 ◽  
Vol 131 ◽  
pp. 104281
Author(s):  
Alaa Alahmadi ◽  
Alan Davies ◽  
Jennifer Royle ◽  
Leanna Goodwin ◽  
Katharine Cresswell ◽  
...  

1994 ◽  
Vol 8 (3) ◽  
pp. 140
Author(s):  
S.C.U. Marsch ◽  
B. Naegeli ◽  
K. Skarvan ◽  
H-G. Schaeler ◽  
I. Castelli ◽  
...  

2012 ◽  
Vol 7 (9) ◽  
pp. 1454-1460 ◽  
Author(s):  
Mari Suzuki ◽  
Takahashi Hiroshi ◽  
Toru Aoyama ◽  
Miho Tanaka ◽  
Hideki Ishii ◽  
...  

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
A Sammani ◽  
E Kayvanpour ◽  
L P Bosman ◽  
F Sedaghat-Hamedani ◽  
T Proctor ◽  
...  

Abstract Background Patients with non-ischemic dilated cardiomyopathy (NIDCM) are at increased risk of ventricular arrhythmias and sudden cardiac death (SCD). However, identifying patients at high risk for life-threatening ventricular arrhythmia (LTVA) who may benefit from an implantable cardioverter defibrillator (ICD) remains challenging. Methods We searched MEDLINE and EMBASE for prognostic studies describing predictors of LTVA (defined as sustained ventricular tachycardia (VT), haemodynamically unstable VT, ventricular fibrillation, (aborted) SCD or appropriate ICD intervention) in patients with NIDCM. We excluded articles with composite heart failure and arrhythmic endpoints but lacking (subgroup) analysis for LTVA. Study quality and risk of bias was assessed using the QUIPS-tool, and articles with high risk of bias in ≥2 areas were excluded from analysis. Univariable hazard ratios of reported predictors were pooled from the remaining studies in a meta-analysis using a random-effects model and presented with 95% confidence interval (CI). Results Out of 1996 unique citations, 51 studies were included comprising 9798 patients with 1493 arrhythmic events. 28 studies were pooled for meta-analysis (mean age 55±4.1 years, 72% male) with a mean follow-up of 3.7±1.9 years. Crude event rate was 4.3% (95% CI 4.02–4.57) per year. From our meta-analysis, hypertension (HR 1.95; CI [1.26–3.00]), history of out of hospital cardiac arrest or sustained VT (HR 4.15; CI [1.32–13.02]), T-wave alternans (HR 6.50; CI [2.46–17.14]), LVEDV per 10ml/m2 increase (HR 1.10; CI [1.10–1.10]), LVESV per 10ml/m2 increase (HR 1.10; CI [1.00–1.22]) and delayed gadolinium enhancement (HR 5.55; CI [4.02–7.67]) were significantly associated with LTVA (figure). The quality of evidence was moderate and there was significant heterogeneity (median i2 57%; IQR 76%) among studies. Additionally from data that could not be pooled, decreased LVEF, history of nsVT and decreased heart rate variability were significantly associated with LTVA. Summary of meta-analysis results Conclusion The risk of LTVA in NIDCM is 4.3% per year and is considerably higher in patients with hypertension, history of LTVA, decreased LVEF, high LVEDV, high LVESV, T-wave alternans, history of nsVT, decreased heart rate variability and delayed gadolinium enhancement. These results may help determine appropriate candidates for ICD implantation. The high heterogeneity in reported results indicate the need for future multicentre studies to further improve risk stratification in NIDCM. Acknowledgement/Funding ERA-CVD JTC2016: DETECTIN-HF, 680969 & Dutch Heart Foundation (2016T096)


2012 ◽  
Vol 12 (04) ◽  
pp. 1240012 ◽  
Author(s):  
GOUTHAM SWAPNA ◽  
DHANJOO N. GHISTA ◽  
ROSHAN JOY MARTIS ◽  
ALVIN P. C. ANG ◽  
SUBBHURAAM VINITHA SREE

The sum total of millions of cardiac cell depolarization potentials can be represented by an electrocardiogram (ECG). Inspection of the P–QRS–T wave allows for the identification of the cardiac bioelectrical health and disorders of a subject. In order to extract the important features of the ECG signal, the detection of the P wave, QRS complex, and ST segment is essential. Therefore, abnormalities of these ECG parameters are associated with cardiac disorders. In this work, an introduction to the genesis of the ECG is given, followed by a depiction of some abnormal ECG patterns and rhythms (associated with P–QRS–T wave parameters), which have come to be empirically correlated with cardiac disorders (such as sinus bradycardia, premature ventricular contraction, bundle-branch block, atrial flutter, and atrial fibrillation). We employed algorithms for ECG pattern analysis, for the accurate detection of the P wave, QRS complex, and ST segment of the ECG signal. We then catagorited and tabulated these cardiac disorders in terms of heart rate, PR interval, QRS width, and P wave amplitude. Finally, we discussed the characteristics and different methods (and their measures) of analyting the heart rate variability (HRV) signal, derived from the ECG waveform. The HRV signals are characterised in terms of these measures, then fed into classifiers for grouping into categories (for normal subjects and for disorders such as cardiac disorders and diabetes) for carrying out diagnosis.


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