Circadian Modulation on T-wave Alternans Activity in Chronic Heart Failure Patients

Conference paper: Computing in Cardiology 2015; 42:845-848. Alba Martín-Yebra, Enrico G Caiani, Pablo Laguna, Violeta Monasterio, Juan Pablo Martíne

DataSheet1_Differences in ventricular wall composition may explain inter-patient variability in the ECG response to variations in serum potassium and calcium.pdf

Objective: Chronic kidney disease patients have a decreased ability to maintain normal electrolyte concentrations in their blood, which increases the risk for ventricular arrhythmias and sudden cardiac death. Non-invasive monitoring of serum potassium and calcium concentration, [K+] and [Ca2+], can help to prevent arrhythmias in these patients. Electrocardiogram (ECG) markers that significantly correlate with [K+] and [Ca2+] have been proposed, but these relations are highly variable between patients. We hypothesized that inter-individual differences in cell type distribution across the ventricular wall can help to explain this variability.Methods: A population of human heart-torso models were built with different proportions of endocardial, midmyocardial and epicardial cells. Propagation of ventricular electrical activity was described by a reaction-diffusion model, with modified Ten Tusscher-Panfilov dynamics. [K+] and [Ca2+] were varied individually and in combination. Twelve-lead ECGs were simulated and the width, amplitude and morphological variability of T waves and QRS complexes were quantified. Results were compared to measurements from 29 end-stage renal disease (ESRD) patients undergoing hemodialysis (HD).Results: Both simulations and patients data showed that most of the analyzed T wave and QRS complex markers correlated strongly with [K+] (absolute median Pearson correlation coefficients, r, ranging from 0.68 to 0.98) and [Ca2+] (ranging from 0.70 to 0.98). The same sign and similar magnitude of median r was observed in the simulations and the patients. Different cell type distributions in the ventricular wall led to variability in ECG markers that was accentuated at high [K+] and low [Ca2+], in agreement with the larger variability between patients measured at the onset of HD. The simulated ECG variability explained part of the measured inter-patient variability.Conclusion: Changes in ECG markers were similarly related to [K+] and [Ca2+] variations in our models and in the ESRD patients. The high inter-patient ECG variability may be explained by variations in cell type distribution across the ventricular wall, with high sensitivity to variations in the proportion of epicardial cells.Significance: Differences in ventricular wall composition help to explain inter-patient variability in ECG response to [K+] and [Ca2+]. This finding can be used to improve serum electrolyte monitoring in ESRD patients.</p

Average values of the QT adaptation delays and (s) measured in the three CAD patient groups (third column), and , and in a simulated endocardial cell, and , with constant <i>β</i>-adrenergic stimulation (fourth column) and with the proposed time-varying <i>β</i>-adrenergic stimulation (fifth column).

Average values of the QT adaptation delays and (s) measured in the three CAD patient groups (third column), and , and in a simulated endocardial cell, and , with constant β-adrenergic stimulation (fourth column) and with the proposed time-varying β-adrenergic stimulation (fifth column).</p

Tested <i>β</i>-adrenergic stimulation patterns.

Simulated APD responses to HR changes for the four analyzed patterns of β-adrenergic stimulation. (PDF)</p

Average values of the QT adaptation delays and measured in the three patient groups (third column), and , and in a simulated endocardial cell, and , with constant <i>β</i>-adrenergic stimulation (fourth column) and with the proposed time-varying <i>β</i>-adrenergic stimulation (fifth column).

Average values of the QT adaptation delays and measured in the three patient groups (third column), and , and in a simulated endocardial cell, and , with constant β-adrenergic stimulation (fourth column) and with the proposed time-varying β-adrenergic stimulation (fifth column).</p

Fitting of linear (red) and hyperbolic (black) regression models to a patient’s QT and RR data (“highCAD-4” in Table 1).

The data clusters correspond to the different windows Wi, i ∈ 1, 2, 3. The residual εrms took values of 4.27 and 1.75 ms for the linear and hyperbolic fittings, respectively.</p

Table 1 -

Left column: codes used to identify the analyzed patients according to the risk group and the patient order number # within the group. Middle column: QT adaptation delay values measured in the exercise and recovery phases of the stress test for each patient, denoted by τe,p and τr,p with p indicating estimated from patients’ data. Right column: mean square error εrms for the linear and hyperbolic regression models calculated using the strategy.</p

Fig 4 -

Left panels: QT adaptation delay between dQT(n) and during exercise and recovery for a patient in the study. Middle panels: APD adaptation delay between dAPD(n) and during exercise and recovery in a simulated endocardial cell for constant β-adrenergic stimulation and the same HR as for the patient in the left. Right panels: APD adaptation delay between dAPD(n) and during exercise and recovery in a simulated endocardial cell for the proposed time-varying β-adrenergic stimulation. Top panels show results using the estimation strategy, while bottom panels use the estimation strategy. The red points correspond to ne, o, ne, e, nr, o and nr, e, calculated as described in section 2.6, which delimit the exercise and recovery ramps in or . The blue points were identified in dQT(n) or dAPD(n) as the nearest samples to the red points having QT or APD values within 2 ms of the corresponding red point value.</p

Hypothesis considered for the derivation of the approximate M-ary LRT and its expected value.

<p>Left: Example of ECG segment (blue line) and its observation window composed of QRS (red line) and noise (black line) frames (<i>M</i> = 5 and <i>r</i> = 1). Right: The most probable hypotheses in and for a transition as shown in left figure.</p

ECG signal in green line (record 108 containing several abnormal shapes, noise and artifacts).

<p>Left: MAP decision in red line based on M( = 3)-ary LRT. Right: A real time implementation of the matched filter-based method <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0110629#pone.0110629-Sornmo1" target="_blank">[15]</a>.</p