María Paula Bonomini

Probability of ventricular fibrillation: allometric model based on the ST deviation

BackgroundAllometry, in general biology, measures the relative growth of a part in relation to the whole living organism. Using reported clinical data, we apply this concept for evaluating the probability of ventricular fibrillation based on the electrocardiographic ST-segment deviation values.MethodsData collected by previous reports were used to fit an allometric model in order to estimate ventricular fibrillation probability. Patients presenting either with death, myocardial infarction or unstable angina were included to calculate such probability as, VFp= δ + β (ST), for three different ST deviations. The coefficients δ and β were obtained as the best fit to the clinical data extended over observational periods of 1, 6, 12 and 48 months from occurrence of the first reported chest pain accompanied by ST deviation.ResultsBy application of the above equation in log-log representation, the fitting procedure produced the following overall coefficients: Average β = 0.46, with a maximum = 0.62 and a minimum = 0.42; Average δ = 1.28, with a maximum = 1.79 and a minimum = 0.92. For a 2 mm ST-deviation, the full range of predicted ventricular fibrillation probability extended from about 13% at 1 month up to 86% at 4 years after the original cardiac event.ConclusionsThese results, at least preliminarily, appear acceptable and still call for full clinical test. The model seems promising, especially if other parameters were taken into account, such as blood cardiac enzyme concentrations, ischemic or infarcted epicardial areas or ejection fraction. It is concluded, considering these results and a few references found in the literature, that the allometric model shows good predictive practical value to aid medical decisions.

The allometric model in chronic myocardial infarction

BackgroundAn allometric relationship between different electrocardiogram (ECG) parameters and infarcted ventricular mass was assessed in a myocardial infarction (MI) model in New Zealand rabbits.MethodsA total of fifteen animals were used, out of which ten underwent left anterior descending coronary artery ligation to induce infarction (7–35% area). Myocardial infarction (MI) evolved and stabilized during a three month-period, after which, rabbits were sacrificed and the injured area was histologically confirmed. Right before sacrifice, ECGs were obtained to correlate several of its parameters to the infarcted mass. The latter was normalized after combining data from planimetry measurements and heart weight. The following ECG parameters were studied: RR and PR intervals, P-wave duration (PD), QRS duration (QRSD) and amplitude (QRSA), Q-wave (QA), R-wave (RA) and S-wave (SA) amplitudes, T-wave peak amplitude (TA), the interval from the peak to the end of the T-wave (TPE), ST-segment deviation (STA), QT interval (QT), corrected QT and JT intervals. Corrected QT was analyzed with different correction formulae, i.e., Bazett (QTB), Framingham (QTFRA), Fridericia (QTFRI), Hodge (QTHO) and Matsunaga (QTMA) and compared thereafter. The former variables and infarcted ventricular mass were then fitted to the allometric equation in terms of deviation from normality, in turn derived after ECGs in 5 healthy rabbits.ResultsSix variables (JT, QTB, QA, SA, TA and STA) presented statistical differences among leads. QT showed the best allometric fit (r = 0.78), followed by TA (r = 0.77), STA (r = 0.75), QTFRA (r = 0.72), TPE (r = 0.69), QTFRI (r = 0.68) and QTMA (r = 0.68). Corrected QT’s (QTFRA, QTFRI and QTMA) performed worse than the uncorrected counterpart (QT), the former scaling allometrically with similar goodness of fits.ConclusionsQT, TA, STA and TPE could possibly be used to assess infarction extent in an old MI event through the allometric model as a first approach. Moreover, the TPE also produced a good allometric scaling, leading to the potential existence of promising allometric indexes to diagnose malignant arrhythmias.

Non-selective His bundle pacing with a biphasic waveform: enhancing septal resynchronization

Aims His bundle pacing has shown to prevent detrimental effects from right ventricular apical pacing (RVA) and proved to resynchronize many conduction disturbances cases. However, the extent of His bundle pacing resynchronization is limited. An optimized stimulation waveform could expand this limit when implemented in His bundle pacing sets. In this work, we temporarily implemented RVA and Non-selective His bundle pacing with a biphasic anodal-first waveform (AF-nHB) and compared their effects against sinus rhythm (SR). Methods and results Fifteen patients referred for electrophysiologic study with conduction disturbances, cardiomyopathy and ejection fraction below 35% were enrolled for the study. The following acute parameters were measured: QRS duration, left ventricular activation (RLVT), time of isovolumic contraction (IVCT), ejection fraction (EF), and dP/dtmax. QRS duration and RLVT decreased markedly under AF-nHB (SR: 169 ± 34 ms vs. nHB: 116 ± 31 ms, P < 0.0005) while RVA significantly increased QRS duration (SR: 169 ms vs. RVA: 198 ms, P < 0.05) and did not change RLVT (P = NS). Consistently, IVCT moderately decreased under AF-nHB (SR: 238 ms vs. RVA: 184 ms, P < 0.05 vs. SR) and dP/dtmax showed a 93.35 [mmHg] average increase under AF-nHB against SR. Also, T-wave inversions were observed during AF-nHB immediately after SR and RVA pacing suggesting the occurrence of cardiac memory. Conclusions AF-nHB corrected bundle branch blocks in patients with severe conduction disturbances, even in those with dilated cardiomiopathy, outstanding from RVA. Also, the occurrence of cardiac memory during AF-nHB turned up as an observational finding of this study.

ECG parameters to predict left ventricular electrical delay

AIMS Left ventricular (LV) dyssynchrony lengthens the left ventricular electrical delay (LVED), measured from QRS onset to the first peak of the LV electrogram. We constructed an ECG model to predict LVED noninvasively. METHODS Intrapatient LVED was measured during a baseline vs nonselective His bundle pacing (nHBP) protocol. This setup provided paired synchronic/non-synchronic LVEDs, allowing intrapatient comparisons. Crosscorrelation of leads II and V6 was accomplished and extracted features together with age and gender fed a linear mixed effects model to predict LVED. RESULTS Hemodynamic increments were consistent with LVED advances under nHBP in a subset of 17 patients (dP/dtmax, baseline: 938.82 ± 241.95 mm Hg/s vs nHBP: 1034.94 ± 253.63 mm Hg/s, p = 6.24e-4). The inclusion of the area under V6 (AV 6) and the time shift of R-peaks obtained from the crosscorrelation signal (CorS) grouped by patient significantly improved LVED estimation with respect to the model based only on QRS duration, age and gender (p = 1.7e-5). CONCLUSIONS Interlead ECG changes explained LVED, providing clues about the electrical impulse conduction within the left ventricle noninvasively.

Left ventricular hypertrophy index based on a combination of frontal and transverse planes in the ECG and VCG: Diagnostic utility of cardiac vectors

The changes that left ventricular hypertrophy (LVH) induces in depolarization and repolarization vectors are well known. We analyzed the performance of the electrocardiographic and vectorcardiographic transverse planes (TP in the ECG and XZ in the VCG) and frontal planes (FP in the ECG and XY in the VCG) to discriminate LVH patients from control subjects. In an age-balanced set of 58 patients, the directions and amplitudes of QRS-complexes and T-wave vectors were studied. The repolarization vector significantly decreased in modulus from controls to LVH in the transverse plane (TP: 0.45±0.17mV vs. 0.24±0.13mV, p<0.0005; XZ: 0.43±0.16mV vs. 0.26±0.11mV, p<0.005) while the depolarization vector significantly changed in angle in the electrocardiographic frontal plane (Controls vs. LVH, FP: 48.24±33.66° vs. 46.84±35.44°, p<0.005, XY: 20.28±35.20° vs. 19.35±12.31°, NS). Several LVH indexes were proposed combining such information in both ECG and VCG spaces. A subset of all those indexes with AUC values greater than 0.7 was further studied. This subset comprised four indexes, with three of them belonging to the ECG space. Two out of the four indexes presented the best ROC curves (AUC values: 0.78 and 0.75, respectively). One index belonged to the ECG space and the other one to the VCG space. Both indexes showed a sensitivity of 86% and a specificity of 70%. In conclusion, the proposed indexes can favorably complement LVH diagnosis

Dynamic Response of Electrocardiographic Indices During Abrupt Heart Rate Changes - Comparison between Young and Middle-aged Subjects

Abnormal modifications in ventricular repolarization dispersion (VRD) have been shown to constitute a substrate for malignant arrhythmias. In this work, we have induced abrupt heart rate (HR) changes to young and middle-aged healthy subjects through a Tilt-test and have analyzed the evolution of several VRD indices. Duration ones, based on electrocardiogram intervals; energy ones, developed through a Principal Components Analysis (PCA) in T-wave; and the morphology ones, extracted feature from an absolute T-wave. In both groups, results have shown significant decreases in early repolarization duration. These changes are responsible for the alterations in the total repolarization duration, because T-wave peak-to-end has not shown statistical significance. Moreover, we have found significant decreases in total, early and late repolarization energy, and in the T-wave amplitude. In another sense, we have observed that the repolarization energy obtained by PCA jointly with early T-wave slope and amplitude have been able to reflect VRD differences between young and middle-aged subjects. Finally, this work provides the range of values for VRD in normal conditions during abrupt HR changes. Outside this range, we could assume that it exists a cardiac risk.

Comparison of Electrocardiographic and Vectorcardiographic Planes on a Set of Left Ventricular Hypertrophy Patients

Most common electrocardiographic diagnostic criteria for left ventricular hypertrophy (LVH) are based on depolarization information. However, reports support that LVH also alters repolarization. Two measures relate depolarization/ repolarization: the angle between the QRS-complex and the T-wave in a plane; the planar QRST (QRST p) and the vector obtained from the cross sum of the depolarization and repolarization vectors (RTa and RTm). We compared the performance of these measures as hypertrophy markers in two sets of planes: the ECG frontal plane (FP) versus the VCG frontal plane (XYP) and the ECG horizontal plane (HP) versus the VCG horizontal plane (XZP). The horizontal views picked up a significant increase of the QRST p (HP controls vs LVH: 40.18±41.20° vs 66.50±51.65°, p<0.05; XZP controls vs LVH: 43.87±39.76° vs 66.35±38.30°, p<0.05) and a consistent behaviour in the frontal views (XYP controls vs LVH: 17.71±37.23° vs 35.60±47.98°, p<0.005). On the other hand, the angle of the equivalent RT vector significantly increased in the HP (HP controls vs LVH: 24.28±26.50° vs 33.53±22.42°, p<0.05). In conclusion, the angular information in their two forms (QRST p and RT α ) relating depolarization and repolarization was the most informative parameter and should be regarded for the construction of more sensitive electrocardiographic LVH indexes.

Characterization of the Temporal Evolution of the ECG Indices under Abrupt Heart Rate Changes in Healthy Subjects

Abnormal alterations in ventricular repolarization dispersion (VRD) have been shown to constitute a substrate for arrhythmias. In this work, we have induced abrupt heart rate (HR) changes to 17 healthy subjects through a Tilttest and have analyzed the evolution of a set of ECG indices: Temporal ones (T-VRD), based on classic ECG intervals, and morphological ones (M-VRD), extracted features from an absolute T-wave. The quantification has been done by computing the alterations in index value and calculating their response time. In T-VRD indices, results have shown statistically significant decreases in T-wave onset-to-peak (TOP). Furthermore, these changes are responsible about the alterations in the T-wave width and in the QT interval. T-wave peak-to-end (TPE) has not shown statistical significance. Regarding M-VRD indices, we have observed that the shape of the T-wave undergoes to a fast initial reduction in amplitude and a posterior slow shifting toward the QRS-complex. The area of the both halves of the T-wave have shown statistically significant decreases, but relationship between themselves has remained practically constant. Finally, several ECG indices have reached the steady state before the RR interval. This study provides the normal range of VRD values in healthy young subjects during HR changes.

Inverse Allometry: Foundations for a Bioinspired LVH-Prediction Model

Allometry, in general biology, measures the relative growth of a part in relation to the whole living organism. Left ventricular hypertrophy (LVH) is the heart adaptation to excessive load (systolic or diastolic) that leads to an increase in left ventricular mass, which in turn, raises the electrocardiographic voltages. If this mass increase followed an allometric law, then it would be possible to design a bioinspired model based on the allometric equation to predict LVH. In this work, we first investigated the validity of this hypothesis and then proposed an LVH marker based on the inverse allometry model. Based on clinical data, we compared the allometric behavior of three different ECG markers of LVH. To do this, the allometric fit AECG = δ + β(VM) relating left ventricular mass (estimated from echocardiographic data) and ECG amplitudes (expressed as the Cornell-Voltage, Sokolow and the ECG overall voltage indexes) were compared. Besides, sensitivity and specificity for each index were analyzed. The more sensitive the ECG criteria, the better the allometric fit. Finally, the Receiver Characteristic Curve (ROC) of an allometric model proposed here was computed. In conclusion: The allometric paradigm should be regarded as the way to design new and more sensitive ECG-based LVH markers.

Cardiac Risk Assessment: When and Who? [Retrospectroscope]

Think about the above lines taken from the Old Testament: At 130 years of age, Adam begat a son and at 800 he kept going, quitting this earthly life at 930. These numbers surpass by far the limits our current experience teaches us, however, perhaps a life span into the hundreds of years is ? What if, in the future, science were to do away with disease? What then would cause people to die: accidents, killings, wars? How old would old age be? Aging has always been a hot topic for research (with considerable quackery, too). For example, animals with a slow metabolism tend to live longer than those with a fast metabolism. Compare the average life span of a mouse with that of a turtle. Apparently, meditators are able to slow their metabolism down [1].