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S A Chiaramida

Publications and source records attributed to S A Chiaramida.

9 recordsLinked to original sources
PubMed2001

Comparison of echocardiography with technetium 99m-gated single photon emission computed tomography as diagnostic tools for left ventricular ejection fraction.

Left ventricular ejection fractions (LVEF) are routinely measured and result in critical decision-making algorithms in cardiology. This study was conducted to compare the accepted standard two-dimensional (2-D) echocardiogram ejection fraction (EF(ECHO)) with single photon emission computed tomography rest ejection fraction (EF(SPECT)). Data were acquired and analyzed from 51 inpatients. EF(ECHO) was obtained using modified Simpson's rule in the four-chamber apical view, and gated EF(SPECT) was computed by an automated method (Siemens ICON software). Comparison between EF(ECHO) and EF(SPECT) was done by linear regression, Bland-Altman, and receiver operator characteristic (ROC) analyses. Linear regression analysis revealed EF(SPECT) = 1.12 x EF(ECHO) - 3.6%, r = 0.93, n = 51, P < 0.0001. Bland-Altman analysis showed that the limits of 95% confidence for the difference between EF(SPECT) and EF(ECHO) were - 12% to 18%. Ninety-eight percent of the datapoints were within the limits of confidence. The ROC analysis showed that the sensitivity and specificity for detecting abnormal EF (< or = 50%) were 85% and 86% with EF(SPECT), and 91% and 90% with EF(ECHO). This study showed good correlation as well as agreement between SPECT and two-dimensional echocardiograms in measuring EF.

Coronary Artery Disease↗
PubMed1997

Sensitivity and specificity of left ventricular ejection fraction by echocardiographic automated border detection: comparison with radionuclide ventriculography.

BACKGROUND: Echocardiographic automated border detection (ABD) provides on-line, beat-to-beat estimation of left ventricular (LV) ejection fraction (EF). Sensitivity and specificity of using ABD-EF for diagnosing LV dysfunction in routine clinical situations have not been previously studied. HYPOTHESIS: Analysis of ABD-EF data based on receiver operating characteristic (ROC) should provide useful information about sensitivity and specificity for clinical diagnosis of LV function based on ABD-EF. METHODS: The study group included 50 consecutive patients with EF measured by both ABD and radionuclide ventriculography (RVG). ABD-EF was recorded for 25 consecutive heart beats in the apical four-chamber view. Data were analyzed statistically by linear regression, Bland-Altman plot, and ROC. In ROC analysis, abnormal LV function was defined RVG-EF < or = 40%. RESULTS: ABD and RVG showed a moderate correlation in the EF measurements: slope = 0.93, intercept = 17%, r = 0.79 (n = 50). Interbeat variability in ABD was diminished by averaging consecutive beats; standard error of estimate (SEE) decreased from 15.6% without averaging to 12.5% with 25-beat averaging. Bland-Altman analysis indicated that ABD-EF compared unfavorably with RVG-EF, with limits of agreement from -11% to 39%. ABD-EF showed a systematic overestimation (p < 0.005), which was compensated by increasing the threshold for abnormal ABD-EF to 56%. With the optimized threshold, ABD-EF provided 89% sensitivity and 89% specificity (85% overall diagnostic accuracy) for diagnosing abnormal LV function. CONCLUSION: This study explored the limitations of on-line echocardiographic measurement of EF in a clinical setting and provided useful data for assessing interbeat variability, sensitivity, and specificity.

Aged↗
PubMed1997

A comprehensive model for right-left heart interaction under the influence of pericardium and baroreflex.

A phenomenological model of the cardiopulmonary circulation is developed with a focus on the interaction between the right heart and the left heart. The model predicts the hemodynamic consequences of changing circulatory parameters in terms of a broad spectrum of pressure and flow waveforms. Hemodynamics are characterized by use of an electrical analog incorporating mechanisms for transseptal pressure coupling, pericardial volume coupling, intrathoracic pressure, and baroreflex control of heart rate. Computer simulations are accomplished by numerically integrating 28 differential equations that contain nonlinear and time-varying coefficients. Validity of the model is supported by its accurate fit to clinical pressure and Doppler echocardiographic recordings. The model characterizes the hemodynamic waveforms for mitral stenosis, mitral regurgitation, left heart failure, right heart failure, cardiac tamponade, pulsus paradoxus, and the Valsalva maneuver. The wave shapes of pulmonary capillary wedge pressure under the above conditions are also accurately represented. Sensitivity analysis reveals that simulated hemodynamics are insensitive to most individual model parameters with the exception of afterload resistance, preload capacitances, intrathoracic pressure, contractility, and pericardial fluid volume. Baseline hemodynamics are minimally affected by transseptal coupling (up to 2%) and significantly affected by pericardial coupling (up to 20%). The model should be useful for quantitative studies of cardiopulmonary dynamics related to the right-left heart interaction under normal and disease conditions.

Animals↗
PubMed1983

Resting and exercise hemodynamics in constrictive pericarditis and a case of cardiac amyloidosis mimicking constriction.

The resting and exercise hemodynamics in seven patients with pericardial constriction and in one patient with cardiac amyloidosis are described. Equalization of left and right heart diastolic filling pressures was observed in all cases, both at rest and during exercise. This hemodynamic response, although typical, should not be considered pathognomonic of pericardial constriction.

Adult↗
PubMedDate not supplied

Gaussian pulse decomposition: an intuitive model of electrocardiogram waveforms.

This study presents a novel approach to modeling the electrocardiogram (ECG): the Gaussian pulse decomposition. Constituent waves of the ECG are decomposed into and represented by Gaussian pulses using an iterative algorithm: the chip away decomposition (ChAD) algorithm. At each iteration, a nonlinear minimization method is used to fit a portion of the ECG waveform with a single Gaussian pulse, which is then subtracted from the ECG waveform. The process iterates on the resulting residual waveform until the normalized mean square error is below an acceptable level. Three different minimization methods were compared for their applicability to the ChAD algorithm; the Nelder-Mead simplex method was found to be more noise-tolerant than the Newton-Raphson method or the steepest descent method. Using morphologically different ECG waveforms from the MIT-BIH arrhythmia database, it was demonstrated that the ChAD algorithm is capable of modeling not only normal beats, but also abnormal beats, including those exhibiting a depressed ST segment, bundle branch block, and premature ventricular contraction. An analytical expression for the spectral contributions of the constituent waves was also derived to characterize the ECG waveform in the frequency domain. The Gaussian pulse model, providing an intuitive representation of the ECG constituent waves by use of a small set of meaningful parameters, should be useful for various purposes of ECG signal processing, including signal representation and pattern recognition.

Algorithms↗