Comparison of ventricular pressure relaxation assessments in human heart failure: Quantitative influence on load and drug sensitivity analysis

OBJECTIVES: We contrasted various methods for assessing ventricular pressure decay time constants to test whether sensitivity to slight data instability or disparities between model-assumed and real decay are systematically altered by cardiac failure. We hypothesized that such discrepancies could result in apparent increased relaxation sensitivity to load and drug stimulation. BACKGROUND: Deviation of relaxation behavior from model-assumed waveforms may be worsened by failure, enhancing instability and apparent load and drug sensitivity of commonly used indexes. METHODS: Pressure-volume relations were measured in patients with normal (n = 14), hypertrophic (hypertrophic cardiomyopathy [HCM], n = 15) and dilated-myopathic (dilated cardiomyopathy [DCM], n = 37) hearts before and during preload reduction or inotropic stimulation. Relaxation parameters (monoexponential [ME] model assuming zero-T(ln) or non-zero-T(D), T(F) asymptote:, hybrid logistic-T(L), linear-T(LR), and pressure halftime-T(1/2)) were contrasted regarding sensitivity to slight data range manipulation and loading or drug changes. RESULTS: In DCM, T(D) and T(F) prolonged 15% to 25% (p < 0.0001) by deletion of only 1-2 data points, whereas this had minimal effect on control or HCM. This stemmed from systematic deviation of relaxation from an ME decay in DCM. T(1/2) and T(ln) were highly sensitive to pure pressure offsets, whereas T(L) was most stable to both manipulations in all hearts. As a result, T(D) and T(F) appeared to be much more sensitive to systolic load in DCM than T(1/2) or T(L) and disproportionately sensitive to increased cyclic adenosine monophosphate (cAMP). CONCLUSIONS: Relaxation consistently deviates from an ME decay in DCM resulting in instability and amplified relaxation systolic load or drug dependence of ME-based indexes in failing versus control (or HCM) hearts. The hybrid-logistic method improves quantitative analyses by providing more consistent data fits with all three heart types.