PULSE PRESSURE AND HEART FAILURE

Pulse Pressure and Heart Failure

Pulse Pressure and Heart Failure

Introduction

Increased pulse pressure (PP) is associated with risk of myocardial infarction1, and stroke3 in both normotensive and hypertensive subjects (Liu et al., 2003, 200-285). Increased PP is also associated with new onset congestive heart failure in middle-aged4 and elderly subjects. Finally, increased PP is related to cardiovascular mortality in patients with symptomatic or asymptomatic systolic heart failure. In all these populations, PP is considered as a surrogate marker of arterial stiffness. Recently however, in two large studies of relatively unselected subjects, including a large proportion of patients with decompensated heart failure (irrespective of the ejection fraction (EF)), an inverse relationship was found between PP and all-cause mortality.8,9 As PP is influenced not only by arterial stiffness but also by cardiac factors such as heart rate (HR) and stroke volume, pulse wave velocity (PWV), a direct marker of aortic stiffness, or the carotid augmentation index (AIx), a marker of aortic stiffness and wave reflection, might have better prognostic value than PP. PWV is independently associated with cardiovascular morbidity and mortality in patients with hypertension or diabetes, and in the elderly, whereas AIx is associated with cardiovascular risk factors (CVR) in the general population13 and with mortality in patients with end-stage renal failure. All these markers are dependent on time domain parameters and on pressure, which may alter their prognostic value in heart failure. The aim of this study was to identify factors influencing brachial PP, carotid AIx and carotidfemoral (CF) or carotid-radial (CR) PWV in patients with chronic heart failure, and to determine whether these parameters are dependent on left ventricular systolic function, as reflected by left ventricular EF (Susic, Varagic, Ahn & Frohlich, 2004, 328-333).

Roc Curve Analysis

The diagnostic performance of a test, or the accuray of a test to discriminate diseased cases from normal cases is evaluated using Receiver Operating Characteristic (ROC) curve analysis (Metz, 1978; Zweig & Campbell, 1993). ROC curves can also be used to compare the diagnostic performance of two or more laboratory or diagnostic tests (Griner et al., 1981).

When you consider the results of a particular test in two populations, one population with a disease, the other population without the disease, you will rarely observe a perfect separation between the two groups. Indeed, the distribution of the test results will overlap, as shown in the following figure.

For every possible cut-off point or criterion value you select to discriminate between the two populations, there will be some cases with the disease correctly classified as positive (TP = True Positive fraction), but some cases with the disease will be classified negative (FN = False Negative fraction). On the other hand, some cases without the disease will be correctly classified as negative (TN = True Negative fraction), but some cases without the disease will be classified as positive (FP = False Positive fraction).

Schematic outcomes of a test

The different fractions (TP, FP, TN, FN) are represented in the following ...