Resting Cardiac Power Index and Prediction of Prognosis in Heart Failure

The cardiovascular system is essentially a complex system of pumps and pipes, and as such it belongs to the branch of fluid dynamics in engineering science, the equivalent of which in cardiovascular physiology is haemodynamics, dealing with measureable variables including pressure and flow. Less well known to cardiologists is the study of cardiovascular energetics which obeys the fundamental Law of Conservation of Energy. According to this Law of physics, to prevent the cessation of the circulation owing to dissipation of energy in the vasculature, the cardiac pump needs to generate and impart a continuous supply of hydraulic energy.1 The primary function of the cardiac pump is therefore to convert chemical energy into hydraulic energy to maintain a physiologically viable circulation.1 How good the cardiac pump is can be represented by how well it can impart hydraulic energy to maintain a circulation that can meet the most demanding physiological stresses.2 The variable that represents this entity is cardiac power output (CPO) at peak stress, which can be calculated by the product of cardiac flow and pressure measured, for example, at maximal exercise. The mean value of CPO is approximated by multiplying cardiac output (CO in L/min), with mean arterial pressure (MAP in mmHg), and a factor to convert into watts (cardiac power output, CPO = CO × MAP × 2.22 × 0−3 W).3 Peak cardiac power output (CPOmax) would therefore best represent the extent of the heart's failure to deliver adequate hydraulic energy sufficient to maintain a circulation that copes with any physiological stresses. It is therefore commonly found in multivariate analyses to be a powerful independent predictor of heart failure (HF) prognosis.4 In this issue of the European Journal of Heart Failure, Grodin and colleagues from Cleveland and Belgium5 extended this scheme of conceptual thinking to study heart function, not from the common and near-ubiquitous perspective of left ventricular ejection fraction (LVEF), but by considering the heart's role as a hydraulic pump at rest, quantified as the cardiac power index (CPI), obtained by indexing resting CPO by body surface area (BSA), during invasive haemodynamic assessment. This retrospective cohort study included 495 sequential ambulatory patients with advanced heart failure on evidence-based treatment followed up for a median of 3.3 years. Their demographics included mean age 54 ± 11 years, 76% male, 48% with ischaemic cardiomyopathy, mean LVEF 20%, 91% in New York Heart Association (NYHA) class III, and 39% having an implantable cardioverter defibrillator (ICD) in situ. Resting CPI less than 0.44 W/m2 was associated with increased adverse outcomes (death, transplant or VAD placement) with a hazard ratio of 2.4. An independent association remained after multivariate analyses for age, gender, pulmonary capillary wedge pressure (PCWP), Fick CI, creatinine, LVEF, B-type natriuretic peptide (BNP), and peak oxygen consumption (VO2max). The authors acknowledge a limitation that this was a single-centre, retrospective study, performed mostly in patients with advanced HF. Furthermore, resting CPI is limited by the fact that it is difficult to determine how much of the peak CPO the patient ‘uses’ at rest. Conceivably minor changes in pulse rate or adrenergic activity (related to stress for example) can lead to increases in CPI thus creating the false assessment of a ‘better’ patient prognosis. Although the definition of advanced HF was rather vague, in advanced HF it is likely that the CPO reserve is relatively low and hence resting CPI may correlate well with peak CPO and therefore provide a good representation of near-maximal cardiac pumping capacity. This assumption should not be made in patients with less severe HF, where peak CPO rather than resting CPI should be assessed. Curiously, one of the earliest indicators of HF prognosis was published in a non-cardiological journal in 1948. In it the authors reported that it is ‘justifiable to consider the prognosis as very grave’ in patients with severe HF if normoblasts are found in their blood and their disappearance was ‘concomitant with an improvement in the heart failure’.6 Since then, there has been a marked proliferation of reported HF prognostic indicators (PrI), which pose a problem for clinicians because of the impossibility of adopting the entire range into routine practice. Over the years, multiple determinants of HF prognosis have been found covering many aspects of the HF syndrome, derived with various statistical tools, especially multivariate analysis, and utilized for worthy clinical and research objectives.7 Now that CPIrest has joined a lengthening list of PrIs for HF, where does it rank amongst other competing PrIs? With so many PrIs already reported in the literature, frequent emergence of newly reported PrIs and questions raised over previously advocated PrIs, practising clinicians are overwhelmed and in danger of becoming inattentive to newer PrIs. The time has come to reappraise and draw a shortlist of PrIs that are worthy of being adopted into routine clinical practice. A major handicap with testing and selecting the best PrI is that, unlike symptoms and functional capacities, it is impossible to measure the prognosis of an individual patient and measure how it is altered by certain therapeutic interventions in that individual.8 Prognosis of HF can only be determined in a cohort of patients over a period of time and the PrIs derived thereby would then require validation in different HF populations to verify their general applicability.9 However, HF populations are changing significantly over time, for example, with evolving demographics such as rising proportions of older patients with preserved LVEF and degenerative valve disease, and with progressively improving pharmacological, device, and interventional therapies. Each multiparametric combination set of PrIs will therefore require periodical redevelopment ad infinitum, followed by validation iteratively in different HF populations. Are there alternative and better ways of selecting PrIs for clinicians? Table 1 lists proposed methods for the classification of HF PrIs. In statistics, it is axiomatic that ‘Bigger is Better’, implying that the larger the sample size, the greater the power, confidence, and generalizability of the PrIs. An ideal approach would be to conduct a mega-study involving millions of consecutive HF patients and entering all known candidate PrIs into conclusive multivariate analyses. However, this approach may not be practical, as it will be necessary to repeat such a mega-study every few years, especially if there are significant drifts in HF management options. An alternative approach would be to test PrIs in smaller studies using a priori hypothesis. They could, for example, be considered according to pathophysiological mechanisms. The HF syndrome is caused by a combination of the cardiac pump's failure to maintain a physiological circulation1 and downstream derangements consequential upon tissue hypoperfusion, congestion, and neurohormonal and inflammatory activation, giving rise to measureable markers. Thus, PrIs can be divided into those pertaining to the primary causes of HF (structural and functional defects) and those pertaining to downstream effects. One might therefore speculate that PrIs related to the primary cardiac dysfunction4 will be more prognostic than PrIs derived from downstream consequences, especially in patients with advanced HF irrespective of aetiology. In this context, measuring CPIrest, and even more so peak CPO, would accurately assess the cardiac pumping capacity and, hence, be strongly associated with outcomes, as shown by Grodin et al.5 Another important consideration when assessing PrIs is to ‘stress’ them longitudinally to verify whether any improvement in their values, however biologically achieved (and not caused by differences in measurement techniques), are accompanied by concordant improvements in HF status and prognosis. For example, applying such a stress test to BNP, it would appear that any alteration in the rates of BNP synthesis, metabolism, or excretion would alter plasma BNP levels unaccompanied by actual alterations in cardiac status and prognosis. This may partly explain why trials of BNP-guided HF therapy have so far produced rather mixed results.10 Peak CPO or resting CPI were not assessed previously in such a study examining their changes with therapies. However, conceivably, resting CPI could be altered by therapies such as inotropes that have not been shown to improve outcomes,11 adding to the caution needed in applying it to clinical practice. What constitutes an optimal and useful PrI? It is one that not only predicts prognosis well, but is also generally applicable to heterogeneous HF patients worldwide and would respond to interventions that change prognosis in a way that correlates with the intervention's effect while not responding to interventions that do not change or worsen prognosis. The selection process for such HF PrIs should now be undertaken in earnest. In addition, as in drug development, where new agents in an existing class are required to demonstrate superiority before approval, the onus should now be on investigators claiming a newly discovered HF PrI to provide evidence of its superiority over existing PrIs. Conflict of interest: none declared.