Optimizing cardiovascular function in order to ensure adequate tissue oxygen delivery is a key objective in the care of critically ill patients. In order to achieve this safely, it is necessary to monitor left ventricular preload and cardiac output (CO) in a significant cohort of ICU patients. The pulmonary artery catheter (PAC) has been used extensively for this purpose for approximately four decades and has formerly been considered the gold standard measure of both left ventricular preload (pulmonary artery occlusion pressure, PAOP) and CO. However, routine right heart catheterization with a PAC is associated with a number of complications and has not been demonstrated to improve mortality or morbidity in a number of studies [1–4]. The National Heart, Lung and Blood Institute's Acute Respiratory Distress Syndrome (ARDS) Network has stated that ‘the pulmonary artery catheter should not be routinely used for the management of acute lung injury’, resulting in a dramatic reduction in the use of PACs in many institutions. This in turn is likely to erode the skill base and familiarity necessary for the safe use of the PAC.
In view of these changes, it has become necessary for every ICU to consider alternative strategies for haemodynamic monitoring. It is acknowledged that many options exist including oesophageal Doppler and transthoracic bioimpedance measurement, the former having acquired a strong evidence base of outcome data justifying its use in perioperative care . Both methods have been expertly reviewed elsewhere [6,7], and will not be further discussed here. In this review, we consider in detail the scientific basis of two popular alternative strategies reliant on intra-arterial monitoring:
- The volumetric approach based on transpulmonary thermal dilution.
- Pulse contour analysis.
This review will focus on the underlying theory and the experimental and clinical evidence that would justify their use in current clinical practice.
A volume-based approach to measuring ventricular preload
Ventricular preload is a function of myocardial stretch and the most direct clinical correlate of this is the end-diastolic ventricular volume (EDV). The traditional approach of estimating EDV using pressure-based measurements such as central venous pressure (CVP) or PAOP has several limitations. Changes in CVP/PAOP have been shown to correlate poorly with changes in ventricular volume in numerous studies [8–10]. As an example, Kumar et al.  assessed the correlation between CVP/PAOP and EDV in response to a 3 l fluid challenge in healthy volunteers during spontaneous ventilation. Neither the initial CVP/PAOP nor the changes in these parameters (ΔCVP/ΔPAOP) were useful in predicting changes in end-diastolic volumes or stroke volume (SV) as measured by trans-oesophageal echocardiography (TOE). Such studies have emphasized the shortcomings of inferring ventricular volumes from pressure measurements and thereby stimulated interest in the use of volume-based estimation of ventricular preload. Although echocardiography can provide an accurate measure of ventricular and SVs, it is inconvenient for repeated use over a prolonged period and is highly operator-dependent. Therefore, devices that enable repeated bed-side assessment of intrathoracic blood volume (ITBV), global end-diastolic volume (GEDV) and extravascular lung water (EVLW) using transpulmonary indicator dilution technology are exciting and require further consideration.
Transpulmonary dilution techniques
The transpulmonary thermal-dye dilution technique
This technique was first developed in the 1970s and allowed the estimation of EVLW using simultaneously derived transpulmonary indocyanin green (IcG) and thermal dilution curves. Thermal-dye dilution technique requires the injection of cold IcG dye into the right atrium or superior vena cava with simultaneous measurement of the temperature and IcG dilution curves in the abdominal aorta. The volume of distribution for each of the indicators (dye and cold) is a function of CO and mean transit time (MTt). Although the ‘thermal bolus’ distributes throughout the entire fluid compartment (intrathoracic thermal volume, ITTV), the volume of distribution of IcG is limited to the intravascular compartment alone, because it is highly protein-bound. This gives an estimate of ITBV. EVLW is then derived as shown in equation :
This approach to EVLW measurement has been validated in animal and human studies. It can reliably detect changes in EVLW of 20% or greater, and compares favourably with gold-standard gravimetric measurement . Although accurate and effective [12–14], this technique is expensive, time consuming and too cumbersome for routine bedside use.
The single transpulmonary thermodilution technique
This technique allows derivation of ITTV, ITBV and EVLW using transpulmonary thermal dilution alone. A key concept is the derivation of pulmonary thermal volume (PTV) using the down slope time of the transpulmonary thermal dilution curve (DStT). The theoretical framework behind the use of DStT to determine PTV was originally described in 1951 by Newman et al.  and has been expertly reviewed more recently by Isakow and Schuster , to which the reader is referred for a more detailed account.
Newman et al. first considered a simple experimental model consisting of a single chamber with an inflow allowing injection of dye, and an outflow for repeated sampling and construction of a dilution curve (Fig. 1). In this model, a relationship between concentration at the sampling port and time after injection was derived based on the following assumptions: injection of dye into the chamber is instantaneous; mixing of dye in the chamber volume is complete and instantaneous; the flow rate of fluid through the chamber (Q) is constant; there is no loss of dye from the system; and after injection, only dye-free fluid flows into the system.
If a known quantity of dye is injected into the chamber of volume V, the rate of change in the number of units of dye remaining in the chamber at a given time is dependent on the concentration and the flow (Q) at that instant. Using this simplified model, Newman et al. demonstrated that it was possible to derive V from knowledge of Q and direct measurement of the gradient of the down slope portion of the dilution curve. In simple terms, subject to assumptions mentioned above, the volume of a compartment upstream of the sampling point may be derived from the gradient of the semi-logarithmic plot of the concentration–time curve (DStT) and the flow.
Newman et al. went on to describe the kinetics of dye flow through a three-compartment model, with a central largest compartment (analogous to the pulmonary circulation) and two smaller compartments on either side joined in series (analogous to the left and right heart). They demonstrated that DStT was dependent primarily on flow and volume of the larger central compartment, allowing pulmonary blood volume (PTV) to be estimated from the DStT as shown in the following equation .
Having ascertained ITTV and PTV, it is then possible to derive an accurate measure related to the volume of blood in the four cardiac chambers. This combined volume has been referred to as the GEDV and is given by the following equation :
If one then assumes that ITBV is always a constant proportion of GEDV, it is possible to calculate ITBV from the GEDV value so derived. The precise numerical relationship between GEDV and ITBV was first defined by Sakka et al.  in a multicentre clinical study and further validated for common physiological perturbations such as acute lung injury and hypovolaemic shock by Nirmalan et al. [18,19]. These studies jointly support the derivation of ITBV using the transpulmonary thermal dilution curve alone using the following equation :
The numbers derived using this approach are related to the volume of blood between the point of injection of the cold bolus (usually the superior vena cava) and the point of detection by the thermistor (usually the femoral or brachial arteries). Therefore, the terms ITBV and GEDV are misnomers in that they do not strictly refer to the volume of blood in the thoracic cavity or the four cardiac chambers, respectively. They are in effect hypothetical chambers that have been shown to be closely linked to the true central compartments of interest. This limitation should be born in mind when deciding the position of the sampling catheter.
A schematic diagram of the relevant volumes as estimated by the indicator dilution technology is shown in Fig. 2.
The derivation of cardiac output using transpulmonary indicator dilution
Accurate measure of CO is a prerequisite in the derivation of ITTV and PTV as described above. CO derived by applying the Stewart Hamilton equation to the transpulmonary indicator dilution curves is used in all commercially available monitors. Temperature (PiCCO; PULSION Medical Systems, Munich, Germany) and lithium (LidCO; LidCO Group PLC, London, UK) have become the most popular indicators. CO measured using transpulmonary dilution of either indicator has been shown to have an excellent correlation and agreement with PAC-derived CO measurements [20–24]. Compared with pulmonary artery thermodilution, transpulmonary dilution techniques involve dilution of the chosen indicator in a significantly greater volume of blood. Therefore, the resolution and accuracy of the dilution curve is greatly influenced by the background concentration of the chosen indicator. As lithium is not a naturally occurring ion in the body, transpulmonary lithium dilution curves might be expected to possess a better signal-to-noise ratio compared with transpulmonary thermal dilution curves. In spite of this potential limitation, transpulmonary thermal dilution has been used successfully to measure CO in normovolaemic as well as hypovolaemic persons in a variety of clinical and experimental conditions.
Value of volumetric assessment of cardiac preload
Several studies have evaluated the usefulness of GEDV and ITBV in determining ventricular preload and the adequacy of volume resuscitation. In general, changes in GEDV/ITBV better reflect changes in CO, SV and oxygen delivery than either ΔCVP or ΔPAOP [10,25–27]. In a study of 57 ICU patients with sepsis or septic shock, Sakka et al.  compared ITBV, CVP and PAOP as predictors of cardiac function as measured by PAC thermodilution. ITBV index was strongly correlated with stroke volume index (SVI, r = 0.66), whereas neither CVP nor PAOP demonstrated any significant correlation (r = 0.10 and 0.06, respectively). Changes in SVI were also more likely to be mirrored by changes in ITBV in this study. As both CO and GEDV are derived from the same thermodilution curve, a potential weakness in the use of GEDV is that any change in CO might lead to a corresponding change in GEDV due to mathematical coupling. In this context, a study by Michard et al.  is important that compared the effects of volume resuscitation and dobutamine infusion on SV and GEDV. This study found that although volume infusion increased both SV and GEDV, dobutamine infusion resulted in an increase in SV without a concurrent increase in GEDV. These results suggest that the positive correlation reported between SV and GEDV cannot be attributed to mathematical coupling alone.
The volumetric assessment of preload is not without drawbacks. As pointed out by Isakow and Schuster , the correlation coefficients between GEDV/ITBV and indices of myocardial performance are usually modest (r = 0.4–0.5) and hence of only of moderate strength. Although this may be a consequence of changes in other factors such as contractility and afterload that may vary during the study period, it is equally plausible that many of the assumptions related to indicator dilution technology do not hold true in a physiological system resulting in an inherent weakness in attempting to measure volumes using the above-mentioned principles. Further theoretical and experimental studies are required to explore the uncertainties arising from pulsatile (rather than constant) flow, the effect of transit time on indicator distribution, injection time (injection can never be an instantaneous process) and other relevant variables.
Pinsky and co-workers [29–31] have highlighted the importance of distinguishing between preload and preload responsiveness. ITBV/GEDV are static indicators of preload, and may prove to be less useful than dynamic measures of preload responsiveness such as systolic and pulse pressure variation (discussed later). Such measures have the advantage that they can be derived from a standard arterial line continuously in real time, whereas ITBV measurement is more invasive and noncontinuous.
Extravascular lung water
EVLW measurement provides an estimate of pulmonary oedema, not reliably detected clinically or by chest radiography. A value of more than 10 ml kg−1 is considered abnormally high, and is consistent with ARDS in the appropriate clinical context. High EVLW is associated with poor outcome; in a retrospective analysis of 373 ICU patients, an EVLW content of more than 15 ml kg−1 was associated with a mortality rate of 65%, approximately double that of patients with EVLW less than 10 ml kg−1 . There is some evidence that using EVLW as an endpoint in treatment algorithms may improve outcome. In a trial of 101 critically ill ICU patients with PAC in situ, Mitchell et al.  randomized 52 patients to fluid management guided by bedside dual indicator EVLW measurement, whereas the remainder were managed based on PAC-derived pulmonary capillary wedge pressure. The EVLW group received significantly less fluid across the course of their illness (median cumulative fluid balance 754 vs. 1600 ml), and had a significantly shorter duration of mechanical ventilation and ICU stay.
As mentioned previously, dual indicator technology is cumbersome for routine bedside use; single indicator transpulmonary thermodilution (as used in the PiCCO system) allowing the derivation of EVLW is far more convenient. EVLW measurements obtained using the PiCCO system have been used as valid endpoints for a therapeutic trial in patients with acute lung injury and ARDS . The authors were able to show significant differences in EVLW in response to a specific therapeutic intervention (intravenous salbutamol). A haemodynamic management protocol using EVLW is available from the PiCCO manufacturer (PULSION Medical Systems), although this has not been independently validated, and is not widely used in the critical care environment. The LiDCO plus system also has provision for determining EVLW during lithium calibration using the dual indicator principle as described previously.
EVLW is not without limitations as a measurement tool. Indicator dilution estimates of EVLW are inherently perfusion dependent. There is a slight overestimation of 5–10% in normal lungs [14,19] due to distribution of the cold bolus in extra-pulmonary tissues (e.g. heart muscle) leading to an overestimation of ITTV. With global or regional hypoperfusion, there is the potential for underestimation of ITTV and hence EVLW [14,16,17]. Although global hypoperfusion effects may be counterbalanced by the increased transit time (allowing more time for diffusion of the cold bolus within extra-pulmonary tissues), these effects are unpredictable, hindering the adoption of EVLW as a reliable endpoint for management protocols. Although the dual indicator technique has been compared favourably with gravimetric measurement , no published human studies have validated the accuracy of PiCCO-derived (single indicator) EVLW measurement.
A well publicized trial  from the ARDS Network group demonstrated improvements in pulmonary function with a conservative fluid strategy, in keeping with early observations on the value of EVLW measurement [12,13]. The association of worsening pulmonary function with a liberal fluid strategy is almost certainly due to interstitial or alveolar oedema, and although the ARDS Network group used the term ‘liberal’ for their control group fluid strategy, the fluid balance in this group (about >1000 ml/24 h) is in keeping with that of patients in previous ARDS Network studies [36,37]. Although fluid therapy will always remain important in critically ill patients, with increasing evidence of adverse pulmonary consequences due to liberal fluid strategies, detailed attention to EVLW and consequent rationalization of fluid therapy may help minimize these adverse consequences.
The appropriateness of EVLW as an endpoint for fluid titration will obviously vary with the clinical circumstances; running an ICU patient with ARDS ‘dry’ by titrating against EVLW might improve outcome, but would be inappropriate in the perioperative management of a patient undergoing major abdominal surgery.
Stroke volume derivation from arterial pulse wave analysis
The existence of a relationship between the arterial waveform and CO has been recognized for over a century . The ability to accurately determine SV on a beat-to-beat basis has obvious attractions. In addition to CO monitoring, this technology offers the prospect of an answer to the key question: ‘will such patients increase their CO in response to a fluid challenge?’ This has been termed preload responsiveness and is discussed further below. The various commercially available arterial pulse wave analysis (APWA) systems derive CO from the arterial waveform using different methods.
Pulse contour analysis: PiCCO
The PiCCO (Pulse Contour Continuous Cardiac Output) system relies on the central tenet that flow is proportional to the driving pressure divided by resistance. If pressure is measured directly and the resistance is known or assumed, the flow (CO) can then be calculated. A measured pressure–time waveform can be transformed into a flow-time waveform by mathematical modelling of the resistance to flow provided by the vascular tree [39,40]. This complex process is summarized diagrammatically in Fig. 3 (modified from Cholley et al. ).
The above model incorporates resistance, impedance and capacitance and allows a flow–time curve to be approximated from a given arterial trace. Other complex factors also affect the relationship between the flow and pressure waveforms in vivo, including wave reflectance phenomena and variation in resistance of the vascular tree. Taking such factors into account allows further refinement of this model.
Pulse power analysis: PulseCO
The PulseCO algorithm employed in the LiDCO monitoring system derives CO through a different theoretical basis to PiCCO, using a three-step approach [41–43]:
- A pressure–volume transformation is first performed on the entire arterial pressure waveform. This pressure–volume conversion makes assumptions about the compliance of the arterial tree, which varies throughout the cardiac cycle, being reduced at higher pressures such that expansion of the vascular tree is less for a given increase in pressure.
- A continuous curve of arterial volume change throughout the cardiac cycle is thus produced. This is used as the basis for calculation of a nominal SV, a process involving application of the mathematical technique of autocorrelation.
- Nominal SV is converted to an actual SV by calibration with the lithium indicator dilution technique.
Both the PICCO and PulseCO systems require calibration initially and at intervals to maintain accuracy. Although pulmonary artery catheterization can be employed [44–46], transpulmonary dilution has become the standard as outlined above. PICCO uses a thermal bolus and requires central venous access and femoral arterial sampling, whereas PulseCO uses lithium dilution and can be performed with a simple intravenous cannula and standard arterial line. Both transpulmonary techniques have been extensively validated as referenced above. With either method, measured CO is factored into the algorithm giving a picture of the individual circulatory characteristics of the patient. A continuous drift in the accuracy of these systems occurs over time, necessitating periodic recalibration, especially if large changes in systemic vascular resistance (SVR) are anticipated .
The FloTrac/Vigileo system (Edwards Life Sciences, California, USA) measures the pulsatility of the arterial waveform by calculating the standard deviation of the arterial pressure wave over a 20-s period. This is multiplied by a constant quantifying arterial compliance and vascular resistance based on patient demographic data (age, sex, height and weight). The system constantly ‘fine tunes’ according to the character of the arterial waveform and autocalibrates every minute . This system is not calibrated by direct CO measurement, as are the other systems, calling into question its accuracy; autocalibration is based on experimentally determined cadaver data.
Value of arterial pulse wave analysis in assessing cardiac output
The value of APWA lies in its ability to provide a continuous measure of CO rather than the snapshots provided by dilution techniques. APWA has been extensively validated with reference to existing methods of CO monitoring, primarily PAC-derived thermodilution. Although the latter has historically been considered the gold standard in clinical practice, it has also long been recognized that numerous sources of error are inherent to the technique. Errors of more than 20% for single measurements and nearly 13% for triplicate measurements can be expected , and new technologies must be judged against this standard. Comparison between methods of CO monitoring is expressed in terms of bias and limits of agreement . Limits of agreement of ±30% are generally considered acceptable for a new method of CO measurement .
Numerous studies have examined the correlation between APWA-derived and PAC-derived CO measurements in cardiac surgical patients, both intra-operatively [32,41,52,53] and postoperatively [54–58]. In a series of 24 postoperative cardiac surgical patients, Goedje et al.  measured CO over the first 24 h by both PAC thermodilution and PiCCO, finding minimal bias (0.07 ± 1.4 l min−1) between the two techniques, and good correlation even during periods of substantial haemodynamic instability. In a series of 10 patients undergoing cardiac surgery, PulseCO-derived CO measurements showed a strong correlation with PAC thermodilution (r 2 = 0.88) . During this study, the PulseCO system tracked phenylephrine-induced alterations in SVR accurately. Good agreement has also been shown in other patient groups (including hepatic surgical , lung transplant  and paediatric  patients) and using other comparators (including oesophageal Doppler  and lithium dilution ).
Not all studies have shown a good correlation with reference methods of CO measurement. Yamashita et al.  compared PulseCO with PAC thermodilution in 23 patients undergoing off-pump coronary artery bypass grafting and found only a weak correlation (r 2 = 0.55), with greater bias than comparable studies. The authors raised the possibility that the rapidly changing arterial compliance characteristics during surgery and pharmacological manipulation might cause drift requiring more frequent recalibration.
The FloTrac/Vigileo system does not require external calibration in contrast to rival commercially available APWA systems discussed above. Although isolated case reports suggest that it may be of some use in guiding rational fluid resuscitation , the majority of studies have found poor limits of agreement with reference methods of CO measurement. In a study of CO measurement in 40 cardiac surgical patients, Mayer et al.  found only moderate agreement with PAC-derived values (r = 0.53), and an average error of 46%. Biais et al.  found poor agreement with PAC-derived CO measurement in patients undergoing liver transplantation (43% error), especially in conditions of low SVR.
The FloTrac algorithm has been refined since its introduction and in its current form (version 01.10) autocalibrates every minute rather than just on initial set-up. It is clearly important to be aware of which version was used when comparing validation studies (by contrast, the LiDCO/PulseCO algorithm remains unchanged since its introduction). Some studies using the latest FloTrac version have shown narrower limits of agreement with reference CO measurement. Mayer et al.  found an acceptable margin of error (24.6%) in cardiac surgical patients both intra-operatively and postoperatively compared with simultaneous PAC-derived pulmonary artery thermodilution. In a similar study, Prasser et al.  found a clinically acceptable percentage error of 26.9%. Other studies using this latest generation of software have not shown such favourable limits of agreement, however. In cirrhotic patients undergoing liver transplantation, Biancofiore et al.  found a percentage error of 54% between FloTrac and PCA-derived CO measurement, with the greatest degree of bias occurring under conditions of low SVR. This result agreed with the findings of Sakka et al.  in an earlier study of septic ICU patients (using the previous software version), casting doubt on the reliability of the device in low-resistance states. In general, most studies show the percentage error of the FloTrac/Vigileo system to exceed the 30% limit of acceptability for clinical use, as summarized elegantly by Biancofiore et al.  in the study quoted above.
Although APWA has been extensively validated as a CO measurement tool, few studies have investigated its outcome. A systematic review  recently failed to demonstrate a survival advantage with the use of the PAC, which might be in part be explained by the morbidity associated with the use of such invasive monitoring. It is tempting to suppose that APWA might avoid such morbidity and improve the risk: benefit ratio of CO monitoring, improving patient outcome. The PAC/PiCCO Use and Likelihood of Success Evaluation (PULSE) study prospectively followed 331 ICU patients requiring CO monitoring by either PAC or PICCO. Use of PiCCO was preferred in septic shock, whereas PAC was preferred in patients with cardiac morbidity. No difference in outcome (ventilator free days, ICU-free days or hospital mortality) was found between the two groups after correction for demographic and clinical differences .
Limitations of arterial pressure waveform analysis
All forms of APWA require some degree of invasive vascular access. The LiDCO system requires only standard arterial and peripheral venous cannulae. The PiCCO system requires a specialized femoral arterial cannula as well as a central venous catheter. Although it has been demonstrated to work with a radial artery waveform , calibration of the PiCCO system by transpulmonary thermodilution has proved to be unreliable using a peripheral (radial) arterial cannula . The FloTrac/Vigileo system requires only a specialized transducer and cable spliced into the patient's existing arterial pressure monitoring set-up.
Both PiCCO and LiDCO employ a transpulmonary technique for system calibration. Transpulmonary thermodilution can be inaccurate in the presence of intrathoracic haemorrhage, or intracardiac shunts. Lithium dilution is unsuitable in patients taking lithium salts (overestimating CO) and can be inaccurate in the presence of certain muscle relaxants including atracurium. Although the FloTrac/Vigileo system does not require such external calibration, this has raised concerns about its accuracy by some commentators.
APWA measurement of CO may be less accurate with significant variation in SVR (assumed to be constant for the purposes of converting an arterial pressure into CO). Although this has been borne out by some studies [50,77], others have demonstrated good performance in the face of changing SVR following calibration [78,79]. Arrhythmias reduce accuracy, as CO is calculated on a beat-to-beat basis, and significant fluctuation may render data meaningless; if the heart rate is reasonably constant, this is less of a problem.
A key objective of critical care is to deliver optimal fluid resuscitation. Fluid requirements will vary with the clinical situation; excessive fluid administration is associated with increased morbidity and mortality in patients with acute lung injury , although under-resuscitation in the first 6 h increases mortality in patients with septic shock . Although the Surviving Sepsis Campaign guidelines advocate a CVP-based approach to fluid resuscitation , static measures of cardiac preload have repeatedly been shown to poorly reflect fluid requirements. Indices such as CVP, PAOP, and ventricular end-diastolic volumes measured by echocardiography have been repeatedly shown to correlate poorly with increase in SV in response to a fluid challenge [8,82–84]. Dynamic markers of fluid responsiveness based on heart–lung interactions across the respiratory cycle have shown promise as better discriminators. The goal of such methods is to determine the position of the patient's heart on the Starling curve, as illustrated in Fig. 4.
Systolic and pulse pressure variation
Mechanical ventilation induces cyclical changes in systolic and pulse pressures, referred to as systolic pressure variation (SPV) and pulse pressure variation (PPV) [85,86]. Many clinicians use this ‘swing on the arterial line’ as the impetus for a fluid challenge. These parameters are themselves surrogates for SV variation (SVV), the physiology of which has been expertly reviewed elsewhere [87,88], and is summarized below.
During mechanical ventilation, inspiration raises intrathoracic pressure, causing a reduction in right ventricular preload and an increase in afterload. Over the course of a few heartbeats, the consequent reduction in right ventricular SV impacts on left ventricular filling, as the right ventricular and left ventricular are in series. This reduction in left ventricular preload (and hence CO) coincides temporally with the expiratory phase of mechanical ventilation. Although a degree of variation across the respiratory cycle is normal, this reduction is magnified in hypovolaemic states for the following reasons:
- In low-volume states, the vena cavae are more collapsible and, therefore, right ventricular preload is more compromised by the transmitted inspiratory increase in intrathoracic pressure.
- The transmission of pressure through the RA (more collapsible in low-volume states) also reduces right heart filling.
- In hypovolaemia, the ventricles operate on a steeper portion of the Frank–Starling curve, hence a more marked change in SV for a given change in preload.
The magnitude of SVV (and SPV/PPV) broadly correlates to the degree of preload responsiveness, a high SVV predicting a larger increase in SV in response to a fluid challenge. SPV and PPV are not entirely interchangeable: SPV is influenced by transmitted changes in pleural pressure, meaning that increasing inspiratory pressures may falsely accentuate SPV. PPV is not influenced in the same way, as inspiratory pressure changes are transmitted to both systolic and diastolic components of the arterial waveform, and pulse pressure remains unchanged. Hence, PPV theoretically reflects changes in CO more accurately, and may be a better index of preload responsiveness. A refinement of SPV is delta Down (ΔDown), defined as the percentage fall in arterial pressure from its maximum value to that at the end of expiration; this measurement requires a short end-expiratory pause. The heart–lung interaction and its relation to mechanical ventilation is summarized in Fig. 5.
Stroke volume variation and pulse contour analysis
Much of the literature on heart–lung interaction has focused on SPV and PPV as surrogates for SVV, as formerly no reliable beat-to-beat SV indicator was available. In animal studies, venesection of 30% of circulating volume in ventilated canine and porcine models has demonstrated a strong correlation between the degree of blood loss and the increase in SPV [89,90]. The same correlation was not observed with static markers such as CVP. Pizov et al.  rendered ventilated dogs hypotensive either by controlled haemorrhage or sodium nitroprusside infusion, demonstrating that PAOP and CVP were similarly reduced in both groups, whereas SPV and ΔDown were significantly increased only in the haemorrhagic group, distinguishing between preload and vasodilatation as a cause of hypotension. This would be a useful distinction in anaesthetized patients, in whom drug-induced vasodilatation or hypovolaemia of various causes might be responsible for hypotension. In patients with septic shock, Tavernier et al.  found that a ΔDown of more than 5 mmHg was a better predictor of a 15% increase in SV in response to a 500 ml fluid challenge than either PAOP or LVEDA. In a similar patient population, Michard et al.  found that a 13% variation in PPV predicted a response [defined as a 15% increase in cardiac index (CI)] to a 500 ml fluid challenge with high sensitivity (94%) and specificity (96%).
The advent of APWA has allowed the study of real-time SV variation monitoring. In postoperative cardiac surgical patients, SVV measured by PiCCO has been shown to correlate well with SPV and to predict fluid responsiveness, being superior in this regard to CVP and PAOP [94,95]. In a more recent study , a SVV of more than 10% measured by FloTrac/Vigileo in cardiac surgical patients predicted a 15% increase in CO in response to a 500 ml fluid challenge with a sensitivity of 82% and a specificity of 87%. In neurosurgical patients, Berkenstadt et al.  found that a SVV of 9.5% or more predicted a SV increase of more than 5% in response to a standard fluid challenge with a sensitivity of 79% and a specificity of 93%. In the same study, CVP was found to be of no better predictive value than random chance.
Although much of the work on SVV has been conducted on elective surgical patients, of perhaps greater interest is the reliability of SVV in physiologically abnormal situations, where greater benefit from appropriate fluid management may be expected. In a canine model, the predictive value of SVV was preserved even in extreme haemorrhage . In septic shock patients, Marx et al.  found a good correlation between SVV and increase in CI in response to a standard fluid challenge. Reuter et al.  found SVV predicted fluid response equally well in both patients with reduced left ventricular function (ejection fraction <35%) and normal cardiac function.
Limitations of heart–lung interaction as predictors of fluid responsiveness
Perhaps the biggest limitation of SVV and PPV is its restriction to mechanically ventilated patients with no spontaneous breathing activity. Due to the completely different heart–lung interaction at play in the spontaneously breathing patient, this work is not generalizable to patients making spontaneous or pressure-supported respiratory efforts, and is, therefore, of limited use in perhaps the majority of ICU patients.
Lower tidal volumes reduce the magnitude of SVV. Many studies validating SVV/SPV have used supranormal tidal volumes of 10–15 ml kg−1, which have been shown to be deleterious in the setting of ARDS . Using volumes of 10 ml kg−1, Wiesenack et al.  were unable to demonstrate a correlation between SVV and fluid response in cardiac surgical patients. De Backer et al.  found that with tidal volumes less than 8 ml kg−1, PPV was a much poorer predictor of a response to fluid challenge in ICU patients. The influence of tidal volume on the interpretation of SVV has been neatly illustrated by Renner et al. , who studied SVV in anaesthetized pigs rendered first hypovolaemic by venesection then hypervolaemic by volume replacement. At low tidal volumes (5 ml kg−1), SVV did not change significantly between different loading conditions. Although the use of higher tidal volumes (10 and 15 ml kg−1) allowed differentiation between different volume states, SVV at 15 ml kg−1 was elevated to a degree suggesting the need for fluid even in the hypervolaemic phase of the study. It seems, therefore, that low tidal volumes may reduce the sensitivity of SVV as a predictor of the need for fluid, whereas high tidal volumes may overestimate fluid requirements.
The Respiratory Systolic Variation Test (RSVT) may offer a solution to this problem. First described by Perel et al. , this test entails delivery of three consecutive mechanical breaths of 10, 20 and 30 cmH2O, causing a progressively increasing ‘swing’ in the arterial trace. The minimum systolic blood pressure during each breath is measured from the arterial pressure waveform, and the slope of the line of best fit for these three points is calculated (Fig. 6). A steeper gradient corresponds to an ‘emptier’ circulation and predicts a greater response to fluid challenge. Preisman et al.  evaluated the RSVT in a group of cardiac surgical patients, comparing its ability to predict preload response with a variety of indices including CVP, left ventricular end diastolic area (LVEDA), ITBV, SVV and PPV. RSVT (alongside PPV) was found to be the most sensitive and specific predictor of a response to a fluid challenge as assessed by transoesophageal echocardiography.
SVV is influenced by other factors besides preload, summarized by Kubitz and Reuter . These include positive end-expiratory pressure (PEEP), chest and lung compliance, heart rate, arrhythmias and ventricular function. Although there is a collective weight of evidence to support SVV as a guide to volume responsiveness, there is considerable heterogeneity between studies with respect to patient group, fluid type, amount and speed of administration and criteria for a positive response to a fluid challenge.
Finally, although APWA has been shown to be accurate in steady-state conditions, it has not been extensively validated as accurate on a beat-to-beat basis, leading Pinsky  to urge caution when using APWA-derived SVV as a basis for clinical decision-making. This has been partly addressed by a recent study in anaesthetized pigs in which PiCCO-derived SVV showed excellent agreement with the gold standard technique of aortic ultrasonic flow probe SVV measurement .
Predicting fluid responsiveness in spontaneously breathing patients
The mechanics of heart–lung interaction are different in spontaneously breathing patients from those who are fully mechanically ventilated. In the spontaneously breathing patient, inspiration reduces intrathoracic pressure, with a consequent increase in right ventricular preload and a more modest increase in afterload. This effectively increases right ventricular SV, which, in turn, increases left ventricular SV a few heartbeats later (coinciding with expiration). In a literature review, Coudray et al.  found that an average of 37.5% of patients in studies of indices of preload responsiveness (such as those outlined above) exhibited spontaneous breathing activity. Few studies have differentiated between mechanically ventilated and spontaneously breathing patients in their analysis.
Of those studies specifically considering the spontaneously breathing patient, the evidence is unconvincing. SVV was shown not to predict fluid responsiveness in a series of 30 ITU patients with septic shock receiving pressure support ventilation . Heenen et al.  conducted a study of 21 critically ill patients receiving either pressure-supported ventilation or breathing through facemask. In this study, PPV failed to predict increase in CO in response to a fluid challenge and was inferior to static indices (right atrial pressure, PAOP). In a recent series of 32 haemodynamically unstable ITU patients with spontaneous breathing activity, PPV of more than 12% was a specific (92%) but not sensitive (63%) predictor of response to a 500 ml fluid bolus .
Unfortunately, perhaps the majority of patients in the intensive care setting are on some form of pressure-supported ventilation, which limits the application of SVV to a small subset of patients. However, those patients who are fully mechanically ventilated are often the most unwell, and, therefore, have the most to gain through rational fluid management and optimization of CO.
Passive leg raising
Passive leg raising (PLR) has been proposed as an alternative means of predicting response to a fluid challenge which may be of greater value in spontaneously breathing patients. Elevation of the patients' legs to 45° effectively autotransfuses around 300 ml of whole blood into the central circulation . This allows the effect of a fluid bolus to be assessed with the advantage of full reversibility, thus avoiding unwanted effects of extracellular fluid accumulation and cardiac overload that may accompany a speculative volume challenge. The response to PLR has been shown to be a sensitive predictor of response to a fluid challenge in mechanically ventilated patients. Boulain et al.  studied PLR in 15 ICU patients with acute circulatory failure and showed that a 4-min PLR caused a mean increase in SV (measured by PAC) of 4 ml, and an increase of 4 mmHg in the radial artery pulse pressure (PPrad). A good correlation was seen between the degree of increase in PPrad in response to SLR and the subsequent increase in SV in response to a 300 ml fluid challenge (r = 0.84).
Monnet et al.  investigated PLR in 71 critically ill patients, 31 of whom displayed spontaneous breathing activity. The effect of PLR was assessed by oesophageal Doppler (OD) measurement of aortic blood flow and by PPV measurement. An increase of more than 10% in OD-measured aortic blood flow in response to PLR was found to be an excellent predictor of subsequent response to fluid challenge (sensitivity 97%, specificity 94%). Although the excellent predictive value of OD measurement was retained in the spontaneously breathing subgroup, PPV failed to discriminate in these patients.
APWA-derived CO measurement may also be of value in PLR assessment of volume responsiveness. Ridel et al.  demonstrated that in patients with spontaneous breathing activity, an increase of more than 12% in the PiCCO-measured CI in response to PLR discriminated between responders and nonresponders to a subsequent fluid challenge with 70% sensitivity and 92% specificity.
Increasingly, the pulmonary artery catheter has come to inhabit a shrinking corner of an expanding marketplace. Numerous alternative means of measuring CO are now available to a clinician, although none ‘ticks all the boxes’, hence the multiplicity of devices. There is little doubt that ITBV and GEDV provide better estimates of cardiac preload than traditional markers such as CVP and PAOP. Their measurement also allows the derivation of EVLW, with the promise of better outcomes through accurate titration of intravenous fluid to the patient's requirements. However, such measurements are not provided in real time and are static rather than dynamic. In most clinical circumstances, what is required is a prediction of preload responsiveness. Pulse contour analysis offers the promise of real-time CO measurement coupled with such functional haemodynamic monitoring. Although questions remain about the ability of this technology to reproducibly predict volume responsiveness with lung protective ventilation (i.e. limited tidal volumes) and expectations of improved patient outcome remain unproven, we may soon inherit a future in which all intensively monitored patients have CO and SV variation monitoring as a standard function of arterial line insertion in theatre or in the ICU.
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