Fluid responsiveness in liver surgery: comparisons of different indices and approaches

Hemodynamic monitoring plays an important role in the management of patients in intensive care and during major surgery. Functional hemodynamic monitoring can be defined as the assessment of the dynamic interactions of hemodynamic variables in response to a defined perturbation [1]. Clinical trials have shown the usefulness of functional hemodynamic monitoring for predicting volume responsiveness and identifying hidden cardiovascular insufficiency [2]. Fluids are primarily administered to reverse hypovolemia and hypotension. Hypovolemia may be due to external fluid losses caused by bleeding or losses from the gastrointestinal or urinary tracts, or internal losses due to extravasation of blood or exudation of body fluids. Optimal fluid resuscitation remains a matter of lively debate; for instance, there is no consensus about which intravenous fluid solution is ideal, and no secure data support a preference for one over another [3]. The end point of fluid resuscitation also remains unclear [3].

In the context of fluid management, the routine administration of fluids to ensure patient hydration must be separated from the maneuver called fluid challenge (FC), which refers to the initial volume expansion period performed to carefully evaluate the response of the patient to fluid infusion [4]. In particular, the importance of the prediction of fluid responsiveness (FR) relies on the fact that the fluid loading in such condition may be hazardous and dangerous for the patient, e.g., by causing or worsening pulmonary edema. Moreover, the fluid management of cirrhotic patients undergoing orthotopic liver transplantation (OLT) may be challenging and still remains controversial [5].

The expected response to fluid infusion is an increase of cardiac output (CO) or stroke volume (SV), and this response depends mostly on the position of an individual patient on the cardiac function curve, i.e., the Frank-Starling curve [6]. The Frank-Starling law of the heart states that the stroke volume of the heart increases in response to an increase in the volume of blood filling the heart, i.e., the end diastolic volume, when all other factors remain constant, but this initial steep increase becomes progressively flat until a plateau is reached; in this last case, a further increase in preload does not lead to any significant increase in SV. According to this concept, the maneuver of fluid bolus infusion can be classified as responsive when it is followed by a significant increase of CO or SV [6]. The problem with this concept is that SV or CO is not a quantity that can be continuously measured. In fact, the available monitoring systems can provide a quantitative measure of cardiac output and stroke volume only at one time instant by the gold standard method of thermodilution, while the beat-to-beat variations of these quantities can be derived indirectly by other measurements such as arterial blood pressure.

Many indices have been proposed to predict FR, which are considered related to the Frank-Starling curve and a sort of an estimation of curve slope, i.e., the so-called dynamic indices (pulse pressure variation (PPV), stroke volume variation (SVV), systolic pressure variation (SPV), and others) [2]. However, these studies mainly concern ICU settings, septic, or postoperative patients [7, 8].

Very few studies have been performed in major surgery and in particular during liver surgery or OLT. Furthermore, these studies differ in terms of observational time window, type of fluid, velocity of administration, acquisition systems, and indices provided [5, 9–12]. All these factors can explain the controversial results.

The originality of this work consists in the comparison between several approaches for the classification of the maneuvers and between indices provided by a commercial monitor and indices computed by algorithms described in literature. In fact, the majority of the works on FR indices take thresholds and criteria to identify responsive maneuver for granted [5, 9–12]. The objective of this work is to improve the knowledge about the performance of these indices in association to different classification approaches in a particular setting, i.e., liver surgery.

The specific aims are (1) the comparison of different CO estimators from invasive ABP measurement with particular attention to the assessment of CO variation after fluid administration and (2) the comparison of several indices for the prediction of fluid responsiveness of maneuvers classified from the CO measurements provided by a commercial monitor (PiCCO™, Pulsion Medical Systems SE, Feldkirchen, Germany).

The next sections will present the experimental protocol and the approaches adopted for the classification of the maneuvers, the comparisons among the different CO estimators and the values obtained from a commercial monitor, the correlation between CO variations and FR predictor indices, and the comparisons between the FR indices of responsive and nonresponsive maneuvers.

This work showed how CO estimated from invasive ABP measurement by different algorithms provided similar performances for the assessment of CO variation after fluid administration. This analysis was not focused on the validation of CO absolute values obtained from the application of different algorithms existing in the literature, but rather to verify the possibility to use such standard methods, in the absence of thermodilution calibration. Also, the estimates of cardiac output provided by commercial monitor systems are not continuous and often, but not always, require calibration; they are ultimately determined by algorithms that are covered by patents, making it hard to evaluate the goodness with respect to other analyses, such as the ones performed offline in this work.

As regards the comparison among the indices for the prediction of fluid responsiveness, the Bland-Altman analysis showed a good agreement between the PPV values estimated according to the definition, i.e., by using the respiratory signal, and the PPV values provided by the monitor PiCCO™ (Pulsion). The SVV indices showed instead a worse agreement with the SVV_PiCCO values. Finally, this work investigated several approaches in order to classify the maneuvers into responsive and nonresponsive to evaluate their performance in liver surgery. The maneuvers were classified as responsive by using the values provided by the commercial monitor (PiCCO™, Pulsion) commonly adopted in operating rooms (CO_PCCO) and by adopting different criteria, i.e., the time of response and the statistics for CO variation values. PPV_PiCCO showed the best performance in terms of AUC, sensitivity, and specificity when the maneuvers were classified according to the criterion of maximum CO variation during the 2 minutes following 1 minute after fluid infusion. Moreover, the threshold value obtained from the ROC curve was 14% similar to those reported in the literature on ICU setting for septic patients (13%) [16] or cardiac surgery (12%) [17]. SVV indices showed good performance too; however, the threshold values were different among the estimators (Table 4) and much higher than those reported in literature [18].

In the work of Biais et al. [12], the FR maneuvers were analyzed in patients undergoing OLT, and the SVV threshold values observed in association with the best classification performance were 9% for the SVV index estimated by a Doppler echography and 10% for the SVV index estimated by FlocTrac (Edward Lifescience, Irvine, CA). However, such differences can be explained by the different protocol: it has not been specified the time interval before fluid infusion and the volume expansion maneuver took 20 min.

Our results suggested that PPV indices are more reliable and computationally feasible than SVV indices. This can be easily explained by the fact that the former ones are estimated from a direct measure, i.e., from ABP signal; the latter ones are assessed in turn from an estimated quantity, i.e., the stroke volume assessed from ABP signal. This further exposes the index to the potential limitations, which should be carefully considered when relying on estimates from monitors.

The value of this work consists in the approaches proposed for the classification of the maneuvers and the thorough analyses of the FR indices both provided by a commercial monitor and computed by algorithms well known in the literature. This solution represents a novelty and can pave the way for further analyses in order to standardize and validate the criteria proposed. Moreover, the commercial monitor provides only the values of the indices, and the variations are often visually observed by the clinicians. This could be a source of errors or, at the very least, a limitation of the effectiveness of monitor-derived estimates. The offline investigation represents a limitation, but it is the first step for the implementation of new monitoring procedures, e.g., monitoring systems for the fluid management, which can control the fluid infusion and monitor the patient response automatically [19].

The main finding of this work is that PPV indices are more reliable and computationally feasible than SVV indices, and PPV_PiCCO has the best performance in terms of AUC, sensitivity, and specificity when the maneuvers were classified according to the maximum values of Δ PCCO estimated during the second time window. The threshold values produced by the PPV indices were similar between the ones provided by the commercial monitor and the computed index, in further agreement with the literature. From these observations, PPV index seems more suitable for FR prediction in liver surgery.