Estimation of microvascular perfusion after esophagectomy: a quantitative model of dynamic fluorescence imaging

The reconstruction of the gastric conduit generally preserves the right gastroepiploic vessels and right gastric artery as the main source of blood supply to the gastric wall. Consequently, part of the gastric conduit close to the anastomosis is only supplied with blood from collateral connections in the gastric wall. We modeled the gastric conduit by introducing four sites, as shown in Fig. 1. This choice was based on the four regions of interest (ROIs) for measurement of fluorescence intensity performed on constructed gastric conduit in the prospective clinical study in Amsterdam Medical Center from October 2015 to June 2016 [12]. Figure 1a shows a frame of constructed gastric conduit of a patient with the ICG visualizing the tissue perfusion. The ROI was a 300 pixels circle with #1 3 cm below the watershed, #2 the watershed, #3 3 cm above the watershed, and #4 the fundus. The measured fluorescence enhancement curves at the four ROIS are shown in Fig. 1b. In the model, the four sites are connected through collateral arteries and veins. Site 3 and site 4 represent the anastomosed areas and perfusion here depends completely on collateral vessels. The distributed nature of the arterial, capillary, and venous networks at each site is represented by a single lumped resistance thought to be situated in the arterioles and capillaries, connecting a proximal arterial volume to a distal capillary/venous volume. The vascular bed in the gastric tube is thus represented by eight compartments, four microvascular connections, three arterial collateral connections, and three venous collateral connections. The system is supplied and drained by large arteries and veins of the lower two sites (Fig. 1c).

We assumed identical vascular conductances and volume for each site and varied multiple model parameters including collateral artery and vein conductance relative to microvascular conductance (G_ca/G_c and G_cv/G_c), relative large vessel conductance (G_LV/G_c), and vascular volume including arterial and venous volume (AV and VV). Quantitative information on gastric vascular branching patterns is not available, but we considered that the above conductance ratios are likely to be highly variable between patients. We therefore evaluated the model over a large parameter space, including the presumed physiological range. Determining the physiological range for vessel conductance is difficult. Total conductance of the arterial and venous system depends on the network connectivity and the diameter of the individual segments in this network. Such data are, to the best of our knowledge, not available for the human stomach. We therefore covered a very wide range of values for G_ca/G_c and G_cv/G_c, spanning 0.01 to 100 for both ratios. The low end of the chosen spectrum reflects absence of collaterals, while the high end indicates a model with identical perfusion of all four sites. Physiological values were estimated to be less than 10, with no definitive lower boundary [13, 14]. The large vessels (LV) include terminal arteries, i.e., right gastric artery and right gastroepiploic terminal artery, and initial veins, i.e., pyloric vein and right gastroepiploic initial vein. Relative conductance of the combined large arteries (G_LV/G_c) was 3 by default and spanned 1 to 100 when studying the effect of this parameter. Large vein conductance was fixed at twice the large artery conductance. We used physiological data describing the proportion of artery, capillary, and vein in a given volume in the systemic circulation to determine the physiological vascular volumes [14, 15]. The total volume of a site was calculated based on the volume of the ROI in the preceding clinical study [12]. The default vascular volumes were taken as 0.24 mL and 0.56 mL for arterial (AV) and venous (VV) compartments, respectively, spanning 0.12 mL to 1.12 mL when studying the effect of vascular volume to contrast dynamics.

Pressure at each branching point in Fig. 1c and flow in each segment was calculated using Kirchhoff’s first law combined with Ohm’s law, assuming laminar flow of a Newtonian fluid [16]. These calculated hemodynamic parameters depend on the pressures in the right gastroepiploic artery and vein and on the conductance of all segments. Arterial input and venous outflow pressure were taken as 70 mmHg and 0, respectively. Conductances were varied to evaluate their influence on perfusion. For each segment, after the pressure gradient was obtained, the flow was calculated. Tissue perfusion at the four sites is reflected by the predicted flow in the segments connecting arterial and venous compartments (F₁ to F₄).

The ICG enters the system from the hepatic artery into the gastroduodenal artery (greater curvature), which leads into right gastroepiploic artery, and the right gastric artery (lesser curvature). The ICG then flows into the various compartments from A₁ and A₂ towards capillaries in the native sites and collateral-dependent sites and leaves the system via V₁ and V₂. ICG from the right gastric and the right gastroepiploic veins drains into the portal and the superior mesenteric vein, respectively. This ICG transport was simulated using the above vessel configuration and well-mixed arterial and venous compartments, where dynamics of dye concentration obey the following differential equation:with C_k the concentration of the dye in compartment k, m, and n the number of upstream and downstream vessel of compartment k, respectively, UC_j the concentration of upstream compartment j, UF_j upstream flow from this compartment, DF_i flow through i-th downstream vessel, and V_k the volume of compartment k [17]. This ordinary differential equation was numerically solved with a single-step solver based on the Dormand-Prince algorithm of Runge-Kutta method which computed C(t) from [C(t-Δt)] [18]. The algorithm had six stages of function evaluation for each partial step and generated fourth-order and fifth-order approximation of C(t). The two approximations was subsequently compared to estimate the error which provides the basis to accept or reject the tentative C(t). The error estimate also modulated the step size, Δt, for the next time step.

The ICG signal intensity for each site was defined as the amount of ICG in both the arterial and venous compartments at time t:

where φ indicates the ICG signal, AC and AV denote the ICG concentration and the volume of the arterial compartment, respectively. VC and VV represent the ICG concentration and the volume of the venous compartment and i denotes the number of site. Figure 2 shows an example of the simulated temporal profile of φ for each site.

For each simulation, the temporal profile of φ at each site was analyzed. This signal forms the base for the estimation of remaining perfusion after surgery. We propose a measure to describe relative impairment of flow in a collateral-dependent site. This measure was based on the time to threshold T_n which was defined as the time at which φ of site n reaches a fraction of the maximum of φ of site 1. For example, in Fig. 2, T₄ is defined as the time it takes for φ to reach 50% of the maximum intensity in site 1 (I₅₀). This approach ensures that T_n can be calculated as long as the maximum φ in site 1 is recorded. We introduce RTT as a parameter on which RRF estimation can be based:

With T_i the time to threshold and F_i the flow in site i. F_presurgery is the flow prior to the ligation. It was calculated from the same model and using the same set of parameter values but with A₃ and A₄ still supplied by the large arteries and V₃ and V₄ still drained by the large veins. A range of intensity thresholds were tested: 20%, 30%, 40%, and 50% of the maximum φ of site 1. The starting point (t = 0) was defined as the first inflection of contrast intensity in site 1. RRF is the ratio of post- and pre-intervention flow. We hypothesized that for a large range of variation in vascular conductances and volumes, RTT is closely related to RRF, such that RTT is a possible measure for the effect of surgery on local perfusion.

Using the simulation scenarios detailed in the Table 1, we first evaluated the effect of G_ca/G_c and G_cv/G_c on the remaining perfusion in anastomosed sites by fixing G_LV/G_c, AV and VV to their default values (simulation 1). The effect of G_LV/G_c was tested while fixing AV and VV to 0.24 and 0.56 ml, respectively (simulation 2). Finally, G_LV/G_c was maintained at its default value (3) while examining the effect of AV and VV (simulation 3). All tests were performed using all intensity thresholds over the full range of collateral conductances. We then computed the RRF for all possible combinations of parameters for a range of possible outcomes (sample space) from Ohm’s and Kirchhoff’s laws. We included curve fits of the relation between RRF and RTT to illustrate the nature of the relation over collateral conductance space. We included curve fits of the relation between RRF and RTT to illustrate the nature of the relation over collateral conductance space. We tested the curve fit using linear, polynomial, and exponential function while evaluating the goodness-of-fit. A good fit was defined as a model that has low sum of squared of errors and high R². The prediction interval was calculated by taking into account the sample mean, sample standard deviation, sample size, and critical value of Student’s t distribution at 95% confidence level. It should be noted that the indicated relations merely described the data and were not a result of mathematical analysis of the model. Hence, these fits do not affect the further outcomes in this study. Finally, we performed receiver operating characteristic (ROC) curve analysis to select the best intensity threshold to estimate perfusion impairment. We arbitrarily chose RRF values to dichotomize the outcome into lacking versus adequate perfusion. For site 3, RRF < 50% was defined as lacking perfusion, and RRF ≥ 50% was adequate perfusion. For site 4, RRF < 40% was defined as lacking perfusion, and RRF ≥ 40% was adequate perfusion. The area of physiological sample space was treated as predictor variables on which logistic regression was applied to produce the ROC curve. The simulation was performed in Matlab on a standard PC running on Windows 7 with 3 GHz 16 CPUs, 32 GB RAM, and NVIDIA Quadro K4200 GPU.

The vascular tree can be modeled using several choices for the level of detail of the branching network. One extreme is to have a full network of vessel segments, with poroelastic models for the smallest vessels [19‐21]. The other extreme would be a single-vessel lumped model that disregards the detailed geometric structure of vascular tree thus reducing the complexity of the model [10, 22, 23]. We believe that the current choice for a limited set of compartments is optimal for the current purpose since the actual small vessel structure in the ROI of the gastric conduit is not well-defined and likely to be highly variable. The four ROIs used in this study, which account for both arterial and venous volumes, are based on a preliminary study using clinical data [12]. The ROI 1, 2, and 3 were equidistant (3 cm apart), while ROI 4 was located in the fundus. We separated arterial and venous network into two compartments of lumped vessels since these compartments influence the contrast dynamics in each ROI. Hence, this model allowed us to evaluate the role of the arterial and venous collateral on the tissue perfusion. Other configuration of the model was not investigated (for instance an eight-site model). This choice would affect the values of the modeling parameters, since clearly the volumes and conductances in each site would be different. Yet, there seems little reason to suspect that the modeling outcomes and further analysis would be fundamentally different for such a more detailed model. Considering the clinical setting we therefore used the four site model.

The validity of the presented model remains a limitation as this study only uses hypothetical data. We partly addressed this limitation by varying several model parameters over large ranges. Yet, many other choices would have been possible. Thus, the assumption of identical collateral conductances for all sites is an oversimplification of real cases. Patient data also show high variability in vascular volume between sites. These variations may yield different relations between RTT and RRF. The accuracy of the predictive value of RTT to predict RRF was based on perfusion thresholds of 50% and 40% of the pre-intervention perfusion. Currently, we do not have data for realistic values of this threshold.

A strong point of this study is that we have created a model in which many parameters can be tested. Therefore, alternative measures (such as intensity based, or absolute times) for estimating the perfusion can be evaluated with this model. However, in the context of the complex surgery, observation periods are limited to 2–3 min and longer periods are a concern [24]. In the fluorescence enhancement curve in Fig. 1b, the fluorescence yield is reasonably similar between the sites and a maximum is obtained in also the fourth site but these are not always the case. Thus, alternative measures of perfusion that rely on normalization to the maximal fluorescence at site 4 or generally the full enhancement curve may have been impractical.

A large number of studies have employed fluorescence imaging in intraoperative applications to assist visualization of blood flow or anatomical features [6, 25‐28]. However, quantitative analysis of ICG fluorescence imaging of the gastric conduit is still limited. Yukaya et al. introduced a quantitative parameter describing the decay of luminance as analyzed with the software tool LumiView to predict anastomotic leakage [29]. They could not find an association between blood flow and anastomotic leakage. A study on quantitative assessment of free jejunal graft used the time-fluorescence intensity curve, showing that time to half maximum is an indicative parameter for venous malperfusion [30]. However, that study had a population of only five patients suffering from venous anastomotic failure. Furthermore, in that study, no direct relation between perfusion deficit and ICG intensity dynamics was studied. A more recent study had been performed to predict anastomotic leakage by quantitatively measuring ICG speed between four predetermined points in the gastric conduit [8]. Also, this study suffered from limited data especially in the anastomotic leakage/malperfusion group.

While there clearly are several limitations to consider, quantitative analysis of contrast dynamics could provide a useful prognostic tool in determining treatment success. We found that time to 20% of the maximum intensity is optimal in the discrimination between intermediate and low perfusion as indicated by the area under the ROC curve. Additionally, if adopted in clinical practice, this low threshold requires only a relatively short measurement time of fluorescence imaging after maximum intensity is reached in ROI 1, which alleviates surgery-related risks.

Since fluorescence imaging allows assessment of temporal ICG intensity for different areas of gastric conduit intraoperatively, the operating clinician can evaluate intensity profiles for selected ROIs. This allows the calculation of the RTT at the anastomosed site and this value can be used to estimate the range of perfusion reduction (Fig. 6). The actual usefulness of RTT as an estimate for local perfusion and predictor of final outcome in esophagectomy remains to be established.