Rapid modeling: a surgical proof-of-concept explained by hemodynamics modeling

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Rapid modeling: a surgical proof-of-concept
explained by hemodynamics modeling
N. Golse, F. Joly, Q. Nicolas, E. Vibert, P. D. Lin & I. Vignon-Clementel
To cite this article: N. Golse, F. Joly, Q. Nicolas, E. Vibert, P. D. Lin & I. Vignon-Clementel
(2020) Rapid modeling: a surgical proof-of-concept explained by hemodynamics modeling,
Computer Methods in Biomechanics and Biomedical Engineering, 23:sup1, S130-S132, DOI:
10.1080/10255842.2020.1816298
To link to this article: https://doi.org/10.1080/10255842.2020.1816298
© 2020 The Author(s). Published by Informa
UK Limited, trading as Taylor & Francis
Group
Published online: 02 Nov 2020.
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Rapid modeling: a surgical
proof-of-concept explained by
hemodynamics modeling
N. Golse
a,b,c
, F. Joly
c
, Q. Nicolas
c
, E. Vibert
a,b
,
P. D. Lin
d,e
and I. Vignon-Clementel
c
a
Department of Surgery, Hepato-Biliary Centre in Paul-Brousse
Hospital, Assistance Publique H^
opitaux de Paris, Villejuif,
France;
b
INSERM, Unit 1193, Villejuif, France;
c
Inria, centre de
recherche de Paris, Paris, France;
d
Department of
Transplantation Medicine, Oslo University Hospital, Oslo,
Norway;
e
Institute of Clinical Medicine, University of
Oslo, Norway
1. Introduction
The RAPID (Resection And Partial Liver Segment 2/
3 Transplantation with Delayed total hepatectomy)
concept is an innovative surgical procedure that was
recently proposed to increase the availability of
grafts (transplantable organs) for patients with unre-
sectable metastases (Line et al. 2015). The RAPID
technique (Figure 1) includes total hepatectomy
(Hx, liver resection) in two steps. First, partial Hx is
performed and the recipient receives a small partial
liver. Then, portal flow, the main input flow to the
liver, is diverted to the graft to facilitate its fast
regeneration. However, to avoid graft portal hyper-
perfusion or barotrauma, portal vein (PV) pressure
monitoring is required (Yagi et al. 2006). As soon as
the graft reaches the target volume, the second stage
resection is performed, ending the native diseased
liver removal.
For such a complex and innovative procedure,
numerically simulating hemodynamics in RAPID
patients could be useful to anticipate hyperperfusion
or barotrauma: this proof of concept study aims at
developing a model to represent these different steps
and see if such a model can predict the outcome of
the critical first steps (Golse et al. 2020).
2. Methods
Recently, our team performed hemodynamic 0D
modeling to simulate major Hx in pigs (Audebert
et al. 2017). To the aim of tailoring the RAPID pro-
cedure, we adapted the 0D model in this setting to
assess its clinical applicability. This electric analog
of the entire blood circulation (Figure 2)consistsof
a 4-chamber heart module, the lung, the digestive
organs, the liver and the other systemic organs. The
lung, digestive organs and other systemic organs are
represented by simple ‘RCR’components. The liver
is dually perfused by the portal vein from the digest-
ive organs and by the hepatic artery from the aorta
and drains into the right side of the heart, each
route being modeled by resistance, with at their
junction a capacitance. The liver is divided into the
right and left uneven hemi-livers that are in parallel.
This 0D model was retrospectively tested on three
patients for which the only available measurements to
parameterize the model were the weight of the liver
subparts and graft at the different stages, and pre-
resection PV pressure, portocaval gradient (PCG) and
mean arterial pressure. The other hemodynamics data
needed to tune the model were based on the litera-
ture, given that patients were not cirrhotic. The grafts
were assumed to have healthy intrinsic resistances.
We simulated the first three steps (Figure 1) and
compared our final predicted PV pressures and porto-
caval gradients to those intraoperatively measured, as
an indication to modulate portal flow relies on
these measures.
3. Results and discussion
Portal pressures measured at the end of the first stage
in patients 1, 2 and 3 were, respectively, of 14, 16 and
12 mmHg while the simulated pressures were of 13.1,
14.8 and 11.5 mmHg. Portocaval gradients measured
after right PV clamping in the three patients were,
respectively, of 10, 11 and 7 mmHg while the simu-
lated gradients were of 9.9, 11.6 and 8.3 mmHg.
Spearman correlation for the 12 paired values was
high (rcoefficient ¼0.94, p ¼0.02), indicating that
the model predictions are good. Differences in the
gradients reflect both the disease state of the liver
(initial resistance), extent of Hx and size of trans-
planted graft.
To anticipate the minimal graft weight that
should be chosen to avoid the need for portal flow
modulation (according to fixed maximal pressure of
ß2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits
unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING
2020, VOL. 23, NO. S1, S130–S132
https://doi.org/10.1080/10255842.2020.1816298

Ppv ¼20 mmHg, or gradients PCG ¼15 mmHg and
10 mmHg), we virtually changed the graft weight of
each patient. The minimal graft weight required to
avoid portal hypertension with different thresholds
proposed for Ppv and portocaval gradients was
below the real graft weight in all cases, except for
the most restrictive portocaval gradient threshold:
patient 1 was borderline (1.5% too low weight),
while the real size of patient 2 was too low by 22%.
This result highlights the need for a consensus in
appropriate thresholds to determine safe transplant-
ation graft sizes.
4. Conclusions
Although more work is warranted to validate it on a
larger cohort, this promising report demonstrates that
0D simulation could be a useful tool to promote this
new surgical procedure by a better understanding of
how the different steps affect the liver perfusion and
pressure and by anticipating depending on the graft
size (and thus potentially avoiding) hypertension for
each patient in the future.
Acknowledgements
This work was supported by an Inria-APHP fellowship.
References
Audebert C, Bekheit M, Bucur P, Vibert E, Vignon-
Clementel IE. 2017. Partial hepatectomy hemodynamics
changes: experimental data explained by closed-loop
lumped modeling. J Biomech. 50:202–208. Jan 4
Golse N, Joly F, Nicolas Q, Vibert E, Line PD, Vignon-
Clementel I. 2020. Partial orthotopic liver transplantation
in combination with two-stage hepatectomy: a proof-of-
Figure 1. Different steps of the RAPID procedure.
Figure 2. 0D model with two hemi-livers.
COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING S131

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KEYWORDS Hemodynamics; modeling; transplantation; liver;
surgical innovation
irene.vignon-clementel@inria.fr
S132 ABSTRACT