Predicting the risk of post-hepatectomy portal hypertension using a digital twin: A clinical proof of concept.
Aged
Clinical Decision-Making
/ methods
Feasibility Studies
Female
Follow-Up Studies
Hepatectomy
/ adverse effects
Humans
Hypertension, Portal
/ diagnostic imaging
Liver Failure
/ diagnostic imaging
Liver Function Tests
Magnetic Resonance Imaging
/ methods
Male
Middle Aged
Models, Theoretical
Portal Pressure
Portal Vein
/ physiopathology
Postoperative Complications
/ diagnostic imaging
Prognosis
Prospective Studies
Risk Factors
Liver failure
Liver resection
Mathematical model
Portal pressure
Risk factors
Journal
Journal of hepatology
ISSN: 1600-0641
Titre abrégé: J Hepatol
Pays: Netherlands
ID NLM: 8503886
Informations de publication
Date de publication:
03 2021
03 2021
Historique:
received:
29
06
2020
revised:
25
10
2020
accepted:
28
10
2020
pubmed:
20
11
2020
medline:
21
1
2022
entrez:
19
11
2020
Statut:
ppublish
Résumé
Despite improvements in medical and surgical techniques, post-hepatectomy liver failure (PHLF) remains the leading cause of postoperative death. High postoperative portal vein pressure (P We prospectively included 47 patients undergoing major hepatectomy. A mathematical (0D) model of the entire blood circulation was assessed and automatically calibrated from patient characteristics. Hepatic flows were obtained from preoperative flow MRI (n = 9), intraoperative flowmetry (n = 16), or estimated from cardiac output (n = 47). Resection was then simulated in these 3 groups and the computed P Simulated post-hepatectomy pressures did not differ between the 3 groups, comparing well with collected data (no significant differences). In the entire cohort, the correlation between measured and simulated P We demonstrated that a 0D model could correctly anticipate postoperative PHT, even using estimated hepatic flow rates as input data. If this major conceptual step is confirmed, this algorithm could change our practice toward more tailor-made procedures, while ensuring satisfactory outcomes. Post-hepatectomy portal hypertension is a major cause of liver failure and death, but no tool is available to accurately anticipate this potentially lethal complication for a given patient. Herein, we propose using a mathematical model to predict the portocaval gradient at the end of liver resection. We tested this model on a cohort of 47 patients undergoing major hepatectomy and demonstrated that it could modify current surgical decision-making algorithms.
Sections du résumé
BACKGROUND & AIMS
Despite improvements in medical and surgical techniques, post-hepatectomy liver failure (PHLF) remains the leading cause of postoperative death. High postoperative portal vein pressure (P
METHODS
We prospectively included 47 patients undergoing major hepatectomy. A mathematical (0D) model of the entire blood circulation was assessed and automatically calibrated from patient characteristics. Hepatic flows were obtained from preoperative flow MRI (n = 9), intraoperative flowmetry (n = 16), or estimated from cardiac output (n = 47). Resection was then simulated in these 3 groups and the computed P
RESULTS
Simulated post-hepatectomy pressures did not differ between the 3 groups, comparing well with collected data (no significant differences). In the entire cohort, the correlation between measured and simulated P
CONCLUSIONS
We demonstrated that a 0D model could correctly anticipate postoperative PHT, even using estimated hepatic flow rates as input data. If this major conceptual step is confirmed, this algorithm could change our practice toward more tailor-made procedures, while ensuring satisfactory outcomes.
LAY SUMMARY
Post-hepatectomy portal hypertension is a major cause of liver failure and death, but no tool is available to accurately anticipate this potentially lethal complication for a given patient. Herein, we propose using a mathematical model to predict the portocaval gradient at the end of liver resection. We tested this model on a cohort of 47 patients undergoing major hepatectomy and demonstrated that it could modify current surgical decision-making algorithms.
Identifiants
pubmed: 33212089
pii: S0168-8278(20)33761-2
doi: 10.1016/j.jhep.2020.10.036
pii:
doi:
Types de publication
Evaluation Study
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
661-669Commentaires et corrections
Type : CommentIn
Type : CommentIn
Informations de copyright
Copyright © 2020 European Association for the Study of the Liver. Published by Elsevier B.V. All rights reserved.
Déclaration de conflit d'intérêts
Conflict of interest The authors declare no conflicts of interest that pertain to this work. Please refer to the accompanying ICMJE disclosure forms for further details.