Computational Modeling in Liver Surgery

The need for extended liver resection is increasing due to the growing incidence of liver tumors in aging societies. Individualized surgical planning is the key for identifying the optimal resection strategy and to minimize the risk of postoperative liver failure and tumor recurrence. Current computational tools provide virtual planning of liver resection by taking into account the spatial relationship between the tumor and the hepatic vascular trees, as well as the size of the future liver remnant. However, size and function of the liver are not necessarily equivalent. Hence, determining the future liver volume might misestimate the future liver function, especially in cases of hepatic comorbidities such as hepatic steatosis. A systems medicine approach could be applied, including biological, medical, and surgical aspects, by integrating all available anatomical and functional information of the individual patient. Such an approach holds promise for better prediction of postoperative liver function and hence improved risk assessment. This review provides an overview of mathematical models related to the liver and its function and explores their potential relevance for computational liver surgery. We first summarize key facts of hepatic anatomy, physiology, and pathology relevant for hepatic surgery, followed by a description of the computational tools currently used in liver surgical planning. Then we present selected state-of-the-art computational liver models potentially useful to support liver surgery. Finally, we discuss the main challenges that will need to be addressed when developing advanced computational planning tools in the context of liver surgery.Peer Reviewe

Towards patient-specific modelling as a pre-operative planning strategy and follow up assessment for the treatment of advanced heart failure with rotary blood pumps

Background: Ventricular Assist Devices (VADs) insertion is an established treatment for patients with end-stage heart failure waiting for a heart transplant or in need for long-term circulatory support (destination therapy). Rotary blood pumps (RBP) are the most popular devices in view of their size and performance. Pre-operative planning strategy for the insertion of a left ventricular assist device (LVAD) requires a timely discussion at a Multi-Disciplinary Team Meeting (MDT). Clinical-decision making is based according to the needs of the patient and must be processed without delays. Nevertheless, thrombus formation remains a feared complication which affects outcome. VADs operate in a flow regime which is difficult to simulate: the transitional region at the boundary of laminar and turbulent flow (low Reynolds number). Different methods have been used but the best approach remains debatable. Computational Fluid Dynamics (CFD) is an attractive and invaluable tool for the study of the interactions between VADs and the cardiovascular system. The aim of this thesis is three-fold: a) to investigate the use of pressure-volume analysis in a clinical setting through the review of six heart failure patients previously discussed at a MDT meeting with a view to predict or guide further management; b) to review the theory behind modelling approaches to VADs and their interactions with the cardiovascular system for better understanding of their clinical use. Then, an overview of computational fluid dynamics (CFD) is considered as a prelude to its application to the analysis of VADs performance. Additionally, the development of a simplified model of centrifugal pump will be used in initial simulations as preliminary analysis; c) to examine an example of a proof-of-concept pilot patient-specific model of an axial flow pump (HeartMate II) as pre-operative planning strategy in a patient-specific model with a view to identify potential critical areas that may affect pump function and outcome in a clinical setting. Material and Methods: 3D reconstruction from CT-scan images of patients who underwent the insertion of rotary blood pumps, namely HeartWare HVAD and HeartMate II. Ansys Fluent has been used for CFD analysis based on the fundamental governing equations of motion. Blood has been modelled as incompressible, Newtonian fluid with density = 1060 and viscosity = 0.0035 kg/m-s. The laminar and SST models have been used for comparison purposes. The rotational motion of the impeller has been implemented using the moving reference frame (MRF) approach. The sliding mesh method has also been used to account for unsteady interaction between stationary and moving part. The no-slip condition has been applied to all walls, which were assumed to be rigid. Boundary conditions consisting of velocity inlet and pressure outlet of the pump based on different settings and constant rotational speed for the impeller. Pressure-velocity coupling has been based on the coupled scheme. Spatial discretisation consisted of the “least square cell based” gradient for velocity and “PRESTO” or second order for pressure. Second order upwind has been set for the momentum, turbulent kinetic energy and specific dissipation rate. First order implicit has been set for transient formulation. The pseudo transient algorithm (steady state), the high order relaxation term and the warped-face gradient correction have been used to add an unsteady term to the solution equations with the aim to improve stability and enhance convergence. Specific settings have been considered for comparison purposes. Results: Pressure-volume simulation analysis in six advanced heart failure patients showed that an integrated model of the cardiovascular system based on lumped-parameter representation, modified time-varying elastance and pressure-volume analysis of ventricular function seems a feasible and suitable approach yielding a sufficiently accurate quantitative analysis in real time, therefore applicable within the time-constraints of a clinical setting. Lumped-parameter models consist of simultaneous ordinary differential equations complemented by an algebraic balance equation and are suitable for examination of global distribution of pressure, flow and volume over a range of physiological conditions with inclusion of the interaction between modelled components. Higher level lumped-parameter modelling is needed to address the interaction between the circulation and other systems based on a compromise between complexity and ability to set the required parameters to personalise an integrated lumped-parameter model for a patient-specific approach. CARDIOSIM© fulfils these requirements and does address the systems interaction with its modular approach and assembly of models with varying degree of complexity although 0-D and 1-D coupling may be required for the evaluation of long-term VAD support. The challenge remains the ability to predict outcome over a longer period of time. The preliminary CFD simulations with the HeartWare HVAD centrifugal pump demonstrated that it is possible to obtain an accurate analysis in a timely manner to complement the clinical review process. The simulations with the pilot patient-specific model of the HeartMate II axial flow pump revealed that a complex 3D reconstruction is feasible in a timely manner and can be used to generate sufficiently accurate results to be used in the context of a MDT meeting for the purposes of clinical decision-making. Overall, these three studies demonstrate that the time frame of the simulations was within hours which may fit the time constraints of the clinical environment in the context of a MDT meeting. More specifically, it was shown that the laminar model may be used for an initial evaluation of the flow development within the pump. Nonetheless, the k- model offers higher accuracy if the timeline of the clinical setting allows for a longer simulation. Conclusion: This thesis aimed at the understanding of the use of computational modelling as a pre-operative planning strategy and follow up assessment for the treatment of advanced heart failure with rotary blood pumps. The novelty lays in the use of both pressure-volume simulation analysis and 3D flow dynamics studies in VADs with a view to treatment optimisation and outcome prediction within the time constraints of a clinical setting in the context of a MDT meeting. The clinical significance and the contribution to the field is a more targeted approach for different groups of patients and a more quantitative evaluation in the clinical decision process based on a pro-active co-operation between clinicians and scientists reducing the potential for “guess work”. The results of this thesis are a proof-of-concept as a prelude to a potential future implementation of patient-specific modelling within a clinical setting on a daily basis demonstrating a clear clinical significance and contribution to the field. The proposed approach does not consider modelling and simulation as a substitute for clinical experience but an additional tool to guide therapeutic intervention and complement the clinical decision process in which the clinician remains the ultimate decision-maker. Such an approach may well add a different dimension to the problem of heart failure with potential for high return in terms of patient’s outcome and long-term surveillance. The same principles would be applicable to other cardiovascular problems in line with the current concept of “Team Approach” such as the Heart Team, the Structural Heart Team or the Aortic Team. The present work has taken this concept closer to clinical delivery and has highlighted its potential but further work remains to be done in refining the technique.Background: Ventricular Assist Devices (VADs) insertion is an established treatment for patients with end-stage heart failure waiting for a heart transplant or in need for long-term circulatory support (destination therapy). Rotary blood pumps (RBP) are the most popular devices in view of their size and performance. Pre-operative planning strategy for the insertion of a left ventricular assist device (LVAD) requires a timely discussion at a Multi-Disciplinary Team Meeting (MDT). Clinical-decision making is based according to the needs of the patient and must be processed without delays. Nevertheless, thrombus formation remains a feared complication which affects outcome. VADs operate in a flow regime which is difficult to simulate: the transitional region at the boundary of laminar and turbulent flow (low Reynolds number). Different methods have been used but the best approach remains debatable. Computational Fluid Dynamics (CFD) is an attractive and invaluable tool for the study of the interactions between VADs and the cardiovascular system. The aim of this thesis is three-fold: a) to investigate the use of pressure-volume analysis in a clinical setting through the review of six heart failure patients previously discussed at a MDT meeting with a view to predict or guide further management; b) to review the theory behind modelling approaches to VADs and their interactions with the cardiovascular system for better understanding of their clinical use. Then, an overview of computational fluid dynamics (CFD) is considered as a prelude to its application to the analysis of VADs performance. Additionally, the development of a simplified model of centrifugal pump will be used in initial simulations as preliminary analysis; c) to examine an example of a proof-of-concept pilot patient-specific model of an axial flow pump (HeartMate II) as pre-operative planning strategy in a patient-specific model with a view to identify potential critical areas that may affect pump function and outcome in a clinical setting. Material and Methods: 3D reconstruction from CT-scan images of patients who underwent the insertion of rotary blood pumps, namely HeartWare HVAD and HeartMate II. Ansys Fluent has been used for CFD analysis based on the fundamental governing equations of motion. Blood has been modelled as incompressible, Newtonian fluid with density = 1060 and viscosity = 0.0035 kg/m-s. The laminar and SST models have been used for comparison purposes. The rotational motion of the impeller has been implemented using the moving reference frame (MRF) approach. The sliding mesh method has also been used to account for unsteady interaction between stationary and moving part. The no-slip condition has been applied to all walls, which were assumed to be rigid. Boundary conditions consisting of velocity inlet and pressure outlet of the pump based on different settings and constant rotational speed for the impeller. Pressure-velocity coupling has been based on the coupled scheme. Spatial discretisation consisted of the “least square cell based” gradient for velocity and “PRESTO” or second order for pressure. Second order upwind has been set for the momentum, turbulent kinetic energy and specific dissipation rate. First order implicit has been set for transient formulation. The pseudo transient algorithm (steady state), the high order relaxation term and the warped-face gradient correction have been used to add an unsteady term to the solution equations with the aim to improve stability and enhance convergence. Specific settings have been considered for comparison purposes. Results: Pressure-volume simulation analysis in six advanced heart failure patients showed that an integrated model of the cardiovascular system based on lumped-parameter representation, modified time-varying elastance and pressure-volume analysis of ventricular function seems a feasible and suitable approach yielding a sufficiently accurate quantitative analysis in real time, therefore applicable within the time-constraints of a clinical setting. Lumped-parameter models consist of simultaneous ordinary differential equations complemented by an algebraic balance equation and are suitable for examination of global distribution of pressure, flow and volume over a range of physiological conditions with inclusion of the interaction between modelled components. Higher level lumped-parameter modelling is needed to address the interaction between the circulation and other systems based on a compromise between complexity and ability to set the required parameters to personalise an integrated lumped-parameter model for a patient-specific approach. CARDIOSIM© fulfils these requirements and does address the systems interaction with its modular approach and assembly of models with varying degree of complexity although 0-D and 1-D coupling may be required for the evaluation of long-term VAD support. The challenge remains the ability to predict outcome over a longer period of time. The preliminary CFD simulations with the HeartWare HVAD centrifugal pump demonstrated that it is possible to obtain an accurate analysis in a timely manner to complement the clinical review process. The simulations with the pilot patient-specific model of the HeartMate II axial flow pump revealed that a complex 3D reconstruction is feasible in a timely manner and can be used to generate sufficiently accurate results to be used in the context of a MDT meeting for the purposes of clinical decision-making. Overall, these three studies demonstrate that the time frame of the simulations was within hours which may fit the time constraints of the clinical environment in the context of a MDT meeting. More specifically, it was shown that the laminar model may be used for an initial evaluation of the flow development within the pump. Nonetheless, the k- model offers higher accuracy if the timeline of the clinical setting allows for a longer simulation. Conclusion: This thesis aimed at the understanding of the use of computational modelling as a pre-operative planning strategy and follow up assessment for the treatment of advanced heart failure with rotary blood pumps. The novelty lays in the use of both pressure-volume simulation analysis and 3D flow dynamics studies in VADs with a view to treatment optimisation and outcome prediction within the time constraints of a clinical setting in the context of a MDT meeting. The clinical significance and the contribution to the field is a more targeted approach for different groups of patients and a more quantitative evaluation in the clinical decision process based on a pro-active co-operation between clinicians and scientists reducing the potential for “guess work”. The results of this thesis are a proof-of-concept as a prelude to a potential future implementation of patient-specific modelling within a clinical setting on a daily basis demonstrating a clear clinical significance and contribution to the field. The proposed approach does not consider modelling and simulation as a substitute for clinical experience but an additional tool to guide therapeutic intervention and complement the clinical decision process in which the clinician remains the ultimate decision-maker. Such an approach may well add a different dimension to the problem of heart failure with potential for high return in terms of patient’s outcome and long-term surveillance. The same principles would be applicable to other cardiovascular problems in line with the current concept of “Team Approach” such as the Heart Team, the Structural Heart Team or the Aortic Team. The present work has taken this concept closer to clinical delivery and has highlighted its potential but further work remains to be done in refining the technique

Blood pressure gradients in cerebral arteries: A clue to pathogenesis of cerebral small vessel disease

Rationale: The role of hypertension in cerebral small vessel disease is poorly understood. At the base of the brain (the \u27vascular centrencephalon\u27), short straight arteries transmit blood pressure directly to small resistance vessels; the cerebral convexity is supplied by long arteries with many branches, resulting in a drop in blood pressure. Hypertensive small vessel disease (lipohyalinosis) causes the classically described lacunar infarctions at the base of the brain; however, periventricular white matter intensities (WMIs) seen on MRI and WMI in subcortical areas over the convexity, which are often also called \u27lacunes\u27, probably have different aetiologies. Objectives: We studied pressure gradients from proximal to distal regions of the cerebral vasculature by mathematical modelling. Methods and results: Blood flow/pressure equations were solved in an Anatomically Detailed Arterial Network (ADAN) model, considering a normotensive and a hypertensive case. Model parameters were suitably modified to account for structural changes in arterial vessels in the hypertensive scenario. Computations predict a marked drop in blood pressure from large and medium-sized cerebral vessels to cerebral peripheral beds. When blood pressure in the brachial artery is 192/113 mm Hg, the pressure in the small arterioles of the posterior parietal artery bed would be only 117/68 mm Hg. In the normotensive case, with blood pressure in the brachial artery of 117/75 mm Hg, the pressure in small parietal arterioles would be only 59/38 mm Hg. Conclusion: These findings have important implications for understanding small vessel disease. The marked pressure gradient across cerebral arteries should be taken into account when evaluating the pathogenesis of small WMIs on MRI. Hypertensive small vessel disease, affecting the arterioles at the base of the brain should be distinguished from small vessel disease in subcortical regions of the convexity and venous disease in the periventricular white matter

Deep-learning based real-time prediction of acute kidney injury after cardiac surgery

The increasing digitisation of medical data and advances in artificial intelligence have enabled us to use the tremendous amount of data that is recorded during a hospital stay in a much more sophisticated way than is currently the case. In the study undertaken and published in the context of this doctoral project, this approach was taken for predicting postoperative acute kidney injury (AKI) – one of the most common and severe complications after cardiothoracic interventions. Using 96 parameters, standardly recorded during a hospital stay, a recurrent neural network (RNN) was developed that predicted AKI within the first seven postoperative days. The training of the model was based on n = 2224 admissions gathered from n = 15,564 admissions at a tertiary care hospital for cardiothoracic surgery. The performance of the model was assessed using an independent test set of n = 350 clinical cases and an area under the curve (AUC) (95% confidence interval) of 0.893 (0.862 - 0.924) was obtained. Additionally, a head-to-head comparison of the RNN against experienced physicians was conducted. The RNN exceeded the physicians in terms of all determined statistical measures (e.g., AUC = 0.901 vs 0.745, p < 0.001). In contrast to the predictions of physicians, who generally underrated the risk of developing AKI, the RNN showed good calibration. The integration of such a model into existing digital medical record systems could allow preventive steps to be taken in time to prevent complications by predicting AKI well before its onset. It could be used as a real-time surveillance system and support physicians' decision-making process. However, when using such a technique, there are several ethical aspects to be considered concerning data protection, model development, and clinical deployment, which are also discussed in this work.Die zunehmende Digitalisierung medizinischer Daten und die Fortschritte im Bereich der künstlichen Intelligenz ermöglichen es, die enorme Menge an Daten, die während eines Krankenhausaufenthalts gesammelt wird, auf viel komplexere Weise zu nutzen, als es bislang der Fall war. In der im Rahmen der Promotion durchgeführten Studie wurde dieser Ansatz für die Echtzeit-Vorhersage von postoperativem akutem Nierenversagen (ANV) verfolgt – eine der häufigsten Komplikationen nach kardiothorakalen Eingriffen. Anhand von 96 Parametern, die standardmäßig während eines Krankenhausaufenthalts aufgezeichnet werden, wurde ein rekurrentes neuronales Netz (RNN) entwickelt, das ANV innerhalb der ersten sieben postoperativen Tage vorhersagen kann. Das Modell wurde mit Daten aus n = 2224 Aufnahmen trainiert, welche aus n = 15.564 klinischen Fällen in einem Krankenhaus der tertiären Versorgung für kardiothorakale Chirurgie zusammengestellt wurden. Die Leistung des RNN wurde anhand eines unabhängigen Testsets aus n = 350 klinischen Fällen bewertet, und es wurde eine area under the curve (AUC) (95 % Konfidenzintervall) von 0,893 (0,862 - 0,924) ermittelt. Zusätzlich wurde ein direkter Vergleich der Vorhersagegüte zwischen dem RNN und erfahrenen ÄrztInnen durchgeführt. Das RNN übertraf die ÄrztInnen in Bezug auf alle ermittelten statistischen Messwerte (z.B. AUC = 0,901 vs. 0,745, p < 0,001). Im Gegensatz zu den Vorhersagen der ÄrztInnen, die das Risiko der Entwicklung eines ANV generell unterschätzten, zeigte das RNN eine gute Kalibrierung. Die Integration eines solchen Modells in bestehende elektronische Patientendatensysteme könnte durch frühzeitige Vorhersage von ANV ermöglichen, präventive Maßnahmen rechtzeitig zu ergreifen, um Komplikationen zu verhindern. Es könnte als Echtzeit-Überwachungssystem eingesetzt werden und die Entscheidungsprozesse der ÄrztInnen unterstützen. Bei der Verwendung eines solchen Systems sind neben seiner Vorhersagegüte aber auch ethische und rechtliche Aspekte zu berücksichtigen, die den Datenschutz, die Modellentwicklung und den klinischen Einsatz betreffen, und die in dieser Arbeit ebenfalls erörtert werden

Prediction of Complications and Prognostication in Perioperative Medicine: A Systematic Review and PROBAST Assessment of Machine Learning Tools

Background: The utilization of artificial intelligence and machine learning as diagnostic and predictive tools in perioperative medicine holds great promise. Indeed, many studies have been performed in recent years to explore the potential. The purpose of this systematic review is to assess the current state of machine learning in perioperative medicine, its utility in prediction of complications and prognostication, and limitations related to bias and validation. Methods: A multidisciplinary team of clinicians and engineers conducted a systematic review using the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) protocol. Multiple databases were searched, including Scopus, Cumulative Index to Nursing and Allied Health Literature (CINAHL), the Cochrane Library, PubMed, Medline, Embase, and Web of Science. The systematic review focused on study design, type of machine learning model used, validation techniques applied, and reported model performance on prediction of complications and prognostication. This review further classified outcomes and machine learning applications using an ad hoc classification system. The Prediction model Risk Of Bias Assessment Tool (PROBAST) was used to assess risk of bias and applicability of the studies. Results: A total of 103 studies were identified. The models reported in the literature were primarily based on single-center validations (75%), with only 13% being externally validated across multiple centers. Most of the mortality models demonstrated a limited ability to discriminate and classify effectively. The PROBAST assessment indicated a high risk of systematic errors in predicted outcomes and artificial intelligence or machine learning applications. Conclusions: The findings indicate that the development of this field is still in its early stages. This systematic review indicates that application of machine learning in perioperative medicine is still at an early stage. While many studies suggest potential utility, several key challenges must be first overcome before their introduction into clinical practice

Determination and Finite Element Validation of the WYPIWYG Strain Energy of Superficial Fascia from Experimental Data

What-You-Prescribe-Is-What-You-Get (WYPIWYG) procedures are a novel and general phenomenological approach to modelling the behavior of soft materials, applicable to biological tissues in particular. For the hyperelastic case, these procedures solve numerically the nonlinear elastic material determination problem. In this paper we show that they can be applied to determine the stored energy density of superficial fascia. In contrast to the usual approach, in such determination no user-prescribed material parameters and no optimization algorithms are employed. The strain energy densities are computed solving the equilibrium equations of the set of experiments. For the case of superficial fascia it is shown that the mechanical behavior derived from such strain energies is capable of reproducing simultaneously the measured load-displacement curves of three experiments to a high accuracy