Virtual pacing of a patient's digital twin to predict left ventricular reverse remodelling after cardiac resynchronization therapy

An in silico guide for ventriculo-ventricular delay programming for left bundle branch-optimized cardiac resynchronization therapy

AIMS

Left bundle branch pacing (LBBP)-optimized cardiac resynchronization therapy (LOT-CRT) can improve left ventricular (LV) activation when LBBP alone or conventional biventricular pacing are ineffective. However, the optimal programming settings for ventriculo-ventricular delay (VVD) for LOT-CRT are unknown. We aim to investigate how to optimally program VVD for LOT-CRT in the presence of various LV conduction substrates using computational modelling.

METHODS AND RESULTS

We simulated ventricular activation on 24 anatomies and validated the model against clinical data. Diffuse LV conduction system and intra-myocardial delay were simulated by slowing the conduction velocity of the LV His-Purkinje system and myocardium, respectively, alone or in combination with proximal left bundle branch block (LBBB). We simulated LOT-CRT with selective or myocardial capture (LV septal pacing, LVSP) with VVD ranging between -100 ms (LBBP/LVSP ahead) and +100 ms [LV epicardial lead (LVepiP), ahead]. Response was quantified with 95% LV activation times (LVAT95). In the presence of diffuse LV conduction system delay, the optimal VVD for LOT-CRT was always negative (LBBB: -42.5 ± 6.6 ms; no LBBB: -36.2 ± 5.6 ms), as delivering LBBP ahead of LVepiP compensates for the slow LV His-Purkinje. In the presence of LV intra-myocardial disease, the shortest LVAT95 with LOT-CRT was achieved by pacing the coronary sinus LV first (optimal VVD for LBBB: 23.3 ± 8.5 ms; no LBBB: 79.2 ± 18.0 ms). The type of capture for LOT-CRT affected the optimal VVD, with myocardial capture favouring negative VVDs (LVSP ahead).

CONCLUSION

The optimal VVD for LOT-CRT depends on the mechanism of delayed LV activation and type of capture achieved, highlighting the importance of VVD optimization.

Numerical accuracy of closed-loop steady state in a zero-dimensional cardiovascular model

Closed-loop cardiovascular models are becoming vital tools in clinical settings, making their accuracy and reliability paramount. While these models rely heavily on steady-state simulations, accuracy because of steady-state convergence is often assumed negligible. Using a reduced-order cardiovascular model created with the CircAdapt framework as a case study, we investigated steady-state convergence behaviour across various integration methods and simulation protocols. To minimize the effect of numerical errors, we first quantified the numerical errors originating from integration methods and model assumptions. We subsequently investigate this steady-state convergence error under two distinct conditions: first without, and then with homeostatic pressure-flow control (PFC), providing a comprehensive assessment of the CircAdapt framework's numerical stability and accuracy. Our results demonstrated that achieving a clinically accurate steady state required 7-15 heartbeats in simulations without regulatory mechanisms. When homeostatic control mechanisms were included to regulate mean arterial pressure and blood volume, more than twice the number of heartbeats was needed. By simulating a variable number of heartbeats tailored to each simulation's characteristics, an efficient balance between computational cost and steady-state accuracy can be achieved. Understanding this balance is crucial as cardiovascular models progress towards clinical use.This article is part of the theme issue 'Uncertainty quantification for healthcare and biological systems (Part 2)'.

Impact of myocardial phenotype on optimal atrioventricular delay settings during biventricular and left bundle branch pacing at rest and during exercise: insights from a virtual patient study

AIMS

Previous studies have not examined the role of non-electrical myocardial disease substrates in determining the optimal atrio-ventricular delay (AVD) settings. We conducted virtual patient simulations to evaluate whether myocardial disease substrates influence the acute response to AVD optimization at rest and during exercise.

METHODS AND RESULTS

The CircAdapt cardiovascular model was used to simulate various left ventricular (LV) remodelling found in cardiac resynchronization therapy candidates. We simulated electrical dyssynchrony, LV dilatation with preserved and reduced contractility, and increased LV passive stiffness. We simulated cardiac resynchronization following biventricular (BiVP) and non-selective LBB pacing (nsLBBP). The paced-AVD ranged from 220 to 40 ms. Cardiac output and heart rate were increased to simulate different levels of exercise. The optimal AVD was the one leading to the highest stroke volume (SV) and the lowest mean left atrial pressure (mLAP). At rest, in simulations with healthy myocardium the gain in SV by AVD optimization was larger compared to those with reduced contractility and stiff myocardium. However, mLAP was comparably decreased by AVD optimization in both healthy and diseased myocardium. During exercise, the optimal AVD shifted to shorter values, and mLAP was more sensitive to AVD, particularly in the presence of hypo-contractile and stiff myocardium.

CONCLUSION

Simulations show that hypocontractility and stiffness reduce the effect of AVD optimization on SV but enhance its benefit in lowering mLAP. Notably, virtual patients with stiff ventricles experience greater benefits from AVD optimization during exercise compared to resting conditions. Furthermore, nsLBBP provides more favourable improvements in mLAP than BiVP.

Digital Twins for Clinical and Operational Decision-Making: Scoping Review

BACKGROUND

The health care industry must align with new digital technologies to respond to existing and new challenges. Digital twins (DTs) are an emerging technology for digital transformation and applied intelligence that is rapidly attracting attention. DTs are virtual representations of products, systems, or processes that interact bidirectionally in real time with their actual counterparts. Although DTs have diverse applications from personalized care to treatment optimization, misconceptions persist regarding their definition and the extent of their implementation within health systems.

OBJECTIVE

This study aimed to review DT applications in health care, particularly for clinical decision-making (CDM) and operational decision-making (ODM). It provides a definition and framework for DTs by exploring their unique elements and characteristics. Then, it assesses the current advances and extent of DT applications to support CDM and ODM using the defined DT characteristics.

METHODS

We conducted a scoping review following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) protocol. We searched multiple databases, including PubMed, MEDLINE, and Scopus, for original research articles describing DT technologies applied to CDM and ODM in health systems. Papers proposing only ideas or frameworks or describing DT capabilities without experimental data were excluded. We collated several available types of information, for example, DT characteristics, the environment that DTs were tested within, and the main underlying method, and used descriptive statistics to analyze the synthesized data.

RESULTS

Out of 5537 relevant papers, 1.55% (86/5537) met the predefined inclusion criteria, all published after 2017. The majority focused on CDM (75/86, 87%). Mathematical modeling (24/86, 28%) and simulation techniques (17/86, 20%) were the most frequently used methods. Using International Classification of Diseases, 10th Revision coding, we identified 3 key areas of DT applications as follows: factors influencing diseases of the circulatory system (14/86, 16%); health status and contact with health services (12/86, 14%); and endocrine, nutritional, and metabolic diseases (10/86, 12%). Only 16 (19%) of 86 studies tested the developed system in a real environment, while the remainder were evaluated in simulated settings. Assessing the studies against defined DT characteristics reveals that the developed systems have yet to materialize the full capabilities of DTs.

CONCLUSIONS

This study provides a comprehensive review of DT applications in health care, focusing on CDM and ODM. A key contribution is the development of a framework that defines important elements and characteristics of DTs in the context of related literature. The DT applications studied in this paper reveal encouraging results that allow us to envision that, in the near future, they will play an important role not only in the diagnosis and prevention of diseases but also in other areas, such as efficient clinical trial design, as well as personalized and optimized treatments.

Cardiac resynchronization therapy (CRT) nonresponders in the contemporary era: A state-of-the-art review

In the 2000s, cardiac resynchronization therapy (CRT) became a revolutionary treatment for heart failure with reduced left ventricular ejection fraction (HFrEF) and wide QRS. However, about one-third of CRT recipients do not show a favorable response. This review of the current literature aims to better define the concept of CRT response/nonresponse. The diagnosis of CRT nonresponder should be viewed as a continuum, and it cannot rely solely on a single parameter. Moreover, baseline features of some patients might predict an unfavorable response. A strong collaboration between heart failure specialists and electrophysiologists is key to overcoming this challenge with multiple strategies. In the contemporary era, new pacing modalities, such as His-bundle pacing and left bundle branch area pacing, represent a promising alternative to CRT. Observational studies have demonstrated their potential; however, several limitations should be addressed. Large randomized controlled trials are needed to prove their efficacy in HFrEF with electromechanical dyssynchrony.

Optimizing outcomes from cardiac resynchronization therapy: what do recent data and insights say?

INTRODUCTION

Cardiac Resynchronization Therapy (CRT) is an effective treatment for heart failure (HF) in approximately two-thirds of recipients, with a third remaining CRT 'non-responders.' There is an increasing body of evidence exploring the reasons behind non-response, as well as ways to preempt or counteract it.

AREAS COVERED

This review will examine the most recent evidence regarding optimizing outcomes from CRT, as well as explore whether traditional CRT indeed remains the best first-line therapy for electrical resynchronization in HF. We will start by discussing methods of preempting non-response, such as refining patient selection and procedural technique, before reviewing how responses can be optimized post-implantation. For the purpose of this review, evidence was gathered from electronic literature searches (via PubMed and GoogleScholar), with a particular focus on primary evidence published in the last 5 years.

EXPERT OPINION

Ever-expanding research in the field of device therapy has armed physicians with more tools than ever to treat dyssynchronous HF. Newer developments, such as artificial intelligence (AI) guided device programming and conduction system pacing (CSP) are particularly exciting, and we will discuss how they could eventually lead to truly personalized care by maximizing outcomes from CRT.

From bits to bedside: entering the age of digital twins in cardiac electrophysiology

This State of the Future Review describes and discusses the potential transformative power of digital twins in cardiac electrophysiology. In this 'big picture' approach, we explore the evolution of mechanistic modelling based digital twins, their current and immediate clinical applications, and envision a future where continuous updates, advanced calibration, and seamless data integration redefine clinical practice of cardiac electrophysiology. Our aim is to inspire researchers and clinicians to embrace the extraordinary possibilities that digital twins offer in the pursuit of precision medicine.

Cardiovascular care with digital twin technology in the era of generative artificial intelligence

Digital twins, which are in silico replications of an individual and its environment, have advanced clinical decision-making and prognostication in cardiovascular medicine. The technology enables personalized simulations of clinical scenarios, prediction of disease risk, and strategies for clinical trial augmentation. Current applications of cardiovascular digital twins have integrated multi-modal data into mechanistic and statistical models to build physiologically accurate cardiac replicas to enhance disease phenotyping, enrich diagnostic workflows, and optimize procedural planning. Digital twin technology is rapidly evolving in the setting of newly available data modalities and advances in generative artificial intelligence, enabling dynamic and comprehensive simulations unique to an individual. These twins fuse physiologic, environmental, and healthcare data into machine learning and generative models to build real-time patient predictions that can model interactions with the clinical environment to accelerate personalized patient care. This review summarizes digital twins in cardiovascular medicine and their potential future applications by incorporating new personalized data modalities. It examines the technical advances in deep learning and generative artificial intelligence that broaden the scope and predictive power of digital twins. Finally, it highlights the individual and societal challenges as well as ethical considerations that are essential to realizing the future vision of incorporating cardiology digital twins into personalized cardiovascular care.

The Ithildin library for efficient numerical solution of anisotropic reaction-diffusion problems in excitable media

Ithildin is an open-source library and framework for efficient parallelized simulations of excitable media, written in the C++ programming language. It uses parallelization on multiple CPU processors via the message passing interface (MPI). We demonstrate the library's versatility through a series of simulations in the context of the monodomain description of cardiac electrophysiology, including the S1S2 protocol, spiral break-up, and spiral waves in ventricular geometry. Our work demonstrates the power of Ithildin as a tool for studying complex wave patterns in cardiac tissue and its potential to inform future experimental and theoretical studies. We publish our full code with this paper in the name of open science.

Digital twins and artificial intelligence in metabolic disease research

Digital twin technology is emerging as a transformative paradigm for personalized medicine in the management of chronic conditions. In this article, we explore the concept and key characteristics of a digital twin and its applications in chronic non-communicable metabolic disease management, with a focus on diabetes case studies. We cover various types of digital twin models, including mechanistic models based on ODEs, data-driven ML algorithms, and hybrid modeling strategies that combine the strengths of both approaches. We present successful case studies demonstrating the potential of digital twins in improving glucose outcomes for individuals with T1D and T2D, and discuss the benefits and challenges of translating digital twin research applications to clinical practice.

Digital twins for cardiac electrophysiology: state of the art and future challenges

Cardiac arrhythmias remain a major cause of death and disability. Current antiarrhythmic therapies are effective to only a limited extent, likely in large part due to their mechanism-independent approach. Precision cardiology aims to deliver targeted therapy for an individual patient to maximize efficacy and minimize adverse effects. In-silico digital twins have emerged as a promising strategy to realize the vision of precision cardiology. While there is no uniform definition of a digital twin, it typically employs digital tools, including simulations of mechanistic computer models, based on patient-specific clinical data to understand arrhythmia mechanisms and/or make clinically relevant predictions. Digital twins have become part of routine clinical practice in the setting of interventional cardiology, where commercially available services use digital twins to non-invasively determine the severity of stenosis (computed tomography-based fractional flow reserve). Although routine clinical application has not been achieved for cardiac arrhythmia management, significant progress towards digital twins for cardiac electrophysiology has been made in recent years. At the same time, significant technical and clinical challenges remain. This article provides a short overview of the history of digital twins for cardiac electrophysiology, including recent applications for the prediction of sudden cardiac death risk and the tailoring of rhythm control in atrial fibrillation. The authors highlight the current challenges for routine clinical application and discuss how overcoming these challenges may allow digital twins to enable a significant precision medicine-based advancement in cardiac arrhythmia management.