Abstract:

AIMS: The effective refractory period (ERP) is one of the main electrophysiological properties governing arrhythmia, yet ERP personalization is rarely performed when creating patient-specific computer models of the atria to inform clinical decision-making. This study evaluates the impact of integrating clinical ERP measurements into personalized in silico models on arrhythmia vulnerability. METHODS AND RESULTS: Clinical ERP measurements were obtained in seven patients from multiple locations in the atria. Atrial geometries from the electroanatomical mapping system were used to generate personalized anatomical atrial models. The Courtemanche M. et al. cellular model was adjusted to reproduce patient-specific ERP. Four modeling approaches were compared: homogeneous (A), heterogeneous (B), regional (C), and continuous (D) ERP distributions. Non-personalized approaches (A and B) were based on literature data, while personalized approaches (C and D) were based on patient measurements. Modeling effects were assessed on arrhythmia vulnerability and tachycardia cycle length, with sensitivity analysis on ERP measurement uncertainty. Mean vulnerability was 3.4 ± 4.0%, 7.7 ± 3.4%, 9.0 ± 5.1%, and 7.0 ± 3.6% for scenarios A-D, respectively. Mean tachycardia cycle length was 167.1 ± 12.6 ms, 158.4 ± 27.5 ms, 265.2 ± 39.9 ms, and 285.9 ± 77.3 ms for scenarios A-D, respectively. Incorporating perturbations to the measured ERP in the range of 2, 5, 10, 20, and 50 ms changed the vulnerability of the model to 5.8 ± 2.7%, 6.1 ± 3.5%, 6.9 ± 3.7%, 5.2 ± 3.5%, and 9.7 ± 10.0%, respectively. CONCLUSION: Increased ERP dispersion had a greater effect on re-entry dynamics than on vulnerability. Inducibility was higher in personalized scenarios compared with scenarios with uniformly reduced ERP; however, this effect was reversed when incorporating fibrosis informed by low-voltage areas. Effective refractory period measurement uncertainty up to 20 ms slightly influenced vulnerability. Electrophysiological personalization of atrial in silico models appears essential and requires confirmation in larger cohorts.

Abstract:

Lernende Systeme oder Machine Learning, so sind sich Fachleute einig, werden auch in der Medizin und der Medizintechnik zukünftig eine große Bedeutung erlangen – mit Vorteilen aber auch mit Risiken für Patientinnen und Patienten, Unternehmen und Fachpersonal. Dabei ergeben sich verschiedenste Herausforderungen im Umgang mit Machine-Learning-Systemen – unter anderem für praktische Behandlungssituationen, für die Qualitätskontrolle, für die Sicherheit in Notfallsituationen oder die Bewertung der vom Computer vorgeschlagenen Diagnosen und Therapiepfade. Die vorliegende acatech POSITION ist das Ergebnis einer Arbeitsgruppe von Wissenschaftlerinnen und Wissenschaftlern aus Medizin und Technik. Die Projektgruppe gibt einen Überblick über heutige Anwendungen von Machine Learning in der Medizintechnik und beleuchtet wichtige zukünftige Anwendungsfelder. Im Fokus stehen darüber hinaus ethische, rechtliche und regulatorische Aspekte sowie kritische Fragen zum Datenschutz und mögliche Veränderungen im Arzt-Patienten-Verhältnis. Neben Vorschlägen zum Aufbau großer medizinischer Datenbanken gibt diese Position auch Handlungsempfehlungen für Ärztinnen und Ärzte, Einrichtungen der Forschungsförderung und die Politik.

Abstract:

ntercellular heterogeneity is fundamental to most biological tissues. For some cell types, heterogeneity is thought to be responsible for distinct cellular phenotypes and functional roles. In the pancreatic islet, subsets of phenotypically distinct beta cells (hub and leader cells) are thought to coordinate electrical activity of the beta cell network. This hypothesis has been addressed by experimental and computational approaches, but none have attempted to reconstruct functional specialization directly from measured heterogeneity. To evaluate if electrophysiologic heterogeneity alone can explain these specialized functional roles, we created a population of human beta cell models (via genetic algorithm optimization) recapitulating the heterogeneity in an extensive patch clamp dataset (1021 pancreatic cells). Then we applied the simplified Kirchhoff network (SKNM) formalism to simulate activity of that population in a connected beta cell network. We could not immediately observe cells with obvious hub or leader phenotypes within the heterogeneous network. However, with this study we built the basis for further ''ground-up'' investigation of relationships between beta cell heterogeneity and human islet function. Moreover, our workflow may be translated to other tissues where large electrophysiologic data sets become available, and heterogeneity is thought to influence tissue function e.g. human atria.

Abstract:

Atrial fibrillation (AF) is the most common sus- tained arrhythmia posing a significant burden to patients and leading to an increased risk of stroke and heart failure. Additional ablation of areas of arrhythmogenic substrate in the atrial body detected by either late gadolinium enhance- ment magnetic resonance imaging (LGE-MRI) or electro- anatomical mapping (EAM) may increase the success rate of restoring and maintaining sinus rhythm compared to the stan- dard treatment procedure of pulmonary vein isolation (PVI). To evaluate if LGE-MRI and EAM identify equivalent sub- strate as potential ablation targets, we divided the left atrium (LA) into six clinically important regions in ten patients. Then, we computed the correlation between both modalities by ana- lyzing the regional extents of identified pathological tissue. In this regional analysis, we observed no correlation between late gadolinium enhancement (LGE) and low voltage areas (LVA), neither in any region nor with regard to the entire atrial surface (−0.3 < 𝑟 < 0.3). Instead, the regional extents identified as pathological tissue varied significantly between both modali- ties. An increased extent of LVA compared to LGE was ob- served in the septal wall of the LA (𝑎 ̃sept.,LVA = 19.63 % and 𝑎 ̃sept.,LGE = 3.94 %, with 𝑎 ̃ = median of the extent of patho- logical tissue in the corresponding region). In contrast, in the inferior and lateral wall, the extent of LGE was higher than the extent of LVA for most geometries (𝑎 ̃inf.,LGE = 27.22% and 𝑎 ̃lat.,LGE = 32.70 % compared to 𝑎 ̃inf .,LVA = 9.21 % and 𝑎 ̃lat.,LVA = 6.69 %). Since both modalities provided dis- crepant results regarding the detection of arrhythmogenic sub- strate using clinically established thresholds, further investiga- tions regarding their constraints need to be performed in order to use these modalities for patient stratification and treatment planning.

Abstract:

Atrial fibrillation (AF) is the most common arrhythmia posing a significant burden to patients and leading to an increased risk of stroke and heart failure. However, the underlying mechanisms of AF are still not entirely understood. While the general treatment of pulmonary vein isolation (PVI) is an effective therapy for patients with paroxysmal AF, the success rate for maintaining sinus rhythm significantly decreases for individuals with persistent AF. It has been reported that this may be due to non-PVI triggers in the atrial body of patients with persistent AF in combination with the presence of structural remodeling processes. Thus, individual treatment strategies need to be elaborated which require regional examinations of the atria. However, since there is no consensus regarding the atrial region division, intra- and inter-, as well as cross-modality quantitative comparisons cannot be performed.To address this challenge, a bi-atrial standard nomenclature with six regions for each atrium was established by considering multiple acquisition modalities and previous approaches proposed in the literature. Furthermore, a robust semi-automatic bi-atrial division pipeline with consistent and reproducible region assignment was developed. This algorithm was tested on 15 bi-atrial and 10 additional left atrial geometries from three independent data sets with multiple acquisition modalities. The results showed qualitative and quantitative accordance with the introduced standard nomenclature, while offering a high degree of autonomy and reproducibility. User intervention was required in 2.7 % of all region assignments.In addition, the atrial division pipeline was used to regionally analyze clinical data of electroanatomical mapping (EAM) and late gadolinium enhancement magnetic resonance imaging (LGE-MRI), which are commonly used modalities to detect arrhythmogenic substrate. However, no accordance was found between late gadolinium enhancement (LGE) and low voltage area (LVA). Instead, the regional extent and spatial distribution of identified pathological tissue varied significantly between both modalities. Moreover, an increased extent of LVA compared to LGE was observed in the septal wall of the left atrium (LA) in two independent data sets. In contrast, LGE was predominantly detected in the lateral wall of the LA. However, these findings need to be investigated further, since the analyzed data set of 12 patients in total was comparatively small.This work provides a unified framework to regionally compare and integrate data from different fields such as research, clinics and technology to further understand the underlying mechanisms of AF and to personalize the treatment strategies.