Artificial intelligence analysis of the impact of fibrosis in arrhythmogenesis and drug response.

Background: Cardiac fibrosis has been identified as a major factor in conduction alterations leading to atrial arrhythmias and modification of drug treatment response. Objective: To perform an in silico proof-of-concept study of Artificial Intelligence (AI) ability to identify susceptibility for conduction blocks in simulations on a population of models with diffused fibrotic atrial tissue and anti-arrhythmic drugs. Methods: Activity in 2D cardiac tissue planes were simulated on a population of variable electrophysiological and anatomical profiles using the Koivumaki model for the atrial cardiomyocytes and the Maleckar model for the diffused fibroblasts (0%, 5% and 10% fibrosis area). Tissue sheets were of 2 cm side and the effect of amiodarone, dofetilide and sotalol was simulated to assess the conduction of the electrical impulse across the planes. Four different AI algorithms (Quadratic Support Vector Machine, QSVM, Cubic Support Vector Machine, CSVM, decision trees, DT, and K-Nearest Neighbors, KNN) were evaluated in predicting conduction of a stimulated electrical impulse. Results: Overall, fibrosis implementation lowered conduction velocity (CV) for the conducting profiles (0% fibrosis: 67.52 ± 7.3 cm/s; 5%: 58.81 ± 14.04 cm/s; 10%: 57.56 ± 14.78 cm/s; p < 0.001) in combination with a reduced 90% action potential duration (0% fibrosis: 187.77 ± 37.62 ms; 5%: 93.29 ± 82.69 ms; 10%: 106.37 ± 85.15 ms; p < 0.001) and peak membrane potential (0% fibrosis: 89.16 ± 16.01 mV; 5%: 70.06 ± 17.08 mV; 10%: 82.21 ± 19.90 mV; p < 0.001). When the antiarrhythmic drugs were present, a total block was observed in most of the profiles. In those profiles in which electrical conduction was preserved, a decrease in CV was observed when simulations were performed in the 0% fibrosis tissue patch (Amiodarone ΔCV: -3.59 ± 1.52 cm/s; Dofetilide ΔCV: -13.43 ± 4.07 cm/s; Sotalol ΔCV: -0.023 ± 0.24 cm/s). This effect was preserved for amiodarone in the 5% fibrosis patch (Amiodarone ΔCV: -4.96 ± 2.15 cm/s; Dofetilide ΔCV: 0.14 ± 1.87 cm/s; Sotalol ΔCV: 0.30 ± 4.69 cm/s). 10% fibrosis simulations showed that part of the profiles increased CV while others showed a decrease in this variable (Amiodarone ΔCV: 0.62 ± 9.56 cm/s; Dofetilide ΔCV: 0.05 ± 1.16 cm/s; Sotalol ΔCV: 0.22 ± 1.39 cm/s). Finally, when the AI algorithms were tested for predicting conduction on input of variables from the population of modelled, Cubic SVM showed the best performance with AUC = 0.95. Conclusion: In silico proof-of-concept study demonstrates that fibrosis can alter the expected behavior of antiarrhythmic drugs in a minority of atrial population models and AI can assist in revealing the profiles that will respond differently.

Combined atrial fibrillation ablation and balloon mitral commissurotomy in patients with rheumatic mitral stenosis.

INTRODUCTION: Ablation of atrial fibrillation (AF) is usually not considered in patients with rheumatic mitral stenosis (RMS). We analyzed the results of a combined procedure of AF ablation and percutaneous balloon mitral commissurotomy (PBMC). METHODS: We prospectively included 22 patients with severe RMS to undergo a combined PBMC + AF ablation procedure. Noninvasive mapping of the atria was also performed. A historical sample of propensity-scored matched patients who underwent PBMC alone was used as controls. The primary endpoint was freedom from AF/AT at 1-year. Multivariate analysis evaluated sinus rhythm (SR) predictors. RESULTS: Successful pulmonary vein isolation and electrocardiographic imaging-based drivers ablation was performed in 20 patients following PBMC. At 1-year, 75% of the patients in the combined group were in SR compared to 40% in the propensity-score matched group (p = 0.004). The composite of AF recurrence, need for mitral surgery and all-cause mortality was also more frequent in the control group (65% vs. 30%; p = 0.005). Catheter ablation (odds ratio [OR] 1.58; 95% confidence interval [CI] [1.17-17.37]; p = 0.04) and AF type (OR 1.46; 95% CI [1.05-82.64]; p < 0.001) were the only independent predictors of SR at 1-year. Noninvasive mapping in the combined group showed that the number of simultaneous rotors (OR 2.10; 95% CI [1.41-10.2]; p = 0.04) was the only independent predictor of AF. CONCLUSION: A combined procedure of AF ablation and PBMC significantly increased the proportion of patients in sinus rhythm at 1-year. Noninvasive mapping may help to improve AF characterization and guide personalized AF treatment.

Cardiovascular Diseases in the Digital Health Era: A Translational Approach from the Lab to the Clinic.

Translational science has been introduced as the nexus among the scientific and the clinical field, which allows researchers to provide and demonstrate that the evidence-based research can connect the gaps present between basic and clinical levels. This type of research has played a major role in the field of cardiovascular diseases, where the main objective has been to identify and transfer potential treatments identified at preclinical stages into clinical practice. This transfer has been enhanced by the intromission of digital health solutions into both basic research and clinical scenarios. This review aimed to identify and summarize the most important translational advances in the last years in the cardiovascular field together with the potential challenges that still remain in basic research, clinical scenarios, and regulatory agencies.

Artificial Intelligence-Driven Algorithm for Drug Effect Prediction on Atrial Fibrillation: An in silico Population of Models Approach.

Background: Antiarrhythmic drugs are the first-line treatment for atrial fibrillation (AF), but their effect is highly dependent on the characteristics of the patient. Moreover, anatomical variability, and specifically atrial size, have also a strong influence on AF recurrence. Objective: We performed a proof-of-concept study using artificial intelligence (AI) that enabled us to identify proarrhythmic profiles based on pattern identification from in silico simulations. Methods: A population of models consisting of 127 electrophysiological profiles with a variation of nine electrophysiological variables (G Na , I NaK , G K1, G CaL , G Kur , I KCa , [Na] ext , and [K] ext and diffusion) was simulated using the Koivumaki atrial model on square planes corresponding to a normal (16 cm2) and dilated (22.5 cm2) atrium. The simple pore channel equation was used for drug implementation including three drugs (isoproterenol, flecainide, and verapamil). We analyzed the effect of every ionic channel combination to evaluate arrhythmia induction. A Random Forest algorithm was trained using the population of models and AF inducibility as input and output, respectively. The algorithm was trained with 80% of the data (N = 832) and 20% of the data was used for testing with a k-fold cross-validation (k = 5). Results: We found two electrophysiological patterns derived from the AI algorithm that was associated with proarrhythmic behavior in most of the profiles, where G K1 was identified as the most important current for classifying the proarrhythmicity of a given profile. Additionally, we found different effects of the drugs depending on the electrophysiological profile and a higher tendency of the dilated tissue to fibrillate (Small tissue: 80 profiles vs Dilated tissue: 87 profiles). Conclusion: Artificial intelligence algorithms appear as a novel tool for electrophysiological pattern identification and analysis of the effect of antiarrhythmic drugs on a heterogeneous population of patients with AF.

Personalized Evaluation of Atrial Complexity of Patients Undergoing Atrial Fibrillation Ablation: A Clinical Computational Study.

Current clinical guidelines establish Pulmonary Vein (PV) isolation as the indicated treatment for Atrial Fibrillation (AF). However, AF can also be triggered or sustained due to atrial drivers located elsewhere in the atria. We designed a new simulation workflow based on personalized computer simulations to characterize AF complexity of patients undergoing PV ablation, validated with non-invasive electrocardiographic imaging and evaluated at one year after ablation. We included 30 patients using atrial anatomies segmented from MRI and simulated an automata model for the electrical modelling, consisting of three states (resting, excited and refractory). In total, 100 different scenarios were simulated per anatomy varying rotor number and location. The 3 states were calibrated with Koivumaki action potential, entropy maps were obtained from the electrograms and compared with ECGi for each patient to analyze PV isolation outcome. The completion of the workflow indicated that successful AF ablation occurred in patients with rotors mainly located at the PV antrum, while unsuccessful procedures presented greater number of driving sites outside the PV area. The number of rotors attached to the PV was significantly higher in patients with favorable long-term ablation outcome (1-year freedom from AF: 1.61 ± 0.21 vs. AF recurrence: 1.40 ± 0.20; p-value = 0.018). The presented workflow could improve patient stratification for PV ablation by screening the complexity of the atria.

Artificial intelligence for a personalized diagnosis and treatment of atrial fibrillation.

Although atrial fibrillation (AF) is the most common cardiac arrhythmia, its early identification, diagnosis, and treatment is still challenging. Due to its heterogeneous mechanisms and risk factors, targeting an individualized treatment of AF demands a large amount of patient data to identify specific patterns. Artificial intelligence (AI) algorithms are particularly well suited for treating high-dimensional data, predicting outcomes, and eventually, optimizing strategies for patient management. The analysis of large patient samples combining different sources of information such as blood biomarkers, electrical signals, and medical images opens a new paradigm for improving diagnostic algorithms. In this review, we summarize suitable AI techniques for this purpose. In particular, we describe potential applications for understanding the structural and functional bases of the disease, as well as for improving early noninvasive diagnosis, developing more efficient therapies, and predicting long-term clinical outcomes of patients with AF.

Generation of NKX2.5GFP Reporter Human iPSCs and Differentiation Into Functional Cardiac Fibroblasts.

Direct cardiac reprogramming has emerged as an interesting approach for the treatment and regeneration of damaged hearts through the direct conversion of fibroblasts into cardiomyocytes or cardiovascular progenitors. However, in studies with human cells, the lack of reporter fibroblasts has hindered the screening of factors and consequently, the development of robust direct cardiac reprogramming protocols.In this study, we have generated functional human NKX2.5GFP reporter cardiac fibroblasts. We first established a new NKX2.5GFP reporter human induced pluripotent stem cell (hiPSC) line using a CRISPR-Cas9-based knock-in approach in order to preserve function which could alter the biology of the cells. The reporter was found to faithfully track NKX2.5 expressing cells in differentiated NKX2.5GFP hiPSC and the potential of NKX2.5-GFP + cells to give rise to the expected cardiac lineages, including functional ventricular- and atrial-like cardiomyocytes, was demonstrated. Then NKX2.5GFP cardiac fibroblasts were obtained through directed differentiation, and these showed typical fibroblast-like morphology, a specific marker expression profile and, more importantly, functionality similar to patient-derived cardiac fibroblasts. The advantage of using this approach is that it offers an unlimited supply of cellular models for research in cardiac reprogramming, and since NKX2.5 is expressed not only in cardiomyocytes but also in cardiovascular precursors, the detection of both induced cell types would be possible. These reporter lines will be useful tools for human direct cardiac reprogramming research and progress in this field.