Computational Medicine: What Electrophysiologists Should Know to Stay Ahead of the Curve

Abstract

Purpose of review: Technology drives the field of cardiac electrophysiology. Recent computational advances will bring exciting changes. To stay ahead of the curve, we recommend electrophysiologists develop a robust appreciation for novel computational techniques, including deterministic, statistical, and hybrid models.

Recent findings: In clinical applications, deterministic models use biophysically detailed simulations to offer patient-specific insights. Statistical techniques like machine learning and artificial intelligence recognize patterns in data. Emerging clinical tools are exploring avenues to combine all the above methodologies. We review three ways that computational medicine will aid electrophysiologists by: (1) improving personalized risk assessments, (2) weighing treatment options, and (3) guiding ablation procedures. Leveraging clinical data that are often readily available, computational models will offer valuable insights to improve arrhythmia patient care. As emerging tools promote personalized medicine, physicians must continue to critically evaluate technology-driven tools they consider using to ensure their appropriate implementation.

Keywords: Arrhythmias; Cardiac electrophysiology; Computational medicine; Computational modeling; Machine learning; Precision medicine.

Figure 1:

Types of computational approaches and their typical implementation in clinical medicine. (A) Biophysically detailed models apply physical and physiologic relationships to patient data, simulating patient-specific cardiac electrophysiology. These models can simulate response to pacing under different conditions or with different patterns of ablation lesions. (B) A general flowchart showing the process for developing statistical models, regardless of the statistical model’s complexity. (C) Examples of common statistical models, compared by relative complexity and interpretability. ML and AI are complex statistical techniques that cannot be readily interpreted. Explainability tools increase the interpretability of these statistical techniques. AI, artificial intelligence; LGE-MRI, late gadolinium enhanced magnetic resonance imaging; ML, machine learning.

Figure 2:

Roles of computational approaches for patients with atrial or ventricular arrhythmias. (A) AI/ML-informed techniques and biophysically detailed models may enhance risk stratification by identifying complex patterns in patient data or testing inducibility in patient-specific models. (B) Computational techniques are preparing to answer meaningful clinical questions, like predicting whether a patient will benefit from an invasive procedure. (C) For both atrial and ventricular ablation procedures, biophysically detailed models are identifying optimal ablation plans to terminate arrhythmias while minimizing the volume of tissue ablated. These ablation plans, which have been successfully implemented in human patients, proactively identify secondary arrhythmia drivers and include this substrate in the patient-specific plan. AF, atrial fibrillation; AI, artificial intelligence; ECG, electrocardiogram; EHR, electronic health record; EP, electrophysiology; LV, left ventricle; ML, machine learning; RV, right ventricle.

Figure 3:

Driving precision medicine through computational techniques. (A) A general framework for implementing computational medicine in electrophysiology. By evaluating the response of biophysically detailed models to different interventions and updating these models, clinical tools can provide individualized treatment recommendations and ablation plans for patients. MRI and left atrial model; adapted with permission under the terms of the CC-BY license from the paper by Ogbomo-Harmitt et al. (2023) Front Physiol [99]. (B) The standard approach recommends different treatments for subsets of patients, whereas the personalized approach tests interventions in biophysically detailed models to identify the optimal strategy for everyone. These predictive models can recommend the minimal intervention (e.g., medication, procedure plan) necessary to terminate a patient’s arrhythmias, reducing the risk of harm while maximizing the likelihood of patient benefit. Ao, aorta; LA, left atrium; LPV, left pulmonary vein; LV, left ventricle; RA, right atrium; RCA, right coronary artery; RPA, right pulmonary artery; RPV, right pulmonary vein; RV, right ventricle.