Atrial fibrillation (AF) is the most common cardiac arrhythmia and is associated with substantial morbidity. There is considerable inter-patient variability in the pathologic processes that promote AF, and this variability likely has a significant genetic basis. Clinically this is reflected by the observation that anti-arrhythmic drugs and interventional procedures have highly variable efficacy, and this highlights the need for adopting a more efficacious personalized approach. We explore recent advancements in both in silico and stem cell disease models that set the stage for a personalized approach. Specifically we highlight new mechanistic insights in AF; the future role of computational models in planning personalized ablation strategies; the potential role of stem cell models as a preclinical platform for drug development; and the potential to use gene-editing technology to create patient-specific stem cell models. Finally, we introduce the concept of integrating stem cell models with computational modelling to create a novel pipeline for patient-specific drug discovery and development.

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Background Patients with hypertrophic cardiomyopathy (HCM) are at risk of ventricular arrhythmia (VA) attributed to abnormal electrical activation arising from myocardial fibrosis and myocyte disarray. We sought to quantify intra‐QRS peaks (QRSp) in high‐resolution ECGs as a measure of abnormal activation to predict late VA in patients with HCM. Methods and Results Prospectively enrolled patients with HCM (n=143, age 53±14 years) with prophylactic implantable cardioverter‐defibrillators had 3‐minute, high‐resolution (1024 Hz), digital 12‐lead ECGs recorded during intrinsic rhythm. For each precordial lead, QRSp was defined as the total number of peaks detected in the QRS complex that deviated from a smoothing filtered version of the QRS. The VA end point was appropriate implantable cardioverter‐defibrillator therapy during 5‐year prospective follow‐up. After 5 years, 21 (16%) patients had VA. Patients who were VA positive had greater QRSp (6.0 [4.0–7.0] versus 4.0 [2.0–5.0]; P <0.01) and lower left ventricular ejection fraction (57±11 versus 62±9; P =0.038) compared with patients who were VA negative, but had similar established HCM risk metrics. Receiver operating characteristic analysis revealed that QRSp discriminated VA (area under the curve=0.76; P <0.001), with a QRSp ≥4 achieving 91% sensitivity and 39% specificity. The annual VA rate was greater in patients with QRSp ≥4 versus QRSp <4 (4.4% versus 0.98%; P =0.012). In multivariable Cox regression, age <50 years (hazard ratio [HR], 2.53; P =0.009) and QRSp (HR per QRS peak, 1.41; P =0.009) predicted VA after adjusting for established HCM risk metrics. In patients aged <50 years, the annual VA rate was 0.0% for QRSp <4 compared with 6.9% for QRSp ≥4 ( P =0.012). Conclusions QRSp predicted VA in patients with HCM who were eligible for an implantable cardioverter‐defibrillator after adjusting for established HCM risk metrics, such that each additional QRS peak increases VA risk by 40%. QRSp <4 was associated with a <1% annual VA risk in all patients, and no VA risk among those aged <50 years. This novel ECG metric may improve patient selection for prophylactic implantable cardioverter‐defibrillator therapy by identifying those with low VA risk. These findings require further validation in a lower risk HCM cohort. Registration URL: https://www.clinicaltrials.gov ; Unique identifier: NCT02560844.

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