Providing therapies tailored to each patient is the vision of precision medicine, enabled by the increasing ability to capture extensive data about individual patients. In this position paper, we argue that the second enabling pillar towards this vision is the increasing power of computers and algorithms to learn, reason, and build the ‘digital twin’ of a patient. Computational models are boosting the capacity to draw diagnosis and prognosis, and future treatments will be tailored not only to current health status and data, but also to an accurate projection of the pathways to restore health by model predictions. The early steps of the digital twin in the area of cardiovascular medicine are reviewed in this article, together with a discussion of the challenges and opportunities ahead. We emphasize the synergies between mechanistic and statistical models in accelerating cardiovascular research and enabling the vision of precision medicine.

Human atrial electrophysiology exhibits high inter-subject variability in both sinus rhythm (SR) and chronic atrial fibrillation (cAF) patients. Variability is however rarely investigated in experimental and theoretical electrophysiological studies, thus hampering the understanding of its underlying causes but also its implications in explaining differences in the response to disease and treatment. In our study, we aim at investigating the ability of populations of human atrial cell models to capture the inter-subject variability in action potential (AP) recorded in 363 patients both under SR and cAF conditions. Human AP recordings in atrial trabeculae (n = 469) from SR and cAF patients were used to calibrate populations of computational SR and cAF atrial AP models. Three populations of over 2000 sampled models were generated, based on three different human atrial AP models. Experimental calibration selected populations of AP models yielding AP with morphology and duration in range with experimental recordings. Populations using the three original models can mimic variability in experimental AP in both SR and cAF, with median conductance values in SR for most ionic currents deviating less than 30% from their original peak values. All cAF populations show similar variations in G_K1, G_Kur and G_to, consistent with AF-related remodeling as reported in experiments. In all SR and cAF model populations, inter-subject variability in I_K1 and I_NaK underlies variability in APD₉₀, variability in I_Kur, I_CaL and I_NaK modulates variability in APD₅₀ and combined variability in I_to and I_Kur determines variability in APD₂₀. The large variability in human atrial AP triangulation is mostly determined by I_K1 and either I_NaK or I_NaCa depending on the model. Experimentally-calibrated human atrial AP models populations mimic AP variability in SR and cAF patient recordings, and identify potential ionic determinants of inter-subject variability in human atrial AP duration and morphology in SR versus cAF.

Abnormalities in repolarization and its rate dependence are known to be related to increased proarrhythmic risk. A number of repolarization-related electrophysiological properties are commonly used as preclinical biomarkers of arrhythmic risk. However, the variability and complexity of repolarization mechanisms make the use of cellular biomarkers to predict arrhythmic risk preclinically challenging. Our goal is to investigate the role of ionic current properties and their variability in modulating cellular biomarkers of arrhythmic risk to improve risk stratification and identification in humans. A systematic investigation into the sensitivity of the main preclinical biomarkers of arrhythmic risk to changes in ionic current conductances and kinetics was performed using computer simulations. Four stimulation protocols were applied to the ten Tusscher and Panfilov human ventricular model to quantify the impact of ±15 and ±30% variations in key model parameters on action potential (AP) properties, Ca²⁺ and Na⁺ dynamics, and their rate dependence. Simulations show that, in humans, AP duration is moderately sensitive to changes in all repolarization current conductances and in L-type Ca²⁺ current ( I_CaL) and slow component of the delayed rectifier current ( I_Ks) inactivation kinetics. AP triangulation, however, is strongly dependent only on inward rectifier K⁺ current ( I_K1) and delayed rectifier current ( I_Kr) conductances. Furthermore, AP rate dependence (i.e., AP duration rate adaptation and restitution properties) and intracellular Ca²⁺ and Na⁺ levels are highly sensitive to both I_CaL and Na⁺/K⁺ pump current ( I_NaK) properties. This study provides quantitative insights into the sensitivity of preclinical biomarkers of arrhythmic risk to variations in ionic current properties in humans. The results show the importance of sensitivity analysis as a powerful method for the in-depth validation of mathematical models in cardiac electrophysiology.

A new method is proposed to evaluate the dynamics of QT interval adaptation in response to heart rate (HR) changes. The method considers weighted averages of RR intervals (R~R~) preceding each cardiac beat to express RR interval history accounting for the influence on repolarization duration. A global optimization algorithm is used to determine the weight distribution leading to the lowest regression residual when curve fitting the [QT, R~R~] data using a patient- specific regression model. From the optimum weight distribution, a memory lag L/sub 90/ is estimated, expressing the delay in the QT adaptation to HR changes. On average, RR intervals of the past 150 beats (approximately 2.5 min) are required to model the QT response accurately. From a clinical point of view, the interval of the initial tens of seconds to one minute seems to be most important in the majority of cases. A measure of the optimum regression residual (r/sub opt/) has been calculated, discriminating between post-myocardial infarction patients at high and low risk of arrhythmic death while on treatment with amiodarone. A similar discrimination has been achieved with a variable expressing the character of QT lag behind the RR interval dynamics.

Background: Congenital long QT syndrome type 2 (abnormal hERG potassium channel) patients can develop flat, asymmetric, and notched T waves. Similar observations have been made with a limited number of hERG ‐blocking drugs. However, it is not known how additional calcium or late sodium block, that can decrease torsade risk, affects T wave morphology. Methods and Results: Twenty‐two healthy subjects received a single dose of a pure hERG blocker (dofetilide) and 3 drugs that also block calcium or sodium (quinidine, ranolazine, and verapamil) as part of a 5‐period, placebo‐controlled cross‐over trial. At pre‐dose and 15 time‐points post‐dose, ECG s and plasma drug concentration were assessed. Patch clamp experiments were performed to assess block of hERG , calcium (L‐type) and late sodium currents for each drug. Pure hERG block (dofetilide) and strong hERG block with lesser calcium and late sodium block (quinidine) caused substantial T wave morphology changes ( P <0.001). Strong late sodium current and hERG block (ranolazine) still caused T wave morphology changes ( P <0.01). Strong calcium and hERG block (verapamil) did not cause T wave morphology changes. At equivalent QT c prolongation, multichannel blockers (quinidine and ranolazine) caused equal or greater T wave morphology changes compared with pure hERG block (dofetilide). Conclusions: T wave morphology changes are directly related to amount of hERG block; however, with quinidine and ranolazine, multichannel block did not prevent T wave morphology changes. A combined approach of assessing multiple ion channels, along with ECG intervals and T wave morphology may provide the greatest insight into drug‐ion channel interactions and torsade de pointes risk. Clinical Trial Registration: URL: http://clinicaltrials.gov/ Unique identifier: NCT 01873950.