Zain Khalpey, Nicholas King
Background Postoperative atrial fibrillation (POAF) is a common complication following cardiac surgery, contributing to increased patient morbidity, mortality, and healthcare expenses. Current risk stratification tools offer limited predictive accuracy and do not support proactive intervention strategies. Objective To develop and validate a digital twin framework utilizing patient-specific electrophysiology models for predicting POAF onset before clinical manifestation, enabling synthetic cohort generation for virtual randomized controlled trials (RCTs) and precision prevention strategies. Methods We applied a digital twin framework integrating multimodal perioperative data, patient-specific electrophysiological models, synthetic cohorts, and virtual randomized trials. Data from 38,742 patients included continuous high-resolution ECG monitoring. Digital twins combined biophysical models (modified Hodgkin-Huxley, bidomain conduction) with machine learning tuned to individual ECG, hemodynamic, and biomarker data. Generative adversarial networks generated synthetic cohorts, enabling virtual trials comparing twin-guided strategies with standard care. Results The digital twin framework achieved superior predictive accuracy (area under the curve: 0.89) compared to traditional risk scores. Synthetic cohorts of 10,000 virtual patients identified optimal intervention windows 48-72 hours preoperatively. Virtual RCTs demonstrated a 34% simulated reduction in POAF incidence through digital twin-guided prophylactic strategies, with improvements in simulated length of stay and stroke incidence. These findings are derived from computational modeling and require prospective validation. Conclusions Digital twin modeling enables proactive POAF prevention, accelerates clinical trial design, and provides a scalable platform for precision intervention strategies with substantial potential for reducing healthcare economic burden while improving patient outcomes.