Background: Atrial fibrillation affects 38 million individuals worldwide, with dofetilide representing a cornerstone class III antiarrhythmic therapy. Current safety protocols mandate three-day inpatient monitoring for torsades de pointes (TdP) during drug initiation. Traditional risk stratification relies on static baseline QTc measurements, which inadequately predict proarrhythmic susceptibility during incremental dosing. Critically, 24% of TdP cases occur after the initial three-day period, indicating that current approaches fail to identify risk beyond discharge. Improved risk stratification methods are needed for delayed proarrhythmic events. We hypothesized that dynamic QTc changes during dofetilide loading would demonstrate significant predictive accuracy for identifying patients at risk for adverse events. Methods: We conducted a retrospective cohort study of 101 consecutive atrial fibrillation patients admitted for dofetilide initiation(July 2022-August 2023). The primary composite endpoint included drug discontinuation or adverse ECG parameters (QTc >500ms and/or ventricular arrhythmia). Receiver operating characteristic curve analysis determined optimal QTc change thresholds. Multivariable logistic regression models, adjusted for gender, heart failure status, baseline creatinine clearance, and baseline QTc, assessed predictive value of QTc changes after sequential doses. Study limitations include single-center design and retrospective methodology potentially limiting generalizability. Results: The cohort demonstrated median age 71.0 years (IQR 63.0-75.0), with 30.7% female participants and mean baseline QTc 440.9±32.6 ms. Forty-six patients (45.5%) experienced the composite endpoint. First-dose absolute QTc increases >48.0ms demonstrated exceptional predictive performance (aOR 17.24, 95% CI 4.34-68.53, p<0.001) with high specificity (83.6%) and overall accuracy (73.3%). First-dose percentage increases >10.54% showed equivalent discriminatory capability (aOR 18.66, 95% CI 4.66-74.75, p<0.001). Cumulative QTc changes from baseline to second dose >50.0ms (aOR 11.01, 95% CI 3.32-36.47, p<0.001) and >11.24% (aOR 10.74, 95% CI 3.23-35.68, p<0.001) maintained robust predictive associations. Conclusion: Dynamic QTc changes demonstrate superior predictive capability compared to static baseline measurements for identifying high-risk patients, with adjusted odds ratios exceeding 10.0 for clinically actionable thresholds. These threshold-based biomarkers enable precision risk stratification that could facilitate targeted continuous mobile ECG monitoring during the vulnerable post-discharge period. Since nearly one-quarter of TdP cases occur after standard monitoring ends, dynamic QTc-guided algorithms with continuous surveillance may substantially improve patient safety outcomes while optimizing resource allocation. Prospective validation studies are warranted to establish clinical implementation protocols for this precision-based monitoring approach.