THERAPEUTIC RISK-BENEFIT INDEX (TRBI): A NOVEL INTEGRATED RISK STRATIFICATION MODEL FOR OPTIMIZING DIRECT ORAL ANTICOAGULANT THERAPY – A RETROSPECTIVE OBSERVATIONAL COHORT STUDY

Background: Direct Oral Anticoagulants (DOACs) are increasingly used for stroke prevention in atrial fibrillation and thromboembolic disorders. Conventional risk tools such as CHA2DS2-VASc and HAS-BLED assess thromboembolic and bleeding risks independently, often generating therapeutic ambiguity and potential overtreatment or undertreatment. Aim: To evaluate the impact of the novel Therapeutic Risk-Benefit Index (TRBI) – an integrated composite score – in guiding DOAC therapy decisions, and to compare its discriminative accuracy with conventional risk stratification models. Methods: A retrospective observational cohort study was conducted over six months at ESIC-MC PGIMSR, Rajajinagar, Bengaluru. A total of 200 patients prescribed DOACs were enrolled. Baseline demographics, comorbidities, and clinical parameters were recorded. TRBI was calculated by integrating thromboembolic and bleeding risk components. Clinical outcomes were compared with conventional CHA2DS2-VASc and HAS-BLED models. Kaplan–Meier survival analysis and ROC curve evaluation were applied to assess predictive accuracy. Multivariate logistic regression and random forest machine-learning models were also constructed. Results: HAS-BLED demonstrated excellent predictive accuracy (AUC = 0.974). The multivariate logistic regression model (AUC = 0.983) and random forest model (AUC = 0.993) outperformed all single-score approaches. CHA2DS2-VASc performed below chance (AUC = 0.460). TRBI meaningfully reclassified approximately 28–30% of patients into more appropriate therapeutic categories. Patients with TRBI > 2.5 showed optimal benefit from DOAC continuation, whereas TRBI ≤ 1.0 was associated with higher adverse event rates. INR was the only laboratory parameter to vary significantly across TRBI categories (H = 25.37, p < 0.001). K-fold cross-validation confirmed model stability (mean AUC = 0.972). Conclusion: TRBI effectively bridges the gap between thromboembolic and bleeding risk, providing a unified, patient-centred framework for anticoagulation management. By integrating multiple predictors, TRBI supports precision medicine and enables individualized DOAC selection, dosing, and initiation timing.