Theses (Master Of Pharmacy Practice)

Background Stroke is the second leading cause of death and it contributes tremendous burden to the society in terms of mortality, morbidity and costs globally. Non-cardioembolic ischaemic stroke accounts for 80% of the ischaemic stroke events. Antiplatelet therapies (APTs) remained the first-line treatment modality for the secondary prevention of stroke. However, randomised controlled trials (RCTs) have demonstrated that oral anticoagulants (ACs) could also be effective for the secondary prevention of stroke. There is no network meta-analysis, comparative efficacy and safety of ACs and APTs together for the secondary prevention of stroke. Hence, the primary objective of this study is to evaluate the comparative effectiveness and safety of different ACs and anti-platelets (monotherapy or combination) for the secondary prevention of stroke in patients with a history of non-cardioembolic ischaemic stroke or transient ischaemic attack (TIA). Methods This systematic review combined pairwise and network meta-analysis of RCTs to analyse the efficacy and safety of ACs and APTs (monotherapy or combination) for the secondary prevention of stroke in patients following either non-cardioembolic stroke or TIA. The primary outcome was the occurrence of recurrent stroke while the safety outcome was an event of major bleeding. Secondary outcomes were all-cause mortality, major cardiovascular events and the occurrence of myocardial infarction. A random-effects network meta-analysis was performed. The relative ranking of interventions was assessed by the surface under the cumulative ranking (SUCRA) probability curve. A higher SUCRA rank (0 to 1) denotes that the intervention is more effective and safer as compared to other candidate interventions. Further subgroup analysis based on different dosing regimens of aspirin was also performed. Results Fifty-two trials with approximately 127,130 participants comparing 16 interventions were selected for analysis. In the primary analysis, all interventions except for vorapaxar statistically significantly reduced the incidence of recurrent stroke compared to placebo. Cilostazol was ranked as the best treatment modality for preventing recurrent stroke (RR: 0.55, 95%CI, 0.44-0.68; SUCRA: 0.94), followed by aspirin-clopidogrel combination (RR: 0.64, 95%CI, 0.54-0.74; SUCRA: 0.82), AC (RR: 0.66, 95%CI 0.51-0.86, SUCRA: 0.72) and aspirin-extended release Dipyridamole combination (RR: 0.69, 95%CI 0.61-0.78, SUCRA: 0.66). For major bleeding events, no intervention demonstrated a statistical significant effect. Network meta-analyses of secondary outcomes demonstrated that aspirin-extended release dipyridamole combination reduced all-cause mortality (RR: 0.83; 95%CI, 0.72-0.96, SUCRA: 0.96), CVD deaths (RR: 0.69, 95%CI, 0.56-0.85, SUCRA: 0.83) and myocardial infarction (RR: 0.66, 95%CI, 0.52-0.84, SUCRA: 0.80) compared to placebo. Overall, the results were robust to the changes in sensitivity analyses and cilostazol remained superior to the other interventions. Subgroup analysis based on different doses of aspirin demonstrated that the very low dose aspirin-clopidogrel combination (RR: 0.62, 95%CI, 0.46-0.84, SUCRA: 0.76) and low dose aspirinclopidogrel combination (RR: 0.63, 95%CI, 0.44-0.90, SUCRA: 0.73) demonstrated the highest probability of being best after cilostazol (RR: 0.55, 95%CI, 0.40-0.78, SUCRA: 0.86) for the efficacy outcome. Conclusion Cilostazol is the most effective and safe intervention for the secondary prevention of stroke in patients with prior non-cardioembolic ischaemic stroke or TIA. However, considering the overall benefit in terms of mortality, aspirin-extended release dipyridamole combination could be the preferred treatment in patients with previous non-cardioembolic stroke or TIA.

Introduction: Bleeding is a significant risk associated with oral anticoagulant (OAC) use, particularly in patients with non-valvular atrial fibrillation (NVAF). Existing bleeding risk scores have shown limited accuracy in predicting clinically relevant bleeding events, necessitating the development of a new predictive model. This study aims to create and validate a novel bleeding risk model that effectively predicts clinically relevant bleeding events. Methods: Using demographic, laboratory, and clinical data collected by Beshir SA et al., five bleeding risk models were developed through machine learning techniques, specifically utilizing recursive feature elimination (RFE) to identify pertinent variables. The dataset was divided into training and internal validation sets at 70:30 ratio. The model performance was assessed with accuracy, precision, sensitivity, specificity, F1 score, and area under the receiver operating characteristic curve (AUROC). Upsampling and downsampling techniques were applied to original dataset with repetition in model building and evaluation. Results: Of 1,017 patients (median age: 67 years, male 52%), 77 patients (7.6%) faced clinically relevant bleeding within first year of observation period. The developed model comprised 10 variables, including renal failure, peripheral vascular disease, history of bleeding, age at diagnosis, hypertension, ischemic heart disease, sex, type II diabetes mellitus, smoking, and race. All five machine learning models had poor predictive performance, with AUROC between 0.49 to 0.50. The SVM-Radial model with upsampling dataset, demonstrated the highest predictive performance, with an AUROC 75.5%, a sensitivity of 86.88%, specificity of 64.18%, accuracy of 75.5.%, precision 70.81%, and F1 score of 78.03%. Conclusions: Feature selection using recursive feature selection – random forest picked 10 clinical predictors for CRB events despite data imbalance in original dataset. Downsampling provided realistic bleeding risk prediction which SVM-Linear performed best among five models. Upsampling improved bleeding risk prediction which SVM-Radial performed best among five models. New techniques to improve bleeding risk prediction are warranted. Keywords: non valvular atrial fibrillation, oral anticoagulants, bleeding, machine learning, bleeding risk score.