Mohammad Umer

Background: While oral anticoagulants (OACs) remain the cornerstone of stroke prevention, left atrial appendage closure (LAAC) is a potential alternative, particularly for patients with high bleeding risk or contraindications to long‐term anticoagulation. Methods: Multiple databases were searched to identify relevant randomized controlled trials (RCTs), and four trials were shortlisted. Study selection, data extraction, and quality assessment were performed independently by two reviewers. Outcomes assessed included stroke (all, ischemic, hemorrhagic), systemic embolism, major bleeding, non‐procedure‐related bleeding, and mortality. Risk of bias was evaluated using Rob 2.0, and meta‐analyses were conducted using RevMan software. Dichotomous outcomes were reported as risk ratios (RR) while continuous outcomes were reported as mean differences (MD), with 95% confidence intervals (CIs). Results: A total of 3116 participants were analyzed across outcomes. LAAC significantly reduced non‐procedure‐related bleeding (RR: 0.48, 95% CI: 0.37–0.61; p < 0.00001) and all‐cause mortality (RR: 0.74, 95% CI: 0.55–0.99; p = 0.04). A marginal reduction was observed in the composite outcome of stroke, systemic embolism, or death (RR: 0.77, 95% CI: 0.59–1.00; p = 0.05). No significant differences were found for major bleeding (RR: 0.82, 95% CI: 0.56–1.22), all strokes (RR: 0.84, 95% CI: 0.56–1.25), ischemic stroke (RR: 1.15, 95% CI: 0.72–1.85), hemorrhagic stroke (RR: 0.46, 95% CI: 0.11–2.02), systemic embolism (RR: 1.52, 95% CI: 0.36–6.41), or cardiovascular/unexplained death (RR: 0.60, 95% CI: 0.29–1.23). Conclusions: LAAC offers a viable alternative to standard anticoagulation in selected AF patients with comparable efficacy and a particular benefit in reducing non–procedure‐related bleeding and mortality advantages.

Background/objective: HCM is a structural disorder of the myocardium that leads to sudden cardiac death in young adults. We synthesized an updated understanding of the role of Cardiac Myosin Inhibitors (CMIs) in HCM by pooling data from RCTs. Methods: We identified six published RCTs, involving 826 participants. Data were extracted pertaining to study characteristics; primary outcomes of interest–(1) change from baseline in resting left ventricular outflow tract (LVOT) peak gradient, (2) change from baseline in Valsalva LVOT peak gradient, and (3) improvement of ≥1 NYHA class–and secondary outcomes. These were pooled using Review Manager 5.4, employing a random-effects model, and reported as odds ratios (ORs) or mean differences (MDs). Results: We found statistically significant between-group difference favoring CMIs in change from baseline in LVOT peak gradient: at rest (MD −39.33; −53.01 to −25.64), post-Valsalva (MD −48.99; −53.96 to −44.03), and post-exercise (MD −37.11; −44.34 to −29.87); ≥1 NYHA class improvement (OR 4.10; 2.79–6.02), change from baseline in peak oxygen uptake (MD −37.11; −44.34 to −29.87), LVOT gradient ≤30 mm hg (RR 14.89; 7.47–29.67), participants eligible for septal reduction therapy (RR 0.26; 0.18–0.36), and change from baseline in KCCQ-CSS score (MD 8.54; 5.36–11.71). Subgrouping by intervention type (mavacamten vs. aficamten) revealed non-significant results for all primary outcomes. Conclusions: CMIs can contribute to improving key efficacy outcomes for patients with HCM while reducing incidence of SRT.

Objective: The purpose of this study is to find out the level of awareness and acceptance of artificial intelligence (AI) in Pakistan’s medical community so as to comment on its future in our healthcare system. Methods: A survey consisting of 15 close-ended questions was conducted. The questions inquired about awareness about AI and discovered the opinions of healthcare professionals regarding its benefits and expected problems. The data were analyzed using SPSS version 26, and descriptive statistics for percentage and frequency were computed. χ² test was used to analyze the subgroups (Significant p value <0.05). Results: A total of 351 participants were included in this study. General familiarity with AI was low. Only 75 (21.3%) participants answered that they had good familiarity with AI, and only 56 (16%) of them had good familiarity with the role of AI in medicine. One hundred sixty-eight (47.9%) participants disagreed that AI would out-compete the physician in the important traits of professionalism. Only 71 (20.2%) participants believed AI to be diagnostically superior to the physician. Two hundred fourteen (61.0%) were worried about completely trusting AI in its decisions, and 204(58.1%) believed that AI systems lacking human traits would not be able to mirror the doctor-patient relationship. Two hundred sixty-one (74.4%) participants believed that AI would be useful in Administrative tasks. A majority, 162 (46.2%), do not believe that AI would replace them. Finally, a huge majority of participants [225 (64.1%)] demanded the integration of AI in Pakistan’s healthcare system. Conclusion: This study suggests that a majority of healthcare professionals in Pakistan do not believe that they are sufficiently aware of the role of AI in healthcare. This was corroborated by their answers to various questions regarding the capabilities of AI. This study indicates the need for a more comprehensive ascertainment of healthcare professionals’ perceptions regarding the role of Artificial Intelligence in medicine and bridging the gap between doctors and technology to further promote a patient-centred approach to medicine.