Clinical Research Directory

Prospective Evaluation of AI-ECG for SHD Detection

This study aims to improve the early detection of undiagnosed heart disease, which causes serious health issues, hospital admissions, and high healthcare costs. Researchers are exploring how artificial intelligence (AI) can analyse routine heart tests, called electrocardiograms (ECGs), to detect heart problems. These tests can be done using both traditional ECG machines and portable, wearable devices like smartwatches, making it easier for people to monitor their heart health at home. While AI has shown promise using past data, this study will involve the collection of ECG data and subsequent testing of its accuracy in real-world settings to ensure it works well for both doctors and patients. The goal is to see if AI can identify conditions like heart muscle weakness, valve issues, and high lung pressure from the ECG data of patients. The researchers will also compare AI's detections with other blood tests commonly used to diagnose heart disease. The AI models that will be used are being tested for research and validation purposes only. They will not be used for clinical decision-making or providing information to influence diagnosis, treatment, or patient care during the study. The AI outputs are not shared with clinicians and will have no impact on the care pathway. This research will demonstrate if AI-powered ECG analysis - whether from traditional or portable devices - can provide a low-cost, non-invasive way to detect heart disease early and improve health assessments.

Comparison of Early Postoperative Outcomes Between Minimally Invasive Valve Surgery Via Right Thoracotomy and Conventional Valve Surgery Via Sternotomy.

This randomized controlled trial aims to compare early postoperative outcomes between Minimally Invasive Valve Surgery (MIVS) via right thoracotomy and Conventional Valve Surgery via median sternotomy in patients undergoing elective, isolated mitral or aortic valve surgery. Minimally invasive techniques are believed to reduce postoperative pain, ventilation time, chest drain output, and wound complications, but evidence from Pakistan is limited. The study will enroll patients of all ages and genders who are scheduled for isolated valvular procedures at Chaudhary Pervaiz Elahi Institute of Cardiology (CPEIC), Multan. Participants will be randomly assigned to undergo either minimally invasive thoracotomy or conventional sternotomy. Primary outcomes include ventilation time, CPB duration, cross-clamp time, pain scores, and chest drain output. Secondary outcomes include wound healing (Day 7 and 30 days), return to routine activity, echocardiographic evaluation, transfusion requirements, and 30-day mortality. Findings from this study may provide evidence to guide the adoption of minimally invasive valvular surgery techniques in low-resource and developing settings.