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.

GUARD-PH: Guided Use of AI-ECG for Risk Detection of PH in Surgery (GUARD-PH Trial)

This study is a prospective, open-label, randomized controlled trial designed to evaluate a new artificial intelligence (AI) tool for heart monitoring. Researchers will use an AI-enabled electrocardiography (ECG) system to screen patients before they undergo surgery. The main goal is to determine if this AI system can accurately detect pulmonary hypertension and related heart diseases in the preoperative setting. The study is being conducted at Taipei Veterans General Hospital.

Doxycycline Host-directed Therapy to Improve Lung Function and Decrease Tissue Destruction in Pulmonary Tuberculosis

Tuberculosis (TB) is a global pandemic that despite successful treatment and bacterial eradication can cause chronic ill health, such as pulmonary impairment after tuberculosis (PIAT) and cardiovascular disease (CVD). A recent Phase 2b double-blind randomised-controlled clinical trial shows that adjunctive doxycycline therapy is safe, accelerates resolution of inflammation, suppresses tissue damaging enzyme activity and decreases pulmonary cavity volume (1). We aim to determine if adjunctive doxycycline can reduce PIAT and improve cardiovascular outcomes in a fully powered Phase III trial of 8 weeks of adjunctive doxycycline alongside standard pulmonary TB (PTB) treatment. The investigators hypothesize that doxycycline inhibits tissue destruction in patients with PTB and thereby leads to improved lung function after treatment. Specific aims 1. To assess improvement in lung function as measured by forced expiratory volume (FEV1) predicted in PTB patients given doxycycline versus placebo. 2. To investigate whether doxycycline will hasten the resolution of pulmonary cavities measured by CT thorax 3. To investigate whether doxycycline can suppress inflammatory markers including matrix metalloproteinases 4. To investigate whether doxycycline can accelerate time to sputum conversion 5. To evaluate the effect of doxycycline on cardiovascular outcomes such as the incidence of acute coronary syndrome (ACS) and pulmonary hypertension 6. To investigate whether doxycycline improves TB drug concentrations in sputum and plasma. 7. To assess the safety profile of doxycycline with concurrent standard anti-tuberculous treatment.

Applying Artificial Intelligence to the 12 Lead ECG for the Diagnosis of Pulmonary Hypertension: an Observational Study

The goal of this observational study is to apply Artificial Intelligence (AI) and machine learning technology to the resting 12-lead electrocardiogram (ECG) and assess whether it can assist doctors in the early diagnosis of Pulmonary Hypertension (PH). Early and accurate diagnosis is an important step for patients with PH. It helps provide effective treatments early which improve prognosis and quality of life. The main questions our study aims to answer are: 1. Can AI technology in the 12-lead ECG accurately predict the presence of PH? 2. Can AI technology in the 12-lead ECG identify specific sub-types of PH? 3. Can AI technology in the 12-lead ECG predict mortality in patients with PH? In this study, the investigators will recruit 12-lead ECGs from consenting participants who have undergone Right heart Catheterisation (RHC) as part of their routine clinical care. AI technology will be applied to these ECGs to assess whether automated technology can predict the presence of PH and it's associated sub-types.