Clinical Research Directory

Prospective Evaluation of AI-ECG for SHD Detection

AI-ECG-SHD is a multicentre prospective observational diagnostic accuracy study evaluating artificial intelligence-enhanced electrocardiography (AI-ECG) for the detection of structural heart disease in adults undergoing clinically indicated transthoracic echocardiography (TTE). Participants undergo standard 12-lead ECG acquisition together with ECG recordings obtained using portable or wearable single-, 3-, and 6-lead devices. Established AI-ECG models will be evaluated for the detection of heart failure, pulmonary hypertension and valvular heart disease using TTE as the clinical reference standard, with performance compared across ECG acquisition modalities within the same prospective cohort. AI-ECG predictions are generated offline and are not provided to treating clinicians or used to alter clinical care. The study will also compare AI-ECG performance with N-terminal pro-B-type natriuretic peptide (NT-proBNP) and evaluate the diagnostic performance of a combined AI-ECG and NT-proBNP approach. The study aims to recruit 590 participants across six NHS hospital sites. A prespecified subgroup of 203 participants will undergo dedicated venous blood sampling for NT-proBNP; eligible same-day NT-proBNP measurements obtained as part of routine clinical care will also be included where available.

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.

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.

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.