Clinical Research Directory

Theoretically Informed Behavioral Intervention

The goal of this waitlist control clinical trial is to learn if the tailored LEARN 2 platform can prevent HIV-related comorbidities with shared risk factors in men ages 18 and older living with HIV. The main question\[s\] are: 1. Can the virtual environment improve quality of life among these participants? 2. Does the LEARN 2 platform effectively serve as prevention education for HIV comorbidity shared risk factors? Researchers will compare participants receiving the LEARN2 virtual environment intervention to those in a waitlist control group to see if the intervention leads to improvements in quality of life and reductions in risk factors. Participants will be asked to: 1. Engage with the virtual environment weekly. 2. Participate in virtual live health educator sessions. 3. Complete daily assessments of personal health behaviors through Ecological Momentary Assessment.

Copenhagen Comorbidity in HIV Infection Study

Despite efficient antiretroviral treatment for HIV infection, decrease in life expectancy remains. Excess mortality is mainly due to non-AIDS co-morbidity including cardiovascular, pulmonary, and liver related diseases. Both HIV-unrelated and HIV-related risk factors probably contribute to this pattern. At present, most evidence regarding co-morbidity in HIV infection rely on cross-study comparisons of HIV-infected persons with published population rates and few prospective studies in U.S. cohorts. Using well characterized participants from the Copenhagen General Population Study (CGPS) as controls, we aim to include \>1500 HIV-infected persons in the COCOMO study to determine if co-morbidity is more prevalent or develops at a higher rate in HIV-infected persons. The study will asses 1) cardiovascular, 2) pulmonary and 3) liver-related co-morbidity using uniformly collected data in the two cohorts. The investigators aim to study the relative impact of HIV-unrelated and HIV-related factors on development of co-morbidity.

HEARTWISE - P-CARDIAC for Chinese: Population-based Study

Cardiovascular disease (CVD) is one of the prominent diseases that affect many people. One cost-effective solution is to identify people at higher risk of CVD by CVD risk prediction model. China-PAR, TRS-2P, and SMART2 are common risk prediction models for prevention. However, these risk scores were mostly based on the routinely self-check health information and multivariable regression without time-varying consideration. Investigators developed a Machine Learning (ML) based risk prediction model, Personalized CARdiovascular DIsease risk Assessment for Chinese (P-CARDIAC) among a predominantly Chinese population in Hong Kong to estimates the 10 years of secondary recurrent CVD risk for the high-risk individuals. The study objective is to evaluate the accuracy of the P-CARDIAC performance in practice among a large-scale Hong Kong population in medicine specialist outpatient clinic (SOPC) and cardiac clinic. The results will reassure cardiologists that the P-CARDIAC risk score is sensitive to the heart disease symptoms. Investigators anticipate that the results may help to facilitate P-CARDIAC in clinical setting and provide more practical information with the development of P-CARDIAC.

EEG/MECG/EMG Evaluating the Severity of Aortic Stenosis, Heart Failure and Ischemic Stroke Through an Artificial Intelligenceassisted System.

The specific objectives and methods of this project are: (1) To test the feasibility and accuracy of integrating EEG, MECG and EMG for detecting the severity of diseases such as aortic stenosis, heart failure and ischemic stroke. (2) Improve the accuracy of this multi-channel brain-heart-muscle device by using an artificial intelligence auxiliary system. (3) Provide tailor-made interdisciplinary treatment strategies for patients with different disease states.