A substantial share of patients at risk of developing cardiovascular disease (CVD) fail to achieve control of CVD risk factors, but clinicians lack a structured approach to identify these patients. We applied machine learning to longitudinal data from two completed randomized controlled trials among 1502 individuals with diabetes in urban India and Pakistan. Using commonly available clinical data, we predict each individual's risk of failing to achieve CVD risk factor control goals or meaningful improvements in risk factors at one year after baseline.
View Article and Find Full Text PDFGelatin hydrogels have drawn attention for their diverse biomedical applications due to their flexible physiochemical properties. However, such gelatin hydrogels are made of toxic crosslinkers and photoinitiators, restricting their non-invasive deep tissue application. The in-situ forming chemical crosslinked without such toxic crosslinker and UV light has not been explored under physiological conditions.
View Article and Find Full Text PDFBackground: Assessment of knowledge, attitudes, and practices regarding cardiovascular diseases (CVD) and cardiovascular risk factors (CVRF) is critical to inform CVD prevention strategies, but limited community-level data exist from developing countries.
Objective: To assess the knowledge, attitudes, and practices regarding CVD and CVRF and acceptability of non-physician health workers and text-message based reminders to guide CVD prevention strategies in India.
Methods: We conducted a telephone-based survey nested in the on-going Centre for Cardiometabolic Risk Reduction in South Asia (CARRS) cohort in Delhi and Chennai, India between January 2021 to February 2021.