Hypertension and its determinants in Abu Dhabi population: a retrospective cohort study.

BACKGROUND: Preventing high blood pressure and its complications requires identifying its risk factors. This study assessed predictors of hypertension and its associated complications among Emirati adults in Abu Dhabi, United Arab Emirates (UAE). METHODS: This retrospective cohort study was conducted by retrieving data from the Electronic Medical Records (EMR) of Emiratis who participated in a national cardiovascular screening program between 2011 and 2013. The study cohort comprised 8456 Emirati adults (18 years and above): 4095 women and 4361 men. The average follow-up period was 9.2 years, with a maximum of 12 years. RESULTS: The age-adjusted hypertension prevalence in Abu Dhabi increased from 24.5% at baseline to 35.2% in 2023. At baseline, 61.8% of hypertensive patients had controlled blood pressure, which increased to 74.3% in 2023. Among those free from hypertension at screening, 835 patients (12.3%) were newly diagnosed during the follow-up period. Using Cox regression, the hypertension prediction model developed included age [P value <0.001, hazard ratio 1.051, 95% confidence interval (CI) 1.046-1.056], SBP (P value <0.001, hazard ratio 1.017, 95% CI 1.011-1.023) and DBP (P value <0.001, hazard ratio 1.029, 95% CI 1.02-1.037), glycated hemoglobin (P < 0.001, hazard ratio 1.132, 95% CI 1.077-1.191), and high-density lipoprotein cholesterol (HDL-C) (P value <0.001, hazard ratio 0.662, 95% CI 0.526-0.832). This prediction model had a c-statistic of 0.803 (95% CI 0.786-0.819). Using survival analysis (Kaplan-Meier), higher blood pressure was associated with more cardiovascular events and mortality during follow-up. CONCLUSION: Targeting population-specific predictors of hypertension can prevent its progression and inform healthcare professionals and policymakers to decrease the incidence, complications, and mortality related to hypertension.

Recent Advancements in Emerging Technologies for Healthcare Management Systems: A Survey.

In recent times, the growth of the Internet of Things (IoT), artificial intelligence (AI), and Blockchain technologies have quickly gained pace as a new study niche in numerous collegiate and industrial sectors, notably in the healthcare sector. Recent advancements in healthcare delivery have given many patients access to advanced personalized healthcare, which has improved their well-being. The subsequent phase in healthcare is to seamlessly consolidate these emerging technologies such as IoT-assisted wearable sensor devices, AI, and Blockchain collectively. Surprisingly, owing to the rapid use of smart wearable sensors, IoT and AI-enabled technology are shifting healthcare from a conventional hub-based system to a more personalized healthcare management system (HMS). However, implementing smart sensors, advanced IoT, AI, and Blockchain technologies synchronously in HMS remains a significant challenge. Prominent and reoccurring issues such as scarcity of cost-effective and accurate smart medical sensors, unstandardized IoT system architectures, heterogeneity of connected wearable devices, the multidimensionality of data generated, and high demand for interoperability are vivid problems affecting the advancement of HMS. Hence, this survey paper presents a detailed evaluation of the application of these emerging technologies (Smart Sensor, IoT, AI, Blockchain) in HMS to better understand the progress thus far. Specifically, current studies and findings on the deployment of these emerging technologies in healthcare are investigated, as well as key enabling factors, noteworthy use cases, and successful deployments. This survey also examined essential issues that are frequently encountered by IoT-assisted wearable sensor systems, AI, and Blockchain, as well as the critical concerns that must be addressed to enhance the application of these emerging technologies in the HMS.