ObjectiveTo compare home blood pressure monitoring (HBPM) versus ambulatory blood pressure monitoring (ABPM) versus office blood pressure monitoring (OBPM) in diagnosis and management of hypertension, and to find the optimal blood pressure measurement and management.MethodsThe following were compared among three BP monitoring, such as cost-effectiveness, prognostic value of target organ damage (TOD), predictive value of the progress in chronic kidney disease (CKD) and blood pressure variety (BPV). ResultsCompared to OBPM, ABPM was the most cost-effective method in the primary diagnosis of hypertension, but HBPM was the optimal method in long-term and self-management in hypertension. In hypertensives, compared to OBPM, HBPM and ABPM, especially HBPM, had a stronger predictive value for cardiovascular events, stroke, end-stage renal dysfunction (ESRD) and all-cause mortality. In hypertensives with renal dysfunction, controlling HBPM and ABPM, especially controlling ABPM, was an effective way to slow the progress in renal dysfunction, to decrease cardiovascular events, and to decrease the need of dialysis. All BPV derived from OBPM, ABPM and HBPM had a predictive significance of cardiovascular events, and HBPM BPV performed the best.ConclusionCompared to OBPM, ABPM is the best method in primary diagnosis of hypertension and BP control in CKD patients, while HBPM is the best method in predicting and in evaluating BPV, as well as in long-term and self-management in hypertension.
Heart failure is a disease that seriously threatens human health and has become a global public health problem. Diagnostic and prognostic analysis of heart failure based on medical imaging and clinical data can reveal the progression of heart failure and reduce the risk of death of patients, which has important research value. The traditional analysis methods based on statistics and machine learning have some problems, such as insufficient model capability, poor accuracy due to prior dependence, and poor model adaptability. In recent years, with the development of artificial intelligence technology, deep learning has been gradually applied to clinical data analysis in the field of heart failure, showing a new perspective. This paper reviews the main progress, application methods and major achievements of deep learning in heart failure diagnosis, heart failure mortality and heart failure readmission, summarizes the existing problems and presents the prospects of related research to promote the clinical application of deep learning in heart failure clinical research.