Assessment of hypertension-mediated organ damage in children and adolescents with hypertension. Blood pressure Arterial hypertension (HT) is a main, potentially reversible cardiovascular risk factor. Long lasting HT leads to hypertension mediated organ damage (HMOD) of heart, vascular bed, and kidneys. Assessment of HMOD is a standard diagnostic procedure in hypertensive adults and presence of HMOD is associated with increased cardiovascular risk. The assessment of main HMOD markers includes the assessment of left ventricular mass, carotid intima-media thickness, arterial stiffness expressed as pulse wave velocity, and assessment of microcirculation. In contrast to adults, proper interpretation of obtained results of HMOD must be adjusted to age and sex referential values. In the last two decades, numerous studies describing HMOD in children with hypertension have been published, including meta-analyses evaluating various methods of HMOD assessment. Here, we present current state of the art and discuss recommendations on HMOD evaluation in hypertensive children. 10.1080/08037051.2023.2212085

Causal Inference for Hypertension Prediction. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Hypertension is a leading cause of cardiovascular disease and premature death worldwide and it puts a heavy burden on the healthcare system. It is, therefore, very important to detect and evaluate hypertension and related cardiovascular events as to for efficient diagnosis, treatment and management. Hypertension can be evaluated with noninvasive cardiac signals, such as electrocardiogram (ECG) and photoplethysmogram (PPG) signals. Most of the previous studies predicted hypertension from ECG and PPG signals with extracted features that are correlated with hypertension. However, correlation is sometimes unreliable and may be affected by confounding factors. In this study, we propose a causal inference based approach to identify feature variables from ECG and PPG signals that are potentially causally related with hypertension. The method of greedy equivalence search was employed to construct the causal graph of features and hypertension. With causal features identified from the causal graph, we used machine learning models to diagnose hypertension. The machine learning classification models achieve great classification performance, among which random forest model has the best classification performance, with accuracy being 0.987, precision being 0.990, recall being 0.981, and F1-score being 0.985. The results show that the causal inference based approach can effectively predict hyper-tension.Clinical relevance- This paper proposes a new hypertension risk prediction method, which uses causality instead of correlation as the feature screening criteria to establish a causal graph of hypertension, which can predict the hypertension more reliably. 10.1109/EMBC40787.2023.10341021