Research, Development, and Application of Intelligent Diagnostic System for Orthostatic Hypotension

Orthostatic hypotension (OH) has a high incidence rate of 30%-50% in the elderly and populations with neurodegenerative diseases. The resulting cerebral hypoperfusion significantly increases the risk of cerebral ischemia, falls, and cognitive decline. Traditional OH diagnosis primarily relies on intermittent cuff blood pressure measurements, leading to low detection rates and an inability to provide scientifically effective OH classification. Furthermore, existing research often overlooks cerebral hemodynamic mechanisms, particularly the assessment of dynamic cerebral autoregulation (dCA), making it difficult to study the mechanisms behind OH and its associated symptoms.

To address these issues, the research team has preliminarily developed an "Intelligent Diagnostic System for Orthostatic Hypotension". This system innovatively integrates synchronous and continuous monitoring of multiple parameters, including non-invasive beat-to-beat blood pressure, transcranial Doppler (TCD) cerebral blood flow velocity, and electrocardiogram (ECG). It also enables the quantitative assessment of dynamic cerebral autoregulation function. The project will collaborate with fifteen high-level clinical centers in China to collect data from 2000 patients with orthostatic hypotension. The aim is to establish and externally validate a risk stratification model for OH. By integrating multimodal clinical and hemodynamic data, the investigators intend to construct an automated, precise intelligent system for the classification, subtyping, and risk stratification of OH. This initiative will establish a standardized diagnostic and management pathway covering early screening, precise classification, early warning, and stratified intervention. The goal is to provide key technological support for enhancing the early identification and standardized management of OH, thereby reducing its associated disability and mortality rates.