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.
This prospective, multicenter, observational cohort study aims to develop and validate an intelligent diagnostic and risk stratification system for orthostatic hypotension (OH). The study plans to enroll approximately 2000 participants from 15 tertiary clinical centers in China between March 2026 and February 2029. The target population comprises adult patients (≥18 years) with Parkinson's disease (PD) or multiple system atrophy (MSA), and patients aged ≥50 years with diabetes mellitus who are suspected or diagnosed with OH. A key technical inclusion criterion is the presence of adequate bilateral temporal bone windows for reliable transcranial Doppler (TCD) monitoring. The core methodology involves synchronous, continuous, and non-invasive monitoring of beat-to-beat blood pressure (BP), bilateral cerebral blood flow velocity (CBFv) in the middle cerebral arteries, electrocardiogram (ECG), and end-tidal carbon dioxide (PetCO₂) during a standardized active standing test. Following a 10-minute supine rest, participants rapidly stand and remain upright for up to 10 minutes. Using this integrated data stream, OH is classified as Initial, Classic, or Delayed per consensus hemodynamic thresholds. Dynamic cerebral autoregulation (dCA) is quantitatively assessed offline via transfer function analysis (TFA) of the BP and CBFv signals, deriving phase, gain (absolute and normalized), and coherence parameters in very low frequency (VLF) and low frequency (LF) bands. Participants are followed for 24 months, with a telephone follow-up at 12 months and an in-person visit at 24 months that includes a repeat stand test and cognitive assessment. The primary technical endpoints are the algorithm-based classification of OH subtype/etiology and the quantitative dCA parameters. Secondary endpoints include the performance (sensitivity, specificity, area under the curve \[AUC\]) of the derived multimodal risk model in predicting clinical events such as falls, syncope, cognitive decline, and all-cause mortality. Data analysis will involve machine learning/statistical modeling on a development cohort to generate the risk stratification model, followed by external validation on a separate cohort to assess generalizability and clinical utility.
Study Type
OBSERVATIONAL
Enrollment
2,000
All participants will undergo a standardized multi-parameter monitoring protocol. After resting in the supine position for at least 10 minutes, participants will perform an active standing test. During this protocol, the following parameters are continuously and synchronously recorded using the integrated intelligent diagnostic system: non-invasive beat-to-beat blood pressure, cerebral blood flow velocity in the middle cerebral artery (assessed via transcranial Doppler, TCD), electrocardiogram (ECG), and end-tidal carbon dioxide (ETCO₂). Monitoring is conducted for a 10-minute baseline period in the supine position and continues for up to 10 minutes following standing.
Xuanwu Hospital, Capital Medical University
Beijing, Beijing Municipality, China
Classification of Orthostatic Hypotension
Orthostatic hypotension will be subtyped according to the timing of blood pressure reduction after standing: Classical, Initial, or Delayed. Additionally, etiology will be classified as Neurogenic or Non-neurogenic, determined by the heart rate response to blood pressure change, quantified as the ratio of change in heart rate to change in systolic blood pressure (ΔHR/ΔSBP ratio).
Time frame: 2years
Dynamic Cerebral Autoregulation Parameters
Quantitative assessment of dynamic cerebral autoregulation (dCA) function will be performed using Transfer Function Analysis (TFA) on continuous blood pressure and cerebral blood flow velocity signals. Parameters include phase difference, gain, and coherence in the following frequency ranges: very low frequency (VLF: 0.02-0.07 Hz), low frequency (LF: 0.07-0.20 Hz), and high frequency (HF: 0.20-0.70 Hz).
Time frame: 2years
Performance of the Risk Stratification Model
The predictive accuracy of the developed risk stratification model for adverse events (e.g., falls, syncope) will be assessed using metrics including sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC).
Time frame: At the end of the 24-month follow-up period, when outcome data for all participants are available for model validation.
Incidence of Adverse Clinical Events
The cumulative incidence of the following clinical events will be recorded and compared between groups: falls, syncope (fainting), fractures, cognitive decline (defined as a decrease in Montreal Cognitive Assessment \[MoCA\] or Mini-Mental State Examination \[MMSE\] score from baseline), and all-cause mortality.
Time frame: Assessed at 12-month and 24-month follow-up visits.
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