Modeling Orthostatic Hypotension in a-Synucleinopathies by Dynamic BP/CBFv

Background: Current management for orthostatic hypotension (OH) in α-synucleinopathies is passive and lacks specificity, and objective tools for accurately assessing the true risk level in OH patients are lacking. This study aimed to develop an objective tool to quantify OH severity, enabling individualized and precision-based clinical management of OH. Methods: From January 2021 to March 2025, patients with OH at Xuanwu Hospital were assigned an OH Symptom-Based Risk Score (0–2 points) based on symptom severity. Active standing tests were performed to continuously collect data including blood pressure (BP) and cerebral blood flow velocity (CBFv). Two predictive models were developed: one to identify patients with versus without OH symptoms, and another to predict high-risk patients prone to critical events among symptomatic patients (scoring 1–2 points). Results: Among 172 patients with OH (86 asymptomatic, 50%), symptomatic patients showed greater orthostatic reductions in BP and CBFv (both maximum and mean decreases, all P < 0.05) than asymptomatic patients. OH Symptom-Based Risk Scores were significantly correlated with all orthostatic BP and CBFv decline parameters (all P < 0.001). Binary logistic regression models successfully identified symptomatic patients and predicted patients at high-risk for critical events among symptomatic patients, with bootstrap-validated accuracy exceeding 70% in multiple models. Orthostatic BP and CBFv decline parameters were independent predictors of OH. Conclusion: The established models provide an objective tool for quantifying OH risk severity, showing significant potential for early clinical warning and optimizing OH management strategies.