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A Support Vector Machine to Predict Impending Pre‐Syncope from Hemodynamic Derived Signals During Progressive Central Hypovolemia

Hypovolemic shock is characterized by a critically reduced central blood volume (CBV). The heart rate and blood pressure responses to a reduced CBV do not predict onset of hypovolemic shock. We hypothesized that a machine learning model detects subtle changes and patterns within hemodynamic signals that predict impending pre‐syncope as an expression of a critically reduced CBV.