The Real-Time Tracking and Alarming the Early Neurological Deterioration Using Continuous Blood Pressure Monitoring in Patient with Acute Ischemic Stroke
In this paper, we develop a real-time prediction of END (Early Neurological Deterioration) using continuous BP (blood pressure) monitoring and clinical parameters and propose to set up an alarming criterion before END. We identified consecutive ischemic stroke patients hospitalized within 48 h of symptom onset from a prospective stroke registry database. BP data during hospitalization were obtained from the electric medical records. Probability of END at each time point of BP measurement was estimated using a logistic model with covariates, which is derived from two models for clinical information and BP parameters. Here, a model for clinical information was fitted using logistic model with clinical characteristics of patients to predict END. A model for BP was fitted using random effects models allowing for temporal correlations at each time point of BP measurement with irregular intervals. Prediction performance was evaluated by sensitivity and specificity. An alarm criterion for a high probability of END at each time point was defined as being above a cutoff point prior to 24 h.
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