Development of a Risk Tracking Model for Neurological Deterioration in Ischemic Stroke Based on Blood Pressure Dynamics.

BACKGROUND

Using the significant link between blood pressure fluctuations and neurological deterioration (ND) in patients with ischemic stroke, this study aims to develop a predictive model capable of tracking ND risk in real time, enabling timely detection of high-risk periods.

METHODS AND RESULTS

A total of 3906 consecutive patients with ischemic stroke were recruited. To develop an initial predictive model, we employed a multinomial logistic regression model incorporating clinical parameters. This model estimates the probability of ND occurring within 2 distinct time windows relative to hospital arrival: within the first 12 hours and between 12 and 72 hours. To refine ND risk assessments over time, we subsequently introduced an iterative risk-tracking model that uses continuously updated blood pressure measurements. We endeavored to integrate these models, assessing their combined discriminative capacity and clinical utility, with a particular emphasis on pinpointing time periods of increased ND risk. ND rates were observed at 6.1% within the first 12 hours and 7.3% between 12 and 72 hours, presenting the variation over time. Multinomial logistic models encountered disparities in significant predictors across different time zones. The iterative risk-tracking model was successfully set up to forecast ND within a 12-hour window at every measurement. The integrated models achieved an area under the receiver operating characteristic curve ranging from 0.68 to 0.76 for narrowing down ND risk identification within 12 hours without sacrificing predictive accuracy across diverse patient groups. At 90% and 70% sensitivity settings, the combined model presented slightly higher or comparable specificity and positive predictive values relative to conventional models.

CONCLUSIONS

This study presents a novel approach for real-time monitoring of ND risk in patients with ischemic stroke, using blood pressure trends to identify critical periods for potential intervention.