Developing a predictive value for predicting stroke recovery based on transcranial doppler ultrasound parameters

Area of Science:

• Neurology
• Medical Imaging
• Biomedical Engineering

Background:

• Acute ischemic stroke (AIS) due to middle cerebral artery (MCA) obstruction is a major cause of disability and death.
• Accurate early functional prognosis is vital for optimal patient care but often limited by conventional methods.
• Existing clinical and imaging techniques lack precision and timeliness in prognostic assessments.

Purpose of the Study:

• To develop a predictive model for functional outcomes in AIS patients with MCA blockage.
• To utilize transcranial Doppler (TCD) ultrasonography characteristics for early stroke prognosis.
• To enhance risk stratification and personalize treatment plans for AIS patients.

Main Methods:

• Assessed TCD parameters (PI, Vm, EDV, PSV) within 24 hours of intravenous thrombolysis (IVT).
• Employed logistic regression analysis to identify independent determinants of functional outcome (modified Rankin Scale - mRS).
• Developed a predictive model incorporating TCD ratios (rPI, rPSV) and clinical indicators.

Main Results:

• Favorable functional outcomes correlated significantly with TCD ratios: pulsatility index to mean flow velocity (rPI) and peak systolic velocity to end-diastolic velocity (rPSV).
• The logistic regression model demonstrated excellent accuracy in predicting three-month functional prognosis.
• TCD parameters, particularly rPSV and rPI, proved to be valuable early prognostic indicators.

Conclusions:

• TCD ultrasonography parameters (rPSV, rPI) are effective prognostic indicators for functional outcomes in MCA-related AIS.
• A quantitative model based on TCD criteria can aid in early risk classification and individualized treatment.
• Future research should focus on validating and refining this predictive model in larger, diverse patient cohorts.