Application and risk prediction of thrombolytic therapy in cardio-cerebrovascular diseases: a review

Area of Science:

• Cardiology and Neurology
• Medical Technology and Artificial Intelligence

Background:

• Cardiocerebrovascular diseases (CVDs) are a leading cause of mortality and significant healthcare expenditure globally.
• Prompt assessment and effective treatment are critical for improving prognosis in CVD patients.
• Thrombolytic therapy is a fundamental non-invasive treatment for achieving recanalization in CVDs but carries inherent risks.

Purpose of the Study:

• To introduce the application of thrombolytic therapy in treating cardiocerebrovascular blockage diseases, specifically coronary heart disease and ischemic stroke.
• To review advancements in risk assessment for thrombolytic therapy.
• To compare the efficacy of traditional risk assessment scales against novel artificial intelligence-based models.

Main Methods:

• Literature review focusing on thrombolytic therapy in cardiocerebrovascular diseases.
• Analysis of traditional risk assessment scales used in conjunction with thrombolytic therapy.
• Evaluation of emerging artificial intelligence (AI)-based risk assessment models for thrombolytic therapy.

Main Results:

• Thrombolytic therapy is a vital treatment for coronary heart disease and ischemic stroke, though its use is limited by risks.
• Traditional risk assessment methods provide a baseline for evaluating thrombolytic therapy risks.
• AI-based risk assessment models show potential for enhanced accuracy and performance in predicting thrombolytic therapy outcomes.

Conclusions:

• Thrombolytic therapy remains a cornerstone in managing cardiocerebrovascular blockage diseases.
• The development of AI-driven risk assessment tools offers a promising avenue for optimizing thrombolytic therapy.
• Further research into AI models is warranted to refine risk stratification and improve patient safety and treatment efficacy.