Prediction Models of Symptomatic Intracerebral Hemorrhage after Intravenous Thrombolysis in Acute Ischemic Stroke: A Systematic Review
YANG Jiaxin, HE Chunyu, LIU Lei, CHEN Wenbo, XIE Yan
2024, 19(1):
76-86.
DOI: 10.3969/j.issn.1673-5765.2024.01.011
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Objective To systematically evaluate the prediction models of symptomatic intracerebral hemorrhage after intravenous thrombolysis in acute ischemic stroke and to provide a reference for clinical decision-making of intravenous thrombolysis.
Methods CNKI, WanFang Data, VIP, PubMed, Embase, Web of Science, and The Cochrane Library databases were searched to collect relevant studies, with a timeframe for searching the database from the establishment of the database to December 18, 2022. Two researchers independently screened the literature, extracted the data, assessed the risk of bias, and then systematically evaluated the basic characteristics and methodology of the included models.
Results Twenty studies were included, with a total of 30 prediction models. The AUC of the included models ranged from 0.42 to 0.94. The overall predictive performance of the 24 (80%) prediction models was good, with significant differences in model differentiation across outcome definitions and algorithms. The most common predictors were NIHSS score, age, imaging signs or scores of infarction, blood glucose, systolic blood pressure, and antiplatelet drugs.
Conclusions In recent years, the prediction models of symptomatic intracerebral hemorrhage after intravenous thrombolysis in acute ischemic stroke have shown the characteristics of diversified modeling algorithms, better model performance, and diversified predictors. However, the overall risk of bias was high, and future studies need to calibrate the models further. More attention should be paid to updating and externally validating the models to improve extrapolation and clinical utility and utilize the models’ clinical practice implications.
