Department of Neurology, People's Hospital of Longhua, Shenzhen, China.
Clinical Nursing Teaching and Research Section, The Second Xiangya Hospital of Central South University, Changsha, China.
Clin Appl Thromb Hemost. 2024 Jan-Dec;30:10760296241279800. doi: 10.1177/10760296241279800.
Thrombolytic therapy is essential for acute ischemic stroke (AIS) management but poses a risk of hemorrhagic transformation (HT), necessitating accurate prediction to optimize patient care. A comprehensive search was conducted across PubMed, Web of Science, Scopus, Embase, and Google Scholar, covering studies from inception until July 10, 2024. Studies were included if they used machine learning (ML) or deep learning algorithms to predict HT in AIS patients treated with thrombolysis. Exclusion criteria included studies involving endovascular treatments and those not evaluating model effectiveness. Data extraction and quality assessment were performed following PRISMA guidelines and using the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) and Prediction Model Risk of Bias Assessment Tool (PROBAST) tools. Out of 1943 identified records, 12 studies were included in the final analysis, encompassing 18 007 AIS patients who received thrombolytic therapy. The ML models demonstrated high predictive performance, with pooled area under the curve (AUC) values ranging from 0.79 to 0.95. Specifically, XGBoost models achieved AUCs of up to 0.953 and Artificial Neural Network (ANN) models reached up to 0.942. Sensitivity and specificity varied significantly, with the highest sensitivity at 0.90 and specificity at 0.99. Significant predictors of HT included age, glucose levels, NIH Stroke Scale (NIHSS) score, systolic and diastolic blood pressure, and radiomic features. Despite these promising results, methodological disparities and limited external validation highlighted the need for standardized reporting and further rigorous testing. ML techniques, especially XGBoost and ANN, show great promise in predicting HT following thrombolysis in AIS patients, enhancing risk stratification and clinical decision-making. Future research should focus on prospective study designs, standardized reporting, and integrating ML assessments into clinical workflows to improve AIS management and patient outcomes.
溶栓治疗对于急性缺血性脑卒中（AIS）的管理至关重要，但存在引起出血性转化（HT）的风险，因此需要进行准确预测以优化患者的治疗。我们对 PubMed、Web of Science、Scopus、Embase 和 Google Scholar 进行了全面检索，涵盖了截至 2024 年 7 月 10 日的研究。如果研究使用机器学习（ML）或深度学习算法来预测接受溶栓治疗的 AIS 患者的 HT，则将其纳入研究。排除标准包括涉及血管内治疗和未评估模型有效性的研究。数据提取和质量评估按照 PRISMA 指南进行，并使用 Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) 和 Prediction Model Risk of Bias Assessment Tool (PROBAST) 工具。在 1943 条鉴定记录中，最终有 12 项研究纳入了最终分析，包括 18007 名接受溶栓治疗的 AIS 患者。ML 模型表现出较高的预测性能，汇总曲线下面积（AUC）值范围为 0.79 至 0.95。具体来说，XGBoost 模型的 AUC 高达 0.953，人工神经网络（ANN）模型的 AUC 高达 0.942。敏感性和特异性差异显著，最高敏感性为 0.90，特异性为 0.99。HT 的显著预测因素包括年龄、血糖水平、国立卫生研究院卒中量表（NIHSS）评分、收缩压和舒张压以及放射组学特征。尽管取得了这些有前景的结果，但方法学差异和有限的外部验证突出表明需要进行标准化报告和进一步严格的测试。ML 技术，尤其是 XGBoost 和 ANN，在预测 AIS 患者溶栓后 HT 方面具有很大的应用前景，可以提高风险分层和临床决策水平。未来的研究应侧重于前瞻性研究设计、标准化报告以及将 ML 评估纳入临床工作流程，以改善 AIS 的管理和患者预后。
