CMH Lahore Medical College, Lahore, Pakistan.
Islamic International Medical College, Rawalpindi, Pakistan.
Neurosurg Rev. 2024 Jul 31;47(1):382. doi: 10.1007/s10143-024-02635-2.
Intracerebral hemorrhage (ICH) is a severe form of stroke with high morbidity and mortality, accounting for 10-15% of all strokes globally. Recent advancements in prognostic biomarkers and predictive models have shown promise in enhancing the prediction and management of ICH outcomes. Serum sestrin2, a stress-responsive protein, has been identified as a significant prognostic marker, correlating with severity indicators such as NIHSS scores and hematoma volume. Its levels predict early neurological deterioration and poor prognosis, offering predictive capabilities comparable to traditional measures. Furthermore, a deep learning-based AI model demonstrated superior performance in predicting early hematoma enlargement, with higher sensitivity and specificity than conventional methods. Additionally, long-term outcome prediction models using CT radiomics and machine learning have achieved high accuracy, particularly with the Random Forest algorithm. These advancements underscore the potential of integrating novel biomarkers and advanced computational techniques to improve prognostication and management of ICH, aiming to enhance patient care and survival rates. The incorporation of serum sestrin2, AI, and machine learning in predictive models represents a significant step forward in the clinical management of ICH, offering new avenues for research and clinical application.
脑出血（ICH）是一种严重的中风形式，具有高发病率和死亡率，占全球所有中风的 10-15%。近年来，预后生物标志物和预测模型的进展表明，在提高 ICH 结局的预测和管理方面具有潜力。血清 sestrin2 是一种应激反应蛋白，已被确定为重要的预后标志物，与 NIHSS 评分和血肿量等严重程度指标相关。其水平预测早期神经功能恶化和预后不良，预测能力可与传统指标相媲美。此外，基于深度学习的人工智能模型在预测早期血肿扩大方面表现出卓越的性能，其敏感性和特异性均高于传统方法。此外，使用 CT 放射组学和机器学习的长期结局预测模型达到了较高的准确性，尤其是随机森林算法。这些进展突显了整合新型生物标志物和先进计算技术以改善 ICH 预后和管理的潜力，旨在提高患者的护理和生存率。血清 sestrin2、人工智能和机器学习在预测模型中的应用代表着 ICH 临床管理的重大进展，为研究和临床应用开辟了新的途径。
