Seetge Jessica, Cséke Balázs, Karádi Zsófia Nozomi, Bosnyák Edit, Szapáry László
Stroke Unit, Department of Neurology, University of Pécs, 7624 Pécs, Hungary.
Department of Emergency Medicine, University of Pécs, 7624 Pécs, Hungary.
J Pers Med. 2025 Jan 4;15(1):18. doi: 10.3390/jpm15010018.
: Acute ischemic stroke (AIS) is a leading cause of disability and mortality worldwide. Despite advances in interventions such as thrombolysis (TL) and mechanical thrombectomy (MT), current treatment protocols remain largely standardized, focusing on general eligibility rather than individual patient characteristics. To address this gap, we introduce the Stroke-SCORE (Simplified Clinical Outcome Risk Evaluation), a predictive tool designed to personalize AIS management by providing data-driven, individualized recommendations to optimize treatment strategies and improve patient outcomes. The Stroke-SCORE was derived using retrospective data from 793 AIS patients admitted to the University of Pécs (February 2023-September 2024). Logistic regression analysis identified age, National Institutes of Health Stroke Scale (NIHSS) score at admission, and pre-morbid modified Rankin Scale (pre-mRS) score as key predictors of unfavorable outcomes at 90 days (defined as modified Rankin Scale [mRS] score > 2). Based on these predictors, a simplified risk score was developed to stratify patients into low-, moderate-, and high-risk groups, guiding treatment decisions on TL, MT, combination therapy (TL + MT), or standard care (SC). Internal validation was performed to assess the model's predictive performance via receiver operating characteristic (ROC) analysis and isotonic regression calibration with bootstrapping. The Stroke-SCORE was moderately positively correlated with a 90-day mRS score > 2 (odds ratio [OR] = 0.70, 95% confidence interval [CI]: 0.58-0.83, < 0.001), with an area under the curve (AUC) of 0.86, a sensitivity and specificity of 79% and 81%, respectively, and an overall accuracy of 80%. Simulations indicated that personalized treatment guided by the Stroke-SCORE significantly reduced unfavorable outcomes. The Stroke-SCORE demonstrates strong predictive performance as a practical, data-driven approach for personalizing AIS treatment decisions. In the future, external, multicenter prospective validation is needed to confirm its applicability in real-world settings.
急性缺血性卒中（AIS）是全球致残和致死的主要原因。尽管在溶栓（TL）和机械取栓（MT）等干预措施方面取得了进展，但目前的治疗方案在很大程度上仍保持标准化，侧重于一般的入选标准而非个体患者特征。为了弥补这一差距，我们引入了卒中评分（Stroke - SCORE，简化临床结局风险评估），这是一种预测工具，旨在通过提供数据驱动的个性化建议来优化治疗策略并改善患者结局，从而实现AIS管理的个性化。卒中评分（Stroke - SCORE）是使用来自佩奇大学（2023年2月至2024年9月）收治的793例AIS患者的回顾性数据得出的。逻辑回归分析确定年龄、入院时的美国国立卫生研究院卒中量表（NIHSS）评分以及病前改良Rankin量表（pre - mRS）评分是90天时不良结局（定义为改良Rankin量表[mRS]评分>2）的关键预测因素。基于这些预测因素，开发了一个简化的风险评分，将患者分为低、中、高风险组，以指导关于TL、MT、联合治疗（TL + MT）或标准治疗（SC）的治疗决策。通过受试者操作特征（ROC）分析和带有自抽样法的等渗回归校准进行内部验证，以评估模型的预测性能。卒中评分（Stroke - SCORE）与90天时mRS评分>2呈中度正相关（比值比[OR]=0.70，95%置信区间[CI]：0.58 - 0.83，<0.001），曲线下面积（AUC）为0.86，敏感性和特异性分别为79%和81%，总体准确率为80%。模拟表明，由卒中评分（Stroke - SCORE）指导的个性化治疗显著降低了不良结局。卒中评分（Stroke - SCORE）作为一种实用的、数据驱动的方法，在个性化AIS治疗决策方面表现出强大的预测性能。未来，需要进行外部多中心前瞻性验证，以确认其在现实环境中的适用性。
