Personalized medicine in acute ischemic stroke requires moving beyond average treatment effects (ATE) to individualized treatment effect (ITE) estimates to support treatment decisions. In acute ischemic stroke, mechanical thrombectomy has been shown to be more effective on average than lysis in randomized controlled trials (RCTs), such as the MR CLEAN study. We aim to identify which individual patients benefit most from mechanical thrombectomy compared to lysis. The outcome of interest is the modified Rankin Scale (mRS) at three months, an ordinal measure of functional disability (0: no symptoms, 6: death). We demonstrate that causal transformation models on directed acyclic graphs (TRAM-DAG) can be used for ITE estimation after being fitted on observational MAGIC multi-center stroke patient data. To ensure comparability with the MR CLEAN population, which we use for validation, we train the TRAM-DAG on a MAGIC sub-population with NIHSS at admission >= 6, corresponding to one inclusion criterion of MR CLEAN. The fitted model is then used to estimate ITEs for stroke patients in the MR CLEAN population. While these ITE estimates cannot be confirmed experimentally, we show that their average is consistent with the trial's reported ATE. Furthermore, the ITE estimates correctly rank trial patients by their observed frequency of a good outcome (mRS at three months <= 2). These findings support the use of causal models like TRAM-DAG for personalized decision-making in stroke care and highlight their ability to bridge the gap between observational evidence and clinical trials.

翻译：急性缺血性脑卒中的个体化医疗需要从平均治疗效果（ATE）转向个体化治疗效果（ITE）估计以支持治疗决策。在急性缺血性脑卒中中，随机对照试验（如MR CLEAN研究）已证实机械取栓术平均优于溶栓治疗。本研究旨在识别哪些患者个体从机械取栓术相比溶栓治疗获益最大。关注的结局指标为三个月时的改良Rankin量表（mRS），该量表为功能性残疾的等级测量指标（0：无症状，6：死亡）。我们证明，基于有向无环图的因果变换模型（TRAM-DAG）在拟合观察性MAGIC多中心卒中患者数据后，可用于ITE估计。为确保与用于验证的MR CLEAN人群的可比性，我们在入院NIHSS评分≥6（对应MR CLEAN入组标准之一）的MAGIC亚群上训练TRAM-DAG。随后，拟合模型用于估计MR CLEAN人群中卒中患者的ITE。虽然这些ITE估计值无法通过实验验证，但我们发现其均值与试验报告的ATE一致。此外，ITE估计值能正确排序试验患者获得良好结局（三个月mRS≤2）的观察频率。这些发现支持将TRAM-DAG等因果模型用于卒中护理的个体化决策，并凸显其在弥合观察性证据与临床试验之间鸿沟的能力。