Department of Neurology Bucheon Sejong Hospital Bucheon-si Gyeonggi-do South Korea.
Department of Obstetrics and Gynecology Seoul National University College of Medicine Seoul South Korea.
J Am Heart Assoc. 2024 Aug 20;13(16):e032216. doi: 10.1161/JAHA.123.032216. Epub 2024 Aug 9.
Perioperative stroke is a severe complication following surgery. To identify patients at risk for perioperative stroke, several prediction models based on the preoperative factors were suggested. Prediction models often focus on preoperative patient characteristics to assess stroke risk. However, most existing models primarily base their predictions on the patient's baseline characteristics before surgery. We aimed to develop a machine-learning model incorporating both pre- and intraoperative variables to predict perioperative stroke.
This study included patients who underwent noncardiac surgery at 2 hospitals with the data of 15 752 patients from Seoul National University Hospital used for development and temporal internal validation, and the data of 449 patients from Boramae Medical Center used for external validation. Perioperative stroke was defined as a newly developed ischemic lesion on diffusion-weighted imaging within 30 days of surgery. We developed a prediction model composed of pre- and intraoperative factors (integrated model) and compared it with a model consisting of preoperative features alone (preoperative model). Perioperative stroke developed in 109 (0.69%) patients in the Seoul National University Hospital group and 11 patients (2.45%) in the Boramae Medical Center group. The integrated model demonstrated superior predictive performance with area under the curve values of 0.824 (95% CI, 0.762-0.880) versus 0.584 (95% CI, 0.499-0.667; <0.001) in the internal validation; and 0.716 (95% CI, 0.560-0.859) versus 0.505 (95% CI, 0.343-0.654; =0.018) in the external validation, compared to the preoperative model.
We suggest that incorporating intraoperative factors into perioperative stroke prediction models can improve their accuracy.
围手术期卒中是手术后的一种严重并发症。为了识别围手术期卒中风险患者，提出了几种基于术前因素的预测模型。预测模型通常侧重于术前患者特征来评估卒中风险。然而，大多数现有模型主要基于手术前患者的基线特征进行预测。我们旨在开发一种机器学习模型，该模型将术前和术中变量结合起来预测围手术期卒中。
本研究纳入了在 2 家医院接受非心脏手术的患者，其中首尔国立大学医院的数据用于开发和时间内内部验证，共纳入 15752 例患者；Boramae 医疗中心的数据用于外部验证，共纳入 449 例患者。围手术期卒中定义为手术 30 天内扩散加权成像上出现新的缺血性病变。我们开发了一个由术前和术中因素组成的预测模型（综合模型），并将其与仅由术前特征组成的模型（术前模型）进行比较。在首尔国立大学医院组中，109 例（0.69%）患者发生围手术期卒中，Boramae 医疗中心组中 11 例（2.45%）患者发生围手术期卒中。综合模型的预测性能更优，内部验证的曲线下面积值为 0.824（95%CI，0.762-0.880），优于术前模型的 0.584（95%CI，0.499-0.667；<0.001）；外部验证的曲线下面积值为 0.716（95%CI，0.560-0.859），优于术前模型的 0.505（95%CI，0.343-0.654；=0.018）。
我们建议将术中因素纳入围手术期卒中预测模型可以提高其准确性。
