Madison Mackenzie, Luo Xiao, Silvey Jackson, Brenner Robert, Gannamaneni Kartik, Sawchuk Alan P
Department of Surgery, School of Medicine, Indiana University, Indianapolis, IN 46202, USA.
Department of Management Science and Information Systems, Oklahoma State University, Stillwater, OK 74078, USA.
J Cardiovasc Dev Dis. 2025 Feb 6;12(2):61. doi: 10.3390/jcdd12020061.
An artificial intelligence (AI) analysis of electronic medical records (EMRs) was performed to analyze the differences between patients with carotid stenosis who developed symptomatic disease and those who remained asymptomatic. The EMRs of 872 patients who underwent a carotid endarterectomy between 2009 and 2022 were analyzed with AI. This included 408 patients who had carotid intervention for symptomatic carotid disease and 464 patients for asymptomatic, >70% stenosis. By analyzing the EMRs, the Support Vector Machine achieved the highest sensitivity at 0.626 for predicting which of these patients would go on to develop a stroke or TIA. Random Forest had the highest specificity at 0.906. The risk for stroke in patients with carotid stenosis was a balance between optimum medical treatment and the underlying disease processes. Risk factors for developing symptomatic carotid disease included elevated glucose, chronic kidney disease, hyperlipidemia, and current or recent smoking, while protective factors included cardiovascular agents, antihypertensives, and beta blockers. An AI review of EMRs can help determine which patients with carotid stenosis are more likely to develop a stroke to assist with decision making as to whether to proceed with intervention or to demonstrate and encourage reduced stroke risk with risk factor modification.
进行了一项对电子病历（EMR）的人工智能（AI）分析，以分析发生症状性疾病的颈动脉狭窄患者与无症状患者之间的差异。对2009年至2022年间接受颈动脉内膜切除术的872例患者的电子病历进行了人工智能分析。这包括408例因症状性颈动脉疾病接受颈动脉干预的患者和464例无症状、狭窄程度>70%的患者。通过分析电子病历，支持向量机在预测哪些患者会发生中风或短暂性脑缺血发作（TIA）方面达到了最高灵敏度，为0.626。随机森林的特异性最高，为0.906。颈动脉狭窄患者的中风风险是最佳药物治疗与潜在疾病过程之间的平衡。发生症状性颈动脉疾病的风险因素包括血糖升高、慢性肾病、高脂血症以及当前或近期吸烟，而保护因素包括心血管药物、抗高血压药和β受体阻滞剂。对电子病历的人工智能审查有助于确定哪些颈动脉狭窄患者更有可能发生中风，以协助决策是否进行干预，或通过调整风险因素来证明并鼓励降低中风风险。
