Yeh Jian-Kuan, Chen Po-Wei, Chang Wei-Ting, Chiu Pin-Hsuan, Su Pei-Fang, Hsu Chih-Hsin, Lin Chih-Chan, Chang Hsien-Yuan
Division of Cardiology, department of Internal Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
Institute of Clinical Medicine, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
Int J Cardiol Heart Vasc. 2025 Jun 3;59:101712. doi: 10.1016/j.ijcha.2025.101712. eCollection 2025 Aug.
Currently, simplified methods based on computed tomography pulmonary angiography (CTPA) to predict clinical deterioration in patients with acute pulmonary embolism (PE) are lacking. We developed a simplified imaging model with good clinical accessibility to predict this outcome.
Patients with acute pulmonary embolism from 2008 to 2019 were retrospectively enrolled from two medical centers to form a study cohort and a validation cohort. Seven models of pulmonary artery obstruction index (PAOI) were developed based on the location and degree of obstruction. The outcome of interest was clinical deterioration during hospitalization. Logistic regression analysis was used to assess the association between different models and clinical deterioration. The category-free net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were used to quantify improvements in predictability.
The study group included 210 patients (mean age: 65 ± 16 years; male: 40 %) and the external validation group included 109 patients (mean age: 64 ± 17 years; male: 43 %). Calculating the nearly total obstruction of 20 peripheral arteries demonstrated good predictive ability (AUC: 0.77). Total obstruction of six peripheral arteries did not increase the odds of clinical deterioration, while total obstruction of ten peripheral arteries nearly doubled the risk of deterioration. Combining PAOI with the simplified pulmonary embolism severity index (sPESI) improved the predictive ability for clinical deterioration compared to using sPESI alone (NRI: 0.09-0.12; IDI: 0.05-0.09).
Calculating totally obstructed pulmonary arteries simplifies the prediction of clinical deterioration. The combination of PAOI and sPESI enhances the ability to predict clinical deterioration in patients with acute PE.
目前,缺乏基于计算机断层扫描肺动脉造影(CTPA)预测急性肺栓塞(PE)患者临床病情恶化的简化方法。我们开发了一种具有良好临床可及性的简化成像模型来预测这一结果。
回顾性纳入2008年至2019年来自两个医疗中心的急性肺栓塞患者,形成一个研究队列和一个验证队列。根据阻塞的部位和程度建立了7种肺动脉阻塞指数(PAOI)模型。感兴趣的结局是住院期间的临床病情恶化。采用逻辑回归分析评估不同模型与临床病情恶化之间的关联。采用无类别净重新分类改善(NRI)和综合判别改善(IDI)来量化预测能力的改善情况。
研究组包括210例患者(平均年龄:65±16岁;男性:40%),外部验证组包括109例患者(平均年龄:64±17岁;男性:43%)。计算20条外周动脉的近乎完全阻塞显示出良好的预测能力(AUC:0.77)。6条外周动脉的完全阻塞并未增加临床病情恶化的几率,而10条外周动脉的完全阻塞使恶化风险几乎翻倍。与单独使用简化肺栓塞严重程度指数(sPESI)相比,将PAOI与sPESI相结合可提高对临床病情恶化的预测能力(NRI:0.09 - 0.12;IDI:0.05 - 0.09)。
计算完全阻塞的肺动脉可简化对临床病情恶化的预测。PAOI与sPESI的结合增强了预测急性PE患者临床病情恶化的能力。