Department of Surgery, University of Toronto, Toronto M5T 1P5, Canada.
Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, University of Toronto, Toronto M5B 1W8, Canada.
J Proteome Res. 2024 Jun 7;23(6):2279-2287. doi: 10.1021/acs.jproteome.4c00254. Epub 2024 Apr 22.
Blood-based biomarkers for abdominal aortic aneurysm (AAA) have been studied individually; however, we considered a panel of proteins to investigate AAA prognosis and its potential to improve predictive accuracy.
Using a prospectively recruited cohort of patients with/without AAA ( = 452), we conducted a prognostic study to develop a model that accurately predicts AAA outcomes using clinical features and circulating biomarker levels. Serum concentrations of 9 biomarkers were measured at baseline, and the cohort was followed for 2 years. The primary outcome was major adverse aortic event (MAAE; composite of rapid AAA expansion [>0.5 cm/6 months or >1 cm/12 months], AAA intervention, or AAA rupture). Using 10-fold cross-validation, we trained a random forest model to predict 2 year MAAE using (1) clinical characteristics, (2) biomarkers, and (3) clinical characteristics and biomarkers.
Two-year MAAE occurred in 114 (25%) patients. Two proteins were significantly elevated in patients with AAA compared with those without AAA (angiopoietin-2 and aggrecan), composing the protein panel. For predicting 2 year MAAE, our random forest model achieved area under the receiver operating characteristic curve (AUROC) 0.74 using clinical features alone, and the addition of the 2-protein panel improved performance to AUROC 0.86.
Using a combination of clinical/biomarker data, we developed a model that accurately predicts 2 year MAAE.
已有研究分别针对腹主动脉瘤（AAA）的血液生物标志物进行了研究；然而，我们考虑了一组蛋白质，以研究 AAA 的预后及其提高预测准确性的潜力。
我们使用前瞻性招募的伴有/不伴有 AAA 的患者队列（=452）进行了一项预后研究，以开发一种使用临床特征和循环生物标志物水平准确预测 AAA 结局的模型。在基线时测量了 9 种生物标志物的血清浓度，并对队列进行了 2 年的随访。主要结局是主要不良主动脉事件（MAAE；AAA 迅速扩张[>0.5 cm/6 个月或>1 cm/12 个月]、AAA 干预或 AAA 破裂的复合结局）。我们使用 10 倍交叉验证，使用（1）临床特征、（2）生物标志物和（3）临床特征和生物标志物来训练随机森林模型，以预测 2 年的 MAAE。
2 年内发生 MAAE 的患者有 114 例（25%）。与无 AAA 的患者相比，AAA 患者有两种蛋白质显著升高（血管生成素-2 和聚集蛋白聚糖），构成了蛋白质谱。对于预测 2 年 MAAE，我们的随机森林模型仅使用临床特征的 AUC 为 0.74，而添加 2 种蛋白质谱可将性能提高到 AUC 0.86。
我们使用临床/生物标志物数据的组合，开发了一种可准确预测 2 年 MAAE 的模型。
