Li Ben, Shaikh Farah, Zamzam Abdelrahman, Raphael Ravel, Syed Muzammil H, Younes Houssam K, Abdin Rawand, Qadura Mohammad
Department of Surgery, University of Toronto, Toronto, Ontario, Canada.
Division of Vascular Surgery, St. Michael's Hospital, Unity Health Toronto, University of Toronto, Toronto, Ontario, Canada.
J Inflamm Res. 2024 Jul 22;17:4865-4879. doi: 10.2147/JIR.S471150. eCollection 2024.
Inflammatory biomarkers associated with peripheral artery disease (PAD) have been examined separately; however, an algorithm that includes a panel of inflammatory proteins to inform prognosis of PAD could improve predictive accuracy. We developed predictive models for 2-year PAD-related major adverse limb events (MALE) using clinical/inflammatory biomarker data.
We conducted a prognostic study using 2 phases (discovery/validation models). The discovery cohort included 100 PAD patients that were propensity-score matched to 100 non-PAD patients. The validation cohort included 365 patients with PAD and 144 patients without PAD (non-matched). Plasma concentrations of 29 inflammatory proteins were determined at recruitment and the cohorts were followed for 2 years. The outcome of interest was 2-year MALE (composite of major amputation, vascular intervention, or acute limb ischemia). A random forest model was trained with 10-fold cross-validation to predict 2-year MALE using the following input features: 1) clinical characteristics, 2) inflammatory biomarkers that were expressed differentially in PAD vs non-PAD patients, and 3) clinical characteristics and inflammatory biomarkers.
The model discovery cohort was well-matched on age, sex, and comorbidities. Of the 29 proteins tested, 5 were elevated in PAD vs non-PAD patients (MMP-7, MMP-10, IL-6, CCL2/MCP-1, and TFPI). For prognosis of 2-year MALE on the validation cohort, our model achieved AUROC 0.63 using clinical features alone and adding inflammatory biomarker levels improved performance to AUROC 0.84.
Using clinical characteristics and inflammatory biomarker data, we developed an accurate predictive model for PAD prognosis.
与外周动脉疾病（PAD）相关的炎症生物标志物已分别进行了研究；然而，一种包含一组炎症蛋白以评估PAD预后的算法可能会提高预测准确性。我们使用临床/炎症生物标志物数据开发了2年PAD相关主要肢体不良事件（MALE）的预测模型。
我们进行了一项预后研究，分为两个阶段（发现/验证模型）。发现队列包括100例PAD患者，这些患者通过倾向评分与100例非PAD患者匹配。验证队列包括365例PAD患者和144例非PAD患者（未匹配）。在入组时测定29种炎症蛋白的血浆浓度，并对队列进行2年随访。感兴趣的结局是2年MALE（主要截肢、血管介入或急性肢体缺血的复合结局）。使用随机森林模型通过10折交叉验证进行训练，以使用以下输入特征预测2年MALE：1）临床特征，2）在PAD与非PAD患者中差异表达的炎症生物标志物，以及3）临床特征和炎症生物标志物。
模型发现队列在年龄、性别和合并症方面匹配良好。在测试的29种蛋白质中，有5种在PAD患者中高于非PAD患者（MMP-7、MMP-10、IL-6、CCL2/MCP-1和TFPI）。对于验证队列中2年MALE的预后，我们的模型仅使用临床特征时AUROC为0.63，添加炎症生物标志物水平后性能提高至AUROC为0.84。
利用临床特征和炎症生物标志物数据，我们开发了一种准确的PAD预后预测模型。
