背景:本研究旨在应用八种机器学习算法来开发最佳模型,以预测外周动脉疾病(PAD)患者首次血运重建后的无截肢生存率(AFS)。
方法:在2011年至2020年的2130例患者中,1260例接受血运重建的患者以8:2的比例随机分配到训练组和验证组。对67项临床参数进行Lasso回归分析。Logistic回归,梯度增压机,随机森林,决策树,极限梯度提升,神经网络,Cox回归,应用随机生存森林(RSF)建立预测模型。从2010年开始,将最佳模型与包含患者的测试集中的GermanVasc评分进行了比较。
结果:术后1/3/5年AFS为90%,79.4%,74.1%。年龄(HR:1.035,95CI:1.015-1.056),心房颤动(HR:2.257,95CI:1.193-4.271),心脏射血分数(HR:0.064,95CI:0.009-0.413),卢瑟福等级≥5(HR:1.899,95CI:1.296-2.782),肌酐(HR:1.03,95CI:1.02-1.04),手术时间(HR:1.03,95CI:1.01-1.05),纤维蛋白原(HR:1.292,95CI:1.098~1.521)为独立危险因素。通过RSF算法建立了最优模型,训练集中1/3/5年AUC为0.866(95%CI:0.819-0.912),0.854(95%CI:0.811-0.896),0.844(95%CI:0.793-0.894),在0.741的验证集中(95%CI:0.580-0.902),0.768(95%CI:0.654-0.882),0.836(95%CI:0.719-0.953),在0.821(95CI:0.711-0.931)的测试集中,0.802(95CI:0.684-0.919),0.798(95CI:0.657-0.939)。该模型的c指数优于GermanVasc评分(0.788vs0.730)。在shinyapp上发布了动态列线图(https://wyy2023。shinyapps.io/截肢/)。
结论:通过RSF算法建立了PAD患者首次血运重建后AFS的最佳预测模型,表现出突出的预测性能。
This study aimed to apply eight machine learning algorithms to develop the optimal model to predict amputation-free survival (AFS) after first revascularization in patients with peripheral artery disease (PAD).
Among 2130 patients from 2011 to 2020, 1260 patients who underwent revascularization were randomly assigned to training set and validation set in an 8:2 ratio. 67 clinical parameters were analyzed by lasso regression analysis. Logistic regression, gradient boosting machine, random forest, decision tree, eXtreme gradient boosting, neural network, Cox regression, and random survival forest (RSF) were applied to develop prediction models. The optimal model was compared with GermanVasc score in testing set comprising patients from 2010.
The postoperative 1/3/5-year AFS were 90%, 79.4%, and 74.1%. Age (HR:1.035, 95%CI: 1.015-1.056), atrial fibrillation (HR:2.257, 95%CI: 1.193-4.271), cardiac ejection fraction (HR:0.064, 95%CI: 0.009-0.413), Rutherford grade ≥ 5 (HR:1.899, 95%CI: 1.296-2.782), creatinine (HR:1.03, 95%CI: 1.02-1.04), surgery duration (HR:1.03, 95%CI: 1.01-1.05), and fibrinogen (HR:1.292, 95%CI: 1.098-1.521) were independent risk factors. The optimal model was developed by RSF algorithm, with 1/3/5-year AUCs in training set of 0.866 (95% CI:0.819-0.912), 0.854 (95% CI:0.811-0.896), 0.844 (95% CI:0.793-0.894), in validation set of 0.741 (95% CI:0.580-0.902), 0.768 (95% CI:0.654-0.882), 0.836 (95% CI:0.719-0.953), and in testing set of 0.821 (95%CI: 0.711-0.931), 0.802 (95%CI: 0.684-0.919), 0.798 (95%CI: 0.657-0.939). The c-index of the model outperformed GermanVasc Score (0.788 vs 0.730). A dynamic nomogram was published on shinyapp (https://wyy2023.shinyapps.io/amputation/).
The optimal prediction model for AFS after first revascularization in patients with PAD was developed by RSF algorithm, which exhibited outstanding prediction performance.