UNASSIGNED: To develop a novel and accurate nomogram to predict survival without amputation in patients with acute lower limb ischemia (ALLI) during the first year following endovascular therapy.
UNASSIGNED: Patients with ALLI who underwent endovascular therapy in our department between January 2012 and September 2020 were screened and included in the research. The included patients were randomly divided into a training and validation cohorts, respectively. Univariate and multivariate analyses were used in the training cohort to identify independent risk factors for amputation-free survival (AFS). A nomogram was then developed according to the identified independent risk factors. The nomogram was then validated in the validation cohort.
UNASSIGNED: 415 Chinese patients with 417 affected limbs were included in this study. Among these patients, 311 patients were classified into the training cohort and 104 patients were assigned to the validation cohort. Most patients were men (n = 240) and the average age of patients was 71.43 (standard deviation 8.86) years old. After the univariate and multivariate analyses, advanced age (p < 0.001), history of smoking (p < 0.001), atrial fibrillation (p < 0.001), and insufficient outflow (p = 0.001) were revealed as independent risk factors for AFS during the first year. The nomogram yielded AUROC values of 0.912 (95 % confidence interval [CI]: 0.873-0.950) and 0.889 (95 % CI: 0.812-0.967) in the training and validation cohorts, respectively.
UNASSIGNED: Advanced age, history of smoking, atrial fibrillation, and insufficient outflow were independent negative predictors for AFS in ALLI patients treated by endovascular therapy. The novel nomogram offered an accurate prediction of AFS in ALLI patients.

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.