Prediction of 2-Year Major Adverse Limb Event-Free Survival After Percutaneous Transluminal Angioplasty and Stenting for Lower Limb Atherosclerosis Obliterans: A Machine Learning-Based Study

Frontiers in Cardiovascular Medicine

article

Tianyue Pan¹  Xiaolang Jiang¹  Hao Liu¹  Yifan Liu¹  Weiguo Fu¹  Zhihui Dong¹ 

[1] Department of Vascular Surgery, Institute of Vascular Surgery, Zhongshan Hospital, Fudan University;National Clinical Research Center for Interventional Medicine

关键词: machine learning;    lower limb atherosclerosis obliterans;    endovascular therapy;    prognosis prediction;    major adverse limb events;   

DOI  :  10.3389/fcvm.2022.783336

学科分类：地球科学(综合)

来源: Frontiers

【 摘 要 】

Background The current scoring systems could not predict prognosis after endovascular therapy for peripheral artery disease. Machine learning could make predictions for future events by learning a specific pattern from existing data. This study aimed to demonstrate machine learning could make an accurate prediction for 2-year major adverse limb event-free survival (MFS) after percutaneous transluminal angioplasty (PTA) and stenting for lower limb atherosclerosis obliterans (ASO). Methods A lower limb ASO cohort of 392 patients who received PTA and stenting was split to the training set and test set by 4:1 in chronological order. Demographic, medical, and imaging data were used to build machine learning models to predict 2-year MFS. The discrimination and calibration of artificial neural network (ANN) and random forest models were compared with the logistic regression model, using the area under the receiver operating curve (ROCAUC) with DeLong test, and the calibration curve with Hosmer–Lemeshow goodness-of-fit test, respectively. Results The ANN model (ROCAUC = 0.80, 95% CI: 0.68–0.89) but not the random forest model (ROCAUC = 0.78, 95% CI: 0.66–0.87) significantly outperformed the logistic regression model (ROCAUC = 0.73, 95% CI: 0.60–0.83, P = 0.01 and P = 0.24). The ANN model the logistic regression model demonstrated good calibration performance ( P = 0.73 and P = 0.28), while the random forest model showed poor calibration ( P < 0.01). The calibration curve of the ANN model was visually the closest to the perfectly calibrated line. Conclusion Machine learning models could accurately predict 2-year MFS after PTA and stenting for lower limb ASO, in which the ANN model had better discrimination and calibration. Machine learning-derived prediction tools might be clinically useful to automatically identify candidates for PTA and stenting.

【 授权许可】

CC BY