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.

Tianyue Pan,Hao Liu,Weiguo Fu,Xiaolang Jiang,Yifan Liu,Zhihui Dong

doi:10.3389/fcvm.2022.783336

Tianyue Pan, Hao Liu + Show 4 more

Open Access

https://doi.org/10.3389/fcvm.2022.783336

Copy DOI

Abstract

BackgroundThe 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).MethodsA 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.ResultsThe 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.ConclusionMachine 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.

Highlights

Peripheral arterial disease (PAD), frequently caused by atherosclerosis, could progress to arterial stenosis or occlusion and lead to chronic or acute limb ischemia
Percutaneous transluminal angioplasty (PTA) with selective stenting is the main strategy of endovascular intervention iliac, superficial femoral, popliteal and infrapopliteal stenotic, and occlusive lesions
Exclusion criteria included: [1] any serious health events which might mislead the assessment of lower limb function including, but not limited to heart failure, symptomatic cerebral apoplexy, and lower limb fracture before admission; [2] lower limb ischemia due to other etiologies including, but not limited to arterial embolism, angiitis, and arterial aneurysm before admission; [3] diagnosed with malignancy at baseline; and [4] any surgery or endovascular intervention for the target lower limb artery performed before

Summary

Introduction

Peripheral arterial disease (PAD), frequently caused by atherosclerosis, could progress to arterial stenosis or occlusion and lead to chronic or acute limb ischemia. Open bypass surgery and endovascular intervention are the main treatment options for ASO-related CLI or severe claudication. We aimed to use machine learning to automatically create an algorithm to calculate the probability of prognosis after receiving lower limb PTA with stenting. This might help identify candidates for PTA and stenting, and discriminate patients unsuitable for endovascular intervention and avoid inappropriate stent placement. 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)

Objectives

Methods

Results

Discussion

Conclusion