Determination of Risk Factors and Establishment of a Prediction Model for Immediate Technical Failure during Endovascular Treatment of Femoropopliteal Occlusive Disease.

BACKGROUND: For long femoropopliteal occlusive lesions, the immediate technical failure (ITF) of endovascular treatment (EVT) is relatively high. Therefore, this study aims to reveal risk factors and establish a prediction model of ITF of EVT in femoropopliteal occlusive disease (FPOD) patients based on preoperative clinical date that may be helpful to the clinical procedures. METHODS: A retrospective analysis of 1,563 FPOD patients who underwent above-the-knee EVT was undertaken. Univariate analysis with chi-squared test was used to screen risk factors from preoperative clinical data. Multivariable analysis with logistic regression was used to generate a model for predicting the ITF rate of EVT, which was evaluated through the receiver operating characteristic curve and another independent cohort of 242 FPOD patients. RESULTS: Risk factors for ITF during EVT in FPOD included age (>80 years, X1), the absence of diabetes mellitus (X2), low-density lipoprotein (>160 mg/dL, X3), lesion calcification (X4), lesion length (>20 cm, X5), ostial occlusion of superficial femoral artery (SFA) (X6), and SFA lesion involving the popliteal artery (X7). A logistic regression model was established based on the equation: -6.504 + 1.236X1 + 0.945X2 + 1.406X3 + 1.136X4 + 1.059X5 + 2.307X6 + 2.194X7. Scores were given to risk factors as follows: X1 (yes = 12, no = 0), X2 (yes = 9, no = 0), X3 (yes = 14, no = 0), X4 (yes = 11, no = 0), X5 (yes = 11, no = 0), X6 (yes = 23, no = 0), and X7 (yes = 22, no = 0). We determined that the optimal comprehensive score for predicting EVT failure was 39, with a sensitivity of 0.847 and a specificity of 0.8. Among these 242 peripheral arterial disease patients, 12 of 14 patients who had failed EVT had a comprehensive score of >39. CONCLUSIONS: We identified a number of risk factors of ITF during the above-the-knee EVT and established a prediction model that may be used for guidance in clinical practice.