Development a practical machine learning model to predict post implantation syndrome after endovascular aneurysm repair surgery

Background: Post-implantation syndrome (PIS) is recognized as a systemic inflammatory response following endovascular aneurysm repair (EVAR), characterized by a high frequency of occurrence and the capacity to provoke cardiovascular complications and extend the duration of hospitalization. The objective of our study is to construct a predictive algorithm through the application of machine learning (ML) techniques to forecast the onset of PIS subsequent to EVAR procedures. Methods: Data of 618 patients were retrospectively withdraw form EHR system from Foshan First People's Hospital. Least absolute shrinkage and selection operator (LASSO) regression is used for data preprocessing and variable selection. Eight ML models are developed to predictive PIS after EVAR surgery. The area under the receiver operating curve (AUC), f1-score, accuracy, sensitivity, specificity, were evaluated as the model performances. Results: According to the exclusion criteria of 618 patients, 594 patients were finally included in the statistical analysis, and the incidence rate of PIS was 16.8%. Our research results show that there are 11 features that predict risk factors for PIS, including intraoperative use of Etomidate, Muscle relaxants, Polyester Bracket(Percutek Therapeutics), Polyester Bracket(MicroPort), Glucocorticoids, Deoxypinephrine, Platelet count, Age, Absolute value of neutral cells, Surgical duration, Serum creatinine. The Linear Discriminant Analysis (LDA) model performs the best among prediction models, with an AUC of 0.794, f1 score of 0.438, sensitivity of 0.7, specificity of 0.697, and accuracy of 0.697. Conclusion: Our study selected 11 preoperative and intraoperative variables to develop a ML model based on LDA for predicting PIS after EVAR surgery and the model may help assisting clinical decision-making.