Ab1471 Machine-Learning Based Models Assist the Identification of Pulmonary Embolism in Autoimmune Diseases-a Retrospective, Multicenter Study

Background: Pulmonary embolism (PE) is a severe and acute cardiovascular syndrome with a high mortality among patients with autoimmune inflammatory rheumatic diseases (AIIRD). Accurate prediction and timely intervention play a pivotal role in enhancing survival rates. However, there is a notable scarcity of practical early prediction and risk assessment systems of PE in patients with are scarce. Objectives: To investigate the potential value of PE prediction in AIIRD patients with different machine learning models. Methods: In the training cohort, 60 AIIRD with PE cases and 180 age-, gender-, and disease-matched AIIRD non-PE cases were identified from 7254 AIIRD cases in Tongji Hospital from 2014 to 2022. Univariate logistic regression and LASSO were used to select the clinical features for the further training with machine learning (ML) methods, including RF, SVM, NN, LR GBDT, CART and C5.0. The performances of these models were subsequently validated using a multi-center validation cohort. Results: In training cohort, 24 and 13 clinical features were selected by univariate LR and LASSO strategies, respectively. All five ML models (RF, SVM, NN, LR, and GBDT) showed promising performances, with an AUC of 0.962–1 in the training cohort, and 0.969–0.999 in the validation cohort. CART and C5.0 trees achieved AUCs of 0.850 and 0.932 respectively in the training cohort, respectively. Using D-dimer as a pre-screening index, the refined C5.0 model achieved an AUC exceeding 0.948 in the training cohort, and an AUC above 0.925 in the validation cohort. These results markedly outperformed the use of D-dimer levels alone (Figure 1). Conclusion: ML-based models prove to be precise for predicting the onset of PE in patients with AIIRD exhibiting clinical suspicion of PE. REFERENCES: NIL. Acknowledgements: NIL. Disclosure of Interests: None declared.