A machine learning-based predictive model for the occurrence of lower extremity deep vein thrombosis after laparoscopic surgery in abdominal surgery.

Background & aims: Deep vein thrombosis, a common complication after laparoscopic surgery, can negatively affect patients' limb motor function and even seriously threaten their lives. Therefore, it is crucial to accurately identify patients at high risk of lower extremity deep vein thrombosis. Thus, the aim of this study was to develop a model to predict the occurrence of deep vein thrombosis in patients after laparoscopy. Methods: We retrospectively analyzed the clinical data of patients who underwent laparoscopic surgery at Wenzhou Central Hospital's Hepatobiliary Surgery Department. Patients with postoperative deep vein thrombosis composed the observation group, while others composed the control group. Eleven key features were identified through group comparisons and used for model development. Twenty machine learning algorithms were evaluated, and the top five algorithms were used to build the final model by stacking. Results: A total of 335 patients underwent laparoscopic abdominal surgery. Patients with deep vein thrombosis (9.9%) differed significantly in age, history of tumor, hemoglobin, red blood cell counts, preoperative blood pressure, duration of the surgery, activated partial thromboplastin time, D-dimer, total protein, albumin, and calcium. According to our model, the most important features influencing the predictions were tumor history, age, time to surgery, and D-dimer level. We employed two interpretability methods: decomposition interpretation and Shapley additive explanation. Decomposition analysis revealed that the three study characteristics with the strongest predictive effect for deep vein thrombosis occurrence after laparoscopy were, in descending order, the time of surgery, patient age, and tumor history. Conversely, for ruling out deep vein thrombosis, the most important features were tumor history, hemoglobin level, and age. Shapley additive explanation revealed that tumor history, age, and time of surgery were the most important factors for predicting and ruling out deep vein thrombosis following laparoscopy. We additionally selected 114 patients for external validation, and the results showed that the ROC of validation set for the LASDVT model was 0.9293 and the AUPRC was 0.6497. The effect of the LASDVT model was statistically different (delong test, Conclusion: We present a model for predicting deep vein thrombosis in laparoscopic surgery patients. This model outperformed the Caprini score in predicting the incidence of deep vein thrombosis.