緊急外来における肺血栓塞栓症の予測精度向上と不要なコンピューター断層撮影検査の削減に向けた機械学習の活用

The diagnosis of pulmonary thromboembolism (PTE) remains challenging due to its nonspecific clinical signs and symptoms. This study aimed to develop a machine learning (ML) model to predict PTE in emergency department patients. We retrospectively analyzed 2,525 emergency department patients suspected of PTE who underwent computed tomography pulmonary angiography (CTPA) within 7 days after elevated D-dimer levels (≥ 0.5 µg/ml) at a tertiary hospital, between January 2012 and December 2021. Clinical and laboratory data were split into training (n = 2025) and test (n = 500) sets. Six ML models-XGBoost, random forest, logistic regression, elastic net regression, support vector machine, and feed-forward neural network-were compared with the revised Geneva score using the area under the receiver operating characteristic curve (AUC). Variable importance was assessed using permutation methods. Of the 2,525 patients, 573 (22.7%) were diagnosed with PTE. XGBoost achieved the highest AUC of 0.814 (95% confidence interval [CI]: 0.759-0.862). All ML models outperformed the revised Geneva score, which had an AUC of 0.622 (95% CI: 0.563-0.675). D-dimer and activated partial thromboplastin time were the most important predictors across all ML models. At sensitivities of 100%, 95%, and 90%, the XGBoost model could reduce the number of CTPA scans by 3.0%, 14.8%, and 33.2%, respectively (all p < 0.001). These findings suggest that ML models, particularly XGBoost, can improve PTE risk prediction compared to the revised Geneva score and may help reduce unnecessary CTPA imaging in the emergency department.