Background: Patients with pulmonary embolism (PE) who prematurely discontinue anticoagulant therapy (<90 days) are at an increased risk for death or recurrences. Methods: We used the data from the RIETE registry to compare the prognostic ability of 5 machine-learning (ML) models and logistic regression to identify patients at increased risk for the composite of fatal PE or recurrent venous thromboembolism (VTE) 30 days after discontinuation. ML models included Decision tree, K-Nearest Neighbors algorithm, Support Vector Machine, Ensemble and Neural Network [NN]. A “full” model with 70 variables and a “reduced” model with 23 were analyzed. Model performance was assessed by confusion matrix metrics on the testing data for each model and a calibration plot. Results: Among 34,447 patients with PE, 1,348 (3.9%) discontinued therapy prematurely. Fifty-one (3.8%) developed fatal PE or sudden death and 24 (1.8%) had non-fatal VTE recurrences within 30 days after discontinuation. ML-NN was the best method for identification of patients experiencing the composite endpoint, predicting the composite outcome with an area under receiver operating characteristics (ROC) curve of 0.96 (95% confidence intervals [CI], 0.95-0.98), using either 70 or 23 variables captured before discontinuation. Similar numbers were obtained for sensitivity, specificity, positive predictive value, negative predictive value and accuracy. The discrimination of logistic regression was inferior (area under ROC curve, 0.76 [95% Cl 0.70-0.81]). Calibration plot showed similar deviations from the perfect line for ML-NN and logistic regression. Conclusions: ML-NN method very well predicted the composite outcome after premature discontinuation of anticoagulation and outperformed traditional logistic regression.

中文翻译：

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机器学习预测过早停止抗凝治疗的急性肺栓塞患者的预后。

背景：过早停止抗凝治疗（<90 天）的肺栓塞 (PE) 患者死亡或复发的风险增加。方法：我们使用 RIETE 注册中心的数据比较 5 种机器学习 (ML) 模型和逻辑回归的预后能力，以确定停药后 30 天致命性 PE 或复发性静脉血栓栓塞 (VTE) 复合风险增加的患者. ML 模型包括决策树、K 最近邻算法、支持向量机、集成和神经网络 [NN]。分析了具有 70 个变量的“完整”模型和具有 23 个变量的“简化”模型。模型性能通过混淆矩阵指标对每个模型的测试数据和校准图进行评估。结果：在 34,447 名 PE 患者中，1,348 名 (3.9%) 提前停止治疗。51 人 (3.8%) 发生致命性 PE 或猝死，24 人 (1.8%) 在停药后 30 天内出现非致命性 VTE 复发。ML-NN 是识别经历复合终点的患者的最佳方法，使用 70或停药前捕获的 23 个变量。在敏感性、特异性、阳性预测值、阴性预测值和准确性方面获得了相似的数字。逻辑回归的辨别力较差（ROC 曲线下面积，0.76 [95% Cl 0.70-0.81]）。校准图显示与 ML-NN 和逻辑回归的完美线有类似的偏差。结论：