Aim To develop a machine learning model to predict the diagnosis of pulmonary embolism (PE). Methods and results We undertook a derivation and internal validation study to develop a risk prediction model for use in patients being investigated for possible PE. The machine learning technique, generalized logistic regression using elastic net, was chosen following an assessment of seven machine learning techniques and on the basis that it optimized the area under the receiver operator characteristic curve (AUC) and Brier score. Models were developed both with and without the addition of D-dimer. A total of 3347 patients were included in the study of whom, 219 (6.5%) had PE. Four clinical variables (O2 saturation, previous deep venous thrombosis or PE, immobilization or surgery, and alternative diagnosis equal or more likely than PE) plus D-dimer contributed to the machine learning models. The addition of D-dimer improved the AUC by 0.16 (95% confidence interval 0.13–0.19), from 0.73 to 0.89 (0.87–0.91) and decreased the Brier score by 14% (10–18%). More could be ruled out with a higher positive likelihood ratio than by the Wells score combined with D-dimer, revised Geneva score combined with D-dimer, or the Pulmonary Embolism Rule-out Criteria score. Machine learning with D-dimer maintained a low-false-negative rate at a true-negative rate of nearly 53%, which was better performance than any of the other alternatives. Conclusion A machine learning model outperformed traditional risk scores for the risk stratification of PE in the emergency department. However, external validation is needed.

中文翻译：

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在肺栓塞风险分层中使用 D-二聚体进行机器学习：推导和内部验证研究

目的 开发一种机器学习模型来预测肺栓塞 (PE) 的诊断。方法和结果 我们进行了一项推导和内部验证研究，以开发一个风险预测模型，用于正在接受可能 PE 调查的患者。机器学习技术，即使用弹性网络的广义逻辑回归，是在对七种机器学习技术进行评估后选择的，并基于它优化了接收者操作特征曲线 (AUC) 和 Brier 评分下的面积。在添加和不添加 D-二聚体的情况下开发了模型。该研究共纳入 3347 名患者，其中 219 名（6.5%）患有 PE。四个临床变量（O2 饱和度、既往深静脉血栓形成或 PE、制动或手术、和 PE 相等或更有可能的替代诊断）加上 D-二聚体有助于机器学习模型。添加 D-二聚体将 AUC 提高了 0.16（95% 置信区间 0.13-0.19），从 0.73 提高到 0.89（0.87-0.91），并将 Brier 评分降低了 14%（10-18%）。与 Wells 评分联合 D-二聚体、修订的日内瓦评分联合 D-二聚体或肺栓塞排除标准评分相比，更高的阳性似然比可以排除更多的可能性。使用 D-二聚体的机器学习保持了低假阴性率，真阴性率接近 53%，这比其他任何替代方案的性能都要好。结论 机器学习模型在急诊科 PE 风险分层方面优于传统风险评分。但是，需要外部验证。