Jenab Yaser, Hosseini Kaveh, Esmaeili Zahra, Tofighi Saeed, Ariannejad Hamid, Sotoudeh Houman
Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran.
Faculty of Medicine, Tehran University of Medical Sciences, Tehran, Iran.
Front Cardiovasc Med. 2023 Mar 14;10:1087702. doi: 10.3389/fcvm.2023.1087702. eCollection 2023.
Pulmonary thromboembolism (PE) is the third leading cause of cardiovascular events. The conventional modeling methods and severity risk scores lack multiple laboratories, paraclinical and imaging data. Data science and machine learning (ML) based prediction models may help better predict outcomes.
In this retrospective registry-based design, all consecutive hospitalized patients diagnosed with pulmonary thromboembolism (based on pulmonary CT angiography) from 2011 to 2019 were recruited. ML based algorithms [Gradient Boosting (GB) and Deep Learning (DL)] were applied and compared with logistic regression (LR) to predict hemodynamic instability and/or all-cause mortality.
A total number of 1,017 patients were finally enrolled in the study, including 465 women and 552 men. Overall incidence of study main endpoint was 9.6%, (7.2% in men and 12.4% in women; -value = 0.05). The overall performance of the GB model is better than the other two models (AUC: 0.94 for GB vs. 0.88 and 0.90 for DL and LR models respectively). Based on GB model, lower O saturation and right ventricle dilation and dysfunction were among the strongest adverse event predictors.
ML-based models have notable prediction ability in PE patients. These algorithms may help physicians to detect high-risk patients earlier and take appropriate preventive measures.
肺血栓栓塞症（PE）是心血管事件的第三大主要病因。传统的建模方法和严重程度风险评分缺乏多个实验室、辅助检查和影像数据。基于数据科学和机器学习（ML）的预测模型可能有助于更好地预测结果。
在这项基于回顾性登记的设计中，纳入了2011年至2019年期间所有连续住院的被诊断为肺血栓栓塞症（基于肺部CT血管造影）的患者。应用基于ML的算法[梯度提升（GB）和深度学习（DL）]，并与逻辑回归（LR）进行比较，以预测血流动力学不稳定和/或全因死亡率。
共有1017例患者最终纳入研究，其中女性465例，男性552例。研究主要终点的总体发生率为9.6%，（男性为7.2%，女性为12.4%；P值=0.05）。GB模型的总体性能优于其他两个模型（AUC：GB为0.94，而DL和LR模型分别为0.88和0.90）。基于GB模型，较低的氧饱和度以及右心室扩张和功能障碍是最强的不良事件预测因素。
基于ML的模型在PE患者中具有显著的预测能力。这些算法可能有助于医生更早地检测出高危患者并采取适当的预防措施。
