Lanfang Feng, Xu Ma, Jun Chen, Jia Zhao, Wenchen Li, Xinghua Jia
Department of Respiratory, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang, China.
Department of Vascular Surgery, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang, China.
Front Med (Lausanne). 2023 May 17;10:1164911. doi: 10.3389/fmed.2023.1164911. eCollection 2023.
Pulmonary embolisms (PE) are clinically challenging because of their high morbidity and mortality. This study aimed to create a nomogram to accurately predict the risk of PE in respiratory department patients in order to enhance their medical treatment and management.
This study utilized a retrospective method to collect information on medical history, complications, specific clinical characteristics, and laboratory biomarker results of suspected PE patients who were admitted to the respiratory department at Affiliated Dongyang Hospital of Wenzhou Medical University between January 2012 and December 2021. This study involved a total of 3,511 patients who were randomly divided into a training group (six parts) and a validation group (four parts) based on a 6:4 ratio. The LASSO regression and multivariate logistic regression were used to develop a scoring model using a nomogram. The performance of the model was evaluated using receiver operating characteristic curve (AUC), calibration curve, and clinical decision curve.
Our research included more than 50 features from 3,511 patients. The nomogram-based scoring model was established using six predictive features including age, smoke, temperature, systolic pressure, D-dimer, and fibrinogen, which achieved AUC values of 0.746 in the training cohort (95% CI 0.720-0.765) and 0.724 in the validation cohort (95% CI 0.695-0.753). The results of the calibration curve revealed a strong consistency between probability predicted by the nomogram and actual probability. The decision curve analysis (DCA) also demonstrated that the nomogram-based scoring model produced a favorable net clinical benefit.
In this study, we successfully developed a novel numerical model that can predict the risk of PE in respiratory department patients suspected of PE, which can not only appropriately select PE prevention strategies but also decrease unnecessary computed tomographic pulmonary angiography (CTPA) scans and their adverse effects.
肺栓塞（PE）因其高发病率和死亡率而在临床上具有挑战性。本研究旨在创建一个列线图，以准确预测呼吸科患者发生PE的风险，从而加强对他们的医疗治疗和管理。
本研究采用回顾性方法，收集了2012年1月至2021年12月期间在温州医科大学附属东阳医院呼吸科住院的疑似PE患者的病史、并发症、特定临床特征和实验室生物标志物结果等信息。本研究共纳入3511例患者，按照6:4的比例随机分为训练组（六份）和验证组（四份）。使用LASSO回归和多变量逻辑回归，通过列线图建立评分模型。使用受试者工作特征曲线（AUC）、校准曲线和临床决策曲线评估模型的性能。
我们的研究纳入了3511例患者的50多个特征。基于列线图的评分模型使用年龄、吸烟、体温、收缩压、D-二聚体和纤维蛋白原这六个预测特征建立，在训练队列中的AUC值为0.746（95%CI 0.720-0.765），在验证队列中的AUC值为0.724（95%CI 0.695-0.753）。校准曲线结果显示，列线图预测的概率与实际概率之间具有很强的一致性。决策曲线分析（DCA）也表明，基于列线图的评分模型产生了良好的净临床效益。
在本研究中，我们成功开发了一种新型数值模型，可预测疑似PE的呼吸科患者发生PE的风险，该模型不仅可以适当选择PE预防策略，还可以减少不必要的计算机断层扫描肺动脉造影（CTPA）扫描及其不良影响。
