A nomogram for predicting the risk of pulmonary embolism in neurology department suspected PE patients: A 10-year retrospective analysis

Frontiers in Neurology

Neurology

Qiang Jianling¹  Jin Lulu¹  Wang Maofeng¹  Li Wenchen²  Qiu Liuyi³  Feng Lanfang⁴  Ma Xu⁵ 

[1] Department of Biomedical Sciences Laboratory, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang, China;Department of Neurology, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang, China;Department of Pathology, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang, China;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;

关键词: pulmonary embolism;    neurology department;    retrospective analysis;    risk assessment models;    nomogram;   

DOI  :  10.3389/fneur.2023.1139598

 received in 2023-01-07, accepted in 2023-03-06,  发布年份 2023

来源: Frontiers

【 摘 要 】

ObjectiveThe purpose of this retrospective study was to establish a numerical model for predicting the risk of pulmonary embolism (PE) in neurology department patients.MethodsA total of 1,578 subjects with suspected PE at the neurology department from January 2012 to December 2021 were considered for enrollment in our retrospective study. The patients were randomly divided into the training cohort and the validation cohort in the ratio of 7:3. The least absolute shrinkage and selection operator regression were used to select the optimal predictive features. Multivariate logistic regression was used to establish the numerical model, and this model was visualized by a nomogram. The model performance was assessed and validated by discrimination, calibration, and clinical utility.ResultsOur predictive model indicated that eight variables, namely, age, pulse, systolic pressure, hemoglobin, neutrophil count, low-density lipoprotein, D-dimer, and partial pressure of oxygen, were associated with PE. The area under the receiver operating characteristic curve of the model was 0.750 [95% confidence interval (CI): 0.721–0.783] in the training cohort and 0.742 (95% CI: 0.689–0.787) in the validation cohort, indicating that the model showed a good differential performance. A good consistency between the prediction and the real observation was presented in the training and validation cohorts. The decision curve analysis in the training and validation cohorts showed that the numerical model had a good net clinical benefit.ConclusionWe established a novel numerical model to predict the risk factors for PE in neurology department suspected PE patients. Our findings may help doctors to develop individualized treatment plans and PE prevention strategies.

【 授权许可】

Unknown   
Copyright © 2023 Jianling, Lulu, Liuyi, Lanfang, Xu, Wenchen and Maofeng.