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Wang, John C. H.

doi:10.1002/0471219282.eot233

Cited by 1 publication

(1 citation statement)

References 15 publications

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“…Currently, a RAM specifically for use with neurology department patients has not been developed. In recent years, we published a number of articles related to PE (15)(16)(17), and on this basis, we wanted to develop a numerical model that could rapidly determine the risk of PE in neurology department patients.…”

Section: Introductionmentioning

confidence: 99%

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

Jianling

Lulu²,

Liuyi³

et al. 2023

Front. Neurol.

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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.

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Section: Introductionmentioning

confidence: 99%