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Nomogram to differentiate between aortic dissection and non-ST segment elevation acute coronary syndrome: a retrospective cohort study

Authors
  • Zhang, Baowei1, 2
  • Wang, Yingying2
  • Guo, Junfang2
  • Zhang, Guohui2
  • Yang, Bing1
  • 1 Tongji University School of Medicine, Shanghai , (China)
  • 2 the affiliated People’s Hospital of Jiangsu University, Zhenjiang , (China)
Type
Published Article
Journal
Cardiovascular Diagnosis and Therapy
Publisher
AME Publishing Company
Publication Date
Apr 01, 2021
Volume
11
Issue
2
Pages
457–466
Identifiers
DOI: 10.21037/cdt-20-935
PMID: 33968623
PMCID: PMC8102240
Source
PubMed Central
Keywords
Disciplines
  • Original Article
License
Unknown

Abstract

Background Aortic dissection (AD) and non-ST segment elevation acute coronary syndrome (ACS) are two of the most life-threatening diseases encountered in the emergency department (ED), but there are no rapid and reliable tools for differentiation. The purpose of this study is to develop and validate a nomogram that incorporates both the clinical characteristics and bedside laboratory tests available to differentiate between AD and non-ST segment elevation ACS (NSTE-ACS). Methods Between January 2016 and July 2018, patients with AD and NSTE-ACS were enrolled and divided into training and validation groups. The least absolute shrinkage and selection operator (LASSO) regression model was used to select the factors with significant value of predicting the diagnosis of AD. A nomogram was built on the basis of multivariable logistic regression analysis. Area under the curve (AUC) of receiver operating characteristic (ROC) curve and the calibration curve were used to assess the performance of the nomogram. Decision curve analysis was performed to assess the clinical utility of the nomogram. Results A final cohort of 263 patients (94 patients with AD and 169 patients with NSTE-ACS) were enrolled. Six variables were incorporated in the nomogram: pain severity, tearing pain, pulse asymmetry, electrocardiogram (ECG), D-dimer level and troponin I level. The AUC of the nomogram to predict the probability of AD was 0.919 (95% CI, 0.876–0.962) in the training group and 0.938 (95% CI, 0.888–0.989) in the validation group. The calibration curve demonstrated a good consistency between the actual clinical results and the predicted outcomes. The decision curve analysis indicated that the nomogram had higher overall net benefits in predicting AD in both the training group and the validation group. Conclusions We developed and validated a predictive nomogram that could be used as a tool to differentiate AD from NSTE-ACS rapidly and accurately.

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