Key result
A Nomogram prediction model incorporating clinical and laboratory factors predicted aortic dissection misdiagnosis with an AUC of 0.924 in the training set and 0.912 in the validation set.
Why the study?
Delayed diagnosis of aortic dissection frequently leads to treatment delays, elevated mortality, and complications, warranting investigation into factors contributing to misdiagnosis.
What factors contribute to the misdiagnosis of aortic dissection, and can a Nomogram model accurately predict misdiagnosis?
Observational (n=219)
No
What factors contribute to the misdiagnosis of aortic dissection, and can a Nomogram model accurately predict misdiagnosis?
Effect estimate: AUC 0.924
A novel Nomogram model incorporating clinical presentation and basic laboratory values can accurately predict the risk of misdiagnosis in patients with aortic dissection.
Objective: Aortic dissection (AD) is a life-threatening cardiovascular emergency. Delayed diagnosis frequently leads to treatment delays, elevated mortality, and complications. This study investigates the factors contributing to the misdiagnosis of AD and proposes strategies for improving its early diagnosis. Methods: A retrospective analysis of 801 patients with AD identified 219 cases for inclusion, which were split into a training set (131 cases) and a validation set (88 cases). A binary logistic regression model was used to identify factors influencing misdiagnosis, while a Nomogram prediction model was developed. Results: The analysis revealed that factors such as the timing and suddenness of symptom onset, typical back pain, walk-in clinic visits, and laboratory results (D-dimer, fibrinogen, and white blood count) were significant in predicting misdiagnosis. The Nomogram model showed high predictive accuracy with an Area under the ROC curve (AUC) of 0.924 in the training set and 0.912 in the validation set, demonstrating good sensitivity and specificity. Conclusion: The model offers potential for improving diagnostic accuracy and clinical outcomes in AD cases.
No takes yet. Share an insight, caveat, or question.
Wang et al. (2025) conducted an observational in Aortic dissection (n=219). Risk factors and Nomogram prediction model was evaluated on Misdiagnosis of aortic dissection (AUC 0.924). A Nomogram prediction model incorporating clinical and laboratory factors predicted aortic dissection misdiagnosis with an AUC of 0.924 in the training set and 0.912 in the validation set.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: