The ensemble Voting machine learning model accurately distinguished aortic dissection from non-ST-elevation myocardial infarction with an AUC of 0.95 and an accuracy of 92% on the test set.
Observational (n=437)
No
Does a machine learning-based diagnostic model improve the accuracy of distinguishing aortic dissection from NSTEMI in patients with chest pain?
A machine learning ensemble model utilizing routine clinical and laboratory data, particularly D-dimer and Troponin T, can accurately and rapidly distinguish between aortic dissection and NSTEMI.
Effect estimate: AUC 0.95 (95% CI 0.91-0.99)
Similar symptoms, such as chest pain, shortness of breath, and palpitations, have been observed in Non-ST elevation myocardial infarction (NSTEMI) and aortic dissection (AD), making diagnosis challenging. Recognizing the distinction between them is essential for prompt treatment. This study was to establish a model based on machine learning (ML) to improve diagnosis accuracy 353 individuals’ clinical characteristics and laboratory results (193 AD, 160 NSTEMI) were analyzed. The Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis was used to identify significant indicators. Four ML models were constructed, and the Voting algorithm was used to conduct an ensemble analysis. Decision Curve Analysis (DCA) assessed the clinical value. And collected a new validation set of 36 AD and 48 NSTEMI patients to assess the generalizability of the optimal model. Shapley Additive explanations (SHAP) was used to evaluate feature contribution With an accuracy of 92%, recall of 94%, F1-score of 91.43%, and an AUC of 0.95 (95 CI%: 0.91–0.99) on the test set, the ensemble Voting model was recognized as the optimal model. DCA provided evidence of the model’s clinical value in AD prediction. The SHAP indicated that Troponin T and D-dimer were crucial predictors We successfully established a machine-learning based diagnosis approach for timely distinguish of AD and NSTEMI. Based on our results, the Voting model performed the best in terms of predicting efficacy. In addition, we used SHAP to provide a personalized risk assessment for the development of the prediction results. This diagnosis model may assist the emergency department to quickly avoiding misdiagnosis of AD with NSTEMI.
Huang et al. (Thu,) conducted a observational in Aortic Dissection and Non-ST-Elevation Myocardial Infarction (n=437). Voting ensemble machine learning model was evaluated on Area Under the Curve (AUC) for distinguishing AD from NSTEMI on the test set (AUC 0.95, 95% CI 0.91-0.99). The ensemble Voting machine learning model accurately distinguished aortic dissection from non-ST-elevation myocardial infarction with an AUC of 0.95 and an accuracy of 92% on the test set.