Accurate Differentiation of Acute Aortic Syndrome from Acute Coronary Syndrome: A Machine Learning Diagnostic Model Based on Routine Admission Variables
4 affiliations
- 1Department of Cardiology, The First Affiliated Hospital of Xinjiang Medical University, No. 137, Liyushan South Road, Xinshi District, Urumqi 830054, China
- 2The First Department of Gastroenterology, The First Affiliated Hospital of Xinjiang Medical University, No. 137, Liyushan South Road, Xinshi District, Urumqi 830054, China
- 3Hammersmith Medicines Research, Nucleus Network, 44 Cumberland Avenue, London NW10 7EW, UK
- 4Cardiology and Aortic Centre, Royal Brompton and Harefield Hospitals, Guy’s and St Thomas’ NHS Foundation Trust, London SW3 6NP, UK
Abstract
Background/Objectives: Acute aortic syndrome (AAS) is an underdiagnosed, time-critical medical emergency. Initial misdiagnosis as acute coronary syndrome (ACS) is common, delaying definitive treatment and exposing patients to inappropriate antithrombotic therapy. We aimed to develop and temporally test a machine learning (ML) model using routine admission variables to distinguish AAS from ACS. Methods: In this single-center retrospective study, we included 2828 consecutive adults with confirmed AAS or ACS (September 2018–April 2025); the initial 1979 patients formed the development cohort and the subsequent 849, a temporally separated test cohort. Least absolute shrinkage and selection operator regression selected predictors from nine prespecified candidates. Seven algorithms were fitted and assigned fixed Youden-derived cutoffs in the development cohort, then applied to the test cohort. Results: Six predictors were selected: D-dimer, leukocyte count, troponin I, C-reactive protein, oxygen saturation, and ST-segment changes. In the test cohort (112 AAS [13.2%]; 737 ACS), area under the receiver operating characteristic curve (AUC) ranged from 0.925 to 0.956 across the seven models. Extreme gradient boosting (XGBoost) showed the highest observed AUC (0.956; 95% confidence interval (CI), 0.935–0.976) and the lowest Brier score (0.047; 95% CI, 0.037–0.057), with sensitivity 0.884, specificity 0.931, and negative predictive value 0.981 at the development-cohort-derived cutoff (0.199); decision curve analysis indicated net benefit within the AAS/ACS test cohort. Shapley additive explanations ranked D-dimer as the dominant contributor. Conclusions: Six routine admission variables discriminated AAS from ACS across all seven ML algorithms in this single-center cohort. These findings support further prospective evaluation in a broader diagnostic population.
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BibTeX
@article{peng2026accurate,
title = {{Accurate Differentiation of Acute Aortic Syndrome from Acute Coronary Syndrome: A Machine Learning Diagnostic Model Based on Routine Admission Variables}},
author = {Xinliang Peng and Baozhu Wang and Tiantian Tang and Suzy Wang and Xiang Ma and Christoph A. Nienaber},
journal = {Journal of Clinical Medicine},
year = {2026},
month = nov,
doi = {10.3390/jcm15218708},
url = {https://www.mdpi.com/2077-0383/15/21/8708},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Accurate Differentiation of Acute Aortic Syndrome from Acute Coronary Syndrome: A Machine Learning Diagnostic Model Based on Routine Admission Variables
AU - Xinliang Peng
AU - Baozhu Wang
AU - Tiantian Tang
AU - Suzy Wang
AU - Xiang Ma
AU - Christoph A. Nienaber
PY - 2026
DA - 2026/11/07
JO - Journal of Clinical Medicine
DO - 10.3390/jcm15218708
UR - https://www.mdpi.com/2077-0383/15/21/8708
AB - Background/Objectives: Acute aortic syndrome (AAS) is an underdiagnosed, time-critical medical emergency. Initial misdiagnosis as acute coronary syndrome (ACS) is common, delaying definitive treatment and exposing patients to inappropriate antithrombotic therapy. We aimed to develop and temporally test a machine learning (ML) model using routine admission variables to distinguish AAS from ACS. Methods: In this single-center retrospective study, we included 2828 consecutive adults with confirmed AAS or ACS (September 2018–April 2025); the initial 1979 patients formed the development cohort and the subsequent 849, a temporally separated test cohort. Least absolute shrinkage and selection operator regression selected predictors from nine prespecified candidates. Seven algorithms were fitted and assigned fixed Youden-derived cutoffs in the development cohort, then applied to the test cohort. Results: Six predictors were selected: D-dimer, leukocyte count, troponin I, C-reactive protein, oxygen saturation, and ST-segment changes. In the test cohort (112 AAS [13.2%]; 737 ACS), area under the receiver operating characteristic curve (AUC) ranged from 0.925 to 0.956 across the seven models. Extreme gradient boosting (XGBoost) showed the highest observed AUC (0.956; 95% confidence interval (CI), 0.935–0.976) and the lowest Brier score (0.047; 95% CI, 0.037–0.057), with sensitivity 0.884, specificity 0.931, and negative predictive value 0.981 at the development-cohort-derived cutoff (0.199); decision curve analysis indicated net benefit within the AAS/ACS test cohort. Shapley additive explanations ranked D-dimer as the dominant contributor. Conclusions: Six routine admission variables discriminated AAS from ACS across all seven ML algorithms in this single-center cohort. These findings support further prospective evaluation in a broader diagnostic population.
SN - 2077-0383
ER -