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Article

Predicting Long-Term Mortality in Patients with Angina across the Spectrum of Dysglycemia: A Machine Learning Approach

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Division of Endocrinology and Metabolism, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung 40705, Taiwan
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Department of Computer Science & Information Engineering, National Taiwan University, Taipei 10617, Taiwan
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School of Medicine, National Yang-Ming University, Taipei 11221, Taiwan
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Institute of Information Systems and Applications, National Tsing Hua University, Hsinchu 30013, Taiwan
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Graduate Institute of Data Science, Taipei Medical University, Taipei 10675, Taiwan
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Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei 10675, Taiwan
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School of Medicine, Chung Shan Medical University, Taichung 40201, Taiwan
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College of Science, Tunghai University, Taichung 40704, Taiwan
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Authors to whom correspondence should be addressed.
Academic Editor: Sameer Antani
Diagnostics 2021, 11(6), 1060; https://doi.org/10.3390/diagnostics11061060
Received: 19 April 2021 / Revised: 2 June 2021 / Accepted: 5 June 2021 / Published: 9 June 2021
(This article belongs to the Section Machine Learning and Artificial Intelligence in Diagnostics)
We aimed to develop and validate a model for predicting mortality in patients with angina across the spectrum of dysglycemia. A total of 1479 patients admitted for coronary angiography due to angina were enrolled. All-cause mortality served as the primary endpoint. The models were validated with five-fold cross validation to predict long-term mortality. The features selected by least absolute shrinkage and selection operator (LASSO) were age, heart rate, plasma glucose levels at 30 min and 120 min during an oral glucose tolerance test (OGTT), the use of angiotensin II receptor blockers, the use of diuretics, and smoking history. This best performing model was built using a random survival forest with selected features. It had a good discriminative ability (Harrell’s C-index: 0.829) and acceptable calibration (Brier score: 0.08) for predicting long-term mortality. Among patients with obstructive coronary artery disease confirmed by angiography, our model outperformed the Global Registry of Acute Coronary Events discharge score for mortality prediction (Harrell’s C-index: 0.829 vs. 0.739, p < 0.001). In conclusion, we developed a machine learning model to predict long-term mortality among patients with angina. With the integration of OGTT, the model could help to identify a high risk of mortality across the spectrum of dysglycemia. View Full-Text
Keywords: angiography; Brier score; Harrell’s C-index; least absolute shrinkage and selection operator; machine learning; oral glucose tolerance test angiography; Brier score; Harrell’s C-index; least absolute shrinkage and selection operator; machine learning; oral glucose tolerance test
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MDPI and ACS Style

Li, Y.-H.; Sheu, W.H.-H.; Yeh, W.-C.; Chang, Y.-C.; Lee, I.-T. Predicting Long-Term Mortality in Patients with Angina across the Spectrum of Dysglycemia: A Machine Learning Approach. Diagnostics 2021, 11, 1060. https://doi.org/10.3390/diagnostics11061060

AMA Style

Li Y-H, Sheu WH-H, Yeh W-C, Chang Y-C, Lee I-T. Predicting Long-Term Mortality in Patients with Angina across the Spectrum of Dysglycemia: A Machine Learning Approach. Diagnostics. 2021; 11(6):1060. https://doi.org/10.3390/diagnostics11061060

Chicago/Turabian Style

Li, Yu-Hsuan, Wayne H.-H. Sheu, Wen-Chao Yeh, Yung-Chun Chang, and I-Te Lee. 2021. "Predicting Long-Term Mortality in Patients with Angina across the Spectrum of Dysglycemia: A Machine Learning Approach" Diagnostics 11, no. 6: 1060. https://doi.org/10.3390/diagnostics11061060

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