AI-based prediction for the risk of coronary heart disease among patients with type 2 diabetes mellitus.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
02 09 2020
02 09 2020
Historique:
received:
23
06
2020
accepted:
27
07
2020
entrez:
4
9
2020
pubmed:
4
9
2020
medline:
25
2
2021
Statut:
epublish
Résumé
Type 2 diabetes mellitus (T2DM) is one common chronic disease caused by insulin secretion disorder that often leads to severe outcomes and even death due to complications, among which coronary heart disease (CHD) represents the most common and severe one. Given a huge number of T2DM patients, it is thus increasingly important to identify the ones with high risks of CHD complication but the quantitative method is still not available. Here, we first curated a dataset of 1,273 T2DM patients including 304 and 969 ones with or without CHD, respectively. We then trained an artificial intelligence (AI) model using randomly selected 4/5 of the dataset and use the rest data to validate the performance of the model. The result showed that the model achieved an AUC of 0.77 (fivefold cross-validation) on the training dataset and 0.80 on the testing dataset. To further confirm the performance of the presented model, we recruited 1,253 new T2DM patients as totally independent testing dataset including 200 and 1,053 ones with or without CHD. And the model achieved an AUC of 0.71. In addition, we implemented a model to quantitatively evaluate the risk contribution of each feature, which is thus able to present personalized guidance for specific individuals. Finally, an online web server for the model was built. This study presented an AI model to determine the risk of T2DM patients to develop to CHD, which has potential value in providing early warning personalized guidance of CHD risk for both T2DM patients and clinicians.
Identifiants
pubmed: 32879331
doi: 10.1038/s41598-020-71321-2
pii: 10.1038/s41598-020-71321-2
pmc: PMC7467935
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
14457Références
WHO Global Report. Global Report on Diabetes. Isbn 978, 6–86 (2016).
Sebastiani, G. et al. Circulating microRNAs and diabetes mellitus: A novel tool for disease prediction, diagnosis, and staging?. J. Endocrinol. Investig. 40, 591–610 (2017).
doi: 10.1007/s40618-017-0611-4
WHO. Diabetes. https://www.who.int/news-room/fact-sheets/detail/diabetes . Accessed 21 Dec 2019.
Internation Diabetes Federation. IDF Diabetes Atlas Ninth (IDF, Dunia, 2019).
American Diabetes Association. 2. Classification and Diagnosis of Diabetes. Diabetes Care 39, S13–S22 (2016).
doi: 10.2337/dc16-er09
Ma, R. C. W. Epidemiology of diabetes and diabetic complications in China. Diabetologia 61, 1249–1260 (2018).
doi: 10.1007/s00125-018-4557-7
Zheng, Y., Ley, S. H. & Hu, F. B. Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Nat. Rev. Endocrinol. 14, 88–98 (2018).
doi: 10.1038/nrendo.2017.151
Zilliox, L. A., Chadrasekaran, K., Kwan, J. Y. & Russell, J. W. Diabetes and cognitive impairment. Curr. Diab. Rep. 16, 87 (2016).
doi: 10.1007/s11892-016-0775-x
Tan, Y. et al. Mechanisms of diabetic cardiomyopathy and potential therapeutic strategies: Preclinical and clinical evidence. Nat. Rev. Cardiol. https://doi.org/10.1038/s41569-020-0339-2 (2020).
doi: 10.1038/s41569-020-0339-2
pubmed: 32080423
Association American Diabetes. 10. Cardiovascular disease and risk management: Standards of medical care in diabetes—2020. Diabetes Care 43, S111–S134 (2020).
doi: 10.2337/dc20-S010
WHO. Cardiovascular diseases (CVDs). WHO https://www.who.int/en/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds ). Accessed 21 Dec 2019.
Maneerat, Y., Prasongsukarn, K., Benjathummarak, S., Dechkhajorn, W. & Chaisri, U. Intersected genes in hyperlipidemia and coronary bypass patients: Feasible biomarkers for coronary heart disease. Atherosclerosis 252, e183–e184 (2016).
doi: 10.1016/j.atherosclerosis.2016.07.854
Nakashima, T. et al. Prognostic impact of spontaneous coronary artery dissection in young female patients with acute myocardial infarction: A report from the Angina Pectoris-Myocardial Infarction Multicenter Investigators in Japan. Int. J. Cardiol. 207, 341–348 (2016).
doi: 10.1016/j.ijcard.2016.01.188
Zebrack, J. S. et al. Usefulness of high-sensitivity C-Reactive protein in predicting long-term risk of death or acute myocardial infarction in patients with unstable or stable angina pectoris or acute myocardial infarction. Am. J. Cardiol. 89, 145–149 (2002).
doi: 10.1016/S0002-9149(01)02190-7
Kim, J. K. & Kang, S. Neural network-based coronary heart disease risk prediction using feature correlation analysis. J. Healthc. Eng. 2017, 1–13 (2017).
Fryar, C. D., Chen, T.-C. & Li, X. Prevalence of Uncontrolled Risk Factors for Cardiovascular Disease: United States, 1999–2010. (2012).
Benjamin, E. J. et al. Heart disease and stroke statistics—2019 update: A report from the American Heart Association. Circulation 139, e56–e528 (2019).
doi: 10.1161/CIR.0000000000000659
Gordon, T. & Kannel, W. B. Multiple risk functions for predicting coronary heart disease: The concept, accuracy, and application. Am. Heart J. 103, 1031–1039 (1982).
doi: 10.1016/0002-8703(82)90567-1
Wilson, P. W. F. et al. Prediction of coronary heart disease using risk factor categories. Circulation 97, 1837–1847 (1998).
doi: 10.1161/01.CIR.97.18.1837
Gordon, T. Diabetes, blood lipids, and the role of obesity in coronary heart disease risk for women. Ann. Intern. Med. 87, 393 (1977).
doi: 10.7326/0003-4819-87-4-393
Narain, R., Saxena, S. & Goyal, A. Cardiovascular risk prediction: A comparative study of Framingham and quantum neural network based approach. Patient Prefer. Adherence 10, 1259–1270 (2016).
doi: 10.2147/PPA.S108203
Nishimura, K. et al. Predicting coronary heart disease using risk factor categories for a Japanese urban population, and comparison with the Framingham risk score: The suita study. J. Atheroscler. Thromb. 21, 784–798 (2014).
doi: 10.5551/jat.19356
Onat, A. Algorithm for predicting CHD death risk in Turkish adults: Conventional factors contribute only moderately in women. Anatol. J. Cardiol. 17, 436–444 (2017).
pubmed: 28315569
pmcid: 5477072
Arabasadi, Z., Alizadehsani, R., Roshanzamir, M., Moosaei, H. & Yarifard, A. A. Computer aided decision making for heart disease detection using hybrid neural network-Genetic algorithm. Comput. Methods Programs Biomed. 141, 19–26 (2017).
doi: 10.1016/j.cmpb.2017.01.004
American Heart Association. Cardiovascular Disease and Diabetes. https://www.heart.org/en/health-topics/diabetes/why-diabetes-matters/cardiovascular-disease--diabetes . Accessed 25 Mar 2020.
Kannel, W. B. Diabetes and cardiovascular disease. The Framingham study. JAMA J. Am. Med. Assoc. 241, 2035–2038 (1979).
doi: 10.1001/jama.1979.03290450033020
McKinney, W. Data structures for statistical computing in Python. In Proceedings of the 9th Python in Science Conference 51–56 (2010).
Varoquaux, G. et al. Scikit-learn. GetMobile Mob Comput. Commun. 19, 29–33 (2015).
doi: 10.1145/2786984.2786995
Tin Kam Ho. The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 20, 832–844 (1998).
doi: 10.1109/34.709601
Breiman, L., Friedman, J. H., Olshen, R. A. & Stone, C. J. Classification and Regression Trees. (Routledge, 2017). https://doi.org/10.1201/9781315139470 .