Predicting long-term mortality in patients with angina across the spectrum of dysglycemia: A machine learning approach

Yu Hsuan Li, Wayne Huey Herng Sheu, Wen Chao Yeh, Yung Chun Chang, I. Te Lee

研究成果: 雜誌貢獻文章同行評審

摘要

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.

原文英語
文章編號1060
期刊Diagnostics
11
發行號6
DOIs
出版狀態已發佈 - 六月 2021

ASJC Scopus subject areas

  • 臨床生物化學

指紋

深入研究「Predicting long-term mortality in patients with angina across the spectrum of dysglycemia: A machine learning approach」主題。共同形成了獨特的指紋。

引用此