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Zhu B, Yang L, Wu M, Wu Q, Liu K, Li Y, Guo W, Zhao Z. Prediction of hyperuricemia in people taking low-dose aspirin using a machine learning algorithm: a cross-sectional study of the National Health and Nutrition Examination Survey. Front Pharmacol 2024; 14:1276149. [PMID: 38313076 PMCID: PMC10834797 DOI: 10.3389/fphar.2023.1276149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Accepted: 12/12/2023] [Indexed: 02/06/2024] Open
Abstract
Background: Hyperuricemia is a serious health problem related to not only gout but also cardiovascular diseases (CVDs). Low-dose aspirin was reported to inhibit uric acid excretion, which leads to hyperuricemia. To decrease hyperuricemia-related CVD, this study aimed to identify the risk of hyperuricemia in people taking aspirin. Method: The original data of this cross-sectional study were obtained from the National Health and Nutrition Examination Survey between 2011 and 2018. Participants who filled in the "Preventive Aspirin Use" questionnaire with a positive answer were included in the analysis. Six machine learning algorithms were screened, and eXtreme Gradient Boosting (XGBoost) was employed to establish a model to predict the risk of hyperuricemia. Results: A total of 805 participants were enrolled in the final analysis, of which 190 participants had hyperuricemia. The participants were divided into a training set and testing set at a ratio of 8:2. The area under the curve for the training set was 0.864 and for the testing set was 0.811. The SHapley Additive exPlanations (SHAP) method was used to evaluate the performances of the modeling. Based on the SHAP results, the feature ranking interpretation showed that the estimated glomerular filtration rate, body mass index, and waist circumference were the three most important features for hyperuricemia in individuals taking aspirin. In addition, triglyceride, hypertension, total cholesterol, high-density lipoprotein, low-density lipoprotein, age, race, and smoking were also correlated with the development of hyperuricemia. Conclusion: A predictive model established by XGBoost algorithms can potentially help clinicians make an early detection of hyperuricemia risk in people taking low-dose aspirin.
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Affiliation(s)
- Bin Zhu
- Department of Pharmacy, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Li Yang
- Department of Pharmacy, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Mingfen Wu
- Department of Pharmacy, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Qiao Wu
- Emergency Department, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Kejia Liu
- DHC Mediway Technology Co., Ltd., Beijing, China
| | - Yansheng Li
- DHC Mediway Technology Co., Ltd., Beijing, China
| | - Wei Guo
- Emergency Department, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, China
| | - Zhigang Zhao
- Department of Pharmacy, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
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Guan J, Leung E, Kwok KO, Chen FY. Correction: A hybrid machine learning framework to improve prediction of all-cause rehospitalization among eldely patients in Hong Kong. BMC Med Res Methodol 2023; 23:38. [PMID: 36782144 PMCID: PMC9926774 DOI: 10.1186/s12874-023-01851-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/15/2023] Open
Affiliation(s)
| | - Eman Leung
- grid.10784.3a0000 0004 1937 0482JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Kin-on Kwok
- grid.10784.3a0000 0004 1937 0482JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, China ,grid.10784.3a0000 0004 1937 0482Stanley Ho Centre for Emerging Infectious Diseases, The Chinese University of Hong Kong, Hong Kong SAR, China ,grid.10784.3a0000 0004 1937 0482Hong Kong Institute of Asia-Pacific Studies, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Frank Youhua Chen
- Department of Management Sciences, City University of Hong Kong, Hong Kong SAR, China.
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