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  • 标题:Hazardous Effect of Low-Dose Aspirin in Patients with Predialysis Advanced Chronic Kidney Disease Assessed by Machine Learning Method Feature Selection
  • 本地全文:下载
  • 作者:Ming-Hsien Tsai ; Hung-Hsiang Liou ; Yen-Chun Huang
  • 期刊名称:Healthcare
  • 电子版ISSN:2227-9032
  • 出版年度:2021
  • 卷号:9
  • 期号:11
  • DOI:10.3390/healthcare9111484
  • 语种:English
  • 出版社:MDPI Publishing
  • 摘要:Low-dose aspirin (100 mg) is widely used in preventing cardiovascular disease in chronic kidney disease (CKD) because its benefits outweighs the harm, however, its effect on clinical outcomes in patients with predialysis advanced CKD is still unclear. This study aimed to assess the effect of aspirin use on clinical outcomes in such group. Methods: Patients were selected from a nationwide diabetes database from January 2009 to June 2017, and divided into two groups, a case group with aspirin use ( n = 3021) and a control group without aspirin use ( n = 9063), by propensity score matching with a 1:3 ratio. The Cox regression model was used to estimate the hazard ratio (HR). Moreover, machine learning method feature selection was used to assess the importance of parameters in the clinical outcomes. Results: In a mean follow-up of 1.54 years, aspirin use was associated with higher risk for entering dialysis (HR, 1.15 [95%CI, 1.10–1.21
  • 关键词:chronic kidney disease; real-world evidence; machine learning; aspirin; nonsteroidal anti-inflammatory drugs; dialysis; feature selection; machine learning
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