scholarly journals Hazardous Effect of Low-Dose Aspirin in Patients with Predialysis Advanced Chronic Kidney Disease Assessed by Machine Learning Method Feature Selection

Healthcare ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1484
Author(s):  
Ming-Hsien Tsai ◽  
Hung-Hsiang Liou ◽  
Yen-Chun Huang ◽  
Tian-Shyug Lee ◽  
Mingchih Chen ◽  
...  

Background: 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]) and death before entering dialysis (1.46 [1.25–1.71]), which were also supported by feature selection. The renal effect of aspirin use was consistent across patient subgroups. Nonusers and aspirin users did not show a significant difference, except for gastrointestinal bleeding (1.05 [0.96–1.15]), intracranial hemorrhage events (1.23 [0.98–1.55]), or ischemic stroke (1.15 [0.98–1.55]). Conclusions: Patients with predialysis advanced CKD and anemia who received aspirin exhibited higher risk of entering dialysis and death before entering dialysis by 15% and 46%, respectively.

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yun Jung Oh ◽  
Ae Jin Kim ◽  
Han Ro ◽  
Jae Hyun Chang ◽  
Hyun Hee Lee ◽  
...  

AbstractThe benefits and risks of aspirin therapy for patients with chronic kidney disease (CKD) who have a high burden of cardiovascular events (CVE) are controversial. To examine the effects of low-dose aspirin on major clinical outcomes in patients with CKD. As a prospective observational cohort study, using propensity score matching, 531 aspirin recipients and non-recipients were paired for analysis from 2070 patients and fulfilled the inclusion criteria among 2238 patients with CKD. The primary outcome was the first occurrence of major CVE. The secondary outcomes were kidney events defined as a > 50% reduction of estimated glomerular filtration rate from baseline, doubling of serum creatinine, or onset of kidney failure with replacement therapy, the all-cause mortality, and bleeding event. The incidence of CVE was significantly greater in low-dose aspirin users than in non-users (HR 1.798; P = 0.011). A significant association between aspirin use and an increased risk of CVE was observed only in the lowest quartile of body weight (HR 4.014; P = 0.019) (Q1 < 60.0 kg). Secondary outcomes were not significantly different between aspirin users and non-users. It needs to be individualized of prescribing low-dose aspirin for the prevention of cardiovascular events in patients with chronic kidney disease, particularly patients with low bodyweight (< 60 kg).


PLoS ONE ◽  
2014 ◽  
Vol 9 (8) ◽  
pp. e104179 ◽  
Author(s):  
Ae Jin Kim ◽  
Hye Jin Lim ◽  
Han Ro ◽  
Kwang-Pil Ko ◽  
Song Yi Han ◽  
...  

Author(s):  
Marian Goicoechea ◽  
Maria Dolores Sanchez-Niño ◽  
Alberto Ortiz ◽  
Soledad García de Vinuesa ◽  
Borja Quiroga ◽  
...  

2013 ◽  
Vol 167 (5) ◽  
pp. 2333-2334 ◽  
Author(s):  
Min Chul Kim ◽  
Youngkeun Ahn ◽  
Keun Ho Park ◽  
Doo Sun Sim ◽  
Nam Sik Yoon ◽  
...  

2021 ◽  
Vol 5 (2) ◽  
pp. 415
Author(s):  
Firdausi Nuzula Zamzami ◽  
Adiwijaya Adiwijaya ◽  
Mahendra Dwifebri P

Information exchange is currently the most happening on the internet. Information exchange can be done in many ways, such as expressing expressions on social media. One of them is reviewing a film. When someone reviews a film he will use his emotions to express their feelings, it can be positive or negative. The fast growth of the internet has made information more diverse, plentiful and unstructured. Sentiment analysis can handle this, because sentiment analysis is a classification process to understand opinions, interactions, and emotions of a document or text that is carried out automatically by a computer system. One suitable machine learning method is the Modified Balanced Random Forest. To deal with the various data, the feature selection used is Mutual Information. With these two methods, the system is able to produce an accuracy value of 79% and F1-scores value of 75%.


2021 ◽  
Vol 77 (18) ◽  
pp. 3083
Author(s):  
Paul A. Gurbel ◽  
Kevin Bliden ◽  
Naval Walia ◽  
Nicole Rapista ◽  
Gordon Ens ◽  
...  

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