2020
DOI: 10.1155/2020/2310307
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Relationship between Uric Acid Level and Severity of Acute Primary Cerebral Infarction: A Cross-Sectional Study

Abstract: Numerous studies have shown that uric acid (UA) is associated with cerebrovascular disease, but whether UA is a protective factor or worsens the risk of developing cerebrovascular disease remains controversial. This study investigated the relationship between UA levels detected at admission and the severity of acute primary cerebral infarction. This cross-sectional study enrolled patients with acute primary cerebral infarction (N=238, 157 men). We designated the levels of serum UA measured at the time of admis… Show more

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Cited by 8 publications
(10 citation statements)
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“…These factors on their own might increase the risk of stroke and result in adverse outcomes following stroke. Further, the SUA level was found to be nonlinearly related to the degree of neurological impairment in patients with acute primary cerebral infarction [ 33 ]. When the SUA was <372 μ mol/L, it was negatively correlated with the degree of neurological impairment in patients with ACI.…”
Section: Discussionmentioning
confidence: 99%
“…These factors on their own might increase the risk of stroke and result in adverse outcomes following stroke. Further, the SUA level was found to be nonlinearly related to the degree of neurological impairment in patients with acute primary cerebral infarction [ 33 ]. When the SUA was <372 μ mol/L, it was negatively correlated with the degree of neurological impairment in patients with ACI.…”
Section: Discussionmentioning
confidence: 99%
“…We further explored the nonlinearity between the TG/HDL-c ratio and unfavorable outcomes using generalized additive models (GAM) and smooth curve fitting (penalized splines). If nonlinearity is detected, we first compute the inflection point using a recursive algorithm (The recursive algorithm begins with an arbitrary initiation followed by filtering/smoothing steps to find the inflection point) and then build a two-piece binary logistic regression model on both sides of the inflection point [ 31 ]. A log-likelihood ratio test was used to determine the most appropriate model describing the association of TG/HDL-c ratio and unfavorable outcomes in patients with AIS.…”
Section: Methodsmentioning
confidence: 99%
“…The recursive algorithm starts with a random initialization and then uses a filtering/smoothing step to find the inflection point. We then built a binary logistic regression model on both sides of the inflection point ( 31 ). The log-likelihood ratio test was used to find the best model to describe the link between BMI and unfavorable outcomes in participants with AIS.…”
Section: Methodsmentioning
confidence: 99%