Evidence map›Paper›PMID 38988602›Full record

ArticleFrontiers in neurology2024

Serum uric acid as a predictor of mortality in patients with stroke: results from National Health and Nutrition Examination Survey 2007-2016.

Xinyu Tong, Chuxin Lyu, Minjie Guo, Jianxiong Gu, Yichun Zhao

Abstract read
In one paragraph

Article in Frontiers in neurology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Review
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Xinyu TongDepartment of Neurology, Wuxi Traditional Chinese Medicine Hospital, Wuxi, China.
Chuxin LyuFirst Clinical Medical School, Nanjing University of Chinese Medicine, Nanjing, China.
Minjie GuoDepartment of Neurology, Wuxi Traditional Chinese Medicine Hospital, Wuxi, China.
Jianxiong GuDepartment of Neurology, Wuxi Traditional Chinese Medicine Hospital, Wuxi, China.
Yichun ZhaoDepartment of Neurology, Wuxi Traditional Chinese Medicine Hospital, Wuxi, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This research endeavors to explore the relationship between serum uric acid (SUA) concentration and all-cause mortality in stroke patients. Methods: We undertook a cross-sectional analysis utilizing data derived from the National Health and Nutrition Examination Survey (NHANES) spanning 2007 to 2016. The concentrations of SUA served as the independent variable, while the dependent variable was defined as all-cause mortality in stroke patients. The quartile method was utilized to classify uric acid levels into four distinct categories. Subsequently, three models were developed, and Cox proportional hazards regression was used to assess the effect of varying uric acid concentrations on the risk of all-cause mortality among stroke patients. Results: The study included a total of 10,805 participants, of whom 395 were stroke patients. Among all populations, the group with elevated levels of uric acid (Q4) exhibited a significant association with the overall mortality risk among stroke patients in all three models (model 1 Conclusion: This cross-sectional investigation reveals a significant correlation between SUA levels and all-cause mortality in stroke patients, with a noticeable trend observed among females. Consequently, SUA may serve as a promising biomarker for assessing the prognosis of individuals affected by stroke.

Indexed as

Cox regressionmortalityNHANESserum uric acidstroke

Identifiers

PMID38988602
PMCPMC11233759

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.