Evidence map›Paper›PMID 38707960›Full record

ArticleJournal of inflammation research2024

Predictors of Inadequate Serum Urate Response to Low-Dose Febuxostat in Male Patients with Gout.

Wenyan Sun, Xuetong Zhao, Nicola Dalbeth, Robert Terkeltaub, Lingling Cui, Zhen Liu, Lin Han, Can Wang, Hui Zhang, Yiming Bao and 2 more

Abstract read
In one paragraph

Article in Journal of inflammation research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

1 citing paper in PubMed.

  1. Review
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

12 authors.

Wenyan Sun *Shandong Provincial Key Laboratory of Metabolic Diseases, Shandong Provincial Clinical Research Center for Immune Diseases, the Affiliated Hospital of Qingdao University, Qingdao, People's Republic of China.
Xuetong Zhao *National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, People's Republic of China.
Nicola Dalbeth *Department of Medicine, University of Auckland, Auckland, New Zealand.
Robert TerkeltaubVA San Diego VA Healthcare Center, University of California San Diego, San Diego, CA, USA.
Lingling CuiShandong Provincial Key Laboratory of Metabolic Diseases, Shandong Provincial Clinical Research Center for Immune Diseases, the Affiliated Hospital of Qingdao University, Qingdao, People's Republic of China.
Zhen LiuShandong Provincial Key Laboratory of Metabolic Diseases, Shandong Provincial Clinical Research Center for Immune Diseases, the Affiliated Hospital of Qingdao University, Qingdao, People's Republic of China.
Lin HanShandong Provincial Key Laboratory of Metabolic Diseases, Shandong Provincial Clinical Research Center for Immune Diseases, the Affiliated Hospital of Qingdao University, Qingdao, People's Republic of China.
Can WangShandong Provincial Key Laboratory of Metabolic Diseases, Shandong Provincial Clinical Research Center for Immune Diseases, the Affiliated Hospital of Qingdao University, Qingdao, People's Republic of China.
Hui ZhangShandong Provincial Key Laboratory of Metabolic Diseases, Shandong Provincial Clinical Research Center for Immune Diseases, the Affiliated Hospital of Qingdao University, Qingdao, People's Republic of China.
Yiming BaoNational Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, People's Republic of China.
Changgui LiShandong Provincial Key Laboratory of Metabolic Diseases, Shandong Provincial Clinical Research Center for Immune Diseases, the Affiliated Hospital of Qingdao University, Qingdao, People's Republic of China.
Jie LuShandong Provincial Key Laboratory of Metabolic Diseases, Shandong Provincial Clinical Research Center for Immune Diseases, the Affiliated Hospital of Qingdao University, Qingdao, People's Republic of China.ORCID 0000-0003-2886-8285

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This study aimed to understand predictors of inadequate response (IR) to low-dose febuxostat treatment based on clinical variables. Methods: We pooled data from 340 patients of an observational cohort and two clinical trials who received febuxostat 20 mg/day for at least 3 months. IR was defined as failure to reach the target serum urate level (sUA<6 mg/dL) at any time point during 3 months treatment. The potential predictors associated with short- or mid-term febuxostat IR after pooling the three cohorts were explored using mixed-effect logistic analysis. Machine learning models were performed to evaluate the predictors for IR using the pooled data as the discovery set and validated in an external test set. Results: Of the 340 patients, 68.9% and 51.8% were non-responders to low-dose febuxostat during short- and mid-term follow-up, respectively. Serum urate and triglyceride (TG) levels were significantly associated with febuxostat IR, but were also selected as significant features by LASSO analysis combined with age, BMI, and C-reactive protein (CRP). These five features in combination, using the best-performing stochastic gradient descent classifier, achieved an area under the receiver operating characteristic curve of 0.873 (95% CI [0.763, 0.942]) and 0.706 (95% CI [0.636, 0.727]) in the internal and external test sets, respectively, to predict febuxostat IR. Conclusion: Response to low-dose febuxostat is associated with early sUA improvement in individual patients, as well as patient age, BMI, and levels of TG and CRP.

Indexed as

febuxostatgoutmachine learning modelurate-lowering therapy

Identifiers

PMID38707960
PMCPMC11069377

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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.