Evidence mapPaperPMID 42007297Full record

ArticleJournal of inflammation research2026

Neutrophil Percentage-to-Albumin Ratio as a Predictor of Urinary Tract Infection in Patients with Urinary Stone Disease: Development a Novel User-Friendly Tool.

Jian Liu, YuXuan Chen, XiaoYing Yan, ChunChun Han, Shuai Jin, HaoChong He

Abstract read
In one paragraph

Article in Journal of inflammation research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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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

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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

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

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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

6 authors.

Jian LiuDepartment of Urology, Jiangmen Central Hospital, Jiangmen, People's Republic of China.
YuXuan ChenDepartment of Nursing, Bengbu Medical University, Bengbu, People's Republic of China.ORCID 0009-0001-5353-1716
XiaoYing YanDepartment of Nursing, Guangdong Pharmaceutical University, Guangzhou, People's Republic of China.
ChunChun HanDepartment of Urology, Jiangmen Central Hospital, Jiangmen, People's Republic of China.
Shuai JinDepartment of Nursing, Capital Medical University, Beijing, People's Republic of China.
HaoChong HeDepartment of Nursing, Guangdong Pharmaceutical University, Guangzhou, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Urinary stone disease (USD) is a prevalent condition, and associated urinary tract infections (UTIs) present significant risks, often leading to severe complications. Current diagnostic approaches for UTIs, such as urine culture, are time-consuming, while existing predictive models often lack dynamic biomarkers or are overly complex. The neutrophil-to-albumin ratio (NPAR), a readily available inflammatory marker, merits investigation as a predictor of UTIs in this patient population. Objective: This study aimed to examine the relationship between NPAR and UTIs in patients with USD and to develop a novel, user-friendly predictive tool for assessing UTIs risk. Methods: A retrospective cohort study was conducted at a single center, including 7000 participants with USD (January 2015 to January 2025). The cohort was randomly split into training and validation sets (7:3). The association between NPAR and UTIs was explored using restricted cubic splines (RCS) with three knots. Both traditional logistic regression and LASSO (Least Absolute Shrinkage and Selection Operator) regression were employed, and model performance was assessed via the area under the receiver operating characteristic (ROC) curve (AUC), calibration curves, and decision curve analysis (DCA). Results: NPAR was independently associated with an increased risk of UTIs (adjusted odds ratio [OR] 1.34, 95% confidence interval [CI]: 1.07-1.69, P < 0.001). Restricted cubic spline analysis revealed a nonlinear relationship, with the risk increasing markedly when NPAR exceeded 1.21. A significant interaction by sex was observed ( Conclusion: NPAR is an independent, easily accessible predictor of UTIs in patients with USD. The developed web-based tool may enable rapid UTI risk stratification, with the potential to support timely intervention and personalized treatment. External validation is needed to confirm its generalizability.

Indexed as

LASSO regressionneutrophil-to-albumin ratiopredictive modelurinary stone diseaseurinary tract infection

Identifiers

PMID42007297
PMCPMC13089248

What Socratic holds

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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 Socratic graph.