Neutrophil count and platelet–lymphocyte ratio as simple predictors of ustekinumab response in patients with Crohn’s disease: a retrospective multicenter study

Introduction: Although ustekinumab (UST) has been recommended by guidelines as a parallel first-line biologic agent for Crohn's disease (CD), its efficacy varies among individuals. This study aimed to identify baseline clinical features and laboratory indicators that can predict clinical remission with UST to assist clinical decision-making. Methods: This retrospective study collected data from patients with CD who received UST induction and maintenance therapy across multiple centers. The primary endpoint was the rate of clinical remission at week 48. Independent predictors of clinical remission were identified by multivariate logistic regression analysis. Receiver operating characteristic (ROC) curves and the Youden index were used to determine optimal cutoff values. Results: /L and <237.912, respectively. Discussion: Baseline neutrophil count and baseline PLR together constitute a simple and effective predictive model. This model may help identify patients who are more responsive to UST before treatment, offering a practical tool for individualized and precise treatment of CD.

Authors

Institutions

Publication Details

Journal
Frontiers in Immunology
Published
2026-04-22
DOI
https://doi.org/10.3389/fimmu.2026.1749683
Primary Topic
Inflammatory Bowel Disease
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Neutrophil count and platelet–lymphocyte ratio as simple predictors of ustekinumab response in patients with Crohn’s disease: a retrospective multicenter study

Xiaoping Niu, Xiaoming Sun, Xuemei Xu, Nianxia Qian et al.
Frontiers in Immunology
Inflammatory Bowel Disease
article

Neutrophil count and platelet–lymphocyte ratio as simple predictors of ustekinumab response in patients with Crohn’s disease: a retrospective multicenter study

Xiaoping Niu, Xiaoming Sun, Xuemei Xu, Nianxia Qian, Ping Lin, Shengbing Li, Ming Deng, Ping Liu, Li Xie, Ying Zhang, Lin Li, Mingtao Xu
article en

Abstract

Introduction: Although ustekinumab (UST) has been recommended by guidelines as a parallel first-line biologic agent for Crohn's disease (CD), its efficacy varies among individuals. This study aimed to identify baseline clinical features and laboratory indicators that can predict clinical remission with UST to assist clinical decision-making. Methods: This retrospective study collected data from patients with CD who received UST induction and maintenance therapy across multiple centers. The primary endpoint was the rate of clinical remission at week 48. Independent predictors of clinical remission were identified by multivariate logistic regression analysis. Receiver operating characteristic (ROC) curves and the Youden index were used to determine optimal cutoff values. Results: /L and <237.912, respectively. Discussion: Baseline neutrophil count and baseline PLR together constitute a simple and effective predictive model. This model may help identify patients who are more responsive to UST before treatment, offering a practical tool for individualized and precise treatment of CD.

Frontiers in ImmunologyVol. 17
The People's Hospital Tongling (CN), Wuhu No 1 People's Hospital (CN), Anqing City Hospital (CN), First Affiliated Hospital of Henan University of Science and Technology (CN), First Affiliated Hospital of Bengbu Medical College (CN), First Affiliated Hospital of Wannan Medical College (CN)
Peace, Justice and strong institutions
Openalex Percentile: Top 21%
Inflammatory Bowel Disease
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.