Development and validation of a nomogram model for identifying post-stroke anxiety symptoms

doi:10.5498/wjp.122517

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Publication Name

World Journal of Psychiatry

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2220-3206

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

Retrospective Study

Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.

World J Psychiatry. Oct 19, 2026; 16(10): 122517
Published online Oct 19, 2026. doi: 10.5498/wjp.122517

Development and validation of a nomogram model for identifying post-stroke anxiety symptoms

Shang-Yu Luo, Li Feng, Yun-Jun Hong, Ji Liang, Yu Peng, Li-Hua Shao, Xiao-Bo Zhang

Shang-Yu Luo, Yun-Jun Hong, Ji Liang, Yu Peng, Li-Hua Shao, Xiao-Bo Zhang, Department of Neurology, Changde Hospital, Xiangya School of Medicine, Central South University (The First People’s Hospital of Changde City), Changde 415000, Hunan Province, China

Li Feng, Department of Neurology, Xiangya Hospital, Central South University, Changsha 410008, Hunan Province, China

Li Feng, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha Hunan Province, 410008, China

Co-corresponding authors: Li-Hua Shao and Xiao-Bo Zhang.

Author contributions: Luo SY wrote the paper; Feng L and Hong YJ analyzed the data; Liang J collected the data; Shao LH, Zhang XB were responsible for study conceptualization, manuscript revision, and final approval of the manuscript, they contributed equally to this article, and are the co-corresponding authors of this manuscript; Luo SY, Feng L, Hong YJ, Liang J, Peng Y, Shao LH, and Zhang XB subsequently critically edited the manuscript, and all read and approved the final edition; all authors were responsible for the decision to submit the manuscript for publication.

AI contribution statement: We did not use AI tools in writing the article.

Supported by the Science and Technology Innovation Program of Changde City, No. CDKJJ20242294.

Institutional review board statement: This study was approved by the Medical Ethics Committee of the First People’s Hospital of Changde City, approval No. 2025-172-01.

Informed consent statement: Written informed consent was obtained from all participants or their families, with family members providing consent on behalf of stroke patients who were unable to do so.

Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.

Data sharing statement: No additional data are available.

Corresponding author: Xiao-Bo Zhang, Chief Physician, Department of Neurology, Changde Hospital, Xiangya School of Medicine, Central South University (The First People’s Hospital of Changde City), No. 818 Renmin Road, Changde 415000, Hunan Province, China. 285058041@qq.com

Received: April 27, 2026
Revised: July 14, 2026
Accepted: September 4, 2026
Published online: October 19, 2026
Processing time: 168 Days and 18.7 Hours

Abstract

BACKGROUND

Currently, no established nomogram models exist for identifying post-stroke anxiety symptoms (PSAS).

AIM

To develop and validate a nomogram for the identification of PSAS.

METHODS

This retrospective cross-sectional study included 947 stroke patients. Demographics, the Generalized Anxiety Disorder 7-item, the Patient Health Questionnaire-9 (PHQ-9), clinical biomarkers, and stroke-related scale assessments were collected. Participants were randomly divided into the training and the internal validation cohorts in a 7:3 ratio. Multivariate logistic regression was employed to identify predictors for constructing the nomogram. The performance of the nomogram was evaluated using receiver operating characteristic curves, calibration plots, and decision curve analysis.

RESULTS

Five independent predictors were identified: Age, stroke type, central post-stroke pain, family history of stroke, and depression. The C-statistic (equivalent to the area under the receiver operating characteristic curve) was 0.86 (95% confidence interval: 0.83-0.90) for the training cohort and 0.82 (95% confidence interval: 0.74-0.90) for the split-sample internal validation cohort, indicating a strong ability to identify patients with PSAS. Calibration curves demonstrated a good agreement between predicted and observed risks in both cohorts. Decision curve analysis confirmed the clinical utility of the nomogram.

CONCLUSION

The nomogram demonstrated good discrimination and calibration in a single-center sample. It may help identify patients at elevated risk for current anxiety symptoms, though external validation is needed.

Keywords: Stroke; Anxiety; Nomogram; Independent associated factors; Retrospective cross-sectional study

Core Tip: This retrospective cross-sectional study sought to develop a clinical recognition model for post-stroke anxiety symptoms. The nomogram exhibited robust discrimination and calibration in a single-center sample, indicating its potential to identify patients at high risk for current anxiety symptoms, pending external validation.