Retrospective Cohort Study
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Clin Cases. Jul 16, 2024; 12(20): 4048-4056
Published online Jul 16, 2024. doi: 10.12998/wjcc.v12.i20.4048
Identification of risk factors and construction of a nomogram predictive model for post-stroke infection in patients with acute ischemic stroke
Xiao-Chen Liu, Xiao-Jie Chang, Si-Ren Zhao, Shan-Shan Zhu, Yan-Yan Tian, Jing Zhang, Xin-Yue Li
Xiao-Chen Liu, Si-Ren Zhao, Shan-Shan Zhu, Jing Zhang, Department of Neurosurgery, Shandong Provincial Third Hospital, Jinan 250031, Shandong Province, China
Xiao-Jie Chang, Yan-Yan Tian, Xin-Yue Li, Department of Neurology, Shandong Provincial Third Hospital, Jinan 250031, Shandong Province, China
Co-first authors: Xiao-Chen Liu and Xiao-Jie Chang.
Author contributions: Liu XC, Chang XJ, Zhao SR, Zhu SS, Tian YY, Zhang J, and Li XY designed the research study; Liu XC, Chang XJ, Zhao SR, and Zhu SS performed the research; Tian YY and Zhang J contributed new reagents and analytic tools; Liu XC, Chang XJ, and Zhao SR analyzed the data and wrote the manuscript; all authors have read and approved the final manuscript.
Supported by Shandong Province Grassroots Health Technology Innovation Program Project, No. JCK22007.
Institutional review board statement: All procedures performed in the study involving human participants were in accordance with the ethical standards of the institutional and/or national research committee(s) and the Helsinki Declaration (as revised in 2013). The medical ethics committee of Shandong Provincial Third Hospital approved this study (No. KYLL-2022042, Date: August 3, 2022).
Informed consent statement: Written informed consent was obtained from the patients or their legal guardians.
Conflict-of-interest statement: All the authors declare that there are no conflicts of interest that could be perceived as prejudicing the impartiality of the research reported.
Data sharing statement: The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.
STROBE statement: The authors have read the STROBE 2010 statement, and the manuscript was prepared and revised according to the STROBE 2010 statement.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Si-Ren Zhao, MD, Doctor, Department of Neurosurgery, Shandong Provincial Third Hospital, No. 11 Middle Wuying Mountain Road, Tianqiao District, Jinan 250031, Shandong Province, China. 871460867@qq.com
Received: March 8, 2024
Revised: May 6, 2024
Accepted: May 31, 2024
Published online: July 16, 2024
Processing time: 114 Days and 16 Hours
Abstract
BACKGROUND

Post-stroke infection is the most common complication of stroke and poses a huge threat to patients. In addition to prolonging the hospitalization time and increasing the medical burden, post-stroke infection also significantly increases the risk of disease and death. Clarifying the risk factors for post-stroke infection in patients with acute ischemic stroke (AIS) is of great significance. It can guide clinical practice to perform corresponding prevention and control work early, minimizing the risk of stroke-related infections and ensuring favorable disease outcomes.

AIM

To explore the risk factors for post-stroke infection in patients with AIS and to construct a nomogram predictive model.

METHODS

The clinical data of 206 patients with AIS admitted to our hospital between April 2020 and April 2023 were retrospectively collected. Baseline data and post-stroke infection status of all study subjects were assessed, and the risk factors for post-stroke infection in patients with AIS were analyzed.

RESULTS

Totally, 48 patients with AIS developed stroke, with an infection rate of 23.3%. Age, diabetes, disturbance of consciousness, high National Institutes of Health Stroke Scale (NIHSS) score at admission, invasive operation, and chronic obstructive pulmonary disease (COPD) were risk factors for post-stroke infection in patients with AIS (P < 0.05). A nomogram prediction model was constructed with a C-index of 0.891, reflecting the good potential clinical efficacy of the nomogram prediction model. The calibration curve also showed good consistency between the actual observations and nomogram predictions. The area under the receiver operating characteristic curve was 0.891 (95% confidence interval: 0.839–0.942), showing predictive value for post-stroke infection. When the optimal cutoff value was selected, the sensitivity and specificity were 87.5% and 79.7%, respectively.

CONCLUSION

Age, diabetes, disturbance of consciousness, NIHSS score at admission, invasive surgery, and COPD are risk factors for post-stroke infection following AIS. The nomogram prediction model established based on these factors exhibits high discrimination and accuracy.

Keywords: Acute ischemic stroke; Infection; Risk factors; Nomogram prediction model; Chronic obstructive pulmonary disease

Core Tip: This study analyzed the risk factors for post-stroke infection in patients with acute ischemic stroke and constructed a nomogram prediction model. Age, diabetes, disturbance of consciousness, National Institutes of Health Stroke Scale score at admission, invasive surgery, and chronic obstructive pulmonary disease were identified to be important risk factors for post-stroke infection in patients with acute ischemic stroke. The nomogram prediction model established based on these factors has high discriminative power and accuracy.