eISSN 2097-6046
ISSN 2096-7446
CN 10-1655/R
Responsible Institution:China Association for Science and Technology
Sponsor:Chinese Nursing Association

Chinese Journal of Emergency and Critical Care Nursing ›› 2026, Vol. 7 ›› Issue (9): 1132-1139.doi: 10.3761/j.issn.2096-7446.2026.09.018

• Evidence Synthesis Research • Previous Articles     Next Articles

Systematic review of aspiration risk prediction models for stroke patients

LI Xiangru1(), ZHOU Hong1,*(), GENG Jing1, MAO Fangju2, ZHOU Juan2, XU Chenyang3, LIU Qiang1   

  1. 1 Yangtze University Health Science CenterJingzhouHubei Province 434023, China
    2 Neurological Intensive Care Unitthe First Affiliated Hospital of Yangtze University,JingzhouHubei Province 434023, China
    3 Department of Rehabilitation MedicineTongji Hospital,Tongji Medical College,Huazhong University of Science and TechnologyWuhan 430030, China
  • Received:2025-09-20 Online:2026-09-10 Published:2026-08-31
  • Contact: * ZHOU Hong,E-mail:1059634547@qq.com
  • Supported by:
    Hubei Provincial Science and Technology Innovation Special Project(2021CFB601)

Abstract:

Objective To systematically evaluate aspiration risk prediction models for stroke patients and provide evidence-based support for the early identification of high-risk patients in clinical practice. Methods A comprehensive search was conducted in multiple databases,including Wan fang,VIP,CNKI,China Biomedical Literature Database,Web of Science,PubMed,and Embase,with a search period from database inception to January 20,2025. Literature screening and data extraction were independently performed by two researchers,and the Prediction Model Risk of Bias Assessment Tool(PROBAST) was used for quality assessment. Results A total of 18 studies was included,encompassing 25 prediction models. The area under the receiver operating characteristic curve ranged from 0.715 to 0.955. Sixteen studies demonstrated good applicability,while two studies had poor applicability. All 18 studies exhibited a high risk of bias,mainly due to inappropriate data source selection,small sample size,insufficient handling of missing data,unreasonable variable selection,and a lack of model efficacy validation. Conclusion The existing stroke aspiration risk prediction models show good clinical applicability but are associated with a high risk of bias. Future research should focus on optimizing study design,enhancing external validation,and further assessing their clinical application value.

Key words: Aspiration, Stroke, Prediction Model, Systematic Review, Nursing Care