ArticleHealthcare (Basel, Switzerland)2023
A Simple Nomogram for Predicting Stroke-Associated Pneumonia in Patients with Acute Ischemic Stroke.
Article in Healthcare (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 8 citations in OpenAlex.
- Development and external validation of a risk stratification model for stroke-associated pneumonia.iScience · 2026Article
- Risk estimation for stroke-associated pneumonia in acute ischemic stroke: a nomogram-based approach.Arquivos de neuro-psiquiatria · 2026Article
- Construction and evaluation of a nomogram model for predicting the risk of hospital-acquired pneumonia in elderly patients with acute ischemic stroke.BMC geriatrics · 2025Article
- Dynamic Nomogram for Predicting the Fall Risk of Stroke Patients: An Observational Study.Clinical interventions in aging · 2025Observational
- Association between fibrinogen levels and stroke-associated pneumonia in acute ischemic stroke patients.BMC neurology · 2024Article
- Risk Factors for the Development of Pneumonia in Stroke Patients: A Systematic Review and Meta-Analysis.Cureus · 2024Review
- Development and validation of a machine learning model for predicting stroke-associated pneumonia in older patients with acute ischemic stroke.Frontiers in neurologyArticle
- Enhancing stroke-associated pneumonia prediction in ischemic stroke: An interpretable Bayesian network approach.Digital healthArticle
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The purpose of this study was to develop a prediction model for stroke-associated pneumonia (SAP) based on risk factors for SAP and to suggest nursing interventions to prevent SAP. In addition, a nomogram was developed to enhance its utility in nursing practice. The retrospective cohort study included 551 patients hospitalized for acute ischemic stroke at a university hospital in South Korea. Data were collected through a structured questionnaire and a review of the electronic medical record (EMR). In the development of a predictive model for SAP, multivariate logistic regression analysis showed that independent risk factors for SAP were age ≥ 65 years, National Institute of Health Stroke Scale (NIHSS) score ≥ 7, nasogastric tube feeding, and C-reactive protein (CRP) ≥ 5.0 mg/dL. The logit model was used to construct the SAP prediction nomogram, and the area under the curve (AUC) of the nomogram was 0.94. Furthermore, the slope of the calibration plot was close to the 45-degree line, indicating that the developed nomogram may be useful for predicting SAP. It is necessary to monitor the age, NIHSS score, nasogastric tube feeding status, and CRP level of stroke patients and identify high-risk groups using the developed nomogram to provide active nursing interventions to prevent SAP.
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Registered trials
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