Trial reportJournal of translational medicine2022
ICH-LR2S2: a new risk score for predicting stroke-associated pneumonia from spontaneous intracerebral hemorrhage.
Trial report in Journal of translational medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 3 of them syntheses that pooled it.
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Who cites it
22 citing papers in PubMed, 3 syntheses or guidelines pooled it, 31 citations in OpenAlex.
- European stroke organisation guideline on stroke-associated pneumonia.European stroke journal · 2026Guideline
- Models of risk prediction for lung infections following a stroke: A systematic review.Medicine · 2026Pooled it
- Risk prediction model for postoperative pneumonia in esophageal cancer patients: A systematic review.Frontiers in oncology · 2024Pooled it
- Clinical utility of the mini nutritional assessment short form in predicting stroke-associated pneumonia in acute stroke patients: a prospective cohort study.Scientific reports · 2026Article
- Correlation between the CALLY index and postoperative stroke-associated pneumonia in patients with spontaneous intracerebral hemorrhage.Neurosurgical review · 2026Article
- Transforming Intracerebral Hemorrhage Care with Artificial Intelligence: Opportunities, Challenges, and Future Directions.Diagnostics (Basel, Switzerland) · 2026Review
- Predicting time-to-event outcomes in critically ill patients with intracerebral hemorrhage using machine learning.The Journal of international medical research · 2026Article
- Construction and validation of a machine learning-based model for predicting pneumonia risk in patients with hemorrhagic stroke.Frontiers in medicine · 2026Article
- Predicting stroke-associated pneumonia for acute ischemic stroke patients post intravenous thrombolysis: the post-IVT-SAP risk score.Frontiers in medicine · 2026Article
- Do conventional intracerebral hemorrhage functional scores apply to mild-to-moderate hemorrhage?Frontiers in neurology · 2026Article
- Innovative nomogram for predictive risk stratification of aspiration pneumonia in post-stroke dysphagia patients.Frontiers in neurology · 2025Article
- Predictors and lesion patterns of dysphagia and swallowing outcomes after acute intracerebral hemorrhage.Therapeutic advances in neurological disorders · 2025Article
- A Novel Nomogram for Predicting the Risk of Pneumonia After Intracerebral Hemorrhage.Journal of inflammation research · 2025Article
- Association between nutritional status and pneumonia in patients with spontaneous intracerebral hemorrhage.Frontiers in nutrition · 2025Article
- Hydrogel applications: a promising frontier in pneumonia therapy.Frontiers in bioengineering and biotechnology · 2025Review
- Influencing factors and prognosis in patients with spontaneous intracerebral hemorrhage combined with pulmonary infection running head: pulmonary infection in ICH patients.Frontiers in neurology · 2025Article
- A novel prediction method for intracerebral hemorrhage-associated pneumonia: A single center analysis.PloS one · 2025Article
- Autonomic dysfunction and treatment strategies in intracerebral hemorrhage.CNS neuroscience & therapeutics · 2024Review
- Machine learning using multimodal and autonomic nervous system parameters predicts clinically apparent stroke-associated pneumonia in a development and testing study.Journal of neurology · 2024Article
- Article
Corrections and comments
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Authors and funding
18 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeWe develop a new risk score to predict patients with stroke-associated pneumonia (SAP) who have an acute intracranial hemorrhage (ICH).
methodWe applied logistic regression to develop a new risk score called ICH-LR2S2. It was derived from examining a dataset of 70,540 ICH patients between 2015 and 2018 from the Chinese Stroke Center Alliance (CSCA). During the training of ICH-LR2S2, patients were randomly divided into two groups - 80% for the training set and 20% for model validation. A prospective test set was developed using 12,523 patients recruited in 2019. To further verify its effectiveness, we tested ICH-LR2S2 on an external dataset of 24,860 patients from the China National Stroke Registration Management System II (CNSR II). The performance of ICH-LR2S2 was measured by the area under the receiver operating characteristic curve (AUROC).
resultsThe incidence of SAP in the dataset was 25.52%. A 24-point ICH-LR2S2 was developed from independent predictors, including age, modified Rankin Scale, fasting blood glucose, National Institutes of Health Stroke Scale admission score, Glasgow Coma Scale score, C-reactive protein, dysphagia, Chronic Obstructive Pulmonary Disease, and current smoking. The results showed that ICH-LR2S2 achieved an AUC = 0.749 [95% CI 0.739-0.759], which outperforms the best baseline ICH-APS (AUC = 0.704) [95% CI 0.694-0.714]. Compared with the previous ICH risk scores, ICH-LR2S2 incorporates fasting blood glucose and C-reactive protein, improving its discriminative ability. Machine learning methods such as XGboost (AUC = 0.772) [95% CI 0.762-0.782] can further improve our prediction performance. It also performed well when further validated by the external independent cohort of patients (n = 24,860), ICH-LR2S2 AUC = 0.784 [95% CI 0.774-0.794].
conclusionICH-LR2S2 accurately distinguishes SAP patients based on easily available clinical features. It can help identify high-risk patients in the early stages of diseases.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.