ArticleNPJ digital medicine2025
GRACE-ICU: A multimodal nomogram-based approach for illness severity assessment of older adults in the ICU.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
3 citing papers in PubMed.
- Bridging data gaps of rare conditions in ICU: a multi-disease adaptation approach for clinical prediction.NPJ digital medicine · 2026Article
- An efficiency assessment of digital technology empowering healthy aging in China: an empirical study of the "internet plus nursing services" pilot program based on the DEA model.Frontiers in digital health · 2026Article
- An explainable machine learning approach to predicting carbapenem resistance inFrontiers in cellular and infection microbiology · 2026Article
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Authors and funding
15 authors.
Funding
Abstract
Clinical notes are crucial for patient assessment in the ICU but can be challenging to accurately and objectively analyze in time-constrained situations. We developed the GRACE-ICU model which integrates clinical notes and structured data to rapidly assess critical illness severity in older adults. Based on a cohort from a large U.S. teaching hospital, we fine-tuned a Clinical-Longformer model on pre-ICU notes and combined it with 10 significant structured variables via logistic regression. The receiver operating characteristic curve, calibration curve, decision curve analysis, and 11 metrics were obtained to evaluate its performance in internal, temporal, and external validations when compared with four types of baseline models. Our model outperformed the single-modal models and clinical commonly-used illness scores in both internal and temporal validation for early prediction of hospital mortality and provides interpretable, data-driven recommendations for clinical decision-making, with potential for broader applications. Further prospective studies are needed before clinical use.
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Registered trials
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