ArticleCureus2025
A New Early Warning System for Predicting Adverse Outcomes in Hospitalized Patients With Neurological Conditions: A Retrospective Cohort Study.
Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
What it found
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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
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Automated Features, Algorithms, and Technologies of Electronic Early Warning/Track-and-Trigger Systems: Systematic Review.Journal of medical Internet research · 2026Pooled it
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction Early Warning Scores (EWS) are widely used to identify acute clinical deterioration in hospitalized patients, particularly when integrated with rapid response teams (RRTs) as part of hospital safety systems. Although EWS have demonstrated benefits in reducing mortality and facilitating timely intensive care transfers, existing models have been primarily validated in general populations. Neurological and neurosurgical patients, despite being a significant subgroup of emergency admissions, are often underrepresented in these validation studies. Their unique clinical presentations - often subtle or atypical - pose additional challenges for early detection. This study aims to assess the predictive accuracy of various EWS for in-hospital mortality and urgent transfers among patients with neurological and neurosurgical diagnoses, and to develop a new, neurological-specific score (NEURO-EWS) tailored to this population. Methods This retrospective cohort study analyzed clinical records of adult patients with neurological conditions, divided into neurological and neurosurgical subgroups. The primary outcome was a composite of in-hospital mortality or urgent transfer to critical care; secondary outcomes analyzed these components separately. The predictive performance of EWS was evaluated at multiple time points using the area under the receiver operating characteristic curve (AUC), and calibration was assessed through probability plots. A novel scoring model, NEURO-EWS, was developed using multivariate logistic regression and validated in a separate cohort. Results Standard EWS showed lower predictive performance than expected. The modified Rapid Emergency Medicine Score (mREMS) achieved the highest accuracy among existing models (AUC: 0.68 in the overall cohort, 0.754 in neurological, and 0.646 in neurosurgical patients). The newly developed NEURO-EWS demonstrated superior performance in neurological patients (AUC: 0.782), outperforming all other scores. Conclusion Disease-specific EWS improves the detection of clinical deterioration in neurological patients. While mREMS remains the most reliable general model, NEURO-EWS provides enhanced predictive value for neurological populations and may support better clinical decision-making.
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Registered trials
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.