Related Experiment Video
Updated: Jun 29, 2026

Evaluation of the Cognitive Performance of Hypertensive Patients with Silent Cerebrovascular Lesions
Published on: April 23, 2021
Prediction of Neurological Deterioration After Intracerebral Hemorrhage: The SIGNALS Score
Quanwei He1, Hongxiu Guo1, Rentang Bi1
1Department of Neurology, Union Hospital, Tongji Medical College Huazhong University of Science and Technology Wuhan Hubei Province China.
Insights
We developed the SIGNALS score to predict neurological deterioration in patients hospitalized with intracerebral hemorrhage. This score helps identify patients at higher risk of worsening stroke.
Area of Science:
- Neurology
- Clinical Medicine
- Stroke Research
Background:
- Intracerebral hemorrhage (ICH) is a severe stroke type with high disability and mortality rates.
- Predicting neurological deterioration during hospitalization is crucial for patient management.
- Existing prediction tools for ICH outcomes require enhancement.
Purpose of the Study:
- To develop and validate a novel clinical prediction score for in-hospital neurological deterioration after ICH.
- To identify key clinical and laboratory factors associated with worsening neurological status in ICH patients.
Main Methods:
- Analysis of data from the Chinese Cerebral Hemorrhage: Mechanism and Intervention (CHERRY) study.
- Development of a multivariable logistic regression model using a training cohort (n=1027).
- Validation of the developed SIGNALS score in an independent cohort (n=515).
Main Results:
- The SIGNALS score incorporates site, size, sex, NIH Stroke Scale, age, white blood cell count, and glucose levels.
- Higher SIGNALS scores correlated with increased risk of neurological deterioration (NIH Stroke Scale increase ≥4 or death).
- The score demonstrated good discrimination (C-statistics 0.821-0.848) and calibration in both training and validation cohorts.
Conclusions:
- The SIGNALS score is a reliable tool for predicting the risk of in-hospital neurological deterioration in ICH patients.
- This score can aid clinicians in risk stratification and timely intervention for ICH.
- Further research may refine the score for broader clinical application.
Abstract:
Background Intracerebral hemorrhage is the most disabling and lethal form of stroke. We aimed to develop a novel clinical score for neurological deterioration during hospitalization after intracerebral hemorrhage. Methods and Results We analyzed data from the CHERRY (Chinese Cerebral Hemorrhage: Mechanism and Intervention) study. Two-thirds of eligible patients were randomly allocated into the training cohort (n=1027) and one-third into the validation cohort (n=515). Multivariable logistic regression was used to identify factors associated with neurological deterioration (an increase in National Institutes of Health Stroke Scale of ≥4 or death) within 15 days after symptom onset. A prediction score was developed based on regression coefficients derived from the logistic model. The site, size, gender, National Institutes of Health Stroke Scale, age, leukocyte, sugar (SIGNALS) score was developed as a sum of individual points (0-8) based on site (1 point for infratentorial location), size (3 points for >20 mL of supratentorial hematoma volume or 2 points for >10 mL of infratentorial hematoma volume), sex (1 point for male sex), National Institutes of Health Stroke Scale score (1 point for >10), age (1 point for ≥70 years), white blood cell (1 point for>9.0×109/L), and fasting blood glucose (1 point>7.0 mmol/L). The proportion of patients who suffered from neurological deterioration increased with higher SIGNALS score, showing good discrimination and good calibration in the training cohort (C statistic, 0.821; Hosmer-Lemeshow test, P=0.687) and in the validation cohort (C statistic, 0.848; Hosmer-Lemeshow test, P=0.592), respectively. Conclusions The SIGNALS score reliably predicts the risk of in-hospital neurological deterioration of patients with intracerebral hemorrhage.
Related Concept Videos
Hemorrhagic Stroke l: Introduction
Hemorrhagic Stroke ll: Pathophysiology

