ArticleScientific reports2025
A novel nomogram for predicting prolonged disorders of consciousness in severe supratentorial hypertensive intracerebral hemorrhage patients.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
This study was designed to develop and validate a risk-prediction nomogram to predict the incidence of prolonged disorders of consciousness (pDoC) in patients with severe supratentorial hypertensive intracerebral hemorrhage (HICH). The clinical data of 222 severe supratentorial HICH patients who were admitted from January 2017 to June 2024 were reviewed, from which 155 patients were enrolled in the training group, while 67 were enrolled in the internal validation group, at a ratio of 7:3. The external data sets containing 197 patients were obtained from another hospital. Independent predictors of pDoC were analyzed using multivariate logistic regression. Furthermore, a nomogram prediction model was constructed using R software. After evaluation of the model, internal and external validations were performed to verify the efficiency of the model using the area under the receiver operating characteristic curves (AUC), calibration plots and decision curve analysis. In multivariate analysis, GCS score (p = 0.002), systolic blood pressure (p = 0.010), hematoma volume (p = 0.012), and modified Graeb Score (p = 0.014) were independent predictors for pDoC in patients with severe supratentorial HICH. The AUC for the training, internal, and external validation cohorts was 0.905 (95% CI: 0.853, 0.958), 0.857 (95% CI: 0.764, 0.950), and 0.897 (95% CI: 0.844, 0.950), respectively, which indicated that the prediction model had an excellent capability of discrimination. Calibration of the model was exhibited by the calibration plots, which showed an optimal concordance between the predicted pDoC probability and actual probability in both training and validation cohorts. Decision curve analysis curves suggested that the predictive model had high clinical utility in practical applications. A prediction model for predicting pDoC in severe supratentorial HICH patients was constructed. The model can help clinicians to identify high-risk patients as soon as possible and prevent the occurrence of pDoC.
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