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Published on: April 13, 2013
A predictive nomogram for intracerebral hematoma expansion based on non-contrast computed tomography and clinical
Xiuping Zhang1, Qianqian Gao1, Kaidong Chen1
1Department of Radiology, The Affiliated Wuxi People's Hospital of Nanjing Medical University, No. 299, Qingyang Road, Liangxi District, Wuxi, 214023, Jiangsu Province, China.
Purpose:
To develop and validate a new nomogram utilizing non-contrast computed tomography (NCCT) signs and clinical factors for predicting hematoma expansion (HE) in patients with spontaneous intracerebral hemorrhage (ICH).
Methods:
HE was defined as > 6 mL or 33% increase in baseline hematoma volume. Multivariable logistic regression analysis was performed to identify the predictors of HE. The discriminatory performance of the proposed model was evaluated via receiver operation characteristic (ROC) analysis, and the predictive accuracy was assessed by a calibration curve. The nomogram was established by R programming language. The decision curve analysis and clinical impact curve were drawn according to the related risk factors.
Results:
A total of 506 patients with spontaneous ICH were recruited in the development cohort, and 103 patients were registered as the external validation cohort. Among the development cohort, 132 (26.09%) experienced HE. Glasgow coma scale (GCS) (P < 0.001), neutrophil to lymphocyte ratio (NLR) (P < 0.001), blend sign (P < 0.001), swirl sign (P < 0.001), and hypodensities (P = 0.003) were significant predictors of HE, by which were used to establish the nomogram. The model demonstrated good performance with high area under the curve both in the development (AUC = 0.908; 95% confidence interval, 0.880-0.936) and the external validation (AUC = 0.844; 95% confidence interval, 0.760-0.908) cohort. The calibration curve illustrated a high accuracy for HE prediction.
Conclusion:
The nomogram derived from NCCT markers and clinical factors outperformed the NCCT signs-only model in predicting HE for patients with ICH, thus providing an effective and noninvasive tool for the risk stratification of HE.
