Development and validation of a nomogram for predicting symptomatic hemorrhage risk in sporadic cerebral cavernous malformations.

Authors

Yang Liu, Huan Kang, Zheng Wen, Jing Yuan, Li Ma, Shuo Zhang, Qingyuan Liu, Shuo Wang, Huan Fan, Jun Wu

Published in

Journal of neurosurgery. Pages 1-11. Aug 07, 2026. Epub Aug 07, 2026.

Abstract

The aim of this study was to develop and externally validate a nomogram to predict individualized 5-year symptomatic hemorrhage risk in patients with sporadic cerebral cavernous malformations (CCMs).
Patients diagnosed with sporadic CCMs who were enrolled in two prospective multicenter cohorts were analyzed. Independent predictors of symptomatic hemorrhage were identified through multivariate Cox regression analysis and integrated into a predictive nomogram. The nomogram's performance was assessed using the C-index, calibration curves, decision curves, and areas under the curve. Patients were stratified into high- and low-risk groups based on optimal nomogram cutoff scores.
The training cohort included 331 patients (173 female, mean age 39.1 years), and the external validation cohort included 57 patients (31 female, mean age 39.2 years). Multivariate Cox regression analysis identified previous hemorrhage (HR 2.66), lesion size > 1.5 cm (HR 2.89), concurrent developmental venous anomalies (HR 2.24), brainstem location (HR 4.04), and Zabramski type I lesions (HR 3.65) as independent predictors of symptomatic hemorrhage. The developed nomogram demonstrated strong predictive accuracy, with a C-index of 0.81 (95% CI 0.75-0.87) in the training cohort and 0.87 (95% CI 0.78-0.95) in the external validation cohort. Patients classified into the high-risk group (≥ 195 points) exhibited significantly higher hemorrhage risk compared with those in the low-risk group.
The developed nomogram effectively integrates clinical and radiological factors to accurately predict individualized 5-year symptomatic hemorrhage risk in patients with sporadic CCMs, providing valuable support for personalized clinical decision-making.

PMID:
42566798
Bibliographic data and abstract were imported from PubMed on 08 Aug 2026.

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