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Updated: May 29, 2025
Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
Published on: September 25, 2019
Development and External Validation of a Prediction Model for Early Postoperative Cerebral Infarction on Computed
Kun Lin1,2, Zhi-Yun Zhan3, Yong-Xiu Tong4
1Department of Neurosurgery, Neurosurgery Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, China.
Background:
Early postoperative cerebral infarction (ePCI) significantly worsens outcomes in patients with spontaneous intracerebral hemorrhage (ICH) undergoing surgery. This study aimed to develop and externally validate a nomogram to assess ePCI risk.
Methods:
Adult patients with spontaneous supratentorial ICH who underwent surgery between May 2015 and September 2022 at a large tertiary referral center (development cohort) and another tertiary referral center (external validation cohort) were retrospectively included. ePCI was defined as a newly identified permanent low-density lesion observed within 72 h of surgery on computed tomography. We developed a nomogram using predictors identified through least absolute shrinkage and selection operator analysis. The model's discrimination, calibration, and clinical utility were evaluated.
Results:
The development cohort (n = 453) had 51 ePCI cases, and the external validation cohort (n = 184) had 20. The model incorporated the Glasgow Coma Scale (GCS), the Original Intracerebral Hemorrhage Scale (oICH), uncal herniation stage, and hematoma volume, demonstrating strong discrimination with an area under the receiver operating characteristic curve (AUC) of 0.915 (95% confidence interval [CI] 0.882-0.948) in the development cohort and an AUC of 0.942 (95% CI 0.897-0.988) in the external independent cohort. The model also showed excellent calibration and clinical applicability.
Conclusions:
This nomogram, including the GCS, the oICH, uncal herniation stage, and hematoma volume, effectively predicts ePCI risk in patients with spontaneous supratentorial ICH.
