Risk Nomogram Prediction Model for Cerebral Edema in Patients With Hypertensive Intracerebral Hemorrhage

Objective To establish and validate a risk prediction model for cerebral edema in patients with hypertensive intracerebral hemorrhage (HICH).

Methods A total of 320 HICH patients from January 2020 to March 2025 were retrospectively selected and divided into a training set (n = 224) and a validation set (n = 96) at a ratio of 7∶3. Based on the occurrence of cerebral edema, the training set was divided into a cerebral edema group (n = 71) and a non-cerebral edema group (n = 153), and the validation set was divided into a cerebral edema group (n = 31) and a non-cerebral edema group (n = 65). LASSO regression was used to screen variables, followed by multivariate logistic analysis, and a nomogram prediction model was established. The model was validated using the receiver operating characteristic (ROC) curve analysis and calibration curve.

Results There were 71 cases (31.70%) in the training set and 31 cases (32.29%) in the validation set with cerebral edema. Statistically significant differences were found in age, hematoma volume, Glasgow Coma Scale (GCS) score, National Institutes of Health Stroke Scale (NIHSS), serum matrix metalloproteinase-9 (MMP-9), angiopoietin-like protein 2 (ANGPTL2), and thrombospondin-1 (TSP-1) levels between the cerebral edema and non-cerebral edema groups (P < 0.05). Multivariate logistic analysis showed that hematoma volume (odds ratio OR = 1.227, 95% confidence interval CI: 1.115-1.351), GCS (OR = 0.700, 95% CI: 0.569-0.862), NIHSS (OR = 1.176, 95% CI: 1.030-1.342), MMP-9 (OR = 1.017, 95% CI: 1.009-1.026), ANGPTL2 (OR = 3.759, 95% CI: 1.784-7.919), and TSP-1 (OR = 1.097, 95% CI: 1.046-1.149) were influencing factors for cerebral edema in HICH patients (P < 0.05). ROC curve analysis showed that the area under the curve (AUC) of the training set was 0.930 (95% CI: 0.886-0.962), and that of the validation set was 0.952 (95% CI: 0.915-0.990). The calibration curve showed that the prediction curves of the training and validation sets were consistent with the standard curve.

Conclusion Hematoma volume, GCS, NIHSS, serum MMP-9, ANGPTL2, and TSP-1 levels are influencing factors for cerebral edema in HICH patients, and the nomogram model constructed based on these factors has demonstrated efficacy.