Introduction:
To develop a risk prediction model of delayed cerebral edema after hypertensive cerebral hemorrhage based on serum inflammatory factors and hemodynamics, and to evaluate its clinical application value.

Material and methods:
Multivariate logistic regression was used to analyze the risk factors of postoperative bleeding and build a nomogram prediction model, and a receiver operating characteristic (ROC) curve and calibration curve were drawn to evaluate the prediction efficiency of the nomogram model, which was verified in the validation set.

Results:
The results of univariate analysis revealed significant differences in interleukin-6 (IL-6), tumor necrosis factor-a (TNF-a), interleukin-1b (IL-1b), macrophage inflammatory protein-1a (MIP-1a), cerebral blood flow (CBF), cerebrovascular reactivity (CVR), high-mobility group Box 1 protein (HMGB1), and C-reactive protein (CRP) between patients with edema and those without edema in training (p < 0.05). Logistic regression analysis showed that IL-6, TNF-a, IL-1b, CBF, CVR, HMGB1 and CRP were independent risk factors for bleeding (p < 0.05). The nomogram model demonstrated good calibration and agreement between prediction and reality in both the training and validation sets (C-index index: 0.792 and 0.799). The results of the Hosmer-Lemeshow test were: c2 = 16.582, p = 0.035 and c2 = 7.472, p = 0.487. ROC curve analysis showed that the area under the curve (AUC) for predicting delayed cerebral edema was 0.794 in the training set and 0.796 in the validation set.

Conclusions:
The nomogram prediction model of delayed cerebral edema after hypertensive cerebral hemorrhage based on serum inflammatory factors (IL-6, TNF-a, IL-1b), hemodynamic parameters (CBF, CVR), and other related factors (HMGB1, CRP) was successfully established. The model can effectively predict the risk of delayed cerebral edema in patients with hypertensive cerebral hemorrhage.