Development and Validation of a Nomogram Prediction Model for Delayed Cerebral Edema After Intracerebral Hemorrhage Based on Serum Inflammatory Markers and Hemodynamics

Introduction To explore the risk prediction model of delayed cerebral edema after hypertensive cerebral hemorrhage based on serum inflammatory factors and hemodynamics and 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 ROC curve and calibration curve were drawn to evaluate the prediction efficiency of nomogram model, which was verified in the verification set. Results The results of univariate analysis showed that there were significant differences in L-6, TNF-α, IL-1β, MIP-1α, CBF, CVR, HMGB1 and CRP between patients with edema and those without edema in training (P<0.05). Logistic regression analysis showed that L-6, TNF-α, IL-1β, CBF, CVR, HMGB1 and CRP were independent risk factors for bleeding (P<0.05). The nomogram model has good calibration and fitting degree between prediction and reality in training set and verification set (C-index index is 0.792 and 0.799). The results of HosmerLemeshow test are χ2=16.582, P =0.035 and χ2=7.472, P = 0.487. The ROC curve is shown in the training set and the validation set. The AUC of the nomogram model in predicting delayed cerebral edema is 0. 794 and 0. 796. Conclusions The nomogram prediction model of delayed cerebral edema after hypertensive cerebral hemorrhage based on serum inflammatory factors (IL-6, TNF-α, IL-1β), 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.