Prognostic value of nomogram model based on clinical risk factors and CT radiohistological features in hypertensive intracerebral hemorrhage

Objective: To construct a nomogram model based on clinical risk factors and CT radiohistological features to predict the prognosis of hypertensive intracerebral hemorrhage (HICH). Methods: A total of 148 patients with HICH from April 2022 to July 2024 were retrospectively selected as the research subjects. According to the modified Rankin scale at the time of discharge, they were divided into good group (Rankin scale score 0–2) and bad group (Rankin scale score 3–6). To compare the clinical data and the changes of CT radiographic characteristics in patients with different prognosis. Relevant factors affecting the prognosis were analyzed, and nomogram model was established based on the influencing factors. The fitting degree, prediction efficiency and clinical net benefit of the nomogram model were evaluated by calibration curve, ROC curve and clinical decision curve (DCA). Results: Compared with the good group, the hematoma volume in the poor group was significantly increased, the serum thromboxane 2(TXB2) and lysophosphatidic acid receptor 1(LPAR1) levels were significantly increased, and the energy balance related protein (Adropin) level was significantly decreased. The proportions of irregular shape, promiscuous sign, midline displacement, island sign and uneven density were all significantly increased (p < 0.05). In Logistic multivariate analysis, hematoma volume, Adropin, TXB2, LPAR1 and CT radiological features were all independent factors influencing the poor prognosis of HICH (p < 0.05). A nomogram prediction model was established based on the influencing factors. The calibration curve showed that the C-index was 0.820 (95% CI: 0.799–0.861), the goodness of fit test χ = 5.479, and p = 0.391 > 0.05, indicating a high degree of fitting. The ROC curve showed that the AUC was 0.896 (95% CI: 0.817–0.923), indicating that this model had high prediction ability. The DCA curve shows that the net benefit of the nomogram model is higher when the threshold probability is 0.1–0.9. Conclusion: The nomogram prediction model established based on hematoma volume, Adropin, TXB2, LPAR1 and other clinical risk factors as well as CT radiographic characteristics has high accuracy and prediction value in the diagnosis of poor prognosis in patients with HICH.