Fusion model of imaging data combined with clinical and laboratory data can predict the prognosis of spontaneous intracerebral hemorrhage

PENG Chengyuan; ZENG Chunyi; WU Zongshan; ZHOU Kunsheng

doi:10.12122/j.issn.1674-4500.2025.01.14

Abstract：Objective To analyze the value of imaging data,clinical-laboratory data model and fusion model in predicting prognosis of patients with spontaneous intracerebral hemorrhage.Methods A total of 363 patients diagnosed with spontaneous cerebral hemorrhage by head CT in Lu'an People's Hospital from September 2021 to March 2023 were retrospectively collected.The functional recovery of patients 6 months after discharge was evaluated using the modified Rankin scale.According to the results,the patients were divided into two groups:the good outcome(n=175)and the poor outcome(n=188).Clinical data such as age and gender,neutrophil and lymphocyte counts and their percentages,D-dimer,and hematoma image data outlined and extracted from the first head CT scan on admission were recorded.Multiple logistic regression method was used to construct clinical-laboratory data model,image data model and fusion model,respectively.Results Clinical and laboratory data of statistical significance between the two groups included GCS score,number and percentage of neutrophils,and percentage of lymphocytes(P<0.001).Imaging data included ventricle presence or absence of hematoma,hematoma sphericity,surface area,and feret diameter(P<0.001).The AUC of clinical-laboratory data model,image data model and fusion model were 0.82(95%CI:0.78-0.86),0.80(95%CI:0.75-0.84)and 0.86(95%CI:0.82-0.89).Delong test showed that the performance of fusion model was significantly different from that of single clinical-laboratory data model and imaging data model(P<0.05).Conclusion The fusion model of imaging data combined with clinical and laboratory data has significant value in predicting the prognosis of spontaneous intracerebral hemorrhage.