Hypertensive intracerebral hemorrhage (HICH) is one of the most serious complications of hypertension, which may be due to persistent hypertension, the pathological changes such as fibrosis or hyalinization appear on the vascular wall of intracranial arterioles, and the elasticity of the vascular wall is weakened, finally leading to vascular rupture and hemorrhage (1). HICH has a rapid onset and is dangerous with a complex pathogenesis. It involves many factors, such as poor blood pressure control, arteriosclerosis, and vascular endothelial injury. Even after active treatment, there are still many patients with poor prognosis, which brings a heavy burden to the family and society (2). Therefore, in-depth study of the poor prognosis factors of hypertensive intracerebral hemorrhage is of great significance to improve the treatment effect and the prognosis of patients. According to relevant data, studies have shown that Adropin can protect the blood–brain barrier (BBB) through the Notch1 signaling pathway, and knocking down Notch1 and Hes1 in vivo eliminates Adropin’s protective effect. This indicates that the Notch1/Hes1 pathway plays a crucial role in Adropin’s protection of the blood–brain barrier (3). Another study has found that changes in the level of thromboxane 2 (TXB2) in cerebrospinal fluid are related to the prognosis of patients with hypertensive intracerebral hemorrhage, and the combined detection of TXB2 and S100B levels has a good predictive effect on the prognosis of patients with hypertensive intracerebral hemorrhage (4). Data shows that high levels of lysophosphatidic acid receptor 1 (LPAR1) may be associated with neurological damage and poor prognosis after cerebral hemorrhage, and therefore can serve as an important biomarker for prognostic evaluation of hypertensive intracerebral hemorrhage patients (5). CT imaging tomography is an emerging technology for comprehensive processing and analysis by extracting a large number of imaging features of tissues. In recent years, radiology has shown great potential in the evaluation of many diseases (including early hematoma enlargement of hypertensive intracerebral hemorrhage, lung cancer, and so on) (6, 7). However, due to the personal experience of the test subjects and the personal situation of the test subjects, there may be different differences. To find a prediction method with high accuracy to maximize the diagnosis and prediction efficiency of HICH prognosis is of great significance for improving the prognosis of patients. Nomogram model is a visual prediction tool, which provides a simple and intuitive method for individual prediction by integrating a variety of risk factors. In the field of medical research, nomogram models have been successfully applied to predict the prognosis of cardiovascular diseases, tumors and other diseases, and the nomogram models have high prediction accuracy, clinical practicability and operability, thus providing powerful support for clinical decision making (8, 9). In this study, we retrospectively analyzed the influencing factors of prognosis in patients with hypertensive intracerebral hemorrhage (HIH) and established an nomogram prediction model based on clinical risk factors and CT radiohistological characteristics, in order to explore its predictive value for prognosis of HIH patients.

In recent years, HICH accounts for 70 to 80% of cases of ICH, among which basal ganglia hemorrhage, as a typical manifestation of HICH, is particularly prominent (11). Although the current treatment strategies for HICH patients have become increasingly mature and efficient, early and accurate identification and intervention of those risk factors that may worsen the prognosis are still invaluable for optimizing the patient’s rehabilitation outcome. Long-term hypertension will gradually erode the cerebrovascular system, triggering the process of hyalinization and fibrosis of the microvascular wall. This pathological change not only reshapes the hemodynamic characteristics of the cerebrovascular system, but also weakens the elastic reserve capacity of the vascular wall, making the cerebrovascular system more vulnerable to blood pressure fluctuations, and finally inducing HICH (9) under the rapid changes of hypertension (12). In addition, the over-activation of the renin-angiotensin-aldosterone system (RAAS) is also considered to be an important factor behind the progression of hypertension and HICH. The imbalance of RAAS not only promotes vasoconstriction and aggravates vascular resistance, but also promotes water and sodium retention, thereby increasing the effective circulating blood volume, forming a vicious circle and aggravating the complexity of the disease and the difficulty of treatment (13, 14). An in-depth understanding of the pathophysiological mechanism of HICH, especially the dynamic evolution of primary and secondary injury, and the role of RAAS system in HICH is of profound significance for formulating more accurate and effective treatment strategies and improving the prognosis of patients.

In terms of clinical risk factors, Logistic analysis showed that hematoma volume, Adropin, TXB2 and LPAR1 were all risk factors affecting the prognosis of HICH patients. Recent studies have shown that in patients with hypertensive intracerebral hemorrhage, the hematoma volume may undergo significant dynamic changes within a period of time after hemorrhage (such as 30 min to several hours). This phenomenon of early hematoma enlargement is considered to be one of the important factors affecting the prognosis (15). Hematoma volume enlargement is common in patients with cerebral hemorrhage. The larger the hematoma volume is, the more severe the compression and destruction of the surrounding brain tissue, and thus affecting the survival and functional recovery of nerve cells (15). Studies have found that about 40% of patients with cerebral hemorrhage may have a significant increase in hematoma, and the increase in hematoma volume may have a significant impact on the survival rate, disability rate and mortality rate of patients (16). Therefore, in the treatment process of hypertensive cerebral hemorrhage, we should pay close attention to the changes of hematoma volume and take corresponding treatment measures to reduce the damage and compression of brain tissue. At the same time, other influencing factors such as bleeding site and blood pressure control should be comprehensively considered in order to formulate personalized treatment plan. Adropin is a secreted protein synthesized by the liver and brain, and has been recognized as a key regulator of cardiovascular health and metabolic balance (17). It has been found that the higher the level of Adropin, the lower the risk of cardiovascular disease, which plays an important role in regulating lipid and glucose metabolism and maintaining energy homeostasis (3). It has been reported that Adropin plays an important role in improving the neurological function of patients with cerebral hemorrhage, which may be related to the fact that Adropin promotes the appreciation of endothelial cells, microvessel formation, inhibits inflammatory reaction, and thus promotes the repair of vascular endothelial cells (18). When hypertensive cerebral hemorrhage occurs, due to the damage of vascular wall and hemodynamic changes, platelets are easily activated and release a variety of active products. TXB2 and LPAR1 are both platelet activation products, which may promote platelet aggregation and aggravate the degree of ischemia and hypoxia of brain tissue. Platelet activation products can also promote the occurrence and development of inflammatory response. After hypertensive cerebral hemorrhage, the inflammatory response is one of the important processes for the damage and repair of brain tissue. However, excessive inflammatory response may aggravate the damage of brain tissue and lead to poor prognosis (19, 20). Studies have found that in the group with poor prognosis of HICH patients, the levels of TXB2 and LPAR1 were significantly increased and correlated with the NIHSS score, which could be used as an important indicator for evaluating the prognosis of patients, with similar results to those in the present study (21, 22).

Radiomics is a field of study concerned with the extraction of quantitative features (i.e., radiomic features) from medical images that capture complex information such as tissue and lesion heterogeneity, shape, and can be used in conjunction with demographic, histological, genomic, or proteomic data to address clinical questions, and in the assessment of hypertensive cerebral hemorrhage, radiomic features may include the shape of the hematoma irregularity, mixed sign, midline displacement, island sign, uneven density, and many other indicators (23). Radiomics characterization requires only relatively simple outlining of ROIs and computer-assisted techniques to achieve the assessment of hematoma heterogeneity, which reduces the physician’s empirical requirements for the identification of imaging signs, and plays an important role in improving the accuracy of clinical diagnosis, prognosis prediction, and therapeutic efficacy, as well as facilitating clinical decision-making (24, 25). Previous studies have found that CT radiomics combined with clinical features have high value in predicting the prognosis of non-small cell lung cancer (26). In addition, CT plain imaging features have good performance in predicting hematoma volume increase in cerebral hemorrhage (27). In this study, the results found that the proportion of patients with HICH in the poor prognosis group with irregular shape, mixed sign, midline displacement, island sign, and uneven density were significantly increased, and the CT radiographic features were the risk factors affecting the patients’ poor prognosis by logistic analysis, the study will further reveal the potential value of radiographic features in evaluating the prognosis of hypertensive cerebral hemorrhage, and provide more accurate bases for clinical decision-making. The study will further reveal the potential value of radiographic features in assessing the prognosis of hypertensive cerebral hemorrhage and provide a more accurate basis for clinical decision-making.

In addition, in this study, the nomogram prediction model was established based on the relevant influencing factors. The results showed that the consistency index C-index of the calibration curve of the nomogram prediction model was 0.820, and the AUC of the ROC curve was 0.854. The model had high fitting degree and accuracy, and the net benefit value of the prediction model was higher when the range was 0.1–0.9. The above indicates that the nomogram prediction model may play a positive role in predicting the prognosis of patients with HICH. Compared with traditional clinical prediction methods, the nomogram model can more intuitively show the relationship between various risk factors and prognosis, providing a quantitative prediction tool for clinicians. In addition, the model helps to improve the accuracy of prediction and provides the basis for patients to develop individual treatment plans. In patients with HICH, targeted treatment measures can be formulated according to the relevant risk factors to adjust the treatment regimen, thereby effectively improving the prognosis of the patient. In order to reduce data collection bias, we ensure the reliability and accuracy of the data sources, rigorously check the integrity of the data, and use appropriate statistical methods to handle missing and outlier values. In order to promote research progress in this field, we have proposed several directions for future research. Firstly, we plan to conduct a large-scale prospective study to validate our findings and explore more factors related to the research topic. Secondly, we will strive to collect more comprehensive patient information, including baseline features, treatment process, and follow-up data, to further improve the accuracy and reliability of the study.

In summary, the nomogram prediction model 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. This study has achieved certain results in exploring the poor prognosis of HICH patients, but there are also some limitations. Among them, the most significant is that this study used a relatively small sample size. Although we have made every effort to ensure sample diversity and representativeness, and adopted appropriate methods in statistical analysis to reduce bias, the small sample size may still limit the generalizability and robustness of the research results. Therefore, our conclusions and model predictive performance need to be validated in larger samples. To overcome this limitation and further improve the predictive accuracy of the model, we plan to adopt a multi center, multi regional approach in future studies to collect more diverse patient demographic data. This will help us build a more comprehensive and accurate model to better predict the prognosis or treatment outcomes of HICH patients. At the same time, we will also explore other factors that may affect the poor prognosis of HICH patients to further improve our research.