Predictive Value of the Internal Cerebral Vein Deviation Angle for Intracranial Pressure Assessment and Prognosis in Patients with Acute Subdural Hematoma

Predictive Value of the Internal Cerebral Vein Deviation Angle for Intracranial Pressure Assessment and Prognosis in Patients with Acute Subdural Hematoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Predictive Value of the Internal Cerebral Vein Deviation Angle for Intracranial Pressure Assessment and Prognosis in Patients with Acute Subdural Hematoma Weiming Xu, Taiping Gao, Hengheng Zhai, Bin Li, Feixiang Min, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9080813/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background The core pathological mechanism of traumatic acute subdural hematoma (ASDH) is elevated intracranial pressure (ICP) leading to secondary brain injury; however, current assessment systems lack indicators that reflect the state of cerebral venous circulation. The internal cerebral vein (ICV), as the main drainage channel for deep veins, may become a novel target for evaluating disease severity through its morphological changes. Methods This study prospectively enrolled patients with ASDH admitted to the Department of Neurosurgery from November 2018 to February 2024. Baseline clinical, laboratory, and radiological data were collected. The ICV deviation angle was measured on cranial computed tomography venography (CTV). Its correlation with invasive ICP and 6-month Glasgow Outcome Scale (GOS) scores was analyzed. LASSO regression was employed for feature selection, and machine learning prognostic models were subsequently constructed. Results The ICV deviation angle was profoundly greater in the high ICP group (19.82400) than either the low ICP group (8.92.1) or when correlated with the ICP values showed a strong positive association (R = 0.91). The ICV deviation angle was very high in the poor prognosis group compared to the good prognosis and had a negative correlation with GCS and GOS scores and was significantly positively correlated with CT scores. An AUC of 0.942 was obtained with the estimation of low prognosis with the help of Support Vector Machine (SVM) model, which included the ICV angle, combining with clinical and laboratory indicators, and the decision curve analysis revealed its positive clinical net benefit. Conclusion The ICV deviation angle is a reliable non-invasive imaging biomarker that reflects ICP levels and is closely associated with 6-month neurological outcomes in ASDH patients. Integrating the ICV angle into a multimodal machine learning model significantly enhances prognostic predictive performance, offering a novel perspective based on cerebral venous circulation for the clinical assessment of ASDH. Acute subdural hematoma Internal cerebral vein deviation angle Intracranial pressure Prediction Prognosis Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Introduction Traumatic acute subdural hematoma (ASDH) is a form of traumatic brain injury with high morbidity and mortality, characterized by an increase in intracranial pressure (ICP) as its primary cause of secondary brain damage and unfavorable neurological impacts[1, 2]. Space-occupying effect of hematoma squeegees nearby brain tissue worsening cerebral edema and ischemia to create the vicious cycle of increasing ICP and cerebral ischemia[1, 3, 4]. Invasive ICP monitoring remains the clinical gold standard for real-time intracranial hemodynamic assessment, but its use is limited by procedural risks and delayed deployment, which fails to capture rapidly evolving intracranial changes and creates a critical unmet need for non-invasive imaging biomarkers to guide timely therapeutic interventions[5, 6]. Emerging evidence links impaired deep cerebral venous drainage to ICP elevation in ASDH; hematoma-induced midline shift compresses deep veins, obstructing outflow and amplifying intracranial hypertension[7]. The internal cerebral vein (ICV) exhibits minimal anatomical variation and serves as a major conduit for deep venous drainage, making it a promising target for non-invasive hemodynamic assessment, while cranial computed tomography venography (CTV) enables rapid, clear visualization of the deep intracranial venous system to reliably quantify ICV morphological alterations[8, 9]. Recent clinical perfusion imaging studies have shown that preoperative cerebral blood flow and mean transit time in ASDH patients correlate with 6-month functional outcomes, indicating that venous hemodynamic compromise significantly impacts patient prognosis[9]. A human-sized porcine model of ASDH further confirmed that secondary injury involves basal ganglia and brainstem regions, with ICP and cerebral oxygenation closely tied to venous outflow obstruction, emphasizing that venous congestion rather than just arterial insufficiency is a core trigger of ICP dysregulation[10]. In recent years, the role of cerebral venous circulation dysfunction, especially deep cerebral veins, in ICP changes in patients with traumatic brain injury has attracted extensive attention. Most earlier research has been directed on the arterial system and this is challenging to elucidate complicated clinical observations like acute brain swelling[11, 12]. As the primary drainage system, the intracranial venous system holding about 70%-80% of the intracranial blood volume has its great influence on cerebral blood flow. During the evolution of imaging methods, the dilemma of venous morphological characteristics in the control of cerebral circulation has gained more and more importance[8]. The mass effect of traumatic ASDH can directly compress and traction intracranial veins, resulting in impaired venous reflux and further elevated ICP. Increased ICP in turn aggravates venous compression, forming a vicious cycle and eventually leading to venous circulatory dysfunction[13]. Among these, the ICV, as an important tributary of the deep venous system with minimal anatomical variation, is crucial for deep venous drainage. Its compression and displacement inevitably impair venous return, causing pathological changes including venous stasis, brain swelling, and elevated ICP[14, 15]. Therefore, observing morphological changes of the ICV is expected to be an effective indicator for evaluating the severity and prognosis of TBI patients. This study investigates the correlation between the ICV deviation angle measured on admission CTV and concurrent invasive ICP values in ASDH patients, and evaluates its association with 6-month functional outcomes. It also develops a novel prognostic tool by integrating multimodal data, aiming to address the critical gap in current clinical assessment systems. 2 Methods 2.1 Population and Feature Collection The sample of the study was a collection of patients with acute subdural hematoma (ASDH) of traumatic nature, who were hospitalized at the Department of Neurosurgery during the period from November 2018 to February 2024. Informed consent was given in writing by all subjects or their legal guardians and all the patients were subjected to pre-operative cranial CTV and invasive ICP measurements before surgery. The inclusion criteria were as follows: (1) a confirmed diagnosis of traumatic brain injury; (2) evidence of cranial CTs of unilateral ASDH in the frontotemporal-parietal area; (3) having signs of ICP monitoring, as per the 2020 Consensus of Chinese Experts on the Management of Neurosurgical Critical Care[16]. Patients were excluded based on the following criteria: (1) a history of severe intracranial diseases, such as cerebral infarction, hydrocephalus, or intracranial hemorrhage; (2) the presence of a contralateral intracranial hematoma; or (3) concurrent epidural hematoma or intraventricular hemorrhage. The retrieval of baseline clinical characteristics was done through the electronic medical record system of the hospital. Parameters were collected; they were gender, age, medical history, consciousness state on arrival, mean arterial pressure (MAP) and Glasgow Coma Scale (GCS) score. Laboratory tests such as a red blood cell (RBC) count, platelet (PLT) count, prothrombin time (PT), international normalized ratio (INR), and D-dimer (D-D) were to be realized. Radiological evaluation through CT scan was conducted to determine the hematoma volume, midline shift (MLS) and the existence of traumatic subarachnoid hemorrhage (tSAH). Each patient was later calculated using Rotterdam CT score. Any treatment plan was arrived at according to the 2020 edition of the Guidelines to the Management of Severe Traumatic Brain Injury, and was classified as either craniotomy with the evacuation of hematoma or conservative medical treatment[3]. The critical point of intervention in regards to ICP is 22 mmHg. In this respect, the patients were categorized into a High ICP Group (ICP 22mm Hg and above) and a Low ICP Group (ICP 22mm Hg and below). This classification is aimed at performing a subgroup analysis so as to further ascertain the clinical importance of the internal cerebral veins (ICV) displacement angle in measuring the ICP levels among patients immediately after ASDH. The Glasgow Outcome Scale (GOS) six months after injury was used as the major criterion in prognostic assessment. According to the scoring criteria, all the patients were divided into two groups (Poor Outcome Group or GOS 1–3), and Good Outcome Group or GOS 4–5). 2.2 ICP Monitoring and ICV Deviation Angle Measurement The incision is made 2 cm lateral to the midline on the same side of the ASDH ICP monitoring and drainage needle, manufactured by Spiegelberg (Germany), is used to puncture the ipsilateral lateral ventricle. Following the puncture, ICP is monitored. According to the interpretation of the 4th Edition of the Guidelines for the Management of Severe Traumatic Brain Injury[3]. If the ICP remains consistently > 22 mmHg after conservative intervention, a craniotomy for ASDH evacuation is performed. If the ICP remains ≤ 22 mmHg after conservative intervention, conservative treatment is continued. The CTV image data were reconstructed using Advantage Window 4.6 software. The venous phase sequences were selected for analysis, and the Maximum Intensity Projection (MIP) method was employed with a reconstruction thickness of 2.5 cm. Measurements were performed on the cross-sectional plane of the ICV. The center point for angle measurement was defined as the midpoint of the line connecting the proximal ends of the bilateral ICVs. One side of the angle was defined as the midline between the bilateral ICVs, while the other side was a line parallel to the brain midline. The acute angle formed by the intersection of these two lines was defined as the ICV deviation angle. 2.3 Model Development and Validation The first step to LASSO regression analysis was to conduct a cross-validation ten times which helps the regression analysis to measure the best penalty parameter (λ) to pick features that are predictive and matters a lot. Four machine learning models were created based on the chosen features; namely: Logistic Regression, Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN). The training and evaluation of each model were done via ten-fold cross-validation. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and F1 score along with the area under the precision-recall curve (AUPR) were used to evaluate model discrimination comprehensively. Moreover, the SHAP values were obtained to define the effect of each feature on the outcomes of the prediction. Lastly, a decision curve analysis (DCA) was performed to analyze the clinical net of the models under various threshold probabilities, and that the calibration curves were constructed by using LOESS in order to estimate the fit between the predicted probabilities and the measured outcomes. 2.4 Statistical Analysis The statistical analysis was done with the help of R software (version 4.2. 3). A P value of less than 0.05 on a two-sided basis was taken as a significant value. The data of continuous variable was given as mean plus standard deviation (SD), when the data followed a normal distribution or median plus interquartile range (IQR) when the data were non-normally distributed. Categorical data were in form of counts and percentages. Student t-test or Mann-Whitney U test were used on continuous variables and Fisher exact test or 0 -test was used on the categorical variables to make between group comparisons. 3 Results 3.1 Baseline characteristics of patients categorized by ICP levels This study included 45 patients with recorded intracranial pressure (ICP), categorized into High (n = 27) and Low (n = 18) ICP groups. No significant differences were observed between the two groups regarding age, gender, comorbidities, or laboratory parameters ( p > 0.05). Patients in the High ICP group exhibited greater clinical severity, characterized by lower GCS scores (77.8% severe, p = 0.012), a higher incidence of pupillary dilation (55.6%, p = 0.022), and a higher rate of subarachnoid hemorrhage (55.6%, p = 0.007). Radiologically, the High ICP group showed significantly higher CT scores ( p < 0.001) and larger hematoma volumes ( p < 0.001). Regarding management, a higher proportion of the High ICP group underwent craniotomy (77.8%, p < 0.001). Although the High ICP group had a higher rate of poor prognosis (70.4% vs. 38.9%), the difference did not reach statistical significance ( p = 0.074) (Table 1 ). Table 1 Baseline characteristics of patients grouped by ICP levels. Variable High (n = 27) Low (n = 18) p Gender (%) female 3 (11.1) 5 (27.8) 0.301 male 24 (88.9) 13 (72.2) Age (mean (SD)) 58.85 (12.92) 54.22 (16.55) 0.299 Prognosis (%) bad 19 (70.4) 7 (38.9) 0.074 good 8 (29.6) 11 (61.1) Diabetes (%) No 20 (74.1) 17 (94.4) 0.176 Yes 7 (25.9) 1 (5.6) Hypertension (%) No 19 (70.4) 17 (94.4) 0.11 Yes 8 (29.6) 1 (5.6) GCS_level (%) Severe (3–8) 21 (77.8) 6 (33.3) 0.012 Moderate (9–12) 3 (11.1) 6 (33.3) Mild (13–15) 3 (11.1) 6 (33.3) Coma (%) No 6 (22.2) 7 (38.9) 0.383 Yes 21 (77.8) 11 (61.1) Pupillary dilation (%) No 12 (44.4) 15 (83.3) 0.022 Yes 15 (55.6) 3 (16.7) Subarachnoid hemorrhage (%) No 12 (44.4) 16 (88.9) 0.007 Yes 15 (55.6) 2 (11.1) CT score (median [IQR]) 5.00 [4.00, 6.00] 3.00 [2.00, 3.00] < 0.001 Hematoma volume (median [IQR]) 45.52 [37.50, 68.35] 18.90 [11.25, 29.71] < 0.001 Systolic Pressure (mean (SD)) 146.19 (21.99) 140.83 (31.06) 0.502 Diastolic Pressure (mean (SD)) 81.56 (12.13) 77.00 (18.35) 0.321 Mean arterial pressure (mean (SD)) 103.19 (13.91) 98.28 (21.03) 0.35 Treatment (%) Conservative Treatment 6 (22.2) 16 (88.9) < 0.001 Craniotomy 21 (77.8) 2 (11.1) RBC (mean (SD)) 4.31 (0.73) 4.35 (0.58) 0.844 HB (mean (SD)) 133.30 (16.40) 131.61 (14.94) 0.728 HCT (mean (SD)) 3.17 (10.01) 2.73 (9.93) 0.884 PLT (mean (SD)) 196.07 (72.82) 207.89 (89.31) 0.629 PT (median [IQR]) 11.10 [10.50, 11.90] 11.20 [10.70, 11.83] 0.693 APTT (median [IQR]) 24.60 [22.65, 27.55] 23.70 [21.08, 27.45] 0.366 Fib (median [IQR]) 2.15 [1.62, 2.62] 1.75 [1.54, 2.26] 0.211 INR (median [IQR]) 1.01 [0.93, 1.11] 1.02 [0.97, 1.08] 0.693 D-D (median [IQR]) 35.20 [7.58, 35.20] 23.62 [10.69, 35.20] 0.436 GOS_level (%) Good (4–5) 8 (29.6) 11 (61.1) 0.074 Poor (1–3) 19 (70.4) 7 (38.9) Note: ICP, intracranial pressure; SD, standard deviation; IQR, interquartile range; GCS, Glasgow Coma Scale; CT, computed tomography; RBC, red blood cell count; HB, hemoglobin; HCT, hematocrit; PLT, platelet count; PT, prothrombin time; APTT, activated partial thromboplastin time; Fib, fibrinogen; INR, international normalized ratio; D-D, D-dimer; GOS, Glasgow Outcome Scale. 3.2 Correlation between ICV Deviation Angle and ICP To investigate the differences in ICV deviation angles between the two groups and their value in ICP assessment, we first compared the radiological parameters across the cohorts. As illustrated in Fig. 1 A, the high ICP group exhibited a significantly greater ICV deviation angle compared to the low ICP group (19.8 ± 2.4° vs. 8.9 ± 1.8°, p < 0.001). To further evaluate the quantitative relationship between these variables, a correlation analysis was performed. The results demonstrated a strong positive correlation between the ICV deviation angle and the actual measured ICP values (Fig. 1 B; R = 0.91, p < 0.001), suggesting that the degree of ICV displacement closely reflects the severity of intracranial hypertension. Note ICV, internal cerebral vein; ICP, intracranial pressure. 3.3 Comparison of baseline characteristics between patients with good and bad clinical outcomes Based on the GOS scores, patients were divided into the good prognosis group (n = 66) and the poor prognosis group (n = 45). As shown in Table 2 , several factors were significantly associated with poor outcomes. Patients in the poor prognosis group were significantly older (59.84 ± 14.13 years vs. 51.00 ± 15.80 years, p = 0.003) and presented with greater clinical severity upon admission, including lower GCS scores (75.6% classified as severe, p < 0.001), higher rates of coma (91.1%, p < 0.001), and higher rates of pupillary dilation (57.8%, p < 0.001). Radiologically, poor prognosis was closely associated with a higher incidence of subarachnoid hemorrhage (51.1%, p < 0.001), higher CT scores (median 5.00 vs. 3.00, p < 0.001), and significantly larger hematoma volumes (median 45.18 vs. 16.60 mL, p < 0.001). Therefore, a greater number of patients in this group were craniotomized (66.7% p 0.001). Moreover, the laboratory analysis had shown that the poor prognosis group had lower hemoglobin (p = 0.012), and worsened coagulant state with significantly prolonged PT ( p = 0.01), APTT ( p < 0.001), increasing INR ( p = 0.006) and elevated D-dimer ( p = 0.003). Table 2 Baseline characteristics of patients grouped by clinical outcomes Variable Bad (n = 45) Good (n = 66) p Gender (%) Female 5 (11.1) 18 (27.3) 0.068 Male 40 (88.9) 48 (72.7) Age (mean (SD)) 59.84 (14.13) 51.00 (15.80) 0.003 Diabetes (%) No 37 (82.2) 61 (92.4) 0.18 Yes 8 (17.8) 5 (7.6) Hypertension (%) No 32 (71.1) 51 (77.3) 0.609 Yes 13 (28.9) 15 (22.7) GCS_level (%) Severe (3–8) 34 (75.6) 19 (28.8) < 0.001 Moderate (9–12) 9 (20.0) 11 (16.7) Mild (13–15) 2 (4.4) 36 (54.5) Coma (%) No 4 (8.9) 37 (56.1) < 0.001 Yes 41 (91.1) 29 (43.9) Pupillary dilation (%) No 19 (42.2) 58 (87.9) < 0.001 Yes 26 (57.8) 8 (12.1) Subarachnoid hemorrhage (%) No 22 (48.9) 60 (90.9) < 0.001 Yes 23 (51.1) 6 (9.1) CT score (median [IQR]) 5.00 [4.00, 6.00] 3.00 [2.00, 3.75] < 0.001 Hematoma volume (median [IQR]) 45.18 [28.85, 69.70] 16.60 [11.62, 30.38] < 0.001 Systolic Pressure (mean (SD)) 143.13 (29.91) 143.03 (23.62) 0.984 Diastolic Pressure (mean (SD)) 82.11 (15.73) 82.44 (14.29) 0.909 Mean arterial pressure (mean (SD)) 102.33 (19.04) 102.98 (15.94) 0.846 Treatment (%) Conservative Treatment 15 (33.3) 51 (77.3) < 0.001 Craniotomy 30 (66.7) 15 (22.7) RBC (mean (SD)) 4.36 (2.08) 4.47 (0.61) 0.686 HB (mean (SD)) 128.08 (19.82) 137.26 (17.65) 0.012 HCT (mean (SD)) 3.84 (11.22) 4.01 (11.52) 0.938 PLT (mean (SD)) 188.02 (64.61) 212.23 (71.63) 0.072 PT (median [IQR]) 11.20 [10.60, 12.70] 10.95 [10.30, 11.30] 0.01 APTT (median [IQR]) 26.60 [23.00, 31.10] 22.95 [20.58, 26.20] < 0.001 Fib (median [IQR]) 1.76 [1.41, 2.56] 1.98 [1.60, 2.38] 0.28 INR (median [IQR]) 1.04 [0.95, 1.17] 0.98 [0.92, 1.03] 0.006 D-D (median [IQR]) 35.20 [14.81, 35.20] 13.57 [4.81, 35.20] 0.003 Note: SD, standard deviation; IQR, interquartile range; GCS, Glasgow Coma Scale; CT, computed tomography; RBC, red blood cell count; HB, hemoglobin; HCT, hematocrit; PLT, platelet count; PT, prothrombin time; APTT, activated partial thromboplastin time; Fib, fibrinogen; INR, international normalized ratio; D-D, D-dimer. 3.4 Association Between ICV Deviation Angle, Prognosis, and Radiological Severity To further clarify the clinical significance of the ICV deviation angle, we compared this metric between different prognosis groups. Figure 2 A shows that ICV deviation angle was quite enormous in the poor prognosis group as compared to the good prognosis group ( p < 0.001). Correlation analysis also showed that there was a significant and negative correlation between the ICV deviation angle and the GCS scores (Fig. 2 B) as well as the GOS scores (Fig. 2 C) ( p < 0.001). Also, there was a substantial positive correlation found between the deviation angle of the ICV and the CT scores (Fig. 2 D; p < 0.001). These findings indicate that a greater ICV displacement is not only associated with more severe neurological impairment upon admission but also serves as a predictor of poorer long-term functional recovery. Furthermore, a RCS analysis was performed to visualize the continuous relationship between the ICV deviation angle and the risk of poor prognosis. As shown in Fig. 2 E, a significant non-linear association was observed (P overall < 0.001, P non−linear = 0.036). The odds of a poor prognosis remained relatively low at smaller deviation angles but demonstrated a sharp, exponential increase once the ICV deviation angle exceeded a specific threshold. 3.5 Feature Selection and Construction of the Prognostic Predictive Model To identify the most robust predictors for clinical outcomes, LASSO regression analysis was performed to minimize multicollinearity among candidate variables. The optimal 10-fold cross validated λ parameter was 0.0139 which was the minimum criteria (λ. min). At the optimal lambda value, 8 significant characteristics with coefficients not equal to zero have been found (Fig. 3 A and 3 B), and their coefficients are outlined in Table 3 . The prognostic predictive performance of the models was then assessed on ROC curves in different algorithmic structures. The ICV deviation angle combined with the use of a single predictor showed a high predictive strength where the Logistic regression model had an AUC of 0.86 (Fig. 3 C). Furthermore, integrating the ICV deviation angle with other LASSO-selected features significantly enhanced the predictive performance; the combined Logistic model reached an AUC of 0.917 (Fig. 3 D). Notably, machine learning algorithms exhibited even higher discriminative performance, with the SVM and RF models achieving AUC of 0.942 and 0.923, respectively (Fig. 3 D). Table 3 Predictors and corresponding coefficients selected by LASSO regression Variable Coefficient (Intercept) -4.828385552 ICV deviation angle 0.112077701809522 GenderMale 0.953721747332812 Age 0.0591216743905087 GCS -0.271818422 CT score 0.552067284617606 HB -0.013199512 PLT -0.002036099 APTT 0.0617950085648794 Note: ICV, Internal Cerebral Vein; GCS, Glasgow Coma Scale; HB, Hemoglobin; PLT, Platelet count; APTT, Activated Partial Thromboplastin Time. Note AUC, area under the curve; CI, confidence interval; RF, random forest; SVM, support vector machine; KNN, k-nearest neighbors. Detailed performance metrics are summarized in Table 4 . Among the evaluated algorithms, the SVM model emerged as the most robust and balanced tool, yielding the highest AUC (0.942), AUPR (0.911), and F1-score (0.831). The RF model achieved the highest specificity (0.924), while the KNN model showed limited discriminative power (AUC: 0.783). Table 4 Predictive performance of machine learning models for poor prognosis in ASDH patients Model AUC AUC_95_CI AUPR Accuracy Sensitivity Specificity PPV NPV F1_Score Logistic 0.917 0.867–0.967 0.889 0.838 0.822 0.848 0.787 0.875 0.804 RF 0.923 0.872–0.973 0.905 0.865 0.778 0.924 0.875 0.859 0.824 SVM 0.942 0.902–0.982 0.911 0.865 0.822 0.894 0.841 0.881 0.831 KNN 0.783 0.695–0.871 0.688 0.748 0.6 0.848 0.73 0.757 0.659 Note: AUC, area under the receiver operating characteristic curve; CI, confidence interval; AUPR, area under the precision-recall curve; PPV, positive predictive value; NPV, negative predictive value; Logistic, Logistic Regression; RF, Random Forest; SVM, Support Vector Machine; KNN, K-Nearest Neighbors. 3.6 Clinical Utility and Model Interpretability The clinical utility of the ICV deviation angle and the developed models was evaluated using DCA and calibration curves. Notably, the baseline model consisting solely of the ICV deviation angle demonstrated substantial clinical net benefit across a broad range of threshold probabilities (Fig. 4 A), confirming its independent value as a potent and accessible imaging marker for clinical decision-making. Furthermore, the full model, which integrated the ICV deviation angle with other clinical features, further enhanced the net benefit, suggesting superior overall effectiveness. The calibration curves showed excellent agreement between the predicted probabilities and actual observed outcomes for both the ICV angle and full models (Fig. 4 B). To enhance model interpretability, SHAP analysis was performed. The SHAP summary plot (Fig. 4 C) identified the ICV deviation angle as the most influential predictor among all variables selected by LASSO. Additionally, the SHAP dependence plot (Fig. 4 D) visualized a non-linear relationship, indicating that the risk of poor prognosis increases sharply once the ICV deviation angle exceeds a specific threshold. 4 Discussion This prospective study investigated the value of the ICV deviation angle in assessing ICP and predicting 6-month clinical outcomes in patients with traumatic ASDH, and further developed machine learning prognostic models incorporating this novel imaging index. Key findings confirmed a strong positive correlation between the ICV deviation angle and invasive ICP measurements, a significant association between an increased ICV deviation angle and poor prognosis, and the superior predictive performance of the ICV deviation angle as a core feature in combined models with the SVM model showing the best discriminative ability for poor outcomes. These results validate the ICV deviation angle as a reliable non-invasive imaging marker for rapid ICP stratification and prognosis prediction in ASDH patients, and fill the gap in clinical assessment of ASDH based on cerebral venous circulation status. The ICV deviation angle in the high ICP group was more than twice that in the low ICP group, with a very strong positive correlation between the ICV deviation angle and measured ICP. This finding is highly consistent with the pathological mechanism of ASDH-induced intracranial hypertension[17]. The space-occupying effect of ASDH causes brain tissue shift, which directly compresses and displaces the ICV, a deep cerebral vein with minimal anatomical variation and fixed bilateral parallel distribution[18, 19]. Elevated ICP further aggravates the compression and drainage impairment of deep cerebral veins, forming a vicious cycle of progressive ICV displacement and worsening intracranial hypertension[20, 21]. In clinical practice, invasive ICP monitoring is the gold standard but is limited by its invasiveness and potential complications such as intracranial infection and hemorrhage[20]. In contrast, the ICV deviation angle can be rapidly measured via routine cranial CTV, providing a feasible non-invasive approach for urgent ICP stratification in ASDH patients, especially those in whom invasive monitoring is not suitable[22]. This study also demonstrated a close association between an increased ICV deviation angle and poor 6-month prognosis in ASDH patients. The ICV deviation angle was negatively correlated with the GCS and GOS scores, and positively correlated with the Rotterdam CT score. Moreover, the risk of poor prognosis increased exponentially when the ICV deviation angle exceeded a specific threshold. This observation can be explained by two core pathological links. First, a larger ICV deviation angle indicates more severe brain tissue shift and intracranial hypertension, which are direct triggers of secondary brain injury and poor neurological outcomes[23]. Second, as a major drainage vessel for deep brain structures such as the basal ganglia and thalamus[24], ICV displacement and angulation directly impair deep cerebral venous drainage, leading to cerebral venous congestion and further neuronal damage. This study is the first to integrate the ICV deviation angle into the ASDH prognostic evaluation system, and SHAP analysis confirmed that its predictive weight surpasses traditional indicators, thus supplementing the missing venous circulation dimension in existing assessment frameworks. The machine learning models constructed in this study further verified the clinical utility of the ICV deviation angle. A simple logistic regression model based solely on the ICV deviation angle achieved a favorable AUC of 0.86, and integration with traditional clinical and laboratory indicators further improved predictive performance with the SVM model reaching an AUC of 0.942. DCA and calibration curves confirmed that the combined models yield significant clinical net benefits and good consistency between predicted and observed outcomes. These results indicate that the ICV deviation angle not only has independent predictive value but also can enhance the discriminative power of combined prognostic models[25]. This model can be further translated into simple clinical tools, such as scoring scales or portable calculation programs, to facilitate the assessment of poor prognosis risk and guide the selection of surgical or conservative treatment for ASDH patients in primary hospitals[26, 27]. This study has several limitations that should be acknowledged. This study is subject to several methodological limitations that warrant careful consideration. First, as a single-center prospective investigation with a restricted sample size limited exclusively to patients presenting with unilateral frontotemporal-parietal ASDH, the findings may lack generalizability across diverse ASDH anatomical distributions and clinical severities. Indeed, ASDH can manifest in atypical locations such as the posterior cranial fossa a rare but clinically critical variant associated with upward herniation and poor outcomes despite aggressive surgical intervention[28, 29] or arise as extra-arachnoid SDH without concomitant subarachnoid hemorrhage or contusions, a subtype that paradoxically demonstrates better neurological recovery despite severe initial presentation and large midline shift[30]. Thus, future multicenter collaborative studies are essential to validate the proposed optimal clinical threshold for the ICV deviation angle across heterogeneous ASDH subtypes. Second, the morphological assessment was confined solely to the ICV deviation angle, omitting other potentially informative venous parameters such as ICV diameter or evidence of venous stenosis, which may collectively reflect cerebral venous drainage status and intracranial compliance. Given that elevated ICPa key determinant of outcome in severe ASDH is influenced by both arterial and venous hemodynamics, a multimodal imaging approach incorporating comprehensive venous morphology could enhance predictive accuracy; notably, recent work has proposed using contralateral global cortical atrophy scores alongside imaging biomarkers to refine patient selection and prognostication in elderly ASDH cohorts[31]. Third, the follow-up duration was limited to 6 months, precluding evaluation of long-term neurological recovery beyond this window. Notably, functional outcomes in ASDH survivors can continue to evolve beyond 6 months, as evidenced by studies reporting persistent disability or quality-of-life impairment at 1 year or longer, particularly among elderly cohorts[32]. Therefore, extended longitudinal follow-up is necessary to fully elucidate the long-term prognostic utility of the ICV deviation angle as a biomarker of venous compromise and neurological trajectory. In conclusion, the ICV deviation angle is a reliable non-invasive imaging marker that accurately reflects ICP levels and clinical prognosis in ASDH patients, and it has important clinical value for urgent ICP stratification and pre-operative prognostic assessment. Integrating this index with traditional clinical indicators can construct high-performance prognostic models for ASDH. This study provides a novel perspective based on cerebral venous circulation for the clinical management of ASDH, and the ICV deviation angle has the potential to be incorporated into routine radiological assessment in neurosurgery, providing evidence-based support for individualized treatment and rehabilitation planning in ASDH patients. Future research will focus on multicenter validation, determination of the optimal clinical threshold, and exploration of the combined application of multiple ICV morphological indicators to further optimize the clinical assessment system for ASDH. Declarations Acknowledgements Not Applicable. Authors’ contributions Weiming Xu. and Taiping Gao designed the study, performed statistical analysis and machine learning model construction, and drafted the main manuscript text. Hengheng Zhai., Bin Li. and Feixiang Min collected clinical, radiological and invasive intracranial pressure data of enrolled patients. Zhaocong Zheng completed the quantitative measurement of internal cerebral vein deviation angle on admission cranial computed tomography venography images. Shousen Wang and Weixin Lin supervised the study design and data interpretation, and revised the manuscript critically for important intellectual content. All authors reviewed and approved the final version of the manuscript. *Weiming Xu and Taiping Gao contributed equally to this work as co-first authors. #Shousen Wang and Weixin Lin are co-corresponding authors who take responsibility for the integrity of the work as a whole. Data availability The CHARLS data used in this research is publicly accessible. Consent for publication Not Applicable. Funding No funding was received for this study Ethics approval and consent to participate CHARLS data has been approved by the Biomedical Ethics Committee of Peking University (approval number: IRB 00001052–11015). This study was conducted in accordance with the Declaration of Helsinki (Ethical Principles for Medical Research Involving Human Subjects), and informed consent was obtained from all participants prior to data collection. Competing interests The authors declare no competing interests. References Basilio, A.V., et al., Simulating Cerebral Edema and Ischemia After Traumatic Acute Subdural Hematoma Using Triphasic Swelling Biomechanics. Ann Biomed Eng, 2024. 52 (10): p. 2818-2830. Maas, A.I.R., et al., Traumatic brain injury: integrated approaches to improve prevention, clinical care, and research. Lancet Neurol, 2017. 16 (12): p. 987-1048. Carney, N., et al., Guidelines for the Management of Severe Traumatic Brain Injury, Fourth Edition. Neurosurgery, 2017. 80 (1): p. 6-15. Stocchetti, N. and A.I. Maas, Traumatic intracranial hypertension. N Engl J Med, 2014. 370 (22): p. 2121-30. Huang, K.T., et al., The Neurocritical and Neurosurgical Care of Subdural Hematomas. Neurocrit Care, 2016. 24 (2): p. 294-307. Shin, D.S. and S.C. Hwang, Neurocritical Management of Traumatic Acute Subdural Hematomas. Korean J Neurotrauma, 2020. 16 (2): p. 113-125. Wang, H.C., et al., Direct visualization of microcirculation impairment after acute subdural hemorrhage in a novel animal model. J Neurosurg, 2018. 129 (4): p. 997-1007. Chihi, M., et al., Analysis of Brain Natriuretic Peptide Levels after Traumatic Acute Subdural Hematoma and the Risk of Post-Operative Cerebral Infarction. J Neurotrauma, 2021. 38 (22): p. 3068-3076. Amorim, R.L., et al., Perfusion tomography in early follow-up of acute traumatic subdural hematoma: a case series. J Clin Monit Comput, 2024. 38 (4): p. 783-789. Kapapa, T., et al., Unravelling Secondary Brain Injury: Insights from a Human-Sized Porcine Model of Acute Subdural Haematoma. Cells, 2024. 14 (1). Zhang, Z., et al., Magnetic resonance analysis of deep cerebral venous vasospasm after subarachnoid hemorrhage in rabbits. Front Cardiovasc Med, 2022. 9 : p. 1013610. Ng, S.Y. and A.Y.W. Lee, Traumatic Brain Injuries: Pathophysiology and Potential Therapeutic Targets. Front Cell Neurosci, 2019. 13 : p. 528. Wang, C., et al., Cranial venous-outflow obstruction promotes neuroinflammation via ADAM17/solTNF-alpha/NF-kappaB pathway following experimental TBI. Brain Res Bull, 2023. 204 : p. 110804. Stolz, E., Intracranial pressure and veins. Vasa, 2022. 51 (6): p. 329-332. Ghoneim, A., et al., Imaging of cerebral venous thrombosis. Clin Radiol, 2020. 75 (4): p. 254-264. Gundorova, R.A. and G.G. Ziangirova, [Microsurgery of uveal tumors localizing in the pre-equatorial portion of the eye]. Vestn Oftalmol, 1974. 2 : p. 15-8. Karibe, H., et al., Surgical management of traumatic acute subdural hematoma in adults: a review. Neurol Med Chir (Tokyo), 2014. 54 (11): p. 887-94. Ishibashi, R., Y. Maki, and H. Ikeda, Less Invasive Management of Endovascular Embolization and Neuroendoscopic Surgery for a Dural Arteriovenous Fistula Presenting with Acute Subdural Hematoma. Asian J Neurosurg, 2022. 17 (2): p. 362-366. Ono, M., et al., Microsurgical anatomy of the deep venous system of the brain. Neurosurgery, 1984. 15 (5): p. 621-57. Godoy, D.A., et al., Intracranial Hypertension After Spontaneous Intracerebral Hemorrhage: A Systematic Review and Meta-analysis of Prevalence and Mortality Rate. Neurocrit Care, 2019. 31 (1): p. 176-187. Murphy, N., et al., Use of intracranial pressure monitoring and risk factors for the development of intracranial hypertension in acute liver failure. J Hepatol, 2025. Shlapak, D.P., et al., Utility of CT venography in monitoring stent patency in idiopathic intracranial hypertension: retrospective single-center study. J Neurointerv Surg, 2021. 13 (5): p. 478-482. El-Abtah, M.E., M.J. Roach, and M.L. Kelly, Outcomes After the Surgical Evacuation of Traumatic Acute Subdural Hematomas: The tASDH Risk Score. World Neurosurg, 2023. 180 : p. e274-e280. Fang, Q., et al., Anatomic comparison of veins of Labbe between autopsy, digital subtraction angiography and computed tomographic venography. Biomed Eng Online, 2017. 16 (1): p. 84. Steyerberg, E.W., et al., Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology, 2010. 21 (1): p. 128-38. de Cassia Almeida Vieira, R., et al., Prognostic Models in Severe Traumatic Brain Injury: A Systematic Review and Meta-analysis. Neurocrit Care, 2022. 37 (3): p. 790-805. Khalili, H., et al., Prognosis prediction in traumatic brain injury patients using machine learning algorithms. Sci Rep, 2023. 13 (1): p. 960. Doi, K., et al., [Surgical Treatment for Traumatic Acute Subdural Hemorrhage in the Posterior Cranial Fossa:Three Cases Reports and Review of the Literature]. No Shinkei Geka, 2017. 45 (12): p. 1101-1107. Takeuchi, S., et al., Traumatic posterior fossa subdural hematomas. J Trauma Acute Care Surg, 2012. 72 (2): p. 480-6. Eaton, J.C., et al., Acute Extra-Arachnoid Subdural Hematomas in Patients 50 Years and Older: When Subdurals Act Like Epidurals. World Neurosurg, 2023. 179 : p. e523-e529. Sam, J.E., et al., Endoscopic Evacuation of Acute Subdural Hematomas: A New Selection Criterion. Asian J Neurosurg, 2024. 19 (2): p. 153-159. Bazarian, J.J., et al., Long-term neurologic outcomes after traumatic brain injury. J Head Trauma Rehabil, 2009. 24 (6): p. 439-51. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 26 Apr, 2026 Reviewers agreed at journal 26 Apr, 2026 Reviewers invited by journal 22 Apr, 2026 Editor invited by journal 26 Mar, 2026 Editor assigned by journal 25 Mar, 2026 Submission checks completed at journal 25 Mar, 2026 First submitted to journal 10 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9080813","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":633483951,"identity":"af61af08-d938-4102-a251-e533434b3db1","order_by":0,"name":"Weiming Xu","email":"","orcid":"","institution":"Fuzhou Changle District People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Weiming","middleName":"","lastName":"Xu","suffix":""},{"id":633483952,"identity":"7bec9274-2220-4074-94ed-c86883a7acdb","order_by":1,"name":"Taiping Gao","email":"","orcid":"","institution":"Longmen County People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Taiping","middleName":"","lastName":"Gao","suffix":""},{"id":633483953,"identity":"176194eb-0e0d-479e-b243-b4ae92544812","order_by":2,"name":"Hengheng Zhai","email":"","orcid":"","institution":"Fuzhou Changle District People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hengheng","middleName":"","lastName":"Zhai","suffix":""},{"id":633483954,"identity":"ca665f28-31a9-4b61-b1ab-8f23cb443d40","order_by":3,"name":"Bin Li","email":"","orcid":"","institution":"Fuzhou Changle District People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Bin","middleName":"","lastName":"Li","suffix":""},{"id":633483955,"identity":"657570c2-2f1b-4a84-99d2-064cfff6dc81","order_by":4,"name":"Feixiang Min","email":"","orcid":"","institution":"Fuzhou Changle District People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Feixiang","middleName":"","lastName":"Min","suffix":""},{"id":633483956,"identity":"903096b8-8701-4336-ad92-1cf12c5a3997","order_by":5,"name":"Zhaocong Zheng","email":"","orcid":"","institution":"900th Hospital, Fuzong Clinical Medical College of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhaocong","middleName":"","lastName":"Zheng","suffix":""},{"id":633483957,"identity":"ef2a5d6e-4b92-402b-8d8d-ed4942647714","order_by":6,"name":"Shousen Wang","email":"","orcid":"","institution":"900th Hospital, Fuzong Clinical Medical College of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shousen","middleName":"","lastName":"Wang","suffix":""},{"id":633483958,"identity":"8e1b7426-9fb9-426d-867a-a6a37a4dae8d","order_by":7,"name":"Weixin Lin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxElEQVRIiWNgGAWjYBACPmYQacDAw8DA3PggoaKGsBY2hBbGZoMHZ44RoQXBZGyTfNjCTIQWdt5jEh8KDsuYsx9sq0hsYGPgb+9OIOAwvjTJGQZpPJY9iW03EnfIMEicObuBgBYes9s8BjY8BjcYgVrOsDEYSOQSoeWPgQRYS0FiGzORWhigtjAQq8X8Zw/QLwZnEpslEs4c4yHoF37+M8YGP/4ctjc4fvjgxx8VNXL87b34tWAAHtKUj4JRMApGwSjACgDM0T9QHbY7WAAAAABJRU5ErkJggg==","orcid":"","institution":"Fuzhou Changle District People's Hospital","correspondingAuthor":true,"prefix":"","firstName":"Weixin","middleName":"","lastName":"Lin","suffix":""}],"badges":[],"createdAt":"2026-03-10 07:54:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9080813/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9080813/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108805837,"identity":"a7c9d39a-a2ca-407c-9fc3-033f40d0737d","added_by":"auto","created_at":"2026-05-08 15:27:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":185701,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of ICV deviation angle between low and high ICP groups and its correlation with ICP value.\u003c/strong\u003e(A) Box-and-scatter plot showing significantly higher ICV deviation angle in the high ICP group compared with the low ICP group; (B) Linear regression analysis of the correlation between ICV deviation angle and ICP.\u003c/p\u003e\n\u003cp\u003eNote: ICV, internal cerebral vein; ICP, intracranial pressure.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9080813/v1/37f3d316216a21c00e3dad26.png"},{"id":108734073,"identity":"729d0597-3ee0-4ee9-971f-8dbdff04bd8f","added_by":"auto","created_at":"2026-05-07 19:48:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":549391,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelationship between ICV deviation angle and clinical outcomes, and its prognostic predictive performance.\u003c/strong\u003e(A) Box-and-scatter plot showing the comparison of ICV deviation angle between the good prognosis and poor prognosis groups; (B) Linear regression analysis reveals a significant negative correlation between ICV deviation angle and GOS score; (C) A significant negative correlation is also observed between ICV deviation angle and GOS score; (D) Linear regression analysis demonstrates a significant positive correlation between ICV deviation angle and CT score;\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9080813/v1/8ad3a5741de0a75886b8ff7c.png"},{"id":108806523,"identity":"0650801b-a936-4bc9-aada-7c6344cb986c","added_by":"auto","created_at":"2026-05-08 15:28:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":424505,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFeature selection using LASSO regression and comparison of prognostic predictive performance among different models. \u003c/strong\u003e(A) LASSO regression coefficient profile plot of features; (B) LASSO coefficient distribution plot; (C) Receiver operating characteristic (ROC) curve of the ICV deviation angle alone for predicting poor prognosis; (D) ROC curves of the combined model (ICV deviation angle plus other LASSO-selected features) for predicting poor prognosis.\u003c/p\u003e\n\u003cp\u003eNote: AUC, area under the curve; CI, confidence interval; RF, random forest; SVM, support vector machine; KNN, k-nearest neighbors.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9080813/v1/e24f447b4392301b646842fc.png"},{"id":108807548,"identity":"0aab2cf8-910c-4a86-8f0c-a38c0d0c0ade","added_by":"auto","created_at":"2026-05-08 15:30:36","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":439214,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eClinical performance evaluation and interpretability analysis of the predictive models. \u003c/strong\u003e(A) DCA comparing the clinical net benefit between the full features and the ICV deviation angle alone; (B) Smoothed calibration curves evaluating the agreement between predicted probabilities and actual observed outcomes; (C) SHAP summary plot displaying the impact weights of predictors selected by LASSO on model output; (D) SHAP dependence plot to show the non-linear relationship between ICV deviation angle and risk.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9080813/v1/cbd9ab85606224c581931af9.png"},{"id":108810352,"identity":"3d72b475-4d63-435b-85a2-8c5c41a66bf7","added_by":"auto","created_at":"2026-05-08 15:58:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2111666,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9080813/v1/70047ffa-0e40-4c78-8f81-9e2373266366.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predictive Value of the Internal Cerebral Vein Deviation Angle for Intracranial Pressure Assessment and Prognosis in Patients with Acute Subdural Hematoma","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eTraumatic acute subdural hematoma (ASDH) is a form of traumatic brain injury with high morbidity and mortality, characterized by an increase in intracranial pressure (ICP) as its primary cause of secondary brain damage and unfavorable neurological impacts[1, 2]. Space-occupying effect of hematoma squeegees nearby brain tissue worsening cerebral edema and ischemia to create the vicious cycle of increasing ICP and cerebral ischemia[1, 3, 4]. Invasive ICP monitoring remains the clinical gold standard for real-time intracranial hemodynamic assessment, but its use is limited by procedural risks and delayed deployment, which fails to capture rapidly evolving intracranial changes and creates a critical unmet need for non-invasive imaging biomarkers to guide timely therapeutic interventions[5, 6].\u003c/p\u003e \u003cp\u003eEmerging evidence links impaired deep cerebral venous drainage to ICP elevation in ASDH; hematoma-induced midline shift compresses deep veins, obstructing outflow and amplifying intracranial hypertension[7]. The internal cerebral vein (ICV) exhibits minimal anatomical variation and serves as a major conduit for deep venous drainage, making it a promising target for non-invasive hemodynamic assessment, while cranial computed tomography venography (CTV) enables rapid, clear visualization of the deep intracranial venous system to reliably quantify ICV morphological alterations[8, 9]. Recent clinical perfusion imaging studies have shown that preoperative cerebral blood flow and mean transit time in ASDH patients correlate with 6-month functional outcomes, indicating that venous hemodynamic compromise significantly impacts patient prognosis[9]. A human-sized porcine model of ASDH further confirmed that secondary injury involves basal ganglia and brainstem regions, with ICP and cerebral oxygenation closely tied to venous outflow obstruction, emphasizing that venous congestion rather than just arterial insufficiency is a core trigger of ICP dysregulation[10].\u003c/p\u003e \u003cp\u003eIn recent years, the role of cerebral venous circulation dysfunction, especially deep cerebral veins, in ICP changes in patients with traumatic brain injury has attracted extensive attention. Most earlier research has been directed on the arterial system and this is challenging to elucidate complicated clinical observations like acute brain swelling[11, 12]. As the primary drainage system, the intracranial venous system holding about 70%-80% of the intracranial blood volume has its great influence on cerebral blood flow. During the evolution of imaging methods, the dilemma of venous morphological characteristics in the control of cerebral circulation has gained more and more importance[8]. The mass effect of traumatic ASDH can directly compress and traction intracranial veins, resulting in impaired venous reflux and further elevated ICP. Increased ICP in turn aggravates venous compression, forming a vicious cycle and eventually leading to venous circulatory dysfunction[13]. Among these, the ICV, as an important tributary of the deep venous system with minimal anatomical variation, is crucial for deep venous drainage. Its compression and displacement inevitably impair venous return, causing pathological changes including venous stasis, brain swelling, and elevated ICP[14, 15]. Therefore, observing morphological changes of the ICV is expected to be an effective indicator for evaluating the severity and prognosis of TBI patients.\u003c/p\u003e \u003cp\u003eThis study investigates the correlation between the ICV deviation angle measured on admission CTV and concurrent invasive ICP values in ASDH patients, and evaluates its association with 6-month functional outcomes. It also develops a novel prognostic tool by integrating multimodal data, aiming to address the critical gap in current clinical assessment systems.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Population and Feature Collection\u003c/h2\u003e \u003cp\u003eThe sample of the study was a collection of patients with acute subdural hematoma (ASDH) of traumatic nature, who were hospitalized at the Department of Neurosurgery during the period from November 2018 to February 2024. Informed consent was given in writing by all subjects or their legal guardians and all the patients were subjected to pre-operative cranial CTV and invasive ICP measurements before surgery. The inclusion criteria were as follows: (1) a confirmed diagnosis of traumatic brain injury; (2) evidence of cranial CTs of unilateral ASDH in the frontotemporal-parietal area; (3) having signs of ICP monitoring, as per the 2020 Consensus of Chinese Experts on the Management of Neurosurgical Critical Care[16]. Patients were excluded based on the following criteria: (1) a history of severe intracranial diseases, such as cerebral infarction, hydrocephalus, or intracranial hemorrhage; (2) the presence of a contralateral intracranial hematoma; or (3) concurrent epidural hematoma or intraventricular hemorrhage.\u003c/p\u003e \u003cp\u003eThe retrieval of baseline clinical characteristics was done through the electronic medical record system of the hospital. Parameters were collected; they were gender, age, medical history, consciousness state on arrival, mean arterial pressure (MAP) and Glasgow Coma Scale (GCS) score. Laboratory tests such as a red blood cell (RBC) count, platelet (PLT) count, prothrombin time (PT), international normalized ratio (INR), and D-dimer (D-D) were to be realized. Radiological evaluation through CT scan was conducted to determine the hematoma volume, midline shift (MLS) and the existence of traumatic subarachnoid hemorrhage (tSAH). Each patient was later calculated using Rotterdam CT score. Any treatment plan was arrived at according to the 2020 edition of the Guidelines to the Management of Severe Traumatic Brain Injury, and was classified as either craniotomy with the evacuation of hematoma or conservative medical treatment[3].\u003c/p\u003e \u003cp\u003eThe critical point of intervention in regards to ICP is 22 mmHg. In this respect, the patients were categorized into a High ICP Group (ICP 22mm Hg and above) and a Low ICP Group (ICP 22mm Hg and below). This classification is aimed at performing a subgroup analysis so as to further ascertain the clinical importance of the internal cerebral veins (ICV) displacement angle in measuring the ICP levels among patients immediately after ASDH. The Glasgow Outcome Scale (GOS) six months after injury was used as the major criterion in prognostic assessment. According to the scoring criteria, all the patients were divided into two groups (Poor Outcome Group or GOS 1\u0026ndash;3), and Good Outcome Group or GOS 4\u0026ndash;5).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 ICP Monitoring and ICV Deviation Angle Measurement\u003c/h2\u003e \u003cp\u003eThe incision is made 2 cm lateral to the midline on the same side of the ASDH ICP monitoring and drainage needle, manufactured by Spiegelberg (Germany), is used to puncture the ipsilateral lateral ventricle. Following the puncture, ICP is monitored. According to the interpretation of the 4th Edition of the Guidelines for the Management of Severe Traumatic Brain Injury[3]. If the ICP remains consistently\u0026thinsp;\u0026gt;\u0026thinsp;22 mmHg after conservative intervention, a craniotomy for ASDH evacuation is performed. If the ICP remains\u0026thinsp;\u0026le;\u0026thinsp;22 mmHg after conservative intervention, conservative treatment is continued.\u003c/p\u003e \u003cp\u003eThe CTV image data were reconstructed using Advantage Window 4.6 software. The venous phase sequences were selected for analysis, and the Maximum Intensity Projection (MIP) method was employed with a reconstruction thickness of 2.5 cm. Measurements were performed on the cross-sectional plane of the ICV. The center point for angle measurement was defined as the midpoint of the line connecting the proximal ends of the bilateral ICVs. One side of the angle was defined as the midline between the bilateral ICVs, while the other side was a line parallel to the brain midline. The acute angle formed by the intersection of these two lines was defined as the ICV deviation angle.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Model Development and Validation\u003c/h2\u003e \u003cp\u003eThe first step to LASSO regression analysis was to conduct a cross-validation ten times which helps the regression analysis to measure the best penalty parameter (λ) to pick features that are predictive and matters a lot. Four machine learning models were created based on the chosen features; namely: Logistic Regression, Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN). The training and evaluation of each model were done via ten-fold cross-validation. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and F1 score along with the area under the precision-recall curve (AUPR) were used to evaluate model discrimination comprehensively. Moreover, the SHAP values were obtained to define the effect of each feature on the outcomes of the prediction. Lastly, a decision curve analysis (DCA) was performed to analyze the clinical net of the models under various threshold probabilities, and that the calibration curves were constructed by using LOESS in order to estimate the fit between the predicted probabilities and the measured outcomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical Analysis\u003c/h2\u003e \u003cp\u003eThe statistical analysis was done with the help of R software (version 4.2. 3). A P value of less than 0.05 on a two-sided basis was taken as a significant value. The data of continuous variable was given as mean plus standard deviation (SD), when the data followed a normal distribution or median plus interquartile range (IQR) when the data were non-normally distributed. Categorical data were in form of counts and percentages. Student t-test or Mann-Whitney U test were used on continuous variables and Fisher exact test or 0 -test was used on the categorical variables to make between group comparisons.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Baseline characteristics of patients categorized by ICP levels\u003c/h2\u003e \u003cp\u003eThis study included 45 patients with recorded intracranial pressure (ICP), categorized into High (n\u0026thinsp;=\u0026thinsp;27) and Low (n\u0026thinsp;=\u0026thinsp;18) ICP groups. No significant differences were observed between the two groups regarding age, gender, comorbidities, or laboratory parameters (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Patients in the High ICP group exhibited greater clinical severity, characterized by lower GCS scores (77.8% severe, p\u0026thinsp;=\u0026thinsp;0.012), a higher incidence of pupillary dilation (55.6%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.022), and a higher rate of subarachnoid hemorrhage (55.6%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007). Radiologically, the High ICP group showed significantly higher CT scores (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and larger hematoma volumes (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Regarding management, a higher proportion of the High ICP group underwent craniotomy (77.8%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Although the High ICP group had a higher rate of poor prognosis (70.4% vs. 38.9%), the difference did not reach statistical significance (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.074) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of patients grouped by ICP levels.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh (n\u0026thinsp;=\u0026thinsp;27)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow (n\u0026thinsp;=\u0026thinsp;18)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3 (11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5 (27.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.301\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24 (88.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13 (72.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58.85 (12.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54.22 (16.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.299\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrognosis (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19 (70.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7 (38.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8 (29.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11 (61.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20 (74.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17 (94.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.176\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7 (25.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19 (70.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17 (94.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8 (29.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGCS_level (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere (3\u0026ndash;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21 (77.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6 (33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate (9\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3 (11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6 (33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMild (13\u0026ndash;15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3 (11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6 (33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComa (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6 (22.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7 (38.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.383\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21 (77.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11 (61.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePupillary dilation (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12 (44.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15 (83.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15 (55.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3 (16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubarachnoid hemorrhage (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12 (44.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16 (88.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15 (55.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2 (11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT score (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.00 [4.00, 6.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.00 [2.00, 3.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHematoma volume (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45.52 [37.50, 68.35]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.90 [11.25, 29.71]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic Pressure (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e146.19 (21.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e140.83 (31.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.502\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic Pressure (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e81.56 (12.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77.00 (18.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.321\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean arterial pressure (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e103.19 (13.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e98.28 (21.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConservative Treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6 (22.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16 (88.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCraniotomy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21 (77.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2 (11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRBC (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.31 (0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.35 (0.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.844\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHB (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e133.30 (16.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e131.61 (14.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.728\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCT (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.17 (10.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.73 (9.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.884\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e196.07 (72.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e207.89 (89.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.629\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePT (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.10 [10.50, 11.90]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.20 [10.70, 11.83]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.693\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPTT (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24.60 [22.65, 27.55]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.70 [21.08, 27.45]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.366\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFib (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.15 [1.62, 2.62]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.75 [1.54, 2.26]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.211\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINR (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01 [0.93, 1.11]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.02 [0.97, 1.08]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.693\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD-D (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35.20 [7.58, 35.20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.62 [10.69, 35.20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.436\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGOS_level (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood (4\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8 (29.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11 (61.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor (1\u0026ndash;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19 (70.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7 (38.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: ICP, intracranial pressure; SD, standard deviation; IQR, interquartile range; GCS, Glasgow Coma Scale; CT, computed tomography; RBC, red blood cell count; HB, hemoglobin; HCT, hematocrit; PLT, platelet count; PT, prothrombin time; APTT, activated partial thromboplastin time; Fib, fibrinogen; INR, international normalized ratio; D-D, D-dimer; GOS, Glasgow Outcome Scale.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Correlation between ICV Deviation Angle and ICP\u003c/h2\u003e \u003cp\u003eTo investigate the differences in ICV deviation angles between the two groups and their value in ICP assessment, we first compared the radiological parameters across the cohorts. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, the high ICP group exhibited a significantly greater ICV deviation angle compared to the low ICP group (19.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u0026deg; vs. 8.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u0026deg;, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). To further evaluate the quantitative relationship between these variables, a correlation analysis was performed. The results demonstrated a strong positive correlation between the ICV deviation angle and the actual measured ICP values (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB; R\u0026thinsp;=\u0026thinsp;0.91, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting that the degree of ICV displacement closely reflects the severity of intracranial hypertension.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003eICV, internal cerebral vein; ICP, intracranial pressure.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Comparison of baseline characteristics between patients with good and bad clinical outcomes\u003c/h2\u003e \u003cp\u003eBased on the GOS scores, patients were divided into the good prognosis group (n\u0026thinsp;=\u0026thinsp;66) and the poor prognosis group (n\u0026thinsp;=\u0026thinsp;45). As shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, several factors were significantly associated with poor outcomes. Patients in the poor prognosis group were significantly older (59.84\u0026thinsp;\u0026plusmn;\u0026thinsp;14.13 years vs. 51.00\u0026thinsp;\u0026plusmn;\u0026thinsp;15.80 years, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003) and presented with greater clinical severity upon admission, including lower GCS scores (75.6% classified as severe, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), higher rates of coma (91.1%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and higher rates of pupillary dilation (57.8%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Radiologically, poor prognosis was closely associated with a higher incidence of subarachnoid hemorrhage (51.1%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), higher CT scores (median 5.00 vs. 3.00, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and significantly larger hematoma volumes (median 45.18 vs. 16.60 mL, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Therefore, a greater number of patients in this group were craniotomized (66.7% p 0.001). Moreover, the laboratory analysis had shown that the poor prognosis group had lower hemoglobin (p\u0026thinsp;=\u0026thinsp;0.012), and worsened coagulant state with significantly prolonged PT (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01), APTT (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), increasing INR (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006) and elevated D-dimer (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of patients grouped by clinical outcomes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBad (n\u0026thinsp;=\u0026thinsp;45)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGood (n\u0026thinsp;=\u0026thinsp;66)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5 (11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18 (27.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40 (88.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48 (72.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59.84 (14.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51.00 (15.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37 (82.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61 (92.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8 (17.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5 (7.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32 (71.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51 (77.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.609\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13 (28.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15 (22.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGCS_level (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere (3\u0026ndash;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34 (75.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19 (28.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate (9\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11 (16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMild (13\u0026ndash;15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36 (54.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComa (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4 (8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37 (56.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41 (91.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29 (43.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePupillary dilation (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19 (42.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58 (87.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26 (57.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8 (12.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubarachnoid hemorrhage (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22 (48.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60 (90.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23 (51.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6 (9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT score (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.00 [4.00, 6.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.00 [2.00, 3.75]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHematoma volume (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45.18 [28.85, 69.70]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.60 [11.62, 30.38]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic Pressure (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e143.13 (29.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e143.03 (23.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.984\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic Pressure (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e82.11 (15.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e82.44 (14.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.909\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean arterial pressure (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e102.33 (19.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e102.98 (15.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.846\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConservative Treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15 (33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51 (77.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCraniotomy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30 (66.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15 (22.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRBC (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.36 (2.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.47 (0.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.686\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHB (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e128.08 (19.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e137.26 (17.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCT (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.84 (11.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.01 (11.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.938\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e188.02 (64.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e212.23 (71.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePT (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.20 [10.60, 12.70]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.95 [10.30, 11.30]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPTT (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26.60 [23.00, 31.10]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.95 [20.58, 26.20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFib (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.76 [1.41, 2.56]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.98 [1.60, 2.38]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINR (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.04 [0.95, 1.17]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.98 [0.92, 1.03]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD-D (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35.20 [14.81, 35.20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.57 [4.81, 35.20]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: SD, standard deviation; IQR, interquartile range; GCS, Glasgow Coma Scale; CT, computed tomography; RBC, red blood cell count; HB, hemoglobin; HCT, hematocrit; PLT, platelet count; PT, prothrombin time; APTT, activated partial thromboplastin time; Fib, fibrinogen; INR, international normalized ratio; D-D, D-dimer.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Association Between ICV Deviation Angle, Prognosis, and Radiological Severity\u003c/h2\u003e \u003cp\u003eTo further clarify the clinical significance of the ICV deviation angle, we compared this metric between different prognosis groups. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA shows that ICV deviation angle was quite enormous in the poor prognosis group as compared to the good prognosis group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Correlation analysis also showed that there was a significant and negative correlation between the ICV deviation angle and the GCS scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB) as well as the GOS scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Also, there was a substantial positive correlation found between the deviation angle of the ICV and the CT scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These findings indicate that a greater ICV displacement is not only associated with more severe neurological impairment upon admission but also serves as a predictor of poorer long-term functional recovery. Furthermore, a RCS analysis was performed to visualize the continuous relationship between the ICV deviation angle and the risk of poor prognosis. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE, a significant non-linear association was observed (P\u003csub\u003eoverall\u003c/sub\u003e\u0026lt; 0.001, P\u003csub\u003enon\u0026minus;linear\u003c/sub\u003e = 0.036). The odds of a poor prognosis remained relatively low at smaller deviation angles but demonstrated a sharp, exponential increase once the ICV deviation angle exceeded a specific threshold.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Feature Selection and Construction of the Prognostic Predictive Model\u003c/h2\u003e \u003cp\u003eTo identify the most robust predictors for clinical outcomes, LASSO regression analysis was performed to minimize multicollinearity among candidate variables. The optimal 10-fold cross validated λ parameter was 0.0139 which was the minimum criteria (λ. min). At the optimal lambda value, 8 significant characteristics with coefficients not equal to zero have been found (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB), and their coefficients are outlined in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The prognostic predictive performance of the models was then assessed on ROC curves in different algorithmic structures. The ICV deviation angle combined with the use of a single predictor showed a high predictive strength where the Logistic regression model had an AUC of 0.86 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Furthermore, integrating the ICV deviation angle with other LASSO-selected features significantly enhanced the predictive performance; the combined Logistic model reached an AUC of 0.917 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Notably, machine learning algorithms exhibited even higher discriminative performance, with the SVM and RF models achieving AUC of 0.942 and 0.923, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePredictors and corresponding coefficients selected by LASSO regression\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-4.828385552\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICV deviation angle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.112077701809522\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenderMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.953721747332812\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0591216743905087\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.271818422\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.552067284617606\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.013199512\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.002036099\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0617950085648794\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eNote: ICV, Internal Cerebral Vein; GCS, Glasgow Coma Scale; HB, Hemoglobin; PLT, Platelet count; APTT, Activated Partial Thromboplastin Time.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003eAUC, area under the curve; CI, confidence interval; RF, random forest; SVM, support vector machine; KNN, k-nearest neighbors.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eDetailed performance metrics are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Among the evaluated algorithms, the SVM model emerged as the most robust and balanced tool, yielding the highest AUC (0.942), AUPR (0.911), and F1-score (0.831). The RF model achieved the highest specificity (0.924), while the KNN model showed limited discriminative power (AUC: 0.783).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePredictive performance of machine learning models for poor prognosis in ASDH patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAUC_95_CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAUPR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAccuracy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePPV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNPV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eF1_Score\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLogistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.867\u0026ndash;0.967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.804\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.872\u0026ndash;0.973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.824\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSVM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.902\u0026ndash;0.982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.831\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKNN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.695\u0026ndash;0.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.659\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eNote: AUC, area under the receiver operating characteristic curve; CI, confidence interval; AUPR, area under the precision-recall curve; PPV, positive predictive value; NPV, negative predictive value; Logistic, Logistic Regression; RF, Random Forest; SVM, Support Vector Machine; KNN, K-Nearest Neighbors.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Clinical Utility and Model Interpretability\u003c/h2\u003e \u003cp\u003eThe clinical utility of the ICV deviation angle and the developed models was evaluated using DCA and calibration curves. Notably, the baseline model consisting solely of the ICV deviation angle demonstrated substantial clinical net benefit across a broad range of threshold probabilities (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), confirming its independent value as a potent and accessible imaging marker for clinical decision-making. Furthermore, the full model, which integrated the ICV deviation angle with other clinical features, further enhanced the net benefit, suggesting superior overall effectiveness. The calibration curves showed excellent agreement between the predicted probabilities and actual observed outcomes for both the ICV angle and full models (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eTo enhance model interpretability, SHAP analysis was performed. The SHAP summary plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC) identified the ICV deviation angle as the most influential predictor among all variables selected by LASSO. Additionally, the SHAP dependence plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD) visualized a non-linear relationship, indicating that the risk of poor prognosis increases sharply once the ICV deviation angle exceeds a specific threshold.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThis prospective study investigated the value of the ICV deviation angle in assessing ICP and predicting 6-month clinical outcomes in patients with traumatic ASDH, and further developed machine learning prognostic models incorporating this novel imaging index. Key findings confirmed a strong positive correlation between the ICV deviation angle and invasive ICP measurements, a significant association between an increased ICV deviation angle and poor prognosis, and the superior predictive performance of the ICV deviation angle as a core feature in combined models with the SVM model showing the best discriminative ability for poor outcomes. These results validate the ICV deviation angle as a reliable non-invasive imaging marker for rapid ICP stratification and prognosis prediction in ASDH patients, and fill the gap in clinical assessment of ASDH based on cerebral venous circulation status.\u003c/p\u003e \u003cp\u003eThe ICV deviation angle in the high ICP group was more than twice that in the low ICP group, with a very strong positive correlation between the ICV deviation angle and measured ICP. This finding is highly consistent with the pathological mechanism of ASDH-induced intracranial hypertension[17]. The space-occupying effect of ASDH causes brain tissue shift, which directly compresses and displaces the ICV, a deep cerebral vein with minimal anatomical variation and fixed bilateral parallel distribution[18, 19]. Elevated ICP further aggravates the compression and drainage impairment of deep cerebral veins, forming a vicious cycle of progressive ICV displacement and worsening intracranial hypertension[20, 21]. In clinical practice, invasive ICP monitoring is the gold standard but is limited by its invasiveness and potential complications such as intracranial infection and hemorrhage[20]. In contrast, the ICV deviation angle can be rapidly measured via routine cranial CTV, providing a feasible non-invasive approach for urgent ICP stratification in ASDH patients, especially those in whom invasive monitoring is not suitable[22].\u003c/p\u003e \u003cp\u003eThis study also demonstrated a close association between an increased ICV deviation angle and poor 6-month prognosis in ASDH patients. The ICV deviation angle was negatively correlated with the GCS and GOS scores, and positively correlated with the Rotterdam CT score. Moreover, the risk of poor prognosis increased exponentially when the ICV deviation angle exceeded a specific threshold. This observation can be explained by two core pathological links. First, a larger ICV deviation angle indicates more severe brain tissue shift and intracranial hypertension, which are direct triggers of secondary brain injury and poor neurological outcomes[23]. Second, as a major drainage vessel for deep brain structures such as the basal ganglia and thalamus[24], ICV displacement and angulation directly impair deep cerebral venous drainage, leading to cerebral venous congestion and further neuronal damage. This study is the first to integrate the ICV deviation angle into the ASDH prognostic evaluation system, and SHAP analysis confirmed that its predictive weight surpasses traditional indicators, thus supplementing the missing venous circulation dimension in existing assessment frameworks.\u003c/p\u003e \u003cp\u003eThe machine learning models constructed in this study further verified the clinical utility of the ICV deviation angle. A simple logistic regression model based solely on the ICV deviation angle achieved a favorable AUC of 0.86, and integration with traditional clinical and laboratory indicators further improved predictive performance with the SVM model reaching an AUC of 0.942. DCA and calibration curves confirmed that the combined models yield significant clinical net benefits and good consistency between predicted and observed outcomes. These results indicate that the ICV deviation angle not only has independent predictive value but also can enhance the discriminative power of combined prognostic models[25]. This model can be further translated into simple clinical tools, such as scoring scales or portable calculation programs, to facilitate the assessment of poor prognosis risk and guide the selection of surgical or conservative treatment for ASDH patients in primary hospitals[26, 27].\u003c/p\u003e \u003cp\u003eThis study has several limitations that should be acknowledged. This study is subject to several methodological limitations that warrant careful consideration. First, as a single-center prospective investigation with a restricted sample size limited exclusively to patients presenting with unilateral frontotemporal-parietal ASDH, the findings may lack generalizability across diverse ASDH anatomical distributions and clinical severities. Indeed, ASDH can manifest in atypical locations such as the posterior cranial fossa a rare but clinically critical variant associated with upward herniation and poor outcomes despite aggressive surgical intervention[28, 29] or arise as extra-arachnoid SDH without concomitant subarachnoid hemorrhage or contusions, a subtype that paradoxically demonstrates better neurological recovery despite severe initial presentation and large midline shift[30]. Thus, future multicenter collaborative studies are essential to validate the proposed optimal clinical threshold for the ICV deviation angle across heterogeneous ASDH subtypes. Second, the morphological assessment was confined solely to the ICV deviation angle, omitting other potentially informative venous parameters such as ICV diameter or evidence of venous stenosis, which may collectively reflect cerebral venous drainage status and intracranial compliance. Given that elevated ICPa key determinant of outcome in severe ASDH is influenced by both arterial and venous hemodynamics, a multimodal imaging approach incorporating comprehensive venous morphology could enhance predictive accuracy; notably, recent work has proposed using contralateral global cortical atrophy scores alongside imaging biomarkers to refine patient selection and prognostication in elderly ASDH cohorts[31]. Third, the follow-up duration was limited to 6 months, precluding evaluation of long-term neurological recovery beyond this window. Notably, functional outcomes in ASDH survivors can continue to evolve beyond 6 months, as evidenced by studies reporting persistent disability or quality-of-life impairment at 1 year or longer, particularly among elderly cohorts[32]. Therefore, extended longitudinal follow-up is necessary to fully elucidate the long-term prognostic utility of the ICV deviation angle as a biomarker of venous compromise and neurological trajectory.\u003c/p\u003e \u003cp\u003eIn conclusion, the ICV deviation angle is a reliable non-invasive imaging marker that accurately reflects ICP levels and clinical prognosis in ASDH patients, and it has important clinical value for urgent ICP stratification and pre-operative prognostic assessment. Integrating this index with traditional clinical indicators can construct high-performance prognostic models for ASDH. This study provides a novel perspective based on cerebral venous circulation for the clinical management of ASDH, and the ICV deviation angle has the potential to be incorporated into routine radiological assessment in neurosurgery, providing evidence-based support for individualized treatment and rehabilitation planning in ASDH patients. Future research will focus on multicenter validation, determination of the optimal clinical threshold, and exploration of the combined application of multiple ICV morphological indicators to further optimize the clinical assessment system for ASDH.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWeiming Xu. and Taiping Gao designed the study, performed statistical analysis and machine learning model construction, and drafted the main manuscript text. Hengheng Zhai., Bin Li. and Feixiang Min collected clinical, radiological and invasive intracranial pressure data of enrolled patients. Zhaocong Zheng completed the quantitative measurement of internal cerebral vein deviation angle on admission cranial computed tomography venography images. Shousen Wang and Weixin Lin supervised the study design and data interpretation, and revised the manuscript critically for important intellectual content. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e*Weiming Xu and Taiping Gao contributed equally to this work as co-first authors.\u003c/p\u003e\n\u003cp\u003e#Shousen Wang and Weixin Lin are co-corresponding authors who take responsibility for the integrity of the work as a whole. \u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe CHARLS data used in this research is publicly accessible.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was received for this study\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCHARLS data has been approved by the Biomedical Ethics Committee of Peking University (approval number: IRB 00001052\u0026ndash;11015). This study was conducted in accordance with the Declaration of Helsinki (Ethical Principles for Medical Research Involving Human Subjects), and informed consent was obtained from all participants prior to data collection.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBasilio, A.V., et al., \u003cem\u003eSimulating Cerebral Edema and Ischemia After Traumatic Acute Subdural Hematoma Using Triphasic Swelling Biomechanics.\u003c/em\u003e Ann Biomed Eng, 2024. \u003cstrong\u003e52\u003c/strong\u003e(10): p. 2818-2830.\u003c/li\u003e\n\u003cli\u003eMaas, A.I.R., et al., \u003cem\u003eTraumatic brain injury: integrated approaches to improve prevention, clinical care, and research.\u003c/em\u003e Lancet Neurol, 2017. \u003cstrong\u003e16\u003c/strong\u003e(12): p. 987-1048.\u003c/li\u003e\n\u003cli\u003eCarney, N., et al., \u003cem\u003eGuidelines for the Management of Severe Traumatic Brain Injury, Fourth Edition.\u003c/em\u003e Neurosurgery, 2017. \u003cstrong\u003e80\u003c/strong\u003e(1): p. 6-15.\u003c/li\u003e\n\u003cli\u003eStocchetti, N. and A.I. 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Kelly, \u003cem\u003eOutcomes After the Surgical Evacuation of Traumatic Acute Subdural Hematomas: The tASDH Risk Score.\u003c/em\u003e World Neurosurg, 2023. \u003cstrong\u003e180\u003c/strong\u003e: p. e274-e280.\u003c/li\u003e\n\u003cli\u003eFang, Q., et al., \u003cem\u003eAnatomic comparison of veins of Labbe between autopsy, digital subtraction angiography and computed tomographic venography.\u003c/em\u003e Biomed Eng Online, 2017. \u003cstrong\u003e16\u003c/strong\u003e(1): p. 84.\u003c/li\u003e\n\u003cli\u003eSteyerberg, E.W., et al., \u003cem\u003eAssessing the performance of prediction models: a framework for traditional and novel measures.\u003c/em\u003e Epidemiology, 2010. \u003cstrong\u003e21\u003c/strong\u003e(1): p. 128-38.\u003c/li\u003e\n\u003cli\u003ede Cassia Almeida Vieira, R., et al., \u003cem\u003ePrognostic Models in Severe Traumatic Brain Injury: A Systematic Review and Meta-analysis.\u003c/em\u003e Neurocrit Care, 2022. \u003cstrong\u003e37\u003c/strong\u003e(3): p. 790-805.\u003c/li\u003e\n\u003cli\u003eKhalili, H., et al., \u003cem\u003ePrognosis prediction in traumatic brain injury patients using machine learning algorithms.\u003c/em\u003e Sci Rep, 2023. \u003cstrong\u003e13\u003c/strong\u003e(1): p. 960.\u003c/li\u003e\n\u003cli\u003eDoi, K., et al., \u003cem\u003e[Surgical Treatment for Traumatic Acute Subdural Hemorrhage in the Posterior Cranial Fossa:Three Cases Reports and Review of the Literature].\u003c/em\u003e No Shinkei Geka, 2017. \u003cstrong\u003e45\u003c/strong\u003e(12): p. 1101-1107.\u003c/li\u003e\n\u003cli\u003eTakeuchi, S., et al., \u003cem\u003eTraumatic posterior fossa subdural hematomas.\u003c/em\u003e J Trauma Acute Care Surg, 2012. \u003cstrong\u003e72\u003c/strong\u003e(2): p. 480-6.\u003c/li\u003e\n\u003cli\u003eEaton, J.C., et al., \u003cem\u003eAcute Extra-Arachnoid Subdural Hematomas in Patients 50 Years and Older: When Subdurals Act Like Epidurals.\u003c/em\u003e World Neurosurg, 2023. \u003cstrong\u003e179\u003c/strong\u003e: p. e523-e529.\u003c/li\u003e\n\u003cli\u003eSam, J.E., et al., \u003cem\u003eEndoscopic Evacuation of Acute Subdural Hematomas: A New Selection Criterion.\u003c/em\u003e Asian J Neurosurg, 2024. \u003cstrong\u003e19\u003c/strong\u003e(2): p. 153-159.\u003c/li\u003e\n\u003cli\u003eBazarian, J.J., et al., \u003cem\u003eLong-term neurologic outcomes after traumatic brain injury.\u003c/em\u003e J Head Trauma Rehabil, 2009. \u003cstrong\u003e24\u003c/strong\u003e(6): p. 439-51.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Acute subdural hematoma, Internal cerebral vein deviation angle, Intracranial pressure, Prediction, Prognosis","lastPublishedDoi":"10.21203/rs.3.rs-9080813/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9080813/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe core pathological mechanism of traumatic acute subdural hematoma (ASDH) is elevated intracranial pressure (ICP) leading to secondary brain injury; however, current assessment systems lack indicators that reflect the state of cerebral venous circulation. The internal cerebral vein (ICV), as the main drainage channel for deep veins, may become a novel target for evaluating disease severity through its morphological changes.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study prospectively enrolled patients with ASDH admitted to the Department of Neurosurgery from November 2018 to February 2024. Baseline clinical, laboratory, and radiological data were collected. The ICV deviation angle was measured on cranial computed tomography venography (CTV). Its correlation with invasive ICP and 6-month Glasgow Outcome Scale (GOS) scores was analyzed. LASSO regression was employed for feature selection, and machine learning prognostic models were subsequently constructed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe ICV deviation angle was profoundly greater in the high ICP group (19.82400) than either the low ICP group (8.92.1) or when correlated with the ICP values showed a strong positive association (R\u0026thinsp;=\u0026thinsp;0.91). The ICV deviation angle was very high in the poor prognosis group compared to the good prognosis and had a negative correlation with GCS and GOS scores and was significantly positively correlated with CT scores. An AUC of 0.942 was obtained with the estimation of low prognosis with the help of Support Vector Machine (SVM) model, which included the ICV angle, combining with clinical and laboratory indicators, and the decision curve analysis revealed its positive clinical net benefit.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe ICV deviation angle is a reliable non-invasive imaging biomarker that reflects ICP levels and is closely associated with 6-month neurological outcomes in ASDH patients. Integrating the ICV angle into a multimodal machine learning model significantly enhances prognostic predictive performance, offering a novel perspective based on cerebral venous circulation for the clinical assessment of ASDH.\u003c/p\u003e","manuscriptTitle":"Predictive Value of the Internal Cerebral Vein Deviation Angle for Intracranial Pressure Assessment and Prognosis in Patients with Acute Subdural Hematoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-07 19:48:38","doi":"10.21203/rs.3.rs-9080813/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-26T06:07:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"157010231956887442768805169011224588706","date":"2026-04-26T05:30:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-23T00:53:22+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-26T11:40:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-25T10:44:43+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-25T10:44:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Imaging","date":"2026-03-10T07:44:57+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"56f8838f-ab95-409b-a5d4-2c8e7e2d57ac","owner":[],"postedDate":"May 7th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-07T19:48:39+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-07 19:48:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9080813","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9080813","identity":"rs-9080813","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}