Ratio of bilateral cortical veins areas on CT venography as a novel predictor of prognosis in patients with acute subdural hematoma

Ratio of bilateral cortical veins areas on CT venography as a novel predictor of 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 Ratio of bilateral cortical veins areas on CT venography as a novel predictor of prognosis in patients with acute subdural hematoma Weiming Xu, Hengheng Zhai, Weixin Lin, Zhenxing Chen, Hang Xiao, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9138821/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background Individuals diagnosed with acute subdural hematoma (ASDH) typically present with a severe clinical condition and an unfavorable prognosis. However, there are a lack of reliable methods for predicting the prognosis of patients with ASDH. Therefore, in this study, the characteristic changes in the cortical and internal cerebral veins among patients with ASDH was prospectively observed. The correlations between these changes and ASDH prognosis was examined with the goal of establishing a novel assessment method that can provide a reference for the development of diagnostic and therapeutic procedures for ASDH. Methods A prospective investigation was conducted involving 101 patients diagnosed with ASDH. Upon admission, all patients underwent cerebral computed tomography venography (CTV). The acquired images were analyzed using ImageJ software to measure the maximum intensity projection(MIP) areas of the bilateral cortical veins adjacent to the superior sagittal sinus. The diameter and displacement of the internal cerebral veins, ambient cistern width, brain midline shift were also measured, and Rotterdam CT score was calculated. Factors influencing prognosis were identified. Results Univariate logistic analysis was performed to identify the factors associated with poor prognosis, including the ratio of the MIP areas of cortical veins on the affected and healthy sides (K-value), Glasgow Coma Scale (GCS), hematoma volume, ambient cistern width, and Rotterdam CT score, et al. Furthermore, multivariate logistic analysis using forward selection (Wald) revealed that the risk of poor prognosis decreased with increasing K-value (odds ratio [OR]: 0.10, 95% confidence interval [CI]: 0.02–0.50, P = 0.005) and GCS score (OR: 0.64, 95% CI: 0.49–0.85, P = 0.002), but increased with increasing hematoma volume (OR: 1.05, 95% CI: 1.01–1.08, P = 0.007). The model based on these three indicators demonstrated high sensitivity (87.88%) and high specificity (91.18%) in predicting the prognosis of patients with ASDH. Conclusions K-value, GCS score, and hematoma volume were identified as independent risk factors for poor prognosis in patients with ASDH. A model built on these factors demonstrated accurate prediction of ASDH prognosis and could provide valuable guidance for clinical decision-making. Trial registration Ratio of bilateral cortical veins areas on CT venography as a novel predictor of prognosis in patients with acute subdural hematoma, researchregistry10196. Registered April 14, 2024, https//researchregistry.knack.com/researchregistry#home/registrationdetails/661bbbfc6ab592002ae6048d/ Computed tomography venography acute subdural hematoma maximum intensity projection area of cortical veins GOS score Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Acute subdural hematoma (ASDH) is a frequent and severe form of secondary craniocerebral trauma [ 1 ]. Despite significant progress in imaging technologies, surgical interventions, and perioperative management, there remains a lack of substantial enhancement in the survival and prognosis of patients with ASDH [ 2 ]. The mortality rate of patients with ASDH has been reported to range from 55% to 70%, with a Glasgow Coma Scale (GCS) score of 8 [ 3 ]. Nonetheless, a significant gap persists in the availability of effective tools for assessing the prognosis of patients with ASDH, and there is a lack of cerebrovascular evidence that informs decision-making and early prognostic evaluation, particularly in terms of cerebral venous circulation [ 4 ]. Imaging evidence for patients with ASDH has been confined to plain brain computed tomography (CT), and the uniformity in imaging findings among admitted patients does not correspond to distinct prognoses following comparable treatments. Cerebral veins play a pivotal role in the cerebral circulation system, serving as primary pathways for cerebral venous return. Previous studies have established a substantial association between ASDH and vein injury [ 5 ]. Advancements in cerebral vein study techniques have led to the widespread clinical application of cerebral computed tomography venography (CTV), with the advantages of rapidity and accuracy [ 6 , 7 ]. Therefore, a preliminary exploration was conducted to prospectively observe the characteristic changes in the cortical and internal cerebral veins among patients with ASDH. These observations were evaluated in conjunction with clinical data to establish their correlations with prognosis, with the aim to introduce a novel assessment tool that can provide a reference for the development of diagnostic and therapeutic procedures for patients with ASDH. Patients and methods Patients In this study, we enrolled adult patients diagnosed with ASDH at Changle District People’s Hospital (Fuzhou City, China) from June 2018 to February 2023. The inclusion criteria were as follows: age ≥ 18 years, history of head trauma, cerebral CT findings indicative of ASDH, and hematoma volume ≥ 10 mL. We excluded patients with ASDH with primary brainstem injury or epidural hematoma, cerebral venous diseases such as cerebral arteriovenous fistula and venous sinus thrombosis, as well as those with intracranial tumors, hydrocephalus, spontaneous intracerebral hemorrhage, or brain inflammation. Of the 615 patients diagnosed with ASDH, 101 were selected according to the criteria, including 80 males and 21 females aged 24–94 years (mean: 54.01 ± 15.79 years). This study was approved by the Ethics Committee of Changle District People’s Hospital (2018-001KY). All procedures were performed in accordance with relevant guidelines and regulations. Informed consent was obtained from all patients or their families. Clinical data Basic information about the patients was collected from their medical records, including medical history, symptoms on admission, consciousness, pupil dilation, GCS score, hemoglobin concentration, platelet count, prothrombin time, fibrinogen, D-dimer, and treatment regimens. The selection of treatment regimen was based on the Guidelines for the Management of Severe Traumatic Brain Injury published in 2020 [ 8 ], including craniotomy and conservative therapy with medicines. Imaging data Cerebral CTV All patients underwent cerebral CTV in the supine position using a GE Optima CT660 multi-slice helical scanner. Helical scans were acquired in an axial plane parallel to the orbitomeatal line. The imaging parameters were as follows: tube voltage, 120 kV; tube current, 200–230 mA; slice thickness, 0.625 mm; spacing between slices, 0.625 mm; 80 mL ioversol (containing 320 mg/mL iodine, injection rate 5 mL/s); and delay time, 29 s. Maximum intensity projection (MIP) reconstruction of the venous phase cerebral CTV was performed using the Advantage Window 4.6 workstation. Evaluation and analysis of the images were conducted by a senior neurosurgeon, senior imaging physician, and junior imaging physician. In the case of disagreement, consensus was achieved through discussion. Calculation of the K-value A MIP image with the superior border of the corpus callosum at the center was selected(Fig. 1 A). The image was imported into Image J software, where the scale was set and the threshold was adjusted to clearly delineate the cerebral veins, specifically highlighting the secondary branches of the parasagittal cortical veins. Additionally, the adjustment was conducted to exclude grayscale values of the brain parenchyma, ventricles, hematoma, and skull (Fig. 1 B). Subsequently, the image was binarized and the bilateral cortical veins were outlined (Fig. 1 C). The MIP areas of cortical veins on the affected and healthy sides were measured using the MIP image, and the K-value was calculated using the following formula: K-value = MIP area of cortical veins on the affected side/MIP area of cortical veins on the healthy side Calculation of the diameter and displacement of the internal cerebral veins from the median plane based on MIP reconstruction Axial MIP reconstruction of the venous phase cerebral CTV was performed with a thickness of 3.0 cm. The MIP image with the internal cerebral veins at the center was selected. Two points 1.5 cm anterior to the confluence of the left/right internal cerebral veins and the vein of Galen were selected for the measurement and calculation of the average diameter of bilateral internal cerebral veins. Subsequent assessments involved measuring the displacement of the midpoint of the line connecting the two points from the median plane and determining the distance between the medial borders of the bilateral internal cerebral veins. Calculation of the Rotterdam CT score Plain scans of cerebral CTV were used to evaluate brain midline shift and ambient cistern width. The presence of intraventricular or cisternal hemorrhage was observed. The Rotterdam CT score was computed following established criteria [ 9 ]. Prognostic analysis In this study, patient consciousness, assessed through the Glasgow Outcome Scale (GOS) score [ 10 ], was used to measure neurologic function [ 11 , 12 ]. Outpatient or telephone follow-up was performed for all patients at 6 months after injury to determine their GOS scores. Based on their consciousness at 6 months post-injury, patients with clear consciousness were categorized into the good prognosis group (GOS score of 3–5), whereas those with unclear consciousness or who had died were categorized into the poor prognosis group (GOS score of 1–2). Statistical analysis The mean and standard deviation were used to describe continuous variables with normal distribution, and Student’s t -test was used for statistical analysis. Median and interquartile ranges were used to describe continuous variables with non-normal distribution, and the rank-sum test was used for statistical analysis. Frequency and constituent ratio were used to describe categorical variables, and the chi-square test was used for statistical analysis. Logistic regression analysis was performed to identify the factors influencing poor prognosis. Variables with a P- value < 0.1 in the univariate model were included in the multivariate model to identify factors associated with poor prognosis. The receiver operating characteristic (ROC) curve was plotted, and the area under the curve (AUC) was calculated to assess the predictive performance of these factors. Optimal cutoff values were determined on the basis of the maximum Youden index. Restricted cubic spline plots were used to analyze the linear relationships between the influencing factors and prognosis. Furthermore, a nomogram model for predicting poor prognosis was constructed on the basis of the influencing factors. A calibration curve was plotted to assess the consistency between the actual and predicted probabilities, and the clinical usefulness of the model was evaluated through decision curve analysis. SPSS software (version 25.0, SPSS Inc., Chicago, USA) was used for statistical description, one-way analysis of variance, and logistic regression. R language (version 4.2.1) was used to plot the nomogram, calibration, ROC, and decision curves using the packages OptimalCutpoints, caret, rms, Hmisc, ROCR, rmda, and smoothHR. All tests were two-sided and P -values < 0.05 were considered to indicate statistical significance. Results Patient characteristics A total of 101 patients with ASDH were included in this study. Among them, 68 (67.3%) patients showed consciousness and a good prognosis at 6 months post-treatment, with GOS scores ranging from 3 to 5. Conversely, 33 (32.67%) patients experienced poor prognosis, with mortality or unconsciousness and GOS scores of 1 to 2. In the good and poor prognosis groups, the mean age was 52.56 ± 16.47 and 57.00 ± 14.05 years and 73.5% and 90.9% of the patients were male, respectively. The baseline characteristics of the patients in the two groups are shown in Table 1 . Notably, at the time of admission, the K-value, GCS score, the average diameter of bilateral internal cerebral veins, the distance between bilateral internal cerebral veins, the displacement of internal cerebral veins from the median plane, and ambient cistern width were higher in the good prognosis group than in the poor prognosis group ( P < 0.05). The Rotterdam CT score, acute subdural hematoma volume, prothrombin time, activated partial prothrombin time, international normalization ratio, and D-dimer were lower in the good prognosis group ( P < 0.05). In the poor prognosis group, 25 (75.8%) patients underwent craniotomy, whereas only 14 (20.6%) patients received craniotomy in the good prognosis group. Furthermore, the incidence of subarachnoid hemorrhage was higher in the poor prognosis group than in the good prognosis group (18 [54.5%] vs. 5 [7.4%]; P < 0.001). Table 1 The baseline characteristics of the two groups of patients Variable Whole population(n = 101) The group of Good prognosis (n = 68) The group of poor prognosis(n = 33) Statistic P- value Age 54.01 ± 15.79 52.56 ± 16.47 57.00 ± 14.05 -1.331 0.186 K-value 1.47 ± 0.74 1.82 ± 0.63 0.74 ± 0.26 12.079 < 0.001 MIP area of cortical veins on the affected side(cm 2 ) 5.49 ± 2.55 6.10 ± 2.41 4.21 ± 2.39 3.718 < 0.001 Mean arterial pressure(mmHg) 102.65 ± 16.34 103.78 ± 16.5 100.33 ± 15.99 0.994 0.323 Hemoglobin concentration(g/L) 133.55 ± 18.51 135.75 ± 17.64 129.03 ± 19.69 1.728 0.087 Hematocrit 0.39 ± 0.05 0.40 ± 0.05 0.38 ± 0.06 1.585 0.116 GCS score 10 (6, 14) 14 (8, 15) 5 (3, 8) -6.403 < 0.001 MIP area of cortical veins on the healthy side(cm 2 ) 3.71 (2.56, 4.99) 3.52 (2.56, 4.31) 4.36 (2.58, 5.43) -1.883 0.060 Average diameter of bilateral internal cerebral veins(cm) 0.17 (0.13, 0.19) 0.18 (0.16, 0.20) 0.12 (0.10, 0.15) -5.459 < 0.001 Displacement of internal cerebral veins from the median plane (cm) 0.27 (0.12, 0.75) 0.19 (0.10, 0.37) 0.94 (0.46, 1.12) -5.555 < 0.001 Distance between bilateral internal cerebral veins(cm) 0.05 (0, 0.18) 0.13 (0, 0.19) 0 (0, 0.05) -3.599 < 0.001 Ambient cistern width (cm) 0.19 (0.11, 0.25) 0.20 (0.16, 0.28) 0.09 (0.04, 0.18) -4.712 < 0.001 Brain midline shift(cm) 0.35 (0.14, 0.87) 0.21 (0.12, 0.44) 1.00 (0.61, 1.34) -5.479 < 0.001 Rotterdam CT score 3 (2, 5) 3 (2, 4) 5 (4, 6) -6.252 < 0.001 Acute subdural hematoma volume(ml) 23.50 (12.75, 48.00) 18.00 (11.63, 28.74) 45.52 (31.05, 76.20) -5.395 < 0.001 Platelet count(10 9 /L) 195.00 (166.00, 233.50) 199.50 (168.00, 235.50) 191.00 (148.00, 229.50) -0.833 0.405 Prothrombin time(second) 11.20 (10.40, 11.80) 10.95 (10.20, 11.40) 11.6 (11.05, 13.90) -3.356 0.001 Activated partial prothrombin time(second) 25.10 (21.20, 27.80) 23.35 (20.56, 26.85) 27.10 (23.85, 36.70) -3.425 0.001 Fibrinogen(g/L) 1.78 (1.52, 2.45) 1.925 (1.54, 2.43) 1.76 (1.35, 2.54) -1.162 0.245 International normalization ratio 1.02 (0.94, 1.08) 0.99 (0.92, 1.04) 1.08 (1.01, 1.34) -3.787 < 0.001 D-dimer(mg/L) 17.475 (5.00, 35.2) 11.64 (3.36, 35.2) 35.2 (14.81, 35.2) -3.192 0.001 Sex(%) Male 80 (79.2) 50 (73.5) 30 (90.9) 4.075 0.066 Female 21 (20.8) 18 (26.5) 3 (9.1) Diabetes(%) No 94 (93.1) 64 (94.1) 30 (90.9) -- 0.680 Yes 7 (6.9) 4 (5.9) 3 (9.1) Smoking (%) No 62 (61.4) 45 (66.2) 17 (51.5) 2.015 0.193 Yes 39 (38.6) 23 (33.8) 16 (48.5) Drinking(%) No 61 (60.4) 41 (60.3) 20 (60.6) 0.001 1.000 Yes 40 (39.6) 27 (39.7) 13 (39.4) History of cranial diseases(%) No 94 (93.1) 65 (95.6) 29 (87.9) -- 0.212 Yes 7 (6.9) 3 (4.4) 4 (12.1) Hypertension(%) No 80 (79.2) 53 (77.9) 27 (81.8) 0.203 0.796 Yes 21 (20.8) 15 (22.1) 6 (18.2) GCS classification(%) 1(13-15points) 38 (37.6) 38 (55.9) 0 32.55 < 0.001 2(9-12points) 19 (18.8) 12 (17.6) 7 (21.2) 3(3-8points) 44 (43.6) 18 (26.5) 26 (78.8) Coma(%) No 40 (39.6) 38 (55.9) 2 (6.1) 23.056 < 0.001 Yes 61 (60.4) 30 (44.1) 31 (93.9) Pupil dilation(%) No 75 (74.3) 61 (89.7) 14 (42.4) 25.983 < 0.001 Yes 26 (25.7) 7 (10.3) 19 (57.6) Subarachnoid hemorrhage(%) No 78 (77.2) 63 (92.6) 15 (45.5) 28.136 < 0.001 Yes 23 (22.8) 5 (7.4) 18 (54.5) Treatment regimens(%) Medication therapy 62 (61.4) 54 (79.4) 8 (24.2) 28.529 < 0.001 Craniotomy 39 (38.6) 14 (20.6) 25 (75.8) Factors associated with poor prognosis Univariate logistic analysis discovered that the following factors were associated with poor prognosis: K-value, cortical veins area on the affected side, acute subdural hematoma volume, the average diameter of bilateral internal cerebral veins, GCS score, the displacement of internal cerebral veins from the median plane, the distance between bilateral internal cerebral veins, ambient cistern width, brain midline shift, and Rotterdam CT score (Table 2 ). Subsequent multivariate logistic analysis using forward selection (Wald) showed that the risk of poor prognosis decreased with increasing K-value (OR: 0.10, 95% CI: 0.02–0.50, P = 0.005) and GCS score (OR: 0.64, 95% CI: 0.49–0.85, P = 0.002). Conversely, the risk of poor prognosis increased with increasing hematoma volume (OR: 1.05, 95% CI: 1.01–1.08, P = 0.007) (Table 3 ). Table 2 Univariate logistic regression analysis of factors associated with poor prognosis Variable Beta OR (95% CI ) P- value Age 0.02 1.02 (0.99–1.05) 0.186 K-value -2.18 0.11 (0.04–0.31) < 0.001 MIP area of cortical veins on the affected side -0.36 0.70 (0.56–0.86) 0.001 Mean arterial pressure -0.01 0.99 (0.96–1.01) 0.321 Hemoglobin concentration -0.02 0.98 (0.96–1.00) 0.091 Hematocrit -6.60 0 (0–5.45) 0.119 GCS score -0.46 0.63 (0.53–0.76) < 0.001 MIP area of cortical veins on the healthy side 0.29 1.34 (1.03–1.74) 0.027 Average diameter of bilateral internal cerebral veins -37.97 0 (0–0) < 0.001 Displacement of internal cerebral veins from the median plane 4.09 59.73 (12.29–290.29) < 0.001 Distance between bilateral internal cerebral veins -11.48 0 (0–0.01) 0.001 Ambient cistern width -11.37 0 (0–0) < 0.001 Brain midline shift 3.16 23.63 (6.61–84.52) < 0.001 Rotterdam CT score 1.43 4.17 (2.45–7.10) < 0.001 Acute subdural hematoma volume 0.05 1.05 (1.03–1.08) < 0.001 Platelet count 0 1.00 (0.99–1.00) 0.383 Prothrombin time 0.53 1.70 (1.22–2.37) 0.002 Activated partial prothrombin time 0.12 1.13 (1.05–1.22) 0.002 Fibrinogen -0.25 0.78 (0.51–1.19) 0.247 International normalization ratio 6.05 423.20 (12.24–14636.05) 0.001 D-dimer 0.06 1.06 (1.02–1.10) 0.001 Sex Male 0 1.00 -- Female -1.28 0.28 (0.08–1.02) 0.054 Diabetes No 0 1.00 -- Yes 0.47 1.60 (0.34–7.60) 0.554 Smoking No 0 1.00 -- Yes 0.61 1.84 (0.79–4.30) 0.158 Drinking No 0 1.00 -- Yes -0.01 0.99 (0.42–2.31) 0.976 History of cranial diseases No 0 1.00 -- Yes 1.1 2.99 (0.63–14.22) 0.169 Hypertension No 0 1.00 -- Yes -0.24 0.79 (0.27–2.25) 0.653 GCS classification 1.85 6.33 (2.94–13.66) < 0.001 Coma No 0 1.00 -- Yes 2.98 19.63 (4.35–88.69) < 0.001 Pupil dilation No 0 1.00 -- Yes 2.47 11.83 (4.17–33.57) < 0.001 Subarachnoid hemorrhage No 0 1.00 -- Yes 2.72 15.12 (4.84–47.26) < 0.001 Treatment regimens Medication therapy 0 1.00 -- Craniotomy 2.49 12.05 (4.48–32.43) < 0.001 Table 3 Multivariate logistic regression analysis of factors associated with poor prognosis using forward selection (Wald) Covariates Beta OR (95% CI) P K-value -2.29 0.10 (0.02–0.50) 0.005 Hemoglobin concentration -0.04 0.96 (0.92–1.00) 0.057 GCS score -0.44 0.64 (0.49–0.85) 0.002 Acute subdural hematoma volume 0.05 1.05 (1.01–1.08) 0.007 Note The model variables included the K-value, hemoglobin concentration, Glasgow Coma Scale(GCS) score, the average diameter of bilateral internal cerebral veins, the displacement of internal cerebral veins from the median plane, the distance between bilateral internal cerebral veins, Rotterdam CT score, acute subdural hematoma volume, prothrombin time, activated partial prothrombin time, international normalization ratio, D-dimer, sex, subarachnoid hemorrhage, and treatment. The Rotterdam CT score was derived from the ambient cistern width and brain midline shift. Consciousness was evaluated using the GCS score, and pupil dilation was interfered by the oculomotor nerve injury caused by a skull base fracture. As a result, the significant variables identified in the univariate analysis, such as ambient cistern width, brain midline shift, consciousness, and pupil dilation, were excluded from the model. Prediction of poor prognosis using K-value, GCS score, and acute subdural hematoma volume The K-value, GCS score, and hematoma volume of patients with good and poor prognoses are illustrated in Fig. 2 . ROC analysis showed that the AUC of the K-value was 0.832 and the cutoff value was 1.04 (rounded to 1.0 with two significant figures). The sensitivity was notably high (100%) and the specificity was 69.70% (Fig. 3 A). Therefore, a good prognosis was associated with a larger cortical veins area on the affected side than on the healthy side, and vice versa (Fig. 4 ). The cutoff value of the GCS score was 8, the sensitivity was 89.70%, and the specificity was 72.73% (Fig. 3 B). The cutoff value of the acute subdural hematoma volume was 31 mL (Fig. 3 C), and the sensitivity and specificity were 78.79% and 79.41%, respectively. As shown in the restricted cubic spline plot (Fig. 5 ), there was a nonlinear relationship between the K-value and prognosis ( P = 0.003). The risk of poor prognosis decreased with increasing K-value and then leveled off as the K-value approached 1. The acute subdural hematoma volume showed a linear relationship with prognosis ( P = 0.341), and the risk of poor prognosis increased with increasing hematoma volume. Prediction of poor prognosis using a nomogram based on K-value, GCS score, and acute subdural hematoma volume A nomogram was constructed based on the K-value, GCS score, and acute subdural hematoma volume (Fig. 6 ). The calibration curve demonstrated a close fit (Fig. 7 ). A decision curve showing the clinical benefit was generated from the prediction model (Fig. 8 A). Furthermore, the risk score was calculated for each patient using the prediction mode (Fig. 8 B). The risk score of all patients ranged from 17.80 to 190.11. The median risk score of patients with a good prognosis was 82.55 (73.34–97.44), while that of patients with a poor prognosis was 143.53 (115.42–168.22). The ROC based on the risk score showed an AUC of 0.923 and a cutoff value of 109.5 (Fig. 9 ). Patients were categorized into high- and low-risk groups based on the cutoff value. Notably, 93.90% and 17.10% of patients in the low-risk and high-risk groups showed good prognoses, respectively. The Kappa value was 0.778 ( p < 0.001), the sensitivity was 87.88%, and the specificity was 98.18%. Discussion In this study, the K-value, GCS score, and acute subdural hematoma volume were identified as factors contributing to poor prognosis. The prediction model incorporating these indicators showed high sensitivity and specificity and can be used to predict the prognosis of patients with ASDH. The GCS score is used worldwide to evaluate the level of consciousness in patients with craniocerebral trauma [ 13 ] as well as their prognosis [ 14 ]. However, the subjectivity of the GCS score and its susceptibility to anesthetics or sedatives, along with its omission of pupil and brainstem assessment[ 15 ], render it unsuitable for application in infants. Acute subdural hematoma volume has been used as an important indicator of surgical interventions in patients with craniocerebral trauma and is closely related to their prognosis [ 16 ]. Nonetheless, the impact of hematoma volume on intracranial pressure (ICP) is influenced by brain tissue compliance and the degree of cerebral atrophy. The results in this study confirmed the significance of these two well-recognized factors associated with poor prognosis. The ratio of bilateral cortical veins areas (K-value) was also introduced as a novel indicator of poor prognosis, with its mechanism thought to be linked to the blood and oxygen supply to the brain tissue. Intracranial hypertension necessitates adaptation in the delivery of oxygen and glucose through the bloodstream to sustain brain cell viability. The K-value objectively and directly reflects the disparity in bilateral cerebral blood and oxygen supply, potentially correlating with brain swelling and prognosis [ 17 ]. In the present study, venous rather than arterial indicators were used to predict the prognosis of ASDH. This choice was informed by the proximity of cortical veins to ASDH and the compression of cortical veins by ASDH. Furthermore, given that cerebral veins function as the principal drainage pathways, cerebral vein injury or venous return obstruction is closely associated with brain edema and ICP [ 18 , 19 ]. Consequently, the cortical venous circulation indicator K-value was introduced, and a K-value < 1 was identified as an independent risk factor for poor prognosis in patients with ASDH. Previous studies have highlighted a significant association between impaired cortical venous circulation and ASDH prognosis. Cortical veins are prone to injury, compression, or thrombotic occlusion secondary to traumatic brain injury because of their superficial location and lack of tunica media and venous valves, resulting in the obstruction of cortical venous return [ 20 ]. Wang et al [ 21 ]. observed that cortical veins occlusion on CTV was indicative of significantly increased ICP and poor prognosis, which is consistent with the results of this study. Building on the qualitative examination of cortical veins mentioned above, this study conducted a quantitative investigation in patients with ASDH. The MIP area of cortical veins was calculated using ImageJ software, and the K-value was used as a novel indicator to quantitatively evaluate the influence of impaired cortical venous circulation on ASDH prognosis. The findings of this study validated the K-value as a straightforward and effective indicator for predicting the prognosis of patients with ASDH. The role of the K-value in predicting the prognosis of ASDH may be related to the regulation of resistance to blood outflow through the bridging veins. Studies have shown that, within a certain range, cerebral venous blood flow can undergo physiological regulation. With a slight or moderate increase in ICP, stenosis of parasagittal bridging veins has been confirmed in pigs [ 22 ] and humans [ 23 ]. Through cell staining, Vignes et al [ 24 ]. identified smooth muscle actin within the walls of the bridging veins, which could contract to enhance the resistance of blood outflow from the bridging veins. Using brain magnetic resonance venography, Si et al [ 25 ]. showed that elevated ICP was associated with the formation of an “ outflow segment cuff ” structure located at the outlet of brain-bridging veins and thus increased the resistance to blood outflow. This, in turn, resulted in compensatory dilatation and blood stasis in the distal segment of the bridging veins or neighboring cortical veins. These findings align with the compensatory changes in the cerebral veins with increased ICP observed in the present study. Elevated ICP has been documented in the cerebral hemisphere affected by ASDH in both animal and human studies [ 26 ]. Compression of the cerebral cortex by ASDH frequently results in cortical venous return obstruction and intracranial venous hypertension [ 5 ]. According to the mechanism of regulating blood return in the parasagittal bridging veins mentioned above, various degrees of venous return obstruction and blood stasis occur at the outlet of the bridging veins during mild or moderate intracranial hypertension, causing the accumulation of contrast agents in cortical veins. In addition, the cortical veins contain numerous anastomotic branches. In response to mild or moderate intracranial hypertension, the mean arterial blood pressure increases to maintain stable cerebral blood flow, causing compensatory dilation of peripheral cortical veins, especially on the ASDH side. This partly explains the dilation in the cortical veins on the affected side observed in this study. Consequently, the MIP area of the cortical veins on the affected side was larger than that on the healthy side, resulting in a K-value > 1. Conversely, excessively high ICP impairs the compensatory function of the cerebral veins, resulting in decompensation of cerebral vein blood flow [ 17 ] As a result, the velocity of the blood flow in the cortical veins decreases and, in some instances, approaches zero, leading to delayed or failed visualization of contrast agents in the cortical veins, particularly on the affected side. In this case, the MIP area of the cortical veins on the affected side was smaller than that on the healthy side, yielding a K-value < 1. This decompensation of cerebral venous blood flow may contribute to diffuse brain swelling and even intraoperative brain bulge (Fig. 4 H), both of which indicate poor prognosis. The role of the K-value in predicting the prognosis of ASDH may be associated with the diameter of the bridging veins. This study represents the first application of Poiseuille’s law in elucidating the mechanism of intracranial venous hypertension in patients with ASDH in China. Poiseuille’s law has been widely employed in the study of vascular diseases. Zamboni et al [ 27 ]. reported that chronic cerebrospinal venous insufficiency was caused by unilateral or bilateral stenosis of the jugular veins and resultant venous obstruction. Through hemodynamic analysis of magnetic resonance imaging data, Varga et al [ 28 ]. found that multiple sclerosis is characterized by inflammation and stenosis of venules, leading to impaired venous return. Patients with multiple sclerosis demonstrate reduced internal jugular vein diameter and significantly increased blood flow resistance [ 29 ]. In fluid dynamics, Poiseuille’s law is expressed as follows: Q = π × r 4 × Δp / (8ηL), where Q is the flow rate, Δ p is the pressure difference between the two ends of the pipe, r is the radius of the pipe, L is the length of the pipe, and η is the viscosity coefficient of the fluid. According to Poiseuille’s law, the blood flow rate is proportional to the fourth power of the vessel diameter. Therefore, the venous blood flow could be significantly reduced in patients with ASDH. Consistent with Poiseuille’s law, we found that ASDH could lead to mild compression on the surface of the cerebral cortex and separation of the cortex from the dura mater [ 30 ]. As a result, the bridging veins between the cerebral cortex and dura mater were stretched and their diameters were reduced (Fig. 4 D). According to Poiseuille’s law, blood flow in the bridging veins is restricted, leading to cortical venous blood stasis. This effect was more severe on the affected side, yielding a K-value > 1. With a continued increase in hematoma volume, further elongation and thinning of bridging veins occurred, resulting in a sharp decline in venous blood flow as well as cerebral edema and a further elevation in ICP [ 31 ]. Excessively high ICP beyond the range of physiological regulation resulted in a progressive increase in ICP. Consequently, blood flow was further reduced, progressive cerebral swelling appeared, and cortical veins were further compressed, leading to cortical veins occlusion or even venous thrombosis. In the present study, brain CTV suggested progressive brain swelling and delayed or failed visualization of the bilateral cortical veins, especially on the affected side. Patients with excessively high ICP showed a K-value < 1 and poor prognosis, which also confirmed acute subdural hematoma volume as an independent risk factor for poor prognosis in this study. Therefore, when the K-value is < 1, the possibility of ICP beyond physiological regulation should be considered. Regular comprehensive evaluation should be conducted to avoid missing the optimal surgical window. In cases where cortical veins on the affected side are barely visible and the K-value is significantly < 1, the cerebral blood flow is extremely slow or stagnant, possibly leading to cerebral venous thrombosis. As a result, craniotomy may be less effective in reversing this condition, thereby contributing to an unfavorable prognosis and limiting the surgical benefit for such patients. In addition, the severity of venous return obstruction may be influenced by brain compliance and the relative anatomical positions of ASDH and bridging veins, which require further investigation. Limitations and prospects CTV reconstruction of the cerebral veins is not without limitations. Skull artifacts can affect the reconstruction of cerebral veins situated close to the skull. In addition, the challenge of visualizing small veins persists, and this limitation may be addressed through further exploration using cerebral magnetic resonance venography or cerebral digital subtraction angiography. Conclusions K-value, GCS score, and hematoma volume were identified as independent risk factors for poor prognosis in patients with ASDH. A model based on these indicators could accurately predict the prognosis of ASDH and guide clinical decision-making. Abbreviations ASDH, acute subdural hematoma; CTV, computed tomography venography; MIP, maximum intensity projection; GCS, Glasgow Coma Scale; GOS, Glasgow Outcome Scale. Declarations Author contributions WSS, XWM, ZHH and WW were responsible for the study concept and design. XWM, LWX, ZHH, CZX and XH performed the clinical data collection. XWM, LYK, LC and YYQ performed the experiments of cerebral CTV. XWM, LWX, ZHH and LYK performed the evaluation and analysis of the images. XWM, WW, GJJ and LWX were responsible for the data analyses. XWM, LWX and ZHH drafted the manuscript. WSS and WW critically reviewed the manuscript. All authors read and approved the final manuscript. Data Availability The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions. Funding The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by Fuzhou Science and Technology Plan Project Fund (2021-S-134); Fujian Provincial Science and Technology Innovation Joint Fund (2019Y9045). Declarations Competing interests The authors declare that there is no conflict of interest. Ethics approval and consent to participate All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. This study was approved by the local Ethics Committee of Fuzhou Changle District People's Hospital (No. 2018-001KY), Informed consent was obtained from all patients or their families. Acknowledgement The authors sincerely thank Dr. Zhou Jiang and Jianbin Lin from Fuzhou Changle District People's Hospital, Dr. Liangfeng Wei from 900th Hospital for advice on experimental design. ORCID iDs Weiming Xu: https://orcid.org/0000-0002-8319-1994 Wei Wang: https://orcid.org/0000-0002-9171-9269 References Mohamed E, El-Abtah,Mary J, Roach,Michael L. Kelly,Outcomes After the Surgical Evacuation of Traumatic Acute Subdural Hematomas: The tASDH. Risk Score [J] World Neurosurg. 2023;09054:e1–7. Karibe H, Hayashi T, Hirano T, et al. Surgical management of traumatic acute subdural hematoma in adults: a review. Neurol Med Chir (Tokyo). 2014;54(11):887–94. Gernsback JE, Kolcun J, Richardson AM et al. Patientem fortuna adiuvat: the delayed treatment of surgical acute subdural hematomas-A case series.World Neurosurg. 2018; 120: e414–20. Xu WM, Lin YK, Lin JB, et al. Views merged method of CT venography display the spatial conformation of subdural hematoma and adjacent veins. Chin J Clin Anat. 2021;39(02):233–6. Wang C, Xian L, Chen XR, et al. Visualization of cortical cerebral blood flow dynamics during craniotomy in acute subdural hematoma using laser speckle imaging in a rat model. Brain Res. 2020;1742:146901. Fang Q, Jiang A, Tao W, et al. Anatomic comparison of veins of Labbé between autopsy, digital subtraction angiography and computed tomographic venography. BioMed Eng OnLine. 2017;16(1):84. Hsieh PC, Wu YM, Wang AY, et al. The venous delay phenomenon in computed tomography angiography: a novel imaging outcome predictor for poor cerebral perfusion after severe aneurysmal subarachnoid hemorrhage. J Neurosurg. 2018;129(4):876–82. Hawryluk G, Rubiano A, Ghajar J, et al. In reply: Guidelines for the management of severe traumatic brain injury: 2020 update of the decompressive craniectomy recommendations. Neurosurgery. 2021;88:E372–3. Kashkoush AI, Potter T, Petitt JC, et al. Novel application of the Rotterdam CT score in the prediction of intracranial hypertension following severe traumatic brain injury. J Neurol Neurosurg. 2023;138(4):392–9. Wu H, Jiang B, Yan X, et al. Effect of decompressive craniectomy with stepwise decompression of the intracranial compartment on postoperative neurologic function, hemodynamics, and glasgow outcome scale score of patients with severe traumatic brain injury. J Neurol Surg Cent Eur Neurosurg. 2022;84(6):536–41. Squitti R, Reale G, Tondolo V, et al. Imbalance of essential metals in traumatic brain injury and its possible link with disorders of consciousness. Int J Mol Sci. 2023;24:6867. Song CJ, SUN QY, Du YX, et al. Accuracy of EEG reactivity, EEG patterns, and the Glasgow Coma Scale in predicting outcomes of comatose patients with severe head injurie. Chin J Neurosurg. 2013;29(02):150–2. McNett M, Amato S, Gianakis A, et al. The four score and GCS as predictors of outcome after traumatic brain injury. Neurocrit Care. 2014;21:52–7. Anestis DM, Marinos K, Tsitsopoulos PP. Comparison of the prognostic validity of three simplified consciousness assessment scales with the Glasgow Coma Scale. Eur J Trauma Emerg Surg. 2023;49:2193–202. Bertotti M, Martins E, Areas F, et al. Glasgow coma scale pupil score (GCS-P) and the hospital mortality in severe traumatic brain injury: analysis of 1,066 Brazilian patients. Arq Neuropsiquiatr. 2023;81:452–9. Wang L, Liu C, Lu E, et al. Total intracranial volume as a covariate for predicting prognosis in patients with primary intracerebral hemorrhage. Clin Neurol Neurosurg. 2022;214:107135. Zhang S, Chen Q, Xian L, et al. Acute subdural haematoma exacerbates cerebral blood flow disorder and promotes the development of intraoperative brain bulge in patients with severe traumatic brain injury. Eur J Med Res. 2023;28:138. Hamahata NT, Nakagawa K. Regional cerebral hypoperfusion from acute subdural hematoma. Neurocrit Care. 2020;32(2):633–5. Jha RM, Kochanek PM, Simard JM. Pathophysiology and treatment of cerebral edema in traumatic brain injury. Neuropharmacology. 2019;145(Pt B):230–46. Lima J, Michl S, Katja B. et a1.Cortical vein thrombosis: the diagnostic value of different imaging modalitiesm. Neuroradiology. 2010;52(10):899–911. Wang YH, Chen R, Cai XJ. et a1.The diagnostic value of CT angiography for secondary vascular damage in severe traumatic brain injury. Chin J Trauma. 2011, (01):22–4. Yu Y, Chen J, Si Z, et al. The hemodynamic response of the cerebral bridging veins to changes in ICP. Neurocrit Care. 2010;12(1):117–23. Chen J, Si ZC, Zhao GY et al. MRI investigation on outflow segment of cerebral venous system and its significance with increased intracranialpressure. Chin J Neuromed. 2010; (04):374–8. Vignes JR, Dagain A, Guérin J, et al. A hypothesis of cerebral venous system regulation based on a study of the junction between the cortical bridging veins and the superior sagittal sinus. Laboratory investigation. J Neurosurg. 2007;107(6):1205–10. Si Z, Luan L, Kong D, et al. MRI-based investigation on outflow segment of cerebral venous system under increased ICP condition. Eur J Med Res. 2008;13(3):121–6. Sahuquillo J, Poca MA, Arribas M, Garnacho A, Rubio E. Interhemispheric supratentorial intracranial pressure gradients in head-injured patients: are they clinically important. J Neurosurg. 1999;90(1):16–26. Zamboni P, Galeotti R, Menegatti E, et al. Chronic cerebrospinal venous insufficiency in patients with multiple sclerosis. J Neurol Neurosurg Psychiatry. 2009;80(4):392–9. Varga AW, Johnson G, Babb JS, et al. White matter hemodynamic abnormalities precede sub-cortical gray matter changes in multiple sclerosis. J Neurol Sci. 2009;282(1–2):28–33. Beggs C, Shepherd S, Zamboni P. Cerebral venous outflow resistance and interpretation of cervical plethysmography data with respect to the diagnosis of chronic cerebrospinal venous insufficiency. Phlebology. 2014;29(3):191–99. Jimmy D, Miller JD, Nader R. Acute subdural hematoma from bridging vein rupture: a potential mechanism for growth. J Neurosurg. 2014;120:1378–84. Liu HB, Hong JF, Wang SS. Recent advance in acute subdural hematoma and intracranial vein circulation. Chin J Neuromed. 2019;18(2):211–4. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 23 Apr, 2026 Reviewers invited by journal 22 Apr, 2026 Editor invited by journal 08 Apr, 2026 Editor assigned by journal 08 Apr, 2026 First submitted to journal 06 Apr, 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-9138821","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":627978342,"identity":"171c2e20-55e6-493e-8b4c-1b6ca110428a","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":627978343,"identity":"fdd7aabf-31a0-4762-b573-37cedc1494d3","order_by":1,"name":"Hengheng Zhai","email":"","orcid":"","institution":"Fuzhou changle District People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hengheng","middleName":"","lastName":"Zhai","suffix":""},{"id":627978344,"identity":"ee9bc5b8-d46e-49a1-a657-51d35140b857","order_by":2,"name":"Weixin Lin","email":"","orcid":"","institution":"Fuzhou Changle District People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Weixin","middleName":"","lastName":"Lin","suffix":""},{"id":627978345,"identity":"9f29b1fd-262d-47c6-a1c2-7d3b6dcf8173","order_by":3,"name":"Zhenxing Chen","email":"","orcid":"","institution":"Fuzhou Changle District People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhenxing","middleName":"","lastName":"Chen","suffix":""},{"id":627978346,"identity":"add7abc9-6ad8-4f4d-b6eb-439ef0f9b695","order_by":4,"name":"Hang Xiao","email":"","orcid":"","institution":"Fuzhou Changle District People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hang","middleName":"","lastName":"Xiao","suffix":""},{"id":627978347,"identity":"cefeaed1-2232-4098-8792-ff78ac5cde94","order_by":5,"name":"Yuke Lin","email":"","orcid":"","institution":"Fuzhou Changle District People's Hostipal","correspondingAuthor":false,"prefix":"","firstName":"Yuke","middleName":"","lastName":"Lin","suffix":""},{"id":627978348,"identity":"12951061-59dc-4ef1-b118-158b955ad3bb","order_by":6,"name":"Cheng Lin","email":"","orcid":"","institution":"Fuzhou Changle District People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Cheng","middleName":"","lastName":"Lin","suffix":""},{"id":627978349,"identity":"26167be2-4984-4f20-b3a3-bd9b19ad255b","order_by":7,"name":"Yanqing Yan","email":"","orcid":"","institution":"Fuzhou Changle District People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yanqing","middleName":"","lastName":"Yan","suffix":""},{"id":627978350,"identity":"1f4d51d8-6f9a-4d5d-9732-edb7121155c1","order_by":8,"name":"Jianjun Gu","email":"","orcid":"","institution":"Henan Provincial People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jianjun","middleName":"","lastName":"Gu","suffix":""},{"id":627978351,"identity":"31e4791a-fa90-4c48-8bbf-f8abe95a4321","order_by":9,"name":"Shousen Wang","email":"","orcid":"","institution":"Fuzong Clinical Medical College of Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shousen","middleName":"","lastName":"Wang","suffix":""},{"id":627978352,"identity":"a086a294-e9df-4b11-9037-574adde672a1","order_by":10,"name":"Wei Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAu0lEQVRIiWNgGAWjYHACAwaGHzVy/OyNjQ8/EK3lYM8xY8mew83GEkRrOcDGnLhhRnqbAA9R6m8kb5P+wMOWuEHyYRuDBIOdnG4DQS1pZRIHLGSMt0sntj0oYEg2NjtAQIvZ7RyzGwd42GR3zk5sN5BgOJC4jTgtbMyMG24ebJPgIUWL4oYbjERqsb//rPzHWXAgJwID2YAIvwCjY7NBBTgqjz98+KHCTo6gFjRgQJryUTAKRsEoGAU4AAC+lUiyfQ/A1wAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-9171-9269","institution":"The First Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":true,"prefix":"","firstName":"Wei","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2026-03-16 14:02:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9138821/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9138821/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108409317,"identity":"9ea2e255-3e61-40e8-8287-27fe797febf3","added_by":"auto","created_at":"2026-05-04 09:59:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3386103,"visible":true,"origin":"","legend":"\u003cp\u003eMeasurement of the MIP areas of cortical veins. A Axial MIP image of a computed tomography venography (CTV) showing acute subdural hematoma (red arrow) and cortical veins (black arrow). B Image after adjusting the grayscale threshold value. C Binarized image showing the cortical veins (red areas within the skull). The areas of the bilateral cortical veins in the gray boxes were measured. The MIP area of the cortical veins was 5.28 cm\u003csup\u003e2\u003c/sup\u003e on the affected side and 2.26 cm\u003csup\u003e2\u003c/sup\u003e on the healthy side. The area on the affected side was significantly larger than that on the healthy side.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-9138821/v1/ca25a90bb0b56d48d8c54bb4.png"},{"id":108492568,"identity":"b18da760-8f52-4375-8027-3522be299fe8","added_by":"auto","created_at":"2026-05-05 09:58:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1372924,"visible":true,"origin":"","legend":"\u003cp\u003eBox plots of the K-value, Glasgow Coma Scale(GCS) score, and acute subdural hematoma volume. A Box plot of the K-value. The K-values were predominantly \u0026gt; 1 for patients with good prognosis and \u0026lt; 1 for patients with poor prognosis. B Box plot of the GCS score; the scores were higher for patients with a good prognosis than for those with a poor prognosis. C Box plot of the hematoma volume; the hematoma volumes were smaller for patients with a good prognosis than for patients with a poor prognosis.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-9138821/v1/1f493d72d955096a04817d22.png"},{"id":108493347,"identity":"8215720e-c8b9-430b-8794-4560d140c829","added_by":"auto","created_at":"2026-05-05 10:00:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":6839020,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic(ROC) curves and cutoff values of the K-value, Glasgow Coma Scale score(GCS), and acute subdural hematoma volume. A For the K-value, the area under the receiver operating characteristic curve (AUC) was 0.832, the cutoff value was 1.04, the sensitivity was 100%, and the specificity was 69.70%. B For the GCS score, the AUC was 0.890 and the cutoff value was 8 points. C For acute subdural hematoma volume, the AUC was 0.832 and the cutoff value was 31 ml.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-9138821/v1/3b4c42cefa4149343c4e0e95.png"},{"id":108409318,"identity":"5058148a-3a29-4034-9e35-879642da2c3b","added_by":"auto","created_at":"2026-05-04 09:59:38","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":10862459,"visible":true,"origin":"","legend":"\u003cp\u003ePrediction of poor prognosis with K-value. Case 1(A–D): A Acute subdural hematoma (red arrow) pushing the cortical veins (black arrow) away from the inner surface of the skull. B The MIP areas of cortical veins were calculated using ImageJ software, and the K-value (2.34) was \u0026gt; 1. C The volume rendering image of the parasagittal cortical veins confirmed that the cortical veins on the affected side were better visualized than the cortical veins on the healthy side (good prognosis). D Passive elongation of the bridging veins (black arrow) and bridging vein rupture and hemorrhage (red arrow), which resulted in venous return obstruction, were observed after the removal of acute subdural hematoma during the procedure. Case 2(E–H): E The cortical veins on the affected side(red arrow) were barely visible, whereas the cortical veins (black arrow) were visible on the healthy side. F The MIP areas of cortical veins were calculated using ImageJ software, and the K-value (0.093) was \u0026lt; 1 and close to 0. G Volume rendering image of parasagittal cortical veins. Bilateral cortical veins were poorly visualized, especially on the affected side. H The brain tissue of the patient bulged out of the bone window during surgery, indicating a very poor prognosis.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-9138821/v1/bd03ff6c3708fa815f29ef4a.png"},{"id":108492488,"identity":"3ea0d776-d124-4012-b433-cef6ff087ee0","added_by":"auto","created_at":"2026-05-05 09:57:53","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":12245187,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic spline plots of correlations between K-value and acute subdural hematoma volume with poor prognosis. A The risk of poor prognosis decreased with increasing K-value and leveled off as the K-value approached 1. B The risk of poor prognosis increased with the acute subdural hematoma volume. Note: A cubic spline plot was not generated for the discrete variable GCS score.\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-9138821/v1/55ca6e07f573c6232c61d7c5.png"},{"id":108409322,"identity":"cb5fd793-eb05-4f00-9c6f-0eadcee22beb","added_by":"auto","created_at":"2026-05-04 09:59:38","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":6540314,"visible":true,"origin":"","legend":"\u003cp\u003eA Nomogram based on the K-value, GSC score, and acute subdural hematoma volume. For example, for Case 2 in Fig. 4, the K-value of 0.093 corresponded to a score of 97.9; the GCS score of 4 corresponded to a score of 42.5; and the acute subdural hematoma volume of 40 mL corresponded to a score of 18.2. The model constructed on the basis of these three indicators yielded a total score of 158.6, indicating a high probability of very poor prognosis because the risk of poor prognosis was much greater than 0.9.\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-9138821/v1/d7883cd881b612d99ea9167e.png"},{"id":108409323,"identity":"23f4e5f6-147b-41ef-8b2a-44b6e8f3f0ac","added_by":"auto","created_at":"2026-05-04 09:59:38","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2174250,"visible":true,"origin":"","legend":"\u003cp\u003eA Nomogram calibration curve. The solid line is the actual curve of the patients, and the dashed diagonal line is the predicted curve, which shows a close fit to the diagonal line.\u003c/p\u003e","description":"","filename":"Fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-9138821/v1/189e358f4fc42896d31a11ce.png"},{"id":108493463,"identity":"baff39df-ff60-4cad-a92f-4ae565f900c2","added_by":"auto","created_at":"2026-05-05 10:00:34","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":7166438,"visible":true,"origin":"","legend":"\u003cp\u003eA Decision Curve Analysis of the nomogram. The solid gray line shows the benefit when all subjects were patients, the solid black line shows the benefit when no subjects were patients (zero net benefit), the dashed line represents the net benefit of the predictive model, and the blue line illustrates the net benefit of cross-validation. The net benefit of the model is higher than that of the extreme curves. B Distribution of risk scores based on the prediction model of acute subdural hematoma prognosis.\u003c/p\u003e","description":"","filename":"Fig8.png","url":"https://assets-eu.researchsquare.com/files/rs-9138821/v1/f936c47376b80903d6aab359.png"},{"id":108409325,"identity":"92707635-4d83-40e2-936e-66eec74f01e7","added_by":"auto","created_at":"2026-05-04 09:59:38","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":10505856,"visible":true,"origin":"","legend":"\u003cp\u003ePredictive ability of the risk score derived from the acute subdural hematoma prognosis prediction model and the cutoff value for high-risk and low-risk patients. A Receiver operating characteristic curve and cutoff value (109.5) of the risk score. B Stratification of high-risk and low-risk patients based on the cutoff value. Notably, 6.10% of low-risk patients and 82.90% of high-risk patients showed poor prognosis. The \u003cem\u003eKappa\u003c/em\u003evalue was 0.778 (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001). Note: Score = (4.5-K-value) × 22.22 + (15-GCS score) × 3.85 + 0.54 × hematoma volume (AUC: Area under the receiver operating characteristic curve).\u003c/p\u003e","description":"","filename":"Fig9.png","url":"https://assets-eu.researchsquare.com/files/rs-9138821/v1/bb0fb22e40f3c21adf2246cf.png"},{"id":109296450,"identity":"40463740-45bd-414d-90b9-0dec646b54f7","added_by":"auto","created_at":"2026-05-15 08:47:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":62697406,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9138821/v1/bc0d62d7-7a25-4b51-bd83-988d2628b36b.pdf"}],"financialInterests":"","formattedTitle":"Ratio of bilateral cortical veins areas on CT venography as a novel predictor of prognosis in patients with acute subdural hematoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAcute subdural hematoma (ASDH) is a frequent and severe form of secondary craniocerebral trauma [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Despite significant progress in imaging technologies, surgical interventions, and perioperative management, there remains a lack of substantial enhancement in the survival and prognosis of patients with ASDH [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The mortality rate of patients with ASDH has been reported to range from 55% to 70%, with a Glasgow Coma Scale (GCS) score of 8 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNonetheless, a significant gap persists in the availability of effective tools for assessing the prognosis of patients with ASDH, and there is a lack of cerebrovascular evidence that informs decision-making and early prognostic evaluation, particularly in terms of cerebral venous circulation [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Imaging evidence for patients with ASDH has been confined to plain brain computed tomography (CT), and the uniformity in imaging findings among admitted patients does not correspond to distinct prognoses following comparable treatments. Cerebral veins play a pivotal role in the cerebral circulation system, serving as primary pathways for cerebral venous return. Previous studies have established a substantial association between ASDH and vein injury [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Advancements in cerebral vein study techniques have led to the widespread clinical application of cerebral computed tomography venography (CTV), with the advantages of rapidity and accuracy [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Therefore, a preliminary exploration was conducted to prospectively observe the characteristic changes in the cortical and internal cerebral veins among patients with ASDH. These observations were evaluated in conjunction with clinical data to establish their correlations with prognosis, with the aim to introduce a novel assessment tool that can provide a reference for the development of diagnostic and therapeutic procedures for patients with ASDH.\u003c/p\u003e"},{"header":"Patients and methods","content":" \u003cp\u003ePatients\u003c/p\u003e \u003cp\u003eIn this study, we enrolled adult patients diagnosed with ASDH at Changle District People\u0026rsquo;s Hospital (Fuzhou City, China) from June 2018 to February 2023. The inclusion criteria were as follows: age\u0026thinsp;\u0026ge;\u0026thinsp;18 years, history of head trauma, cerebral CT findings indicative of ASDH, and hematoma volume\u0026thinsp;\u0026ge;\u0026thinsp;10 mL. We excluded patients with ASDH with primary brainstem injury or epidural hematoma, cerebral venous diseases such as cerebral arteriovenous fistula and venous sinus thrombosis, as well as those with intracranial tumors, hydrocephalus, spontaneous intracerebral hemorrhage, or brain inflammation. Of the 615 patients diagnosed with ASDH, 101 were selected according to the criteria, including 80 males and 21 females aged 24\u0026ndash;94 years (mean: 54.01\u0026thinsp;\u0026plusmn;\u0026thinsp;15.79 years). This study was approved by the Ethics Committee of Changle District People\u0026rsquo;s Hospital (2018-001KY). All procedures were performed in accordance with relevant guidelines and regulations. Informed consent was obtained from all patients or their families.\u003c/p\u003e \u003cp\u003eClinical data\u003c/p\u003e \u003cp\u003eBasic information about the patients was collected from their medical records, including medical history, symptoms on admission, consciousness, pupil dilation, GCS score, hemoglobin concentration, platelet count, prothrombin time, fibrinogen, D-dimer, and treatment regimens. The selection of treatment regimen was based on the Guidelines for the Management of Severe Traumatic Brain Injury published in 2020 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], including craniotomy and conservative therapy with medicines.\u003c/p\u003e \u003cp\u003eImaging data\u003c/p\u003e \u003cp\u003eCerebral CTV\u003c/p\u003e \u003cp\u003eAll patients underwent cerebral CTV in the supine position using a GE Optima CT660 multi-slice helical scanner. Helical scans were acquired in an axial plane parallel to the orbitomeatal line. The imaging parameters were as follows: tube voltage, 120 kV; tube current, 200\u0026ndash;230 mA; slice thickness, 0.625 mm; spacing between slices, 0.625 mm; 80 mL ioversol (containing 320 mg/mL iodine, injection rate 5 mL/s); and delay time, 29 s. Maximum intensity projection (MIP) reconstruction of the venous phase cerebral CTV was performed using the Advantage Window 4.6 workstation. Evaluation and analysis of the images were conducted by a senior neurosurgeon, senior imaging physician, and junior imaging physician. In the case of disagreement, consensus was achieved through discussion.\u003c/p\u003e \u003cp\u003eCalculation of the K-value\u003c/p\u003e \u003cp\u003eA MIP image with the superior border of the corpus callosum at the center was selected(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). The image was imported into Image J software, where the scale was set and the threshold was adjusted to clearly delineate the cerebral veins, specifically highlighting the secondary branches of the parasagittal cortical veins. Additionally, the adjustment was conducted to exclude grayscale values of the brain parenchyma, ventricles, hematoma, and skull (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Subsequently, the image was binarized and the bilateral cortical veins were outlined (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). The MIP areas of cortical veins on the affected and healthy sides were measured using the MIP image, and the K-value was calculated using the following formula:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eK-value\u0026thinsp;=\u0026thinsp;MIP area of cortical veins on the affected side/MIP area of cortical veins on the healthy side\u003c/p\u003e \u003cp\u003eCalculation of the diameter and displacement of the internal cerebral veins from the median plane based on MIP reconstruction\u003c/p\u003e \u003cp\u003eAxial MIP reconstruction of the venous phase cerebral CTV was performed with a thickness of 3.0 cm. The MIP image with the internal cerebral veins at the center was selected. Two points 1.5 cm anterior to the confluence of the left/right internal cerebral veins and the vein of Galen were selected for the measurement and calculation of the average diameter of bilateral internal cerebral veins. Subsequent assessments involved measuring the displacement of the midpoint of the line connecting the two points from the median plane and determining the distance between the medial borders of the bilateral internal cerebral veins.\u003c/p\u003e \u003cp\u003eCalculation of the Rotterdam CT score\u003c/p\u003e \u003cp\u003ePlain scans of cerebral CTV were used to evaluate brain midline shift and ambient cistern width. The presence of intraventricular or cisternal hemorrhage was observed. The Rotterdam CT score was computed following established criteria [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrognostic analysis\u003c/p\u003e \u003cp\u003eIn this study, patient consciousness, assessed through the Glasgow Outcome Scale (GOS) score [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], was used to measure neurologic function [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Outpatient or telephone follow-up was performed for all patients at 6 months after injury to determine their GOS scores. Based on their consciousness at 6 months post-injury, patients with clear consciousness were categorized into the good prognosis group (GOS score of 3\u0026ndash;5), whereas those with unclear consciousness or who had died were categorized into the poor prognosis group (GOS score of 1\u0026ndash;2).\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe mean and standard deviation were used to describe continuous variables with normal distribution, and Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test was used for statistical analysis. Median and interquartile ranges were used to describe continuous variables with non-normal distribution, and the rank-sum test was used for statistical analysis. Frequency and constituent ratio were used to describe categorical variables, and the chi-square test was used for statistical analysis. Logistic regression analysis was performed to identify the factors influencing poor prognosis. Variables with a \u003cem\u003eP-\u003c/em\u003evalue\u0026thinsp;\u0026lt;\u0026thinsp;0.1 in the univariate model were included in the multivariate model to identify factors associated with poor prognosis. The receiver operating characteristic (ROC) curve was plotted, and the area under the curve (AUC) was calculated to assess the predictive performance of these factors. Optimal cutoff values were determined on the basis of the maximum Youden index. Restricted cubic spline plots were used to analyze the linear relationships between the influencing factors and prognosis. Furthermore, a nomogram model for predicting poor prognosis was constructed on the basis of the influencing factors. A calibration curve was plotted to assess the consistency between the actual and predicted probabilities, and the clinical usefulness of the model was evaluated through decision curve analysis. SPSS software (version 25.0, SPSS Inc., Chicago, USA) was used for statistical description, one-way analysis of variance, and logistic regression. R language (version 4.2.1) was used to plot the nomogram, calibration, ROC, and decision curves using the packages OptimalCutpoints, caret, rms, Hmisc, ROCR, rmda, and smoothHR. All tests were two-sided and \u003cem\u003eP\u003c/em\u003e-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered to indicate statistical significance.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003ePatient characteristics\u003c/p\u003e \u003cp\u003eA total of 101 patients with ASDH were included in this study. Among them, 68 (67.3%) patients showed consciousness and a good prognosis at 6 months post-treatment, with GOS scores ranging from 3 to 5. Conversely, 33 (32.67%) patients experienced poor prognosis, with mortality or unconsciousness and GOS scores of 1 to 2. In the good and poor prognosis groups, the mean age was 52.56\u0026thinsp;\u0026plusmn;\u0026thinsp;16.47 and 57.00\u0026thinsp;\u0026plusmn;\u0026thinsp;14.05 years and 73.5% and 90.9% of the patients were male, respectively. The baseline characteristics of the patients in the two groups are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Notably, at the time of admission, the K-value, GCS score, the average diameter of bilateral internal cerebral veins, the distance between bilateral internal cerebral veins, the displacement of internal cerebral veins from the median plane, and ambient cistern width were higher in the good prognosis group than in the poor prognosis group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The Rotterdam CT score, acute subdural hematoma volume, prothrombin time, activated partial prothrombin time, international normalization ratio, and D-dimer were lower in the good prognosis group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In the poor prognosis group, 25 (75.8%) patients underwent craniotomy, whereas only 14 (20.6%) patients received craniotomy in the good prognosis group. Furthermore, the incidence of subarachnoid hemorrhage was higher in the poor prognosis group than in the good prognosis group (18 [54.5%] vs. 5 [7.4%]; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\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\u003eThe baseline characteristics of the two groups of patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eWhole population(n\u0026thinsp;=\u0026thinsp;101)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eThe group of Good prognosis (n\u0026thinsp;=\u0026thinsp;68)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eThe group of poor prognosis(n\u0026thinsp;=\u0026thinsp;33)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStatistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e54.01\u0026thinsp;\u0026plusmn;\u0026thinsp;15.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52.56\u0026thinsp;\u0026plusmn;\u0026thinsp;16.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57.00\u0026thinsp;\u0026plusmn;\u0026thinsp;14.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.186\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eK-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMIP area of cortical veins on the affected side(cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e5.49\u0026thinsp;\u0026plusmn;\u0026thinsp;2.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.10\u0026thinsp;\u0026plusmn;\u0026thinsp;2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.21\u0026thinsp;\u0026plusmn;\u0026thinsp;2.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.718\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMean arterial pressure(mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e102.65\u0026thinsp;\u0026plusmn;\u0026thinsp;16.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e103.78\u0026thinsp;\u0026plusmn;\u0026thinsp;16.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100.33\u0026thinsp;\u0026plusmn;\u0026thinsp;15.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.323\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHemoglobin concentration(g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e133.55\u0026thinsp;\u0026plusmn;\u0026thinsp;18.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e135.75\u0026thinsp;\u0026plusmn;\u0026thinsp;17.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e129.03\u0026thinsp;\u0026plusmn;\u0026thinsp;19.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHematocrit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eGCS score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e10 (6, 14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14 (8, 15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5 (3, 8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-6.403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMIP area of cortical veins on the healthy side(cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e3.71 (2.56, 4.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.52 (2.56, 4.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.36 (2.58, 5.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAverage diameter of bilateral internal cerebral veins(cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.17 (0.13, 0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.18 (0.16, 0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.12 (0.10, 0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-5.459\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDisplacement of internal cerebral veins from the median plane (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.27 (0.12, 0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.19 (0.10, 0.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.94 (0.46, 1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-5.555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDistance between bilateral internal cerebral veins(cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.05 (0, 0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.13 (0, 0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0, 0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-3.599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAmbient cistern width (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.19 (0.11, 0.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.20 (0.16, 0.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.09 (0.04, 0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-4.712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBrain midline shift(cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.35 (0.14, 0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.21 (0.12, 0.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00 (0.61, 1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-5.479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eRotterdam CT score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e3 (2, 5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (2, 4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5 (4, 6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-6.252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAcute subdural hematoma volume(ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e23.50 (12.75, 48.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.00 (11.63, 28.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e45.52 (31.05, 76.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-5.395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePlatelet count(10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e195.00 (166.00, 233.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e199.50 (168.00, 235.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e191.00 (148.00, 229.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.405\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eProthrombin time(second)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e11.20 (10.40, 11.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.95 (10.20, 11.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.6 (11.05, 13.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-3.356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eActivated partial prothrombin time(second)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e25.10 (21.20, 27.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.35 (20.56, 26.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27.10 (23.85, 36.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-3.425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFibrinogen(g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.78 (1.52, 2.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.925 (1.54, 2.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.76 (1.35, 2.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.245\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eInternational normalization ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.02 (0.94, 1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.99 (0.92, 1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.08 (1.01, 1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-3.787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eD-dimer(mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e17.475 (5.00, 35.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.64 (3.36, 35.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35.2 (14.81, 35.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-3.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80 (79.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50 (73.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30 (90.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (20.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18 (26.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3 (9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\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\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94 (93.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64 (94.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30 (90.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.680\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3 (9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62 (61.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45 (66.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17 (51.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.193\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39 (38.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23 (33.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16 (48.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61 (60.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41 (60.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20 (60.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40 (39.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27 (39.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13 (39.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of cranial diseases(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94 (93.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e65 (95.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29 (87.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.212\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4 (12.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\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\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80 (79.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53 (77.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27 (81.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.796\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (20.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15 (22.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6 (18.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGCS classification(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1(13-15points)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38 (37.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38 (55.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e32.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2(9-12points)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12 (17.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7 (21.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e3(3-8points)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44 (43.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18 (26.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26 (78.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\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\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40 (39.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38 (55.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2 (6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e23.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61 (60.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30 (44.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31 (93.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePupil dilation(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75 (74.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e61 (89.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14 (42.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25.983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (25.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7 (10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19 (57.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSubarachnoid hemorrhage(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78 (77.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e63 (92.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15 (45.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e28.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23 (22.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 (7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18 (54.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment regimens(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMedication therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62 (61.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54 (79.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8 (24.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e28.529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCraniotomy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39 (38.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14 (20.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25 (75.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFactors associated with poor prognosis\u003c/p\u003e \u003cp\u003eUnivariate logistic analysis discovered that the following factors were associated with poor prognosis: K-value, cortical veins area on the affected side, acute subdural hematoma volume, the average diameter of bilateral internal cerebral veins, GCS score, the displacement of internal cerebral veins from the median plane, the distance between bilateral internal cerebral veins, ambient cistern width, brain midline shift, and Rotterdam CT score (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Subsequent multivariate logistic analysis using forward selection (Wald) showed that the risk of poor prognosis decreased with increasing K-value (OR: 0.10, 95% CI: 0.02\u0026ndash;0.50, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005) and GCS score (OR: 0.64, 95% CI: 0.49\u0026ndash;0.85, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002). Conversely, the risk of poor prognosis increased with increasing hematoma volume (OR: 1.05, 95% CI: 1.01\u0026ndash;1.08, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\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\u003eUnivariate logistic regression analysis of factors associated with poor prognosis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR (95% \u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.02 (0.99\u0026ndash;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.186\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eK-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.11 (0.04\u0026ndash;0.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMIP area of cortical veins on the affected side\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.70 (0.56\u0026ndash;0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMean arterial pressure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99 (0.96\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.321\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHemoglobin concentration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98 (0.96\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHematocrit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-6.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0\u0026ndash;5.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eGCS score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.63 (0.53\u0026ndash;0.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMIP area of cortical veins on the healthy side\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.34 (1.03\u0026ndash;1.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAverage diameter of bilateral internal cerebral veins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-37.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0\u0026ndash;0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDisplacement of internal cerebral veins from the median plane\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59.73 (12.29\u0026ndash;290.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDistance between bilateral internal cerebral veins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-11.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0\u0026ndash;0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAmbient cistern width\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-11.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0\u0026ndash;0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBrain midline shift\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.63 (6.61\u0026ndash;84.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eRotterdam CT score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.17 (2.45\u0026ndash;7.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAcute subdural hematoma volume\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.05 (1.03\u0026ndash;1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePlatelet count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (0.99\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.383\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eProthrombin time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.70 (1.22\u0026ndash;2.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eActivated partial prothrombin time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.13 (1.05\u0026ndash;1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFibrinogen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.78 (0.51\u0026ndash;1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.247\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eInternational normalization ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e423.20 (12.24\u0026ndash;14636.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eD-dimer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.06 (1.02\u0026ndash;1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.28 (0.08\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.054\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 \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.60 (0.34\u0026ndash;7.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.554\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.84 (0.79\u0026ndash;4.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99 (0.42\u0026ndash;2.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.976\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of cranial diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.99 (0.63\u0026ndash;14.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.169\u003c/p\u003e \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 \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.79 (0.27\u0026ndash;2.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.653\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGCS classification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.33 (2.94\u0026ndash;13.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003eComa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.63 (4.35\u0026ndash;88.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003ePupil dilation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.83 (4.17\u0026ndash;33.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSubarachnoid hemorrhage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.12 (4.84\u0026ndash;47.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003eTreatment regimens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedication therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e--\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCraniotomy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.05 (4.48\u0026ndash;32.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \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\u003eMultivariate logistic regression analysis of factors associated with poor prognosis using forward selection (Wald)\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCovariates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.10 (0.02\u0026ndash;0.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin concentration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.96 (0.92\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGCS score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.64 (0.49\u0026ndash;0.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcute subdural hematoma volume\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.05 (1.01\u0026ndash;1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNote The model variables included the K-value, hemoglobin concentration, Glasgow Coma Scale(GCS) score, the average diameter of bilateral internal cerebral veins, the displacement of internal cerebral veins from the median plane, the distance between bilateral internal cerebral veins, Rotterdam CT score, acute subdural hematoma volume, prothrombin time, activated partial prothrombin time, international normalization ratio, D-dimer, sex, subarachnoid hemorrhage, and treatment. The Rotterdam CT score was derived from the ambient cistern width and brain midline shift. Consciousness was evaluated using the GCS score, and pupil dilation was interfered by the oculomotor nerve injury caused by a skull base fracture. As a result, the significant variables identified in the univariate analysis, such as ambient cistern width, brain midline shift, consciousness, and pupil dilation, were excluded from the model.\u003c/p\u003e \u003cp\u003ePrediction of poor prognosis using K-value, GCS score, and acute subdural hematoma volume\u003c/p\u003e \u003cp\u003eThe K-value, GCS score, and hematoma volume of patients with good and poor prognoses are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e. ROC analysis showed that the AUC of the K-value was 0.832 and the cutoff value was 1.04 (rounded to 1.0 with two significant figures). The sensitivity was notably high (100%) and the specificity was 69.70% (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Therefore, a good prognosis was associated with a larger cortical veins area on the affected side than on the healthy side, and vice versa (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The cutoff value of the GCS score was 8, the sensitivity was 89.70%, and the specificity was 72.73% (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The cutoff value of the acute subdural hematoma volume was 31 mL (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eC), and the sensitivity and specificity were 78.79% and 79.41%, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs shown in the restricted cubic spline plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e), there was a nonlinear relationship between the K-value and prognosis (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003). The risk of poor prognosis decreased with increasing K-value and then leveled off as the K-value approached 1. The acute subdural hematoma volume showed a linear relationship with prognosis (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.341), and the risk of poor prognosis increased with increasing hematoma volume.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePrediction of poor prognosis using a nomogram based on K-value, GCS score, and acute subdural hematoma volume\u003c/p\u003e \u003cp\u003eA nomogram was constructed based on the K-value, GCS score, and acute subdural hematoma volume (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The calibration curve demonstrated a close fit (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003e). A decision curve showing the clinical benefit was generated from the prediction model (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). Furthermore, the risk score was calculated for each patient using the prediction mode (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). The risk score of all patients ranged from 17.80 to 190.11. The median risk score of patients with a good prognosis was 82.55 (73.34\u0026ndash;97.44), while that of patients with a poor prognosis was 143.53 (115.42\u0026ndash;168.22). The ROC based on the risk score showed an AUC of 0.923 and a cutoff value of 109.5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e9\u003c/span\u003e). Patients were categorized into high- and low-risk groups based on the cutoff value. Notably, 93.90% and 17.10% of patients in the low-risk and high-risk groups showed good prognoses, respectively. The \u003cem\u003eKappa\u003c/em\u003e value was 0.778 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), the sensitivity was 87.88%, and the specificity was 98.18%.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, the K-value, GCS score, and acute subdural hematoma volume were identified as factors contributing to poor prognosis. The prediction model incorporating these indicators showed high sensitivity and specificity and can be used to predict the prognosis of patients with ASDH. The GCS score is used worldwide to evaluate the level of consciousness in patients with craniocerebral trauma [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] as well as their prognosis [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, the subjectivity of the GCS score and its susceptibility to anesthetics or sedatives, along with its omission of pupil and brainstem assessment[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], render it unsuitable for application in infants. Acute subdural hematoma volume has been used as an important indicator of surgical interventions in patients with craniocerebral trauma and is closely related to their prognosis [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Nonetheless, the impact of hematoma volume on intracranial pressure (ICP) is influenced by brain tissue compliance and the degree of cerebral atrophy. The results in this study confirmed the significance of these two well-recognized factors associated with poor prognosis. The ratio of bilateral cortical veins areas (K-value) was also introduced as a novel indicator of poor prognosis, with its mechanism thought to be linked to the blood and oxygen supply to the brain tissue. Intracranial hypertension necessitates adaptation in the delivery of oxygen and glucose through the bloodstream to sustain brain cell viability. The K-value objectively and directly reflects the disparity in bilateral cerebral blood and oxygen supply, potentially correlating with brain swelling and prognosis [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the present study, venous rather than arterial indicators were used to predict the prognosis of ASDH. This choice was informed by the proximity of cortical veins to ASDH and the compression of cortical veins by ASDH. Furthermore, given that cerebral veins function as the principal drainage pathways, cerebral vein injury or venous return obstruction is closely associated with brain edema and ICP [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Consequently, the cortical venous circulation indicator K-value was introduced, and a K-value\u0026thinsp;\u0026lt;\u0026thinsp;1 was identified as an independent risk factor for poor prognosis in patients with ASDH. Previous studies have highlighted a significant association between impaired cortical venous circulation and ASDH prognosis. Cortical veins are prone to injury, compression, or thrombotic occlusion secondary to traumatic brain injury because of their superficial location and lack of tunica media and venous valves, resulting in the obstruction of cortical venous return [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Wang et al [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. observed that cortical veins occlusion on CTV was indicative of significantly increased ICP and poor prognosis, which is consistent with the results of this study. Building on the qualitative examination of cortical veins mentioned above, this study conducted a quantitative investigation in patients with ASDH. The MIP area of cortical veins was calculated using ImageJ software, and the K-value was used as a novel indicator to quantitatively evaluate the influence of impaired cortical venous circulation on ASDH prognosis. The findings of this study validated the K-value as a straightforward and effective indicator for predicting the prognosis of patients with ASDH.\u003c/p\u003e \u003cp\u003eThe role of the K-value in predicting the prognosis of ASDH may be related to the regulation of resistance to blood outflow through the bridging veins. Studies have shown that, within a certain range, cerebral venous blood flow can undergo physiological regulation. With a slight or moderate increase in ICP, stenosis of parasagittal bridging veins has been confirmed in pigs [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] and humans [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Through cell staining, Vignes et al [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. identified smooth muscle actin within the walls of the bridging veins, which could contract to enhance the resistance of blood outflow from the bridging veins. Using brain magnetic resonance venography, Si et al [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. showed that elevated ICP was associated with the formation of an \u0026ldquo; outflow segment cuff \u0026rdquo; structure located at the outlet of brain-bridging veins and thus increased the resistance to blood outflow. This, in turn, resulted in compensatory dilatation and blood stasis in the distal segment of the bridging veins or neighboring cortical veins. These findings align with the compensatory changes in the cerebral veins with increased ICP observed in the present study. Elevated ICP has been documented in the cerebral hemisphere affected by ASDH in both animal and human studies [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Compression of the cerebral cortex by ASDH frequently results in cortical venous return obstruction and intracranial venous hypertension [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. According to the mechanism of regulating blood return in the parasagittal bridging veins mentioned above, various degrees of venous return obstruction and blood stasis occur at the outlet of the bridging veins during mild or moderate intracranial hypertension, causing the accumulation of contrast agents in cortical veins. In addition, the cortical veins contain numerous anastomotic branches. In response to mild or moderate intracranial hypertension, the mean arterial blood pressure increases to maintain stable cerebral blood flow, causing compensatory dilation of peripheral cortical veins, especially on the ASDH side. This partly explains the dilation in the cortical veins on the affected side observed in this study. Consequently, the MIP area of the cortical veins on the affected side was larger than that on the healthy side, resulting in a K-value\u0026thinsp;\u0026gt;\u0026thinsp;1. Conversely, excessively high ICP impairs the compensatory function of the cerebral veins, resulting in decompensation of cerebral vein blood flow [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] As a result, the velocity of the blood flow in the cortical veins decreases and, in some instances, approaches zero, leading to delayed or failed visualization of contrast agents in the cortical veins, particularly on the affected side. In this case, the MIP area of the cortical veins on the affected side was smaller than that on the healthy side, yielding a K-value\u0026thinsp;\u0026lt;\u0026thinsp;1. This decompensation of cerebral venous blood flow may contribute to diffuse brain swelling and even intraoperative brain bulge (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eH), both of which indicate poor prognosis.\u003c/p\u003e \u003cp\u003eThe role of the K-value in predicting the prognosis of ASDH may be associated with the diameter of the bridging veins. This study represents the first application of Poiseuille\u0026rsquo;s law in elucidating the mechanism of intracranial venous hypertension in patients with ASDH in China. Poiseuille\u0026rsquo;s law has been widely employed in the study of vascular diseases. Zamboni et al [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. reported that chronic cerebrospinal venous insufficiency was caused by unilateral or bilateral stenosis of the jugular veins and resultant venous obstruction. Through hemodynamic analysis of magnetic resonance imaging data, Varga et al [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. found that multiple sclerosis is characterized by inflammation and stenosis of venules, leading to impaired venous return. Patients with multiple sclerosis demonstrate reduced internal jugular vein diameter and significantly increased blood flow resistance [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In fluid dynamics, Poiseuille\u0026rsquo;s law is expressed as follows: Q\u0026thinsp;=\u0026thinsp;π\u0026thinsp;\u0026times;\u0026thinsp;r\u003csup\u003e4\u003c/sup\u003e\u0026thinsp;\u0026times;\u0026thinsp;Δp / (8ηL), where Q is the flow rate, Δ\u003cem\u003ep\u003c/em\u003e is the pressure difference between the two ends of the pipe, r is the radius of the pipe, L is the length of the pipe, and η is the viscosity coefficient of the fluid. According to Poiseuille\u0026rsquo;s law, the blood flow rate is proportional to the fourth power of the vessel diameter. Therefore, the venous blood flow could be significantly reduced in patients with ASDH. Consistent with Poiseuille\u0026rsquo;s law, we found that ASDH could lead to mild compression on the surface of the cerebral cortex and separation of the cortex from the dura mater [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. As a result, the bridging veins between the cerebral cortex and dura mater were stretched and their diameters were reduced (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). According to Poiseuille\u0026rsquo;s law, blood flow in the bridging veins is restricted, leading to cortical venous blood stasis. This effect was more severe on the affected side, yielding a K-value\u0026thinsp;\u0026gt;\u0026thinsp;1. With a continued increase in hematoma volume, further elongation and thinning of bridging veins occurred, resulting in a sharp decline in venous blood flow as well as cerebral edema and a further elevation in ICP [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Excessively high ICP beyond the range of physiological regulation resulted in a progressive increase in ICP. Consequently, blood flow was further reduced, progressive cerebral swelling appeared, and cortical veins were further compressed, leading to cortical veins occlusion or even venous thrombosis. In the present study, brain CTV suggested progressive brain swelling and delayed or failed visualization of the bilateral cortical veins, especially on the affected side. Patients with excessively high ICP showed a K-value\u0026thinsp;\u0026lt;\u0026thinsp;1 and poor prognosis, which also confirmed acute subdural hematoma volume as an independent risk factor for poor prognosis in this study. Therefore, when the K-value is \u0026lt;\u0026thinsp;1, the possibility of ICP beyond physiological regulation should be considered. Regular comprehensive evaluation should be conducted to avoid missing the optimal surgical window. In cases where cortical veins on the affected side are barely visible and the K-value is significantly\u0026thinsp;\u0026lt;\u0026thinsp;1, the cerebral blood flow is extremely slow or stagnant, possibly leading to cerebral venous thrombosis. As a result, craniotomy may be less effective in reversing this condition, thereby contributing to an unfavorable prognosis and limiting the surgical benefit for such patients.\u003c/p\u003e \u003cp\u003eIn addition, the severity of venous return obstruction may be influenced by brain compliance and the relative anatomical positions of ASDH and bridging veins, which require further investigation.\u003c/p\u003e \u003cp\u003eLimitations and prospects\u003c/p\u003e \u003cp\u003eCTV reconstruction of the cerebral veins is not without limitations. Skull artifacts can affect the reconstruction of cerebral veins situated close to the skull. In addition, the challenge of visualizing small veins persists, and this limitation may be addressed through further exploration using cerebral magnetic resonance venography or cerebral digital subtraction angiography.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eK-value, GCS score, and hematoma volume were identified as independent risk factors for poor prognosis in patients with ASDH. A model based on these indicators could accurately predict the prognosis of ASDH and guide clinical decision-making.\u003c/p\u003e "},{"header":"Abbreviations","content":"\u003cp\u003eASDH, acute subdural hematoma; CTV, computed tomography venography; MIP, maximum intensity projection; GCS, Glasgow Coma Scale; GOS, Glasgow Outcome Scale.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAuthor contributions\u003c/p\u003e\n\u003cp\u003eWSS, XWM, ZHH and WW were responsible for the study concept and design. XWM, LWX, ZHH, CZX and\u0026nbsp;XH\u0026nbsp;performed the clinical data collection. XWM, LYK,\u0026nbsp;LC\u0026nbsp;and\u0026nbsp;YYQ\u0026nbsp;performed the experiments of cerebral CTV. XWM, LWX, ZHH and LYK performed the evaluation and analysis of the images. XWM, WW, GJJ and LWX were responsible for the data analyses. XWM, LWX and ZHH drafted the manuscript. WSS and WW critically reviewed the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eData Availability\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThe authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by Fuzhou Science and Technology Plan Project Fund (2021-S-134); Fujian Provincial Science and Technology Innovation Joint Fund (2019Y9045).\u003c/p\u003e\n\u003cp\u003eDeclarations\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that there is no conflict of interest.\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eAll procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. This study was approved by the local Ethics Committee \u0026nbsp;of Fuzhou Changle District People's Hospital (No. 2018-001KY), Informed consent was obtained from all patients or their families.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAcknowledgement\u003c/p\u003e\n\u003cp\u003eThe authors sincerely thank Dr. Zhou Jiang and Jianbin Lin from Fuzhou Changle District People's Hospital, Dr. Liangfeng Wei from 900th Hospital for advice on experimental design.\u003c/p\u003e\n\u003cp\u003eORCID iDs\u003c/p\u003e\n\u003cp\u003eWeiming Xu: https://orcid.org/0000-0002-8319-1994\u003c/p\u003e\n\u003cp\u003eWei Wang: https://orcid.org/0000-0002-9171-9269\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMohamed E, El-Abtah,Mary J, Roach,Michael L. Kelly,Outcomes After the Surgical Evacuation of Traumatic Acute Subdural Hematomas: The tASDH. Risk Score [J] World Neurosurg. 2023;09054:e1\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaribe H, Hayashi T, Hirano T, et al. Surgical management of traumatic acute subdural hematoma in adults: a review. Neurol Med Chir (Tokyo). 2014;54(11):887\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGernsback JE, Kolcun J, Richardson AM et al. Patientem fortuna adiuvat: the delayed treatment of surgical acute subdural hematomas-A case series.World Neurosurg. 2018; 120: e414\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu WM, Lin YK, Lin JB, et al. Views merged method of CT venography display the spatial conformation of subdural hematoma and adjacent veins. Chin J Clin Anat. 2021;39(02):233\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang C, Xian L, Chen XR, et al. 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Glasgow coma scale pupil score (GCS-P) and the hospital mortality in severe traumatic brain injury: analysis of 1,066 Brazilian patients. Arq Neuropsiquiatr. 2023;81:452\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang L, Liu C, Lu E, et al. Total intracranial volume as a covariate for predicting prognosis in patients with primary intracerebral hemorrhage. Clin Neurol Neurosurg. 2022;214:107135.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang S, Chen Q, Xian L, et al. Acute subdural haematoma exacerbates cerebral blood flow disorder and promotes the development of intraoperative brain bulge in patients with severe traumatic brain injury. Eur J Med Res. 2023;28:138.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHamahata NT, Nakagawa K. Regional cerebral hypoperfusion from acute subdural hematoma. Neurocrit Care. 2020;32(2):633\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJha RM, Kochanek PM, Simard JM. Pathophysiology and treatment of cerebral edema in traumatic brain injury. Neuropharmacology. 2019;145(Pt B):230\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLima J, Michl S, Katja B. et a1.Cortical vein thrombosis: the diagnostic value of different imaging modalitiesm. Neuroradiology. 2010;52(10):899\u0026ndash;911.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang YH, Chen R, Cai XJ. et a1.The diagnostic value of CT angiography for secondary vascular damage in severe traumatic brain injury. Chin J Trauma. 2011, (01):22\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu Y, Chen J, Si Z, et al. The hemodynamic response of the cerebral bridging veins to changes in ICP. 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Interhemispheric supratentorial intracranial pressure gradients in head-injured patients: are they clinically important. J Neurosurg. 1999;90(1):16\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZamboni P, Galeotti R, Menegatti E, et al. Chronic cerebrospinal venous insufficiency in patients with multiple sclerosis. J Neurol Neurosurg Psychiatry. 2009;80(4):392\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVarga AW, Johnson G, Babb JS, et al. White matter hemodynamic abnormalities precede sub-cortical gray matter changes in multiple sclerosis. J Neurol Sci. 2009;282(1\u0026ndash;2):28\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeggs C, Shepherd S, Zamboni P. Cerebral venous outflow resistance and interpretation of cervical plethysmography data with respect to the diagnosis of chronic cerebrospinal venous insufficiency. Phlebology. 2014;29(3):191\u0026ndash;99.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJimmy D, Miller JD, Nader R. Acute subdural hematoma from bridging vein rupture: a potential mechanism for growth. J Neurosurg. 2014;120:1378\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu HB, Hong JF, Wang SS. Recent advance in acute subdural hematoma and intracranial vein circulation. Chin J Neuromed. 2019;18(2):211\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e\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":"neurocritical-care","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"neca","sideBox":"Learn more about [Neurocritical Care](http://link.springer.com/journal/12028)","snPcode":"12028","submissionUrl":"https://www.editorialmanager.com/neca/default2.aspx","title":"Neurocritical Care","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Computed tomography venography, acute subdural hematoma, maximum intensity projection area of cortical veins, GOS score","lastPublishedDoi":"10.21203/rs.3.rs-9138821/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9138821/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eIndividuals diagnosed with acute subdural hematoma (ASDH) typically present with a severe clinical condition and an unfavorable prognosis. However, there are a lack of reliable methods for predicting the prognosis of patients with ASDH. Therefore, in this study, the characteristic changes in the cortical and internal cerebral veins among patients with ASDH was prospectively observed. The correlations between these changes and ASDH prognosis was examined with the goal of establishing a novel assessment method that can provide a reference for the development of diagnostic and therapeutic procedures for ASDH.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA prospective investigation was conducted involving 101 patients diagnosed with ASDH. Upon admission, all patients underwent cerebral computed tomography venography (CTV). The acquired images were analyzed using ImageJ software to measure the maximum intensity projection(MIP) areas of the bilateral cortical veins adjacent to the superior sagittal sinus. The diameter and displacement of the internal cerebral veins, ambient cistern width, brain midline shift were also measured, and Rotterdam CT score was calculated. Factors influencing prognosis were identified.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eUnivariate logistic analysis was performed to identify the factors associated with poor prognosis, including the ratio of the MIP areas of cortical veins on the affected and healthy sides (K-value), Glasgow Coma Scale (GCS), hematoma volume, ambient cistern width, and Rotterdam CT score, et al. Furthermore, multivariate logistic analysis using forward selection (Wald) revealed that the risk of poor prognosis decreased with increasing K-value (odds ratio [OR]: 0.10, 95% confidence interval [CI]: 0.02\u0026ndash;0.50, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005) and GCS score (OR: 0.64, 95% CI: 0.49\u0026ndash;0.85, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002), but increased with increasing hematoma volume (OR: 1.05, 95% CI: 1.01\u0026ndash;1.08, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007). The model based on these three indicators demonstrated high sensitivity (87.88%) and high specificity (91.18%) in predicting the prognosis of patients with ASDH.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eK-value, GCS score, and hematoma volume were identified as independent risk factors for poor prognosis in patients with ASDH. A model built on these factors demonstrated accurate prediction of ASDH prognosis and could provide valuable guidance for clinical decision-making.\u003c/p\u003e\u003ch2\u003eTrial registration\u003c/h2\u003e \u003cp\u003eRatio of bilateral cortical veins areas on CT venography as a novel predictor of prognosis in patients with acute subdural hematoma, researchregistry10196. 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