Prognostic Value of the IntracerebralHematoma Volume-to-Cerebrospinal Fluid Volume Ratio in Patients with Spontaneous Basal Ganglia Hemorrhage Undergoing Non-Surgical Treatment

Prognostic Value of the IntracerebralHematoma Volume-to-Cerebrospinal Fluid Volume Ratio in Patients with Spontaneous Basal Ganglia Hemorrhage Undergoing Non-Surgical Treatment | 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 Prognostic Value of the IntracerebralHematoma Volume-to-Cerebrospinal Fluid Volume Ratio in Patients with Spontaneous Basal Ganglia Hemorrhage Undergoing Non-Surgical Treatment Zhangwei Yan, Tao Sun, Xingwang Zhou, Han Peng, Xu Xu, Xiaoyu Wang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8300264/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Objective This study aims to evaluate the prognostic value of the ratio of intracerebral hematoma volume (IHV) to cerebrospinal fluid volume (CSFV) in patients with basal ganglia hemorrhage undergoing non-surgical treatment. By providing a quantitative reference for clinical decision-making, this research seeks to facilitate the early identification of patients with poor prognoses and to support the selection of optimal treatment strategies tailored to individual needs. Methods This retrospective study included patients with spontaneous basal ganglia hemorrhage who received non-surgical treatment at our hospital between January 2017 and October 2020. IHV, CSFV, and the maximum transverse diameter of the third ventricle (MTDTV) were measured, and baseline characteristics were collected. Prognosis was assessed at three months using the modified Rankin Scale (mRS), with scores ≤ 2 indicating good outcomes and scores ≥ 3 indicating poor outcomes. Correlations between clinical variables and prognosis were analyzed. Results A total of 125 patients were included in this study, among whom 74 (59.2%) experienced poor outcomes. Univariate analysis revealed significant differences in NIHSS score, GCS score, IHV, systolic blood pressure (SBP) and diastolic blood pressure (DBP) at admission, CSFV, IHV/CSFV, and MTDTV (P < 0.05). Logistic regression analysis identified IHV/CSFV (OR = 1.074, P = 0.041) and SBP at admission (OR = 1.052, P = 0.005) as independent predictors of patient outcomes, with IHV/CSFV showing the strongest predictive capability. The area under the ROC curve for IHV/CSFV was 0.810 (95% CI: 0.733–0.887), with a cutoff value of 18.35% for predicting poor outcomes, yielding a sensitivity of 67.57% and specificity of 92.16%. Conclusion For patients with spontaneous basal ganglia hemorrhage undergoing non-surgical treatment, admission SBP and the ratio of IHV/CSFV were identified as independent predictors of poor prognosis. Among these, IHV/CSFV demonstrated the strongest association with outcomes, offering a reliable and non-invasive method for prognosis prediction. Additionally, CSFV was significantly correlated with MTDTV, suggesting that CSFV could be effectively estimated through MTDTV measurements, further aiding clinical decision-making. Spontaneous intracerebral hemorrhage basal ganglia cerebrospinal fluid volume hematoma volume maximum transverse diameter of the third ventricle Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Spontaneous intracerebral hemorrhage (ICH) refers to bleeding within the brain caused by non-traumatic factors. Primary causes include hypertension and cerebral amyloid angiopathy, while secondary factors involve intracranial aneurysms, cerebral arteriovenous malformations, and amyloidosis. ICH accounts for approximately 10–15% of all strokes, with most survivors left with varying degrees of neurological impairment 1 . Numerous studies have highlighted intracranial pressure (ICP) as a key factor influencing patient outcomes 2 , 3 . According to the Monro-Kellie doctrine, ICP depends on the pressure-volume relationship among intracranial cerebrospinal fluid volume (CSFV), brain tissue, and blood, which can compensate for volume changes within certain limits 4 . However, when intracranial volume exceeds compensatory capacity, ICP increases exponentially 5 – 7 , potentially leading to clinical manifestations such as headache, vomiting, and, in severe cases, brain herniation, respiratory arrest, or death. While brain tissue occupies the largest volume within the cranial cavity, it cannot compress rapidly to adjust ICP. Instead, cerebrospinal fluid (CSF) plays a primary compensatory role. During ICP elevation, CSF is displaced from the cranial cavity into the subarachnoid space, with secretion reduced and absorption increased 8 , 9 . This mechanism effectively lowers ICP. Thus, the volume of CSF is directly related to tolerance for elevated ICP and significantly impacts the outcomes of ICH patients. CSF is generated in the ventricles and circulates into the subarachnoid space of the brain and spinal cord through the lateral and median apertures of the fourth ventricle 10 . Due to individual anatomical variations, intracranial CSFV differs widely between individuals, and precise measurement has historically been challenging. Early methods relied on invasive techniques, such as cadaver studies and pneumoencephalography 11 , but advances in neuroimaging now allow non-invasive and accurate measurement through imaging modalities 12 – 14 . Emerging evidence has suggested that CSFV has predictive value for disease progression and therapeutic outcomes. For instance, the ratio of ischemic lesion volume to CSFV has been shown to accurately predict malignant middle cerebral artery infarction 15 . Building on this, our study employs CT imaging to measure CSFV and investigate the prognostic value of the ratio of intracerebral hematoma volume (IHV) to CSFV (IHV/CSFV) in non-surgically treated patients with spontaneous basal ganglia hemorrhage. This research aims to provide clinicians with evidence-based tools for decision-making and early identification of patients at risk for poor outcomes. Materials and Methods Patients With the approval of the Ethics Committee of our hospital, this study retrospectively enrolled non-surgically treated patients with spontaneous basal ganglia hemorrhage admitted to the Affiliated Hospital of Guizhou Medical University between January 2017 and October 2020. Patient data were retrieved from the hospital's Health Information System (HIS). Inclusion Criteria Patients with spontaneous basal ganglia hemorrhage who underwent head CT scans on the Siemens Somaris/5 syngo CT 2007S at admission and did not receive surgical intervention. Age range: 10–80 years. Head CT performed within 24 hours of hemorrhage onset. Cases of initial bleeding with no subsequent rebleeding. Exclusion Criteria Patients with hemorrhages secondary to other conditions, including aneurysms, intracranial tumors, or trauma. Patients who underwent surgical interventions during hospitalization. Patients diagnosed with hydrocephalus. Patients presenting with intraventricular hemorrhage. Patients on antithrombotic medication. Patients with severe pre-existing physical or mental illnesses, or significant disabilities prior to the hemorrhage. Pregnant patients.(Figure 1 .) Data Collection General Patient Information Patient medical records were accessed via the HIS system. Following previous literature, factors potentially influencing patient outcomes were identified and collected. These included gender, age, the Glasgow Coma Scale (GCS) 16 score at admission (categorized into two groups: 0–12 and 13–15 points), the National Institute of Health Stroke Scale (NIHSS) 17 score at admission (categorized into two groups: 0–15 and 16–42 points), and blood pressure at admission. Three-month follow-up evaluations were conducted through telephone interviews or outpatient visits to obtain the patients’ modified Rankin Scale (mRS) 18 scores. Cranial CT Imaging Cranial CT data were collected for all subjects. The CT scans were performed using a CT machine calibrated with the baseline aligned to the Orbitomeatal Line (OML). The imaging parameters included a scan field of 24.0 mm × 24.0 mm, a resolution of 512 × 512 pixels, and a slice thickness of 3 mm. Measurement Techniques CSFV For patients meeting the inclusion criteria, cranial CT image data were utilized to reconstruct 3-mm-thick slices with 3-mm intervals on a workstation for CSFV measurement. The Volume software integrated into the Siemens CT system was employed to calculate CSFV. Using this software, regions of interest were manually outlined across all slices, setting CT attenuation values to 0–20 Hounsfield units to isolate CSF. The software subsequently quantified CSFV in the subarachnoid space, ventricles, and various brain cisterns, collectively representing the total CSFV (Fig. 2 ). IHV and the maximum transverse diameter of the third ventricle(MTDTV) The hematoma volume was measured using the ABC/2 formula, as proposed by Tada. Building on our previous studies 19 , 20 , which demonstrated a strong correlation between MTDTV and CSFV, this study further investigated the relationship between MTDTV, patient prognosis, and CSFV. The measurement process involved a radiologist and a neurosurgeon. By visual assessment, the imaging level with the largest MTDTV was identified. Using the distance measurement tool integrated into the workstation’s imaging software, the MTDTV was quantified (Fig. 3 ). To ensure reliability, measurements were conducted twice, and the mean value was recorded. Statistical Analysis Statistical analyses were conducted using SPSS version 26. The patient characteristics analyzed included gender, age, GCS score at admission, NIHSS score at admission, blood pressure at admission, IHV, CSFV, MTDTV, IHV/CSFV, IHV/MTDTV, and prognosis. Continuous data following a normal distribution were expressed as mean ± standard deviation and compared using independent sample t-tests. Non-normally distributed continuous data were presented as median and interquartile range [M (P25, P75)] and analyzed with the Mann-Whitney U test. Categorical variables were reported as frequencies and compared using the chi-square test.To identify significant influencing factors, logistic regression analysis was performed, providing odds ratios (ORs) with 95% confidence intervals (CIs). If the IHV/CSFV demonstrated an association with patient prognosis, a receiver operating characteristic (ROC) curve was constructed to determine its critical value for predicting an mRS score ≥ 3. The area under the curve (AUC), sensitivity, specificity, and other performance metrics were calculated. Additionally, correlation analyses were conducted to evaluate the relationships between CSFV and age, as well as CSFV and the MTDTV. Statistical significance was defined as P < 0.05 for all tests. Results Baseline Characteristics A total of 125 patients were included in the current study, of whom 51 (40.8%) achieved a good prognosis (mRS score ≤ 2), while 74 (59.2%) had a poor prognosis (mRS score ≥ 3). The cohort consisted of 82 male patients (65.6%) and 43 female patients (34.4%). Upon admission, 50 patients (40%) had a GCS score below 13, whereas 75 patients (60%) had a score of 13 or higher. Additionally, 88 patients (70.4%) presented with a NIHSS score under 16, while 37 patients (29.6%) scored 16 or above. Regarding IHV, 88 cases (70.4%) measured less than 20 mL, and 37 cases (29.6%) had an IHV of 20 mL or more (Table 1 ). Table 1 General information of patients Features Number Percentage(%) Prognosis mRS ≤ 2 51 40.8 mRS ≥ 3 74 59.2 Gender Male 82 65.6 Female 43 34.4 GCS <13 50 40.0 ≥ 13 75 60.0 NIHSS <16 88 70.4 ≥ 16 37 29.6 IHV <20ml 88 70.4 ≥ 20ml 37 29.6 Univariate Analysis A normality test was performed on variables including patient age, SBP at admission, DBP at admission, CSFV, IHV/CSFV, the MTDTV, and IHV/MTDTV. The results indicated that patient age and SBP at admission followed a normal distribution, whereas the other variables did not. Consequently, t-tests were applied to variables with normal distributions, and non-parametric tests were used for those without normal distributions to assess their association with patient prognosis. Additionally, chi-square tests were utilized to analyze categorical variables such as gender, GCS score at admission, NIHSS score at admission, and IHV in relation to prognosis (Table 2 ). Table 2 Univariate analysis of prognosis Features mRS(<3) mRS(≥3) X 2 / t /Z P value Gender Male 36(70.6) 46(62.2) 0.950 0.330 Female 15(29.4) 28(37.8) GCS <13 5(9.8) 45(60.8) 32.729 <0.001 ≥ 13 46(90.2) 29(39.2) NIHSS <16 48(94.1) 40(54.1) 23.256 <0.001 ≥ 16 3(5.9) 34(45.9) IHV <20ml 46(90.2) 42(56.8) 16.201 <0.001 ≥ 20ml 5(9.8) 32(43.2) Age 55.31 ± 11.190 57.84 ± 11.092 -1.246 0.215 Adimission SBP 151.55 ± 20.364 170.93 ± 27.690 -4.265 <0.001 Adimission DBP 89(81,100) 101(91, 111) -3.403 <0.001 CSFV 71.6(61.2, 96.4) 55.75(41.05, 72.375) -3.843 <0.001 IHV/CSFV 9.93(5.28, 14.36) 30.22(14.13, 44.06) -5.875 <0.001 MTDTV 0.58(0.46, 0.80) 0.40(0.31,0.6025) -3.490 <0.001 IHV/ MTDTV 127.60 (120.30, 139.03) 134.36 (119.80, 152.23) -1.140 0.254 The results demonstrated that at 3 months post-hemorrhage, statistically significant differences (P < 0.05) were observed between patients with good and poor prognosis in terms of GCS score, NIHSS score, SBP, DBP, IHV, CSFV, IHV/CSFV, and MTDTV upon admission. In contrast, no significant differences were found concerning gender, age, or IHV/MTDTV (P > 0.05). Multivariate Logistic Regression Analysis Independent variables with statistical significance from the univariate analysis, including admission GCS, NIHSS, SBP, DBP, IHV, CSFV, IHV/CSFV, and MTDTV, were entered into a multivariate logistic regression model to identify independent predictors of prognosis. Results demonstrated that IHV/CSFV (OR = 1.074, P = 0.041) and admission SBP (OR = 1.052, P = 0.005) were significant predictors of poor prognosis, with IHV/CSFV showing the strongest predictive ability (Table 3 ). Table 3 multivariate logistic analysis of prognosis Features β SE Walds P Odd Ratio(95%CI) GCS<13 1.213 0.953 1.618 0.203 3.362(0.519,21.778) NIHSS ≥ 16 0.487 1.133 0.185 0.667 1.628(0.177,14.998) IHV(≥20ml) 0.186 1.031 0.032 0.857 1.204(0.160,9.086) Adimission SBP 0.050 0.018 7.878 0.005 1.052(1.015,1.089) Adimission DBP -0.032 0.024 1.831 0.176 0.968(0.924,1.015) CSFV 0.017 0.019 0.801 0.371 1.018(0.979,1.057) IHV/CSFV 0.071 0.035 4.171 0.041 1.074(1.003,1.150) MTDTV -2.703 2.505 1.164 0.281 0.067(0.0004, 9.090) Receiver Operating Characteristic (ROC) Curve Analysis To evaluate the predictive performance of IHV/CSFV, an ROC curve was plotted. The area under the curve (AUC) was 0.810 (95% CI: 0.733–0.887). The optimal cutoff value for predicting poor prognosis was 18.35%, indicating that when IHV/CSFV exceeded 18.35%, the probability of a poor prognosis increased significantly. At this threshold, sensitivity was 67.57% and specificity was 92.16% (Fig. 4 ). Correlation Between CSFV, Age, and MTDTV Spearman correlation analysis was conducted to explore the relationship between CSFV, age, and MTDTV(Table 4 ). Results revealed a strong positive correlation between CSFV and MTDTV (r = 0.926, P < 0.01), suggesting significant research value. In contrast, the correlation between CSFV and age was weak (r = 0.313, P < 0.01). Table 4 Spearman analysis of CSFV, age and maximum transverse diameter of the third ventricle CSFV Age MTDTV CSFV 1.000 .313** .926** Age .313** 1.000 .319** MTDTV .926** .319** 1.000 Linear Regression Analysis Scatter plots and residual analyses confirmed a linear relationship between CSFV and MTDTV. Linear regression yielded the equation: CSFV = 117.7 × MTDTV + 6.948, providing a basis for estimating CSFV using MTDTV measurements. Discussion Spontaneous basal ganglia hemorrhage is a predominant subtype of ICH. Traditionally, hematoma volumes exceeding 30 mL have been associated with poor prognosis 21 – 24 , and a volume over 60 mL combined with a GCS score below 8 predicts a 30-day mortality rate exceeding 90% 20 . Consequently, a 30 mL hematoma is often considered a critical threshold for surgical intervention in basal ganglia hemorrhage 25 , 26 .However, clinical observations frequently reveal exceptions: patients with hematoma volumes ≥ 30 mL sometimes achieve favorable outcomes with conservative treatment, while those with volumes < 30 mL can still face life-threatening complications. Determining whether and when to opt for surgical intervention remains contentious.This study investigates hypertensive ICH, given its prevalence and controversial treatment options. Focusing on ICP regulation mechanisms, we measured the IHV and CSFV, examining their relationship with patient outcomes. To minimize treatment bias, we analyzed 125 non-surgically treated patients with basal ganglia hemorrhage. Unconditional logistic regression analysis showed a strong association between IHV/CSFV and patient prognosis (P = 0.041; OR = 1.074; 95% CI = 1.003–1.150). IHV/CSFV > 18.35% predicted poor outcomes, with a sensitivity of 67.57% and a specificity of 92.16%.The findings confirm that IHV/CSFV is a non-invasive, reliable prognostic marker for cerebral hemorrhage, consistent with the Monro-Kellie doctrine, which links intracranial volume changes to ICP alterations 6 . By integrating CSFV—a major compensatory component in ICP regulation—this study provides a comprehensive evaluation of mass effects and their prognostic implications. Previous studies have emphasized that the mass effect of a hematoma and the pathological changes it induces are critical factors contributing to poor prognosis in patients with spontaneous basal ganglia hemorrhage 27 – 31 . Masè et al. 32 conducted a retrospective analysis of 138 patients treated conservatively for supratentorial spontaneous intracerebral hemorrhage, identifying key prognostic indicators such as ventricular hemorrhage, GCS score, IHV, midline shift, abnormal pupils, eye deviation, and limb paralysis. Among these, IHV, GCS score, and ventricular extension of the hematoma were independent predictors of 30-day mortality. Similarly, Salihović et al. 33 reported mortality rates of 85%, 62.5%, and 36% for IHV > 60 mL, 30–60 mL, and 60 mL compared to those with volumes < 30 mL.Our findings corroborate these conclusions, highlighting hematoma volume as a critical prognostic factor. In our study, patients with IHV ≥ 20 mL experienced an 86.5% rate of poor outcomes, while those with IHV < 20 mL had a 47.7% rate of poor outcomes (P < 0.05). However, our research extends previous work by incorporating CSFV as a modifier of the hematoma's impact on ICP. While prior studies have largely focused on hematoma volume in isolation, overlooking the compensatory role of CSF, our integrative approach provides a more nuanced perspective on intracranial dynamics. This approach identifies subgroups of patients who may achieve favorable outcomes despite large hematoma volumes, thereby refining prognostic assessments. Furthermore, unconditional logistic regression analysis revealed that admission SBP was independently associated with poor prognosis in patients with ICH (P = 0.005; OR = 1.052; 95% CI = 1.015–1.089). Current research on the impact of blood pressure in ICH patients primarily focuses on the relationship between blood pressure levels and hematoma expansion. For example, Mokin et al. 34 observed a positive correlation between baseline SBP after thrombolysis and initial hematoma volume, alongside a negative correlation between SBP reduction and hematoma growth. Several studies have confirmed that elevated SBP is linked to hematoma expansion and unfavorable clinical outcomes in acute ICH patients 35 – 38 . However, contrasting opinions exist, with some studies suggesting no significant association between blood pressure and hematoma expansion 38 . In this study, measuring CSFV presents both a challenge and a focal point of interest. Historically, early methods relied on postmortem data, which often introduced significant inaccuracies due to postmortem changes in brain tissue and the effects of tissue fixation on brain volume and morphology. Another method, pneumoencephalography, involved injecting gas via a lumbar puncture, which, despite its ability to estimate CSFV, was invasive and associated with risks such as intracranial infection and brain herniation, ultimately compromising patient safety and measurement accuracy due to the gas-induced expansion of CSFV. CT, the primary imaging tool for diagnosing ICH, provides a non-invasive, accessible, and reliable method to analyze brain structures. However, CT’s lower resolution limits its ability to distinguish CSF from surrounding brain tissue in certain regions, leading to segmentation challenges and inter-researcher variability in CSFV measurements. Although MRI provides high-resolution imaging and clearer CSF visualization, its manual segmentation process is time-consuming and may compromise precision. Additionally, MRI’s cost and time constraints make it less practical in many clinical settings. Advances in medical image segmentation technology have yet to fully address these challenges, leaving room for improvement in the accurate, efficient, and cost-effective assessment of CSFV. Head CT is the most commonly utilized imaging modality for diagnosing ICH and is widely recognized for its accessibility. Compared to pneumoencephalography, CT scans are easy to obtain, non-invasive, repeatable, and eliminate the risks of iatrogenic intracranial infections and surgical complications. Although MRI provides higher-resolution images, its application is limited by higher costs, longer examination times, and significant noise, which makes it less suitable for patients with altered consciousness or agitation due to ICH. Consequently, this study selected head CT scans as the primary imaging data for measuring CSFV.Furthermore, most CT imaging software includes integrated volume measurement tools, which facilitate rapid and accurate calculations. These features minimize human errors, reduce costs, and present a practical solution for promoting CSFV evaluation in clinical settings. Our previous research demonstrated a notable correlation between CSFV and several linear dimensions within the cranium 19 , 20 . Specifically, CSFV can be estimated using MTDTV, the width of the lateral ventricle at the intersection of the thalamus plane and the anterior corpus callosum, and the intracranial longitudinal diameter at the choroid plexus plane. Among these, MTDTV consistently exhibited the strongest association with CSFV in our prior studies. Consequently, this study focused on measuring MTDTV in patients.Spearman correlation analysis revealed a significant and linear relationship between MTDTV and CSFV (r = 0.98). This finding underscores the utility of MTDTV as a reliable proxy for estimating CSFV. In clinical settings, this relationship enables the calculation of IHV/CSFV based on MTDTV measurements, significantly simplifying the prediction of patient prognosis in cases of spontaneous basal ganglia hemorrhage. Despite its strengths, this study has several limitations. The sample size, though sufficient for preliminary analysis, may restrict the generalizability of the findings. Furthermore, the exclusion of patients with hydrocephalus and intraventricular hemorrhage limits the applicability of the results to these populations. Future research should focus on validating IHV/CSFV in larger, multicenter cohorts and exploring its relevance across more diverse patient groups. Additionally, advancements in imaging technologies, such as high-resolution MRI, could significantly improve the precision of CSFV measurements, thereby refining this predictive model. To summarize, accurately predicting patient prognosis at an early stage is essential for devising effective treatment strategies. For patients identified by the model as having a poor prognosis, timely interventions—such as early reduction of intracranial pressure, blood pressure management, and hematoma evacuation—can be implemented based on a thorough assessment of the patient's overall condition. Conclusion For non-surgically treated patients with spontaneous basal ganglia hemorrhage, admission SBP and the ratio of IHV/CSFV were identified as independent risk factors for poor prognosis. Among these, IHV/CSFV demonstrated the strongest correlation with patient outcomes, enabling precise prognosis prediction. Additionally, CSFV showed a significant positive correlation with MTDTV, suggesting that CSFV could be reliably estimated through MTDTV measurements. Declarations Ethics Approval This study has been approved by the Ethics Committee of the Affiliated Hospital of Guizhou Medical University.All procedures performed in this study involving human participants were in accordance with the Declaration of Helsinki (as revised in 2013) and the Ethical Guidelines for Medical Research Involving Human Subjects issued by the National Health Commission of the People's Republic of China. Consent to Participate All patients included in this study (or their legal representatives, if the patient was unable to provide consent due to neurological impairment caused by acute basal ganglia hemorrhage) signed written informed consent forms prior to the use of their clinical data and imaging materials. For patients with impaired consciousness at admission, informed consent was obtained from their legal next of kin, and additional written consent was obtained from the patients themselves once they regained the capacity to communicate and understand the study purpose. Consent for publication Not Applicable Competing Interests The authors declare no conflict of interest. Funding We express our heartfelt thanks to the dedicated staff of the Department of Neurosurgery at The Affiliated Hospital of Guizhou Medical University for their invaluable support. Our research was generously funded by one grant, the 2023 Central Government Subsidy Fund allocated for Enhancing Medical Services and Security Capabilities, specifically targeting the development of Medical and Health Institutions [National Key Clinical Specialist - Neurosurgery, Grant No. 2023-95]. Additionally, we are immensely grateful to the patients who kindly consented to provide pathological specimens for our study. Their contributions were absolutely essential to the realization of this research. Author Contribution Zhangwei Yan, Tao Sun, Hua Yang and Xin Xiang designed the project. Xingwang Zhou, Xiaoyu Wang and Xu Xu collected and analyzed the data. Zhangwei Yan drafted the manuscript. Hua Yang, Xin Xiang and Junshuan Cui revised the manuscript. All authors approved the final version of the manuscript. Acknowledgement Not applicable. Data Availability The datasets used and/or analyzed during the present study are available from the authors on reasonable request. 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ISRN neuroscience vol. 2013 327968. 8 May. 2013. 10.1155/2013/327968 Mokin M, et al. Blood pressure management and evolution of thrombolysis-associated intracerebral hemorrhage in acute ischemic stroke. J stroke Cerebrovasc diseases: official J Natl Stroke Association vol. 2012;21:852–9. 10.1016/j.jstrokecerebrovasdis.2011.05.006 . Rodriguez-Luna D et al. Prehospital Systolic Blood Pressure Is Related to Intracerebral Hemorrhage Volume on Admission. Stroke 49,1 (2018): 204–6. 10.1161/STROKEAHA.117.018485 Ohwaki K et al. Blood pressure management in acute intracerebral hemorrhage: relationship between elevated blood pressure and hematoma enlargement. Stroke 35,6 (2004): 1364–7. 10.1161/01.STR.0000128795.38283.4b Kazui S et al. Predisposing factors to enlargement of spontaneous intracerebral hematoma. Stroke 28,12 (1997): 2370–5. 10.1161/01.str.28.12.2370 Jauch EC et al. Lack of evidence for an association between hemodynamic variables and hematoma growth in spontaneous intracerebral hemorrhage. Stroke 37,8 (2006): 2061–5. 10.1161/01.STR.0000229878.93759.a2 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 02 Feb, 2026 Reviews received at journal 30 Jan, 2026 Reviews received at journal 17 Jan, 2026 Reviewers agreed at journal 16 Jan, 2026 Reviewers agreed at journal 12 Jan, 2026 Reviewers invited by journal 09 Jan, 2026 Editor assigned by journal 09 Jan, 2026 Editor invited by journal 05 Jan, 2026 Submission checks completed at journal 05 Jan, 2026 First submitted to journal 05 Jan, 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. 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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-8300264","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":573085572,"identity":"10a247b2-3a4f-4029-884d-467bb80d46e0","order_by":0,"name":"Zhangwei Yan","email":"","orcid":"","institution":"Affiliated Hospital of Guizhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhangwei","middleName":"","lastName":"Yan","suffix":""},{"id":573085573,"identity":"c1761034-a89e-4241-b13d-d42917da9e9b","order_by":1,"name":"Tao Sun","email":"","orcid":"","institution":"Third Affiliated Hospital of Sun Yat-sen 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2","display":"","copyAsset":false,"role":"figure","size":24150,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDetermination of cerebrospinal fluid volume （a）Circle selection of survey area；（b）Display of threshold area；（c）The result of the calculation of the regional content product by Volume\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8300264/v1/3996197f2d5da8281bc05b90.jpg"},{"id":100396758,"identity":"1343a5e5-6e6b-41d3-9a6b-da1adaf50f95","added_by":"auto","created_at":"2026-01-16 11:41:08","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":31103,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMeasurement of the maximum transverse diameter of the third ventricle\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8300264/v1/cc9249864c5b4434839afd25.jpg"},{"id":100397154,"identity":"fe9a3d51-bdac-40f3-a87b-4c561ac76274","added_by":"auto","created_at":"2026-01-16 11:41:38","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":13519,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC curve of IHV/CSFV\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8300264/v1/e86cdb51e952083e1e0b28ac.jpg"},{"id":100414125,"identity":"17e7266b-7ea8-44d1-9f0e-9ae726faae0d","added_by":"auto","created_at":"2026-01-16 13:18:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1217357,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8300264/v1/f61142ac-6103-4bb6-a52d-df23a7f6f0bd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prognostic Value of the IntracerebralHematoma Volume-to-Cerebrospinal Fluid Volume Ratio in Patients with Spontaneous Basal Ganglia Hemorrhage Undergoing Non-Surgical Treatment","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSpontaneous intracerebral hemorrhage (ICH) refers to bleeding within the brain caused by non-traumatic factors. Primary causes include hypertension and cerebral amyloid angiopathy, while secondary factors involve intracranial aneurysms, cerebral arteriovenous malformations, and amyloidosis. ICH accounts for approximately 10\u0026ndash;15% of all strokes, with most survivors left with varying degrees of neurological impairment\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNumerous studies have highlighted intracranial pressure (ICP) as a key factor influencing patient outcomes\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. According to the Monro-Kellie doctrine, ICP depends on the pressure-volume relationship among intracranial cerebrospinal fluid volume (CSFV), brain tissue, and blood, which can compensate for volume changes within certain limits\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. However, when intracranial volume exceeds compensatory capacity, ICP increases exponentially\u003csup\u003e\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, potentially leading to clinical manifestations such as headache, vomiting, and, in severe cases, brain herniation, respiratory arrest, or death.\u003c/p\u003e \u003cp\u003eWhile brain tissue occupies the largest volume within the cranial cavity, it cannot compress rapidly to adjust ICP. Instead, cerebrospinal fluid (CSF) plays a primary compensatory role. During ICP elevation, CSF is displaced from the cranial cavity into the subarachnoid space, with secretion reduced and absorption increased\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. This mechanism effectively lowers ICP. Thus, the volume of CSF is directly related to tolerance for elevated ICP and significantly impacts the outcomes of ICH patients.\u003c/p\u003e \u003cp\u003eCSF is generated in the ventricles and circulates into the subarachnoid space of the brain and spinal cord through the lateral and median apertures of the fourth ventricle\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Due to individual anatomical variations, intracranial CSFV differs widely between individuals, and precise measurement has historically been challenging. Early methods relied on invasive techniques, such as cadaver studies and pneumoencephalography\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, but advances in neuroimaging now allow non-invasive and accurate measurement through imaging modalities\u003csup\u003e\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eEmerging evidence has suggested that CSFV has predictive value for disease progression and therapeutic outcomes. For instance, the ratio of ischemic lesion volume to CSFV has been shown to accurately predict malignant middle cerebral artery infarction\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Building on this, our study employs CT imaging to measure CSFV and investigate the prognostic value of the ratio of intracerebral hematoma volume (IHV) to CSFV (IHV/CSFV) in non-surgically treated patients with spontaneous basal ganglia hemorrhage. This research aims to provide clinicians with evidence-based tools for decision-making and early identification of patients at risk for poor outcomes.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003e With the approval of the Ethics Committee of our hospital, this study retrospectively enrolled non-surgically treated patients with spontaneous basal ganglia hemorrhage admitted to the Affiliated Hospital of Guizhou Medical University between January 2017 and October 2020. Patient data were retrieved from the hospital's Health Information System (HIS).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eInclusion Criteria\u003c/h3\u003e\n\u003cp\u003ePatients with spontaneous basal ganglia hemorrhage who underwent head CT scans on the Siemens Somaris/5 syngo CT 2007S at admission and did not receive surgical intervention.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eAge range: 10\u0026ndash;80 years.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eHead CT performed within 24 hours of hemorrhage onset.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCases of initial bleeding with no subsequent rebleeding.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e\n\u003ch3\u003eExclusion Criteria\u003c/h3\u003e\n\u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003ePatients with hemorrhages secondary to other conditions, including aneurysms, intracranial tumors, or trauma.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePatients who underwent surgical interventions during hospitalization.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePatients diagnosed with hydrocephalus.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePatients presenting with intraventricular hemorrhage.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePatients on antithrombotic medication.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePatients with severe pre-existing physical or mental illnesses, or significant disabilities prior to the hemorrhage.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePregnant patients.(Figure\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.)\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eGeneral Patient Information\u003c/h2\u003e \u003cp\u003ePatient medical records were accessed via the HIS system. Following previous literature, factors potentially influencing patient outcomes were identified and collected. These included gender, age, the Glasgow Coma Scale (GCS)\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e score at admission (categorized into two groups: 0\u0026ndash;12 and 13\u0026ndash;15 points), the National Institute of Health Stroke Scale (NIHSS) \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003escore at admission (categorized into two groups: 0\u0026ndash;15 and 16\u0026ndash;42 points), and blood pressure at admission. Three-month follow-up evaluations were conducted through telephone interviews or outpatient visits to obtain the patients\u0026rsquo; modified Rankin Scale (mRS)\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e scores.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCranial CT Imaging\u003c/h2\u003e \u003cp\u003eCranial CT data were collected for all subjects. The CT scans were performed using a CT machine calibrated with the baseline aligned to the Orbitomeatal Line (OML). The imaging parameters included a scan field of 24.0 mm \u0026times; 24.0 mm, a resolution of 512 \u0026times; 512 pixels, and a slice thickness of 3 mm.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMeasurement Techniques\u003c/h3\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eCSFV\u003c/h2\u003e \u003cp\u003eFor patients meeting the inclusion criteria, cranial CT image data were utilized to reconstruct 3-mm-thick slices with 3-mm intervals on a workstation for CSFV measurement. The Volume software integrated into the Siemens CT system was employed to calculate CSFV. Using this software, regions of interest were manually outlined across all slices, setting CT attenuation values to 0\u0026ndash;20 Hounsfield units to isolate CSF. The software subsequently quantified CSFV in the subarachnoid space, ventricles, and various brain cisterns, collectively representing the total CSFV (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eIHV and the maximum transverse diameter of the third ventricle(MTDTV)\u003c/h2\u003e \u003cp\u003eThe hematoma volume was measured using the ABC/2 formula, as proposed by Tada. Building on our previous studies\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, which demonstrated a strong correlation between MTDTV and CSFV, this study further investigated the relationship between MTDTV, patient prognosis, and CSFV. The measurement process involved a radiologist and a neurosurgeon. By visual assessment, the imaging level with the largest MTDTV was identified. Using the distance measurement tool integrated into the workstation\u0026rsquo;s imaging software, the MTDTV was quantified (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). To ensure reliability, measurements were conducted twice, and the mean value was recorded.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were conducted using SPSS version 26. The patient characteristics analyzed included gender, age, GCS score at admission, NIHSS score at admission, blood pressure at admission, IHV, CSFV, MTDTV, IHV/CSFV, IHV/MTDTV, and prognosis. Continuous data following a normal distribution were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation and compared using independent sample t-tests. Non-normally distributed continuous data were presented as median and interquartile range [M (P25, P75)] and analyzed with the Mann-Whitney U test. Categorical variables were reported as frequencies and compared using the chi-square test.To identify significant influencing factors, logistic regression analysis was performed, providing odds ratios (ORs) with 95% confidence intervals (CIs). If the IHV/CSFV demonstrated an association with patient prognosis, a receiver operating characteristic (ROC) curve was constructed to determine its critical value for predicting an mRS score\u0026thinsp;\u0026ge;\u0026thinsp;3. The area under the curve (AUC), sensitivity, specificity, and other performance metrics were calculated. Additionally, correlation analyses were conducted to evaluate the relationships between CSFV and age, as well as CSFV and the MTDTV. Statistical significance was defined as P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for all tests.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eBaseline Characteristics\u003c/h2\u003e \u003cp\u003eA total of 125 patients were included in the current study, of whom 51 (40.8%) achieved a good prognosis (mRS score\u0026thinsp;\u0026le;\u0026thinsp;2), while 74 (59.2%) had a poor prognosis (mRS score\u0026thinsp;\u0026ge;\u0026thinsp;3). The cohort consisted of 82 male patients (65.6%) and 43 female patients (34.4%). Upon admission, 50 patients (40%) had a GCS score below 13, whereas 75 patients (60%) had a score of 13 or higher. Additionally, 88 patients (70.4%) presented with a NIHSS score under 16, while 37 patients (29.6%) scored 16 or above. Regarding IHV, 88 cases (70.4%) measured less than 20 mL, and 37 cases (29.6%) had an IHV of 20 mL or more (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeneral information of patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeatures\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrognosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emRS\u0026thinsp;\u0026le;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emRS\u0026thinsp;\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNIHSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIHV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;20ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;20ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eUnivariate Analysis\u003c/h2\u003e \u003cp\u003eA normality test was performed on variables including patient age, SBP at admission, DBP at admission, CSFV, IHV/CSFV, the MTDTV, and IHV/MTDTV. The results indicated that patient age and SBP at admission followed a normal distribution, whereas the other variables did not. Consequently, t-tests were applied to variables with normal distributions, and non-parametric tests were used for those without normal distributions to assess their association with patient prognosis. Additionally, chi-square tests were utilized to analyze categorical variables such as gender, GCS score at admission, NIHSS score at admission, and IHV in relation to prognosis (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\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 analysis of 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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeatures\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003emRS(\u0026lt;3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003emRS(\u0026ge;3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eX\u003csup\u003e2\u003c/sup\u003e/\u003cem\u003et\u003c/em\u003e/Z\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36(70.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46(62.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.330\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15(29.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28(37.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45(60.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e32.729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46(90.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29(39.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNIHSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48(94.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40(54.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e23.256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34(45.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIHV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;20ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46(90.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42(56.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e16.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;20ml\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32(43.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.31\u0026thinsp;\u0026plusmn;\u0026thinsp;11.190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.84\u0026thinsp;\u0026plusmn;\u0026thinsp;11.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.215\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdimission SBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e151.55\u0026thinsp;\u0026plusmn;\u0026thinsp;20.364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e170.93\u0026thinsp;\u0026plusmn;\u0026thinsp;27.690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-4.265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdimission DBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89(81,100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e101(91, 111)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSFV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71.6(61.2, 96.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.75(41.05, 72.375)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIHV/CSFV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.93(5.28, 14.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.22(14.13, 44.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-5.875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMTDTV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.58(0.46, 0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.40(0.31,0.6025)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIHV/ MTDTV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e127.60\u003c/p\u003e \u003cp\u003e(120.30, 139.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e134.36\u003c/p\u003e \u003cp\u003e(119.80, 152.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.254\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\u003eThe results demonstrated that at 3 months post-hemorrhage, statistically significant differences (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were observed between patients with good and poor prognosis in terms of GCS score, NIHSS score, SBP, DBP, IHV, CSFV, IHV/CSFV, and MTDTV upon admission. In contrast, no significant differences were found concerning gender, age, or IHV/MTDTV (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eMultivariate Logistic Regression Analysis\u003c/h2\u003e \u003cp\u003eIndependent variables with statistical significance from the univariate analysis, including admission GCS, NIHSS, SBP, DBP, IHV, CSFV, IHV/CSFV, and MTDTV, were entered into a multivariate logistic regression model to identify independent predictors of prognosis. Results demonstrated that IHV/CSFV (OR\u0026thinsp;=\u0026thinsp;1.074, P\u0026thinsp;=\u0026thinsp;0.041) and admission SBP (OR\u0026thinsp;=\u0026thinsp;1.052, P\u0026thinsp;=\u0026thinsp;0.005) were significant predictors of poor prognosis, with IHV/CSFV showing the strongest predictive ability (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\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 analysis of prognosis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeatures\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eWalds\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eOdd Ratio(95%CI)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGCS\u0026lt;13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.362(0.519,21.778)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNIHSS\u0026thinsp;\u0026ge;\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.628(0.177,14.998)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIHV(\u0026ge;20ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.204(0.160,9.086)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdimission SBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.052(1.015,1.089)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdimission DBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.968(0.924,1.015)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSFV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.018(0.979,1.057)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIHV/CSFV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.074(1.003,1.150)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMTDTV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.067(0.0004, 9.090)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eReceiver Operating Characteristic (ROC) Curve Analysis\u003c/h2\u003e \u003cp\u003eTo evaluate the predictive performance of IHV/CSFV, an ROC curve was plotted. The area under the curve (AUC) was 0.810 (95% CI: 0.733\u0026ndash;0.887). The optimal cutoff value for predicting poor prognosis was 18.35%, indicating that when IHV/CSFV exceeded 18.35%, the probability of a poor prognosis increased significantly. At this threshold, sensitivity was 67.57% and specificity was 92.16% (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation Between CSFV, Age, and MTDTV\u003c/h2\u003e \u003cp\u003eSpearman correlation analysis was conducted to explore the relationship between CSFV, age, and MTDTV(Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Results revealed a strong positive correlation between CSFV and MTDTV (r\u0026thinsp;=\u0026thinsp;0.926, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), suggesting significant research value. In contrast, the correlation between CSFV and age was weak (r\u0026thinsp;=\u0026thinsp;0.313, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSpearman analysis of CSFV, age and maximum transverse diameter of the third ventricle\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"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\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCSFV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMTDTV\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSFV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.313**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.926**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.313**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.319**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMTDTV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.926**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.319**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003eLinear Regression Analysis\u003c/h2\u003e \u003cp\u003eScatter plots and residual analyses confirmed a linear relationship between CSFV and MTDTV. Linear regression yielded the equation: CSFV\u0026thinsp;=\u0026thinsp;117.7 \u0026times; MTDTV\u0026thinsp;+\u0026thinsp;6.948, providing a basis for estimating CSFV using MTDTV measurements.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eSpontaneous basal ganglia hemorrhage is a predominant subtype of ICH. Traditionally, hematoma volumes exceeding 30 mL have been associated with poor prognosis\u003csup\u003e\u003cspan additionalcitationids=\"CR22 CR23\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, and a volume over 60 mL combined with a GCS score below 8 predicts a 30-day mortality rate exceeding 90%\u003csup\u003e20\u003c/sup\u003e. Consequently, a 30 mL hematoma is often considered a critical threshold for surgical intervention in basal ganglia hemorrhage\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.However, clinical observations frequently reveal exceptions: patients with hematoma volumes\u0026thinsp;\u0026ge;\u0026thinsp;30 mL sometimes achieve favorable outcomes with conservative treatment, while those with volumes\u0026thinsp;\u0026lt;\u0026thinsp;30 mL can still face life-threatening complications. Determining whether and when to opt for surgical intervention remains contentious.This study investigates hypertensive ICH, given its prevalence and controversial treatment options. Focusing on ICP regulation mechanisms, we measured the IHV and CSFV, examining their relationship with patient outcomes. To minimize treatment bias, we analyzed 125 non-surgically treated patients with basal ganglia hemorrhage. Unconditional logistic regression analysis showed a strong association between IHV/CSFV and patient prognosis (P\u0026thinsp;=\u0026thinsp;0.041; OR\u0026thinsp;=\u0026thinsp;1.074; 95% CI\u0026thinsp;=\u0026thinsp;1.003\u0026ndash;1.150). IHV/CSFV\u0026thinsp;\u0026gt;\u0026thinsp;18.35% predicted poor outcomes, with a sensitivity of 67.57% and a specificity of 92.16%.The findings confirm that IHV/CSFV is a non-invasive, reliable prognostic marker for cerebral hemorrhage, consistent with the Monro-Kellie doctrine, which links intracranial volume changes to ICP alterations\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. By integrating CSFV\u0026mdash;a major compensatory component in ICP regulation\u0026mdash;this study provides a comprehensive evaluation of mass effects and their prognostic implications.\u003c/p\u003e \u003cp\u003ePrevious studies have emphasized that the mass effect of a hematoma and the pathological changes it induces are critical factors contributing to poor prognosis in patients with spontaneous basal ganglia hemorrhage\u003csup\u003e\u003cspan additionalcitationids=\"CR28 CR29 CR30\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Mas\u0026egrave; et al.\u003csup\u003e32\u003c/sup\u003e conducted a retrospective analysis of 138 patients treated conservatively for supratentorial spontaneous intracerebral hemorrhage, identifying key prognostic indicators such as ventricular hemorrhage, GCS score, IHV, midline shift, abnormal pupils, eye deviation, and limb paralysis. Among these, IHV, GCS score, and ventricular extension of the hematoma were independent predictors of 30-day mortality. Similarly, Salihović et al.\u003csup\u003e33\u003c/sup\u003e reported mortality rates of 85%, 62.5%, and 36% for IHV\u0026thinsp;\u0026gt;\u0026thinsp;60 mL, 30\u0026ndash;60 mL, and \u0026lt;\u0026thinsp;29 mL, respectively. Roquer et al.\u003csup\u003e28\u003c/sup\u003e further demonstrated a 3.6-fold increased risk of death in patients with IHV\u0026thinsp;\u0026gt;\u0026thinsp;60 mL compared to those with volumes\u0026thinsp;\u0026lt;\u0026thinsp;30 mL.Our findings corroborate these conclusions, highlighting hematoma volume as a critical prognostic factor. In our study, patients with IHV\u0026thinsp;\u0026ge;\u0026thinsp;20 mL experienced an 86.5% rate of poor outcomes, while those with IHV\u0026thinsp;\u0026lt;\u0026thinsp;20 mL had a 47.7% rate of poor outcomes (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, our research extends previous work by incorporating CSFV as a modifier of the hematoma's impact on ICP. While prior studies have largely focused on hematoma volume in isolation, overlooking the compensatory role of CSF, our integrative approach provides a more nuanced perspective on intracranial dynamics. This approach identifies subgroups of patients who may achieve favorable outcomes despite large hematoma volumes, thereby refining prognostic assessments.\u003c/p\u003e \u003cp\u003eFurthermore, unconditional logistic regression analysis revealed that admission SBP was independently associated with poor prognosis in patients with ICH (P\u0026thinsp;=\u0026thinsp;0.005; OR\u0026thinsp;=\u0026thinsp;1.052; 95% CI\u0026thinsp;=\u0026thinsp;1.015\u0026ndash;1.089). Current research on the impact of blood pressure in ICH patients primarily focuses on the relationship between blood pressure levels and hematoma expansion. For example, Mokin et al.\u003csup\u003e34\u003c/sup\u003e observed a positive correlation between baseline SBP after thrombolysis and initial hematoma volume, alongside a negative correlation between SBP reduction and hematoma growth. Several studies have confirmed that elevated SBP is linked to hematoma expansion and unfavorable clinical outcomes in acute ICH patients\u003csup\u003e\u003cspan additionalcitationids=\"CR36 CR37\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. However, contrasting opinions exist, with some studies suggesting no significant association between blood pressure and hematoma expansion\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this study, measuring CSFV presents both a challenge and a focal point of interest. Historically, early methods relied on postmortem data, which often introduced significant inaccuracies due to postmortem changes in brain tissue and the effects of tissue fixation on brain volume and morphology. Another method, pneumoencephalography, involved injecting gas via a lumbar puncture, which, despite its ability to estimate CSFV, was invasive and associated with risks such as intracranial infection and brain herniation, ultimately compromising patient safety and measurement accuracy due to the gas-induced expansion of CSFV. CT, the primary imaging tool for diagnosing ICH, provides a non-invasive, accessible, and reliable method to analyze brain structures. However, CT\u0026rsquo;s lower resolution limits its ability to distinguish CSF from surrounding brain tissue in certain regions, leading to segmentation challenges and inter-researcher variability in CSFV measurements. Although MRI provides high-resolution imaging and clearer CSF visualization, its manual segmentation process is time-consuming and may compromise precision. Additionally, MRI\u0026rsquo;s cost and time constraints make it less practical in many clinical settings. Advances in medical image segmentation technology have yet to fully address these challenges, leaving room for improvement in the accurate, efficient, and cost-effective assessment of CSFV.\u003c/p\u003e \u003cp\u003eHead CT is the most commonly utilized imaging modality for diagnosing ICH and is widely recognized for its accessibility. Compared to pneumoencephalography, CT scans are easy to obtain, non-invasive, repeatable, and eliminate the risks of iatrogenic intracranial infections and surgical complications. Although MRI provides higher-resolution images, its application is limited by higher costs, longer examination times, and significant noise, which makes it less suitable for patients with altered consciousness or agitation due to ICH. Consequently, this study selected head CT scans as the primary imaging data for measuring CSFV.Furthermore, most CT imaging software includes integrated volume measurement tools, which facilitate rapid and accurate calculations. These features minimize human errors, reduce costs, and present a practical solution for promoting CSFV evaluation in clinical settings.\u003c/p\u003e \u003cp\u003eOur previous research demonstrated a notable correlation between CSFV and several linear dimensions within the cranium\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Specifically, CSFV can be estimated using MTDTV, the width of the lateral ventricle at the intersection of the thalamus plane and the anterior corpus callosum, and the intracranial longitudinal diameter at the choroid plexus plane. Among these, MTDTV consistently exhibited the strongest association with CSFV in our prior studies. Consequently, this study focused on measuring MTDTV in patients.Spearman correlation analysis revealed a significant and linear relationship between MTDTV and CSFV (r\u0026thinsp;=\u0026thinsp;0.98). This finding underscores the utility of MTDTV as a reliable proxy for estimating CSFV. In clinical settings, this relationship enables the calculation of IHV/CSFV based on MTDTV measurements, significantly simplifying the prediction of patient prognosis in cases of spontaneous basal ganglia hemorrhage.\u003c/p\u003e \u003cp\u003eDespite its strengths, this study has several limitations. The sample size, though sufficient for preliminary analysis, may restrict the generalizability of the findings. Furthermore, the exclusion of patients with hydrocephalus and intraventricular hemorrhage limits the applicability of the results to these populations. Future research should focus on validating IHV/CSFV in larger, multicenter cohorts and exploring its relevance across more diverse patient groups. Additionally, advancements in imaging technologies, such as high-resolution MRI, could significantly improve the precision of CSFV measurements, thereby refining this predictive model.\u003c/p\u003e \u003cp\u003eTo summarize, accurately predicting patient prognosis at an early stage is essential for devising effective treatment strategies. For patients identified by the model as having a poor prognosis, timely interventions\u0026mdash;such as early reduction of intracranial pressure, blood pressure management, and hematoma evacuation\u0026mdash;can be implemented based on a thorough assessment of the patient's overall condition.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eFor non-surgically treated patients with spontaneous basal ganglia hemorrhage, admission SBP and the ratio of IHV/CSFV were identified as independent risk factors for poor prognosis. Among these, IHV/CSFV demonstrated the strongest correlation with patient outcomes, enabling precise prognosis prediction. Additionally, CSFV showed a significant positive correlation with MTDTV, suggesting that CSFV could be reliably estimated through MTDTV measurements.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eEthics Approval\u003c/h2\u003e \u003cp\u003e This study has been approved by the Ethics Committee of the Affiliated Hospital of Guizhou Medical University.All procedures performed in this study involving human participants were in accordance with the Declaration of Helsinki (as revised in 2013) and the Ethical Guidelines for Medical Research Involving Human Subjects issued by the National Health Commission of the People's Republic of China.\u003c/p\u003e \u003c/div\u003e\u003cp\u003e \u003ch2\u003eConsent to Participate\u003c/h2\u003e \u003cp\u003e All patients included in this study (or their legal representatives, if the patient was unable to provide consent due to neurological impairment caused by acute basal ganglia hemorrhage) signed written informed consent forms prior to the use of their clinical data and imaging materials. For patients with impaired consciousness at admission, informed consent was obtained from their legal next of kin, and additional written consent was obtained from the patients themselves once they regained the capacity to communicate and understand the study purpose.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot Applicable\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting Interests\u003c/h2\u003e \u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eWe express our heartfelt thanks to the dedicated staff of the Department of Neurosurgery at The Affiliated Hospital of Guizhou Medical University for their invaluable support. Our research was generously funded by one grant, the 2023 Central Government Subsidy Fund allocated for Enhancing Medical Services and Security Capabilities, specifically targeting the development of Medical and Health Institutions [National Key Clinical Specialist - Neurosurgery, Grant No. 2023-95]. Additionally, we are immensely grateful to the patients who kindly consented to provide pathological specimens for our study. Their contributions were absolutely essential to the realization of this research.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eZhangwei Yan, Tao Sun, Hua Yang and Xin Xiang designed the project. Xingwang Zhou, Xiaoyu Wang and Xu Xu collected and analyzed the data. Zhangwei Yan drafted the manuscript. Hua Yang, Xin Xiang and Junshuan Cui revised the manuscript. All authors approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analyzed during the present study are available from the authors on reasonable request. Code used throughout this study is available upon reasonable request from the corresponding authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAriesen MJ et al. 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Lack of evidence for an association between hemodynamic variables and hematoma growth in spontaneous intracerebral hemorrhage. Stroke 37,8 (2006): 2061\u0026ndash;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/01.STR.0000229878.93759.a2\u003c/span\u003e\u003cspan address=\"10.1161/01.STR.0000229878.93759.a2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\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":"bmc-neurology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurl","sideBox":"Learn more about [BMC Neurology](http://bmcneurol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurl","title":"BMC Neurology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Spontaneous intracerebral hemorrhage, basal ganglia, cerebrospinal fluid volume, hematoma volume, maximum transverse diameter of the third ventricle","lastPublishedDoi":"10.21203/rs.3.rs-8300264/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8300264/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study aims to evaluate the prognostic value of the ratio of intracerebral hematoma volume (IHV) to cerebrospinal fluid volume (CSFV) in patients with basal ganglia hemorrhage undergoing non-surgical treatment. By providing a quantitative reference for clinical decision-making, this research seeks to facilitate the early identification of patients with poor prognoses and to support the selection of optimal treatment strategies tailored to individual needs.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis retrospective study included patients with spontaneous basal ganglia hemorrhage who received non-surgical treatment at our hospital between January 2017 and October 2020. IHV, CSFV, and the maximum transverse diameter of the third ventricle (MTDTV) were measured, and baseline characteristics were collected. Prognosis was assessed at three months using the modified Rankin Scale (mRS), with scores\u0026thinsp;\u0026le;\u0026thinsp;2 indicating good outcomes and scores\u0026thinsp;\u0026ge;\u0026thinsp;3 indicating poor outcomes. Correlations between clinical variables and prognosis were analyzed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 125 patients were included in this study, among whom 74 (59.2%) experienced poor outcomes. Univariate analysis revealed significant differences in NIHSS score, GCS score, IHV, systolic blood pressure (SBP) and diastolic blood pressure (DBP) at admission, CSFV, IHV/CSFV, and MTDTV (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Logistic regression analysis identified IHV/CSFV (OR\u0026thinsp;=\u0026thinsp;1.074, P\u0026thinsp;=\u0026thinsp;0.041) and SBP at admission (OR\u0026thinsp;=\u0026thinsp;1.052, P\u0026thinsp;=\u0026thinsp;0.005) as independent predictors of patient outcomes, with IHV/CSFV showing the strongest predictive capability. The area under the ROC curve for IHV/CSFV was 0.810 (95% CI: 0.733\u0026ndash;0.887), with a cutoff value of 18.35% for predicting poor outcomes, yielding a sensitivity of 67.57% and specificity of 92.16%.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eFor patients with spontaneous basal ganglia hemorrhage undergoing non-surgical treatment, admission SBP and the ratio of IHV/CSFV were identified as independent predictors of poor prognosis. Among these, IHV/CSFV demonstrated the strongest association with outcomes, offering a reliable and non-invasive method for prognosis prediction. Additionally, CSFV was significantly correlated with MTDTV, suggesting that CSFV could be effectively estimated through MTDTV measurements, further aiding clinical decision-making.\u003c/p\u003e","manuscriptTitle":"Prognostic Value of the IntracerebralHematoma Volume-to-Cerebrospinal Fluid Volume Ratio in Patients with Spontaneous Basal Ganglia Hemorrhage Undergoing Non-Surgical Treatment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-16 08:27:31","doi":"10.21203/rs.3.rs-8300264/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-02T14:43:32+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-30T17:12:29+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-17T16:23:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"122022619235208233730174433232653456","date":"2026-01-16T13:39:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"164016574717483906164654537223282577363","date":"2026-01-12T08:33:09+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-09T11:48:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-09T11:45:38+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-05T23:46:38+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-05T15:09:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Neurology","date":"2026-01-05T15:01:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-neurology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurl","sideBox":"Learn more about [BMC Neurology](http://bmcneurol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurl","title":"BMC Neurology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"536418bd-257f-4158-a016-7be3a2ab8439","owner":[],"postedDate":"January 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-16T18:53:40+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-16 08:27:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8300264","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8300264","identity":"rs-8300264","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}