Analysis of associated factors affecting hematoma evacuation rates in spontaneous intracerebral hemorrhage with stereotactic aspiration combined with catheter drainage

Analysis of associated factors affecting hematoma evacuation rates in spontaneous intracerebral hemorrhage with stereotactic aspiration combined with catheter drainage | 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 Analysis of associated factors affecting hematoma evacuation rates in spontaneous intracerebral hemorrhage with stereotactic aspiration combined with catheter drainage Xinqun Luo, Keming Song, Lingyun Zhuo, Fuxin Lin, Zhuyu Gao, Qiu He, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4576662/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The aim of this study was to delve into the relevant factors affecting hematoma evacuation (HE) rates during the treatment of sICH with stereotactic aspiration combined with catheter drainage. We pooled individual data from our prospective ICH databas, encompassing patients who underwent stereotactic aspiration and catheter drainage between July 2019 and July 2023. The primary outcome was HE rates prior to extubation, the secondary outcome was HE rates within 24 hours postoperatively. Logistic regression was employed to assess relevant clinical and radiological characteristics to establish a predictive model for achieving HE rates ≥ 70%. The model was validated by the ROC curve. Of the 894 patients with sICH enrolled in our database, 106 were eligible for this analysis. Factors affecting HE on the initial postoperative day were determined as preoperative hematoma volume (OR, 0.913; 95% CI, 0.836-0.997; P =0.042), blend sign (OR, 9.457; 95% CI, 0.999-89.508; P =0.050), and the catheter position score (OR, 5.551; 95% CI, 1.231-25.019; P =0.026). The positive blend sign (OR, 4.120; 95% CI, 1.344-12.630; P =0.013), absence of irregular hematoma morphology (OR, 0.291; 95% CI, 0.095-0.893; P =0.031), and hematoma edge not linked to the ventricle (OR, 0.185; 95% CI, 0.036-0.950; P =0.043) emerged as independent predictors for achieving HE rates ≥ 70% prior to extubation. Then, we developed two predictive models: one for early HE rates≥ 70%with a score from 0 to 7, and another for prior to extubation, scoring from 0 to 3. The ROC curve revealed AUC values of 0.871 and 0.753 for each model, respectively, and cutoff values of 5.5 and 1.5, accordingly. The predictive model of HE rates ≥ 70% within 24 hours postoperatively and prior to extuation has demonstrated remarkable predictive capability, holds the potential to assist clinicians in optimizing surgical efficiency. Trial registration ClinicalTrials.gov Identifier NCT03862729. hematoma evacuation rates predictors spontaneous intracerebral hemorrhage stereotactic aspiration Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Spontaneous intracerebral hemorrhage (sICH) is one of the most serious types of stroke, with high mortality and disability rates worldwide [1,2,3], and is the second leading cause of death globally [4]. The pathological mechanism usually involve the non-traumatic rupture of intracranial arteries, veins, or capillaries, leading to blood flow into the brain parenchyma, resulting in a mass effect and subsequent secondary damage from red blood cell lysis [5,6,7,8]. The treatment of sICH is still controversial [9]. In recent years, with the continuous development of surgical treatment of cerebral hemorrhage, minimally invasive surgery combined with catheterization thrombolysis has been proved to be safe and effective [10,11]. If the residual volume of hematoma is controlled within 15ml or the evacuation rates of hematoma are more than 70%, it is promising to improve the neurological status one year after operation [10,12]. Compared with traditional craniotomy, stereotactic surgery has less trauma, faster postoperative recovery, shorter hospital stays, and a lower economic burden on patients [13,14,15], so it is suitable for grassroots hospitals. Hematoma volume is the most important prognostic factor in patients with sICH [16,17]. However, due to blood clot coagulation, this surgical technique cannot completely remove the hematoma at one time. It is of great significance to improve the hematoma evacuation (HE) rates of stereotactic hematoma puncture aspiration and catheter drainage. However, there are still few studies focusing on the factors affecting the evacuation rates of intracerebral hematoma after stereotactic surgery. Therefore, this study evaluated the clinical and imaging characteristics of sICH patients who underwent stereotactic aspiration and catheter drainage from a prospective, multicenter intracerebral hemorrhage database and analyzed the factors affecting the evacuation rates of hematoma. Methods Patients We recruited sICH patients from our prospectively maintained ICH patient database (Risa-MIS-ICH, ClinicalTrials.gov Identifier: NCT03862729) between July 2019 and July 2023. The criteria for inclusion are as follows: (1) Patients underwent stereotactic hematoma puncture aspiration and catheter drainage without other surgical treatment (such as endoscopic hematoma resection, craniotomy hematoma removal, or only external ventricular drainage, etc.); (2) Supratentorial cerebral parenchymal hemorrhage was diagnosed by ccomputed tomography (CT) before surgery, and complete CT image data were available for postoperative follow-up; (3) There are no metal artifacts on CT images, which can accurately calculate the volume of hematoma. The surgical operations are executed with precision, adhering strictly to standardized protocols. While the stereotactic equipment may vary among different medical centers due to disparities in resources, the fundamental approach remains consistent: minimally invasive hematoma puncture aspiration and catheter placement for drainage. All related treatments adhere to the guideline of the American Heart Association / American Stroke Association (AHA/ASA) for the management of spontaneous intracerebral hemorrhage [18,19]. This study was approved by the Ethics Committee of the First Affiliated Hospital of Fujian Medical University (Ethical Approval Number: MRCTA, ECFAH of FMU [2018] 082 − 1) and complied with relevant laws and regulations. Data Collection Initial and follow-up CT image data for all patients were archived in the Digital Imaging and Communications in Medicine (DICOM) format. The hematoma was detected layer by layer on the axial section through 3Dslicer (version No. 5.2.2), and the threshold value of hematoma was set between 44 and 100 Hounsfield Unit (HU) [20]. The specific value could fluctuate depending on the actual situation; the upper limit should not exceed 100 HU, and the lower limit should not be lower than 35 HU. The region of interest (ROI) was delineated, and the volume and CT value of hematoma were calculated by automatic segmentation. In order to prevent retrograde infection, the drainage tube is usually left in place for 3 to 5 days. The primary endpoint of this study aimed to evaluate the HE rates prior to the removal of the drainage tube following stereotactic aspiration coupled with catheter thrombolysis. Additionally, the secondary endpoint encompassed the initial HE rates achieved within 24 hours following the surgical intervention. The calculation of HE efficacy was meticulously determined as follows: HE rates = [(Hematoma volume recorded within six hours before surgery) – (Hematoma volume measured either within 24 hours or prior to extubation postoperatively)] / (Hematoma volume within the six hours prior to surgery). General information of patients was extracted from the database, such as gender, age, current or previous smoking and drinking, history of diabetes and hypertension, medication history (such as anticoagulant drugs, antiplatelet drugs), admission GCS score, duration of hospitalization, total hospitalization expenses, and routine laboratory assay, such as white blood cell count, hemoglobin and platelet counts, erythrocyte ratio, blood glucose, blood calcium, coagulation function, and D-dimer. In addition, surgery-related information was collected, such as the number of drainage tubes, simultaneous insertion of external ventricular drainage, use of urokinase, and duration of catheter placement. Functional status at discharge and functional prognosis during the 3 to 12 months follow-up period were evaluated using the modified Rankin Scale (mRS). The mRS score of 0 to 3 was defined as favorable functional outcomes. Baseline non-contrast CT (NCCT) markers were examined by two independent investigators in all patients. The researchers did not know the patients' clinical history and follow-up CT results. The detected NCCT markers and their definitions are as follows: (1) Black hole sign: a relatively hypoattenuating area encapsulated with a hyperattenuating hematoma. The boundary is clear, and there is a density difference of at least 28 HU between the two regions. The low-density area can be round, oval, or strip but should not be connected to the adjacent brain tissue [21]; (2) Island sign: at the same level, there are ≥ 3 scattered small hematoma separated from the main hematoma, or ≥ 4 small hematoma, some or all of which are connected to the main hematoma [22]; (3) Satellite sign: there is a high-density small hematoma separated from the main hematoma on at least one level. The maximum diameter of the small hematoma is less than 10mm, the maximum distance between the small hematoma and the main hematoma is less than 20mm, and intraventricular involvement or subarachnoid extension of the main hematoma are excluded [23]; (4) Blend sign: a relatively high-density area next to a low-density area of the hematoma. The boundary between the two regions is well-defined and can be identified by the naked eye, and the CT value difference is more than 18HU. The hypoattenuating area is not surrounded by the hyperattenuating area [24]; (5) Fluid level: there is at least one distinct ICH region with a low CT attenuation region above (hypodense compared to brain) and a high CT attenuation region below (hyperdense compared to brain), separated by a clear horizontal line [25]. Furthermore, irregular hematoma morphology is defined by the Barras shape scale as two or more irregular shapes on the margins of the hematoma, which is assessed on the axial slice [26]. The definition of the hematoma edge linked to the ventricle is that the parenchymal hematoma breaks into the ventricle, and the hematoma edge is tangent to the ventricle. Other definitions include deep hematoma: hematoma greater than 1cm from the cortex [27]; Postoperative hematoma enlargement: manifesting as an augmentation of 6 ml or a 33% expansion relative to its preoperative dimensions [28]. Based on previous experience [29,30], we employed the following catheter position scoring system: a score of 3 was awarded for lateral catheter holes positioned precisely at the center of the hematoma, a score of 2 was given for those located within the cavity of the hematoma but outside the center, a score of 1 was designated for holes situated at the hematoma's edge, and a score of 0 was assigned to those positioned completely outside the hematoma, signifying an inadequate location. Discrepancies about the occurrence of CT markers were settled by joint discussion of the two readers. Statistical Analysis Continuous variables consistent with normal distribution were presented as mean and standard deviations, while those consistent with non-normal distribution were presented as median (interquartile range). Categorical data are presented as proportions. The demographic characteristics, clinical data, and CT features between patients with and without HE rates exceeding 70% were compared using the Pearson chi-square test, Fisher exact test, Student’s t-test, or the Mann–Whitney U test, as appropriate. Univariable logistic regression preliminarily determined the potential factors affecting HE. Baseline variables that were considered relevant or candidate variables with a P < 0.1 on univariable analysis were entered into a multivariate logistics regression model. The receiver operating characteristic (ROC) curve and the area under the curve (AUC) determine the predictive efficiency of the model. IBM SPSS Statistics 27 (Version 27.0. Armonk, NY: IBM Corp) were used for analysis, P < 0.05 was considered statistically significant. Result A total of 894 patients with ICH were screened, of which only 140 patients underwent stereotactic aspiration and catheter drainage for sICH, the following situations were excluded: 22 cases due to the absence of preoperative imaging data, 7 cases of patients with the hard channel puncture tube resulting in significant artifacts in the follow-up images, 1 case of surgery performed after 17 days of onset, 4 cases of emergency decompressive craniectomy necessitated by postoperative hematoma enlargement. Finally, 106 patients with sICH across 12 medical centers were included in this analysis. In the entire cohort, 101 patients had complete CT imaging data within 24 hours postoperatively (group A), while imaging data were obtainable for 82 patients prior to extubation (group B) (Fig. 1 ). Baseline characteristics In the entire cohort who received stereotactic aspiration and catheter drainage, the average age was 59.0 ± 15.1 years old, including 82 men (77.4%) and 24 women (22.6%). The median interval from onset to stereotactic surgery was 1(1–2) days. The preoperative median hematoma volume was 39.01(31.16–53.95) ml (Fig. 2 . A), and the median CT value was 61.02(58.43–64.17) HU. The preoperative CT showed that 68 patients (64.2%) had deep cerebral hemorrhage, including thalamus and basal ganglia, 38 patients (35.8%) had lobar hemorrhage and 52 patients (49.1%) had intraventricular hemorrhage, but did not lead to obstructive hydrocephalus. Two drainage tubes were placed in 6 patients, which were perpendicular to the long axis and short axis of the hematoma, respectively. Among the patients with 2 drainage tubes, 5 cases had hematoma evacuation rate ≥ 70%. Based on morphologic features of hematoma on preoperative CT, the number of patients exhibiting the black hole sign, island sign, satellite sign, blend sign and the fluid level were 40 (37.7%), 25 (23.6%), 26 (24.5%), 33 (31.1%), and 4 (3.8%), respectively. According to the Barras shape scale, hematoma morphology was divided into regular and irregular, irregular hematoma was found in 35 patients (33.0%). Based on the scoring system pertaining to the catheter’s placement, we observed 50(47.2%) patients scoring 3, 25(23.6%) scoring 2, and 29(27.4%) scoring 1. Notably, only 2(1.9%) patients exhibited unsatisfactory catheter positioning, necessitating readjustment. The median duration of indwelling drainage tube placement was 4(2–5) days (Table 1 ). As for the available data, within 24 hours postoperatively, 10 patients (9.9%) exhibited HE rates of 70% or higher. Prior to extubation, the number of patients with HE rates ≥ 70% reached 39 (47.6%). Table 1 Clinical characteristics of patients undergoing stereotactic aspiration and catheter drainage Clinical characteristics Number (proportion, %) Patients number 106 Age, year, mean ± SD 59.0 ± 15.1 Gender Male 82(77.4%) Female 24(22.6%) Current or ever smoker, n(%)a 23(21.7%) Regular drinkers, n(%)a 20(18.9%) Medical history Diabetes, n(%) 11(10.4%) Hypertension, n(%) 67(63.2%) Medication history Anticoagulation therapy, n(%) 2(1.9%) Antiplatelet therapy, n(%) 5(4.7%) Admission GCS scores, median (IQR) 11.0(9.0–14.0) laboratory assay Leucocyte,10^9/L, median(IQR) 9.36(7.55–13.26) Neutrophilic,10^9/L, median(IQR) 7.35(4.81–11.15) Lymphocyte,10^9/L, median(IQR) 1.26(0.81–1.83) Hemoglobin, g/L, median(IQR) 143.00(129.00-156.25) Thrombocyte,10^9/L, mean ± SD 210.39 ± 59.04 APTT, median (IQR) 25.1(22.8–27.6) INR, median (IQR) 1.0(0.9–1.1) The time from onset to operation, day, median (IQR) 1(1–2) Pre-op radiological features Hematoma location Deep, n(%) 68(64.2%) Lobe, n(%) 38(35.8%) Pre-op hematoma volume, ml, median (IQR) 39.01(31.16–53.95) Pre-op mean hematoma CT value, HU, median (IQR) 61.02(58.43–64.17) Black hole sign, n(%) 40(37.7%) Island sign, n(%) 25(23.6%) Satellite sign, n(%) 26(24.5%) Blend sign, n(%) 33(31.1%) Table 1 Continued Clinical characteristics Number (proportion, %) Fluid level, n(%) 4(3.8%) Hematoma shape regular, n(%) 71(67.0%) Irregular, n(%) 35(33.0%) Intraventricular hemorrhage, n(%) 52(49.1%) Hematoma edges linked to ventricles, n(%) 28(26.4%) External ventricular drainage, n(%) 9(8.5%) Catheter position score, n(%) 0 2(1.9%) 1 29(27.4%) 2 25(23.6%) 3 50(47.2%) Two drainage tubes, n(%) 6(5.7%) Intubation time, day, median (IQR) a 4(2–5) a, missing data; APTT, activated partial thromboplastin time; GCS, Glasgow Coma Scale; IQR, interquartile range; INR, international normalized ratio; Pre-op, preoperative; SD, standard deviation. Factors associated with Day 1 post-op HE rates ≥ 70% A meticulous multivariate logistic regression analysis revealed that preoperative hematoma volume (OR, 0.913; 95% CI, 0.836–0.997; P = 0.042), the presence of blend sign (OR, 9.457; 95% CI, 0.999–89.508; P = 0.050), and the catheter position score (OR, 5.551; 95% CI, 1.231–25.019; P = 0.026) emerged as significant factors in influencing HE on the first postoperative day (Table 2 ). The cutoff values for hematoma volume and catheter position score were determined to be 45.46ml and 2.5 points, respectively. We have developed a logistic regression model, formulated as follows: Y = 2.911–1.634*X1 + 0.287*X2–0.091*X3–0.095*X4 + 2.247*X5 + 0.299*X6 + 1.714*X7. In this model, Y represents the predicted log-odds, and the probability, P, of the outcome is calculated using the formula P = 1/[1 + e^(-Y)]. The predictors included in this model encompassing X1, which represents lymphocyte count; X2, indicating the time elapsed from onset to surgery; X3, preoperative hematoma volume; X4, preoperative mean hematoma CT value; X5, the presence of blend sign; X6, intraventricular hemorrhage; and X7, catheter position score. This sophisticated model allows us to accurately estimate the predictive probability of the HE rates exceeding 70% within 24 hours postoperatively. To enhance clinical applicability, according to the regression coefficients, we categorized patients into two groups based on preoperative hematoma volume: those with a volume exceeding 45.46ml were assigned a score of 0, while those with a volume of 45.46ml or less received a score of 3. For catheter position, the corresponding score is identical to the score for catheter location. Regarding the blend sign, a negative manifestation was assigned a score of 0, while a positive manifestation received a score of 1. Utilizing these criteria, we developed a predictive model with a score from 0 to 7 aimed at estimating HE rates ≥ 70% within 24 hours postoperatively (Table 4 ). Notably, the ROC of our model exhibited exceptional predictive capacity, with an AUC of 0.871 and a cutoff point 5.5. This predictive prowess surpasses that of relying solely on the catheter position score (AUC = 0.729) or preoperative hematoma volume (AUC = 0.724) as predictors (Fig. 2 . C). Table 2 Univariate logistic and multiple logistic regression of correlation between pre-op CT or clinical features and Day 1 post-op HE rates ≥ 70% Variable Univariate logistic regression Multiple logistic regression OR 95% CI P value OR 95% CI P value Gender 2.367 0.608–9.211 0.214 Age 1.007 0.964–1.052 0.762 Lymphocyte 0.332 0.093–1.190 0.091 0.195 0.035–1.089 0.063 The time from onset to operation 1.430 0.975–2.096 0.067 1.332 0.767–2.313 0.309 Admission GCS scores 1.216 0.939–1.576 0.138 Pre-op hematoma volume 0.940 0.887–0.997 0.039 0.913 0.836–0.997 0.042 Pre-op mean hematoma CT value 0.876 0.774–0.993 0.038 0.909 0.772–1.071 0.254 Black hole sign 0.382 0.077–1.902 0.240 Blend sign 2.370 0.634–8.861 0.200 9.457 0.999–89.508 0.050 Irregular shape 0.461 0.092–2.302 0.345 Intraventricular hemorrhage 0.256 0.051–1.270 0.095 1.348 0.151–12.058 0.789 Hematoma edge linked to ventricle 0.739 0.146–3.735 0.715 Catheter position score 4.009 1.090-14.753 0.037 5.551 1.231–25.019 0.026 Two drainage tubes 3.259 0.306–34.686 0.327 CI, confidence interval; CT, computed tomography; GCS, Glasgow Coma Scale; HE, hematoma evacuation; OR, odds ratio; pre-op, preoperative; post-op, postoperative. Factors associated with HE rates ≥ 70% prior to extubation Our multivariate logistic regression analysis has demonstrated that positive blend sign (OR, 4.120; 95% CI, 1.344–12.630; P = 0.013), absence of irregular hematoma morphology (OR, 0.291; 95% CI, 0.095–0.893; P = 0.031), and the absence of the hematoma edge linked to the ventricle (OR, 0.185; 95% CI, 0.036–0.950; P = 0.043) are independent predictors for achieving HE rates of at least 70% (Fig. 2 . B, Table 3 ). We have crafted a logistic regression model, defined as Y = 0.197 + 1.416*X1–1.234*X2–0.133*X3–1.685*X4, with the predictive probability P calculated as 1/[1 + e^(-Y)]. In this framework, X1 captures the presence of the blend sign, X2 characterizes irregular shape, X3 reflects intraventricular hemorrhage, and X4 denotes the linkage of the hematoma edge to the ventricle. Similarly, to enhance clinical applicability, according to the regression coefficients, we assigned preoperative CT blend sign a score of 0 for absence and 1 for presence. Hematoma morphology received a score of 0 for irregularity and 1 for regularity. Additionally, we assigned a score of 0 when the hematoma edge was linked to the ventricle, and a score of 1 when they were not linked. Using these criteria, we developed a predictive model with a score from 0 to 3 aimed at estimating HE rates ≥ 70% prior to extubation (Table 4 ). Our ROC curve analysis revealed solid predictive efficacy with an AUC of 0.753 and a cutoff point 1.5 (Fig. 2 . D). Table 3 Univariate logistic and multiple logistic regression of correlation between pre-op CT or clinical features and prior to extubation HE rates ≥ 70% Variable Univariate logistic regression Multiple logistic regression OR 95% CI P value OR 95% CI P value Gender 0.556 0.169–1.830 0.334 Age 0.994 0.965–1.024 0.698 Diabetes 0.278 0.054–1.429 0.125 Hypertension 1.168 0.477–2.859 0.735 Lymphocyte 0.730 0.436–1.223 0.232 The time from onset to operation 1.447 0.880–2.377 0.145 Admission GCS scores 0.962 0.830–1.114 0.601 Pre-op hematoma volume 1.006 0.982–1.031 0.624 Pre-op mean hematoma CT value 0.970 0.902–1.043 0.407 Black hole sign 0.956 0.393–2.325 0.921 Blend sign 3.135 1.217–8.073 0.018 4.120 1.344–12.630 0.013 Irregular shape 0.326 0.122–0.871 0.025 0.291 0.095–0.893 0.031 Intraventricular hemorrhage 0.320 0.129–0.796 0.014 0.875 0.258–2.969 0.831 Hematoma edge linked to ventricle 0.193 0.058–0.644 0.007 0.185 0.036–0.950 0.043 Catheter position score 1.122 0.680–1.854 0.652 Two drainage tubes 6.176 0.688–55.416 0.104 CI, confidence interval; CT, computed tomography; GCS, Glasgow Coma Scale; pre-op; HE, hematoma evacuation; OR, odds ratio; preoperative; post-op, postoperative. Table 4 The predictive model of HE rates ≥ 70% Day 1 post-op HE rates ≥ 70% Prior to extubation HE rates ≥ 70% Pre-op hematoma volume Score Feature Score ≤ 45.46ml 3 Irregular shape P/N 0/1 >45.46ml 0 Blend sign P/N 1/0 Blend sign P/N 1/0 Hematoma edge linked to ventricle P/N 0/1 Catheter position score Score 0 0 1 1 2 2 3 3 HE, hematoma evacuation; N, negative; pre-op, preoperative; post-op, postoperative; P, positive. Discussion We analyzed the clinical and imaging data of patients with supratentorial sICH who underwent stereotactic aspiration and catheter drainage, aiming to identify factors conducive to HE and improve the effect of minimally invasive surgery. The presence of a positive NCCT blend sign prior to surgery significantly enhances HE on both the first postoperative day and before tube removal. Conversely, a favorable catheter position score only promotes HE on the initial postoperative day, exhibiting no significant correlation with HE at the later time point. Furthermore, our analysis demonstrates that a larger hematoma volume is deleterious to HE on the first postoperative day, with an optimal cutoff value identified as 45.46ml. Notably, the presence of irregular hematoma morphology and a hematoma edge contiguous with the ventricle are adverse factors for HE prior to extubation (Fig. 3 ). Previous scholarly investigations have demonstrated that the blend sign can serve as an indicator of active bleeding and reflect various stages of hematoma development [24]. Notably, the attenuation values observed on CT scans are intimately linked to the hemoglobin content within the hematoma [31]. In the initial phase of hemorrhage, red blood cells remain intact, resulting in a relatively low concentration of hemoglobin within the hematoma. Consequently, this manifests as low attenuation on CT images. Subsequently, as the red blood cells undergo lysis, hemoglobin is released, and the clot undergoes shrinkage, leading to the separation of serum [32]. This process significantly elevates the hemoglobin concentration, ultimately resulting in a high attenuation signal on CT. Consequently, a positive blend sign suggests the presence of a mobile liquid hematoma, which is more amenable to drainage, thereby facilitating HE. Analysis of preoperative hematoma volume indicates that a larger hematoma is disadvantageous for HE within 24 hours postoperatively, with a threshold value of 45.46ml. The traditional surgical indication for cerebral hemorrhage is a hematoma volume of ≥ 30ml, and the standard surgical approach involves craniotomy for hematoma evacuation, optionally with decompressive craniectomy [18,19]. The Surgical Treatment for Intracerebral Hemorrhage (STICH) study suggested that craniotomy does not significantly improve the prognosis of patients [27,33]. Consequently, minimally invasive surgical techniques have emerged as viable alternatives. Nevertheless, given their inherent limitations, in cases involving substantial hematoma volumes, direct visualization of hematoma removal, such as through endoscopic or microscopic approaches, may still be necessary to enhance HE rates. In addition, for sICH patients, more accurate catheterization may be needed. MISTIE II taught us that efficacy of the surgical task is directly related to satisfactory catheter placement [34]. Our profound research has also revealed that the precise placement of the catheter can effectively facilitate the early elimination of intracranial hematoma. Catheters that are accurately positioned within the hematoma enables surgeons to effectively aspirate the blood and clot, reducing the size of the hematoma and relieving pressure on surrounding tissues. Furthermore, as an NCCT marker, the irregular shape of hematoma is associated with hematoma expansion [24]. This may reflect the multi-source bleeding in the hematoma, along the multi-path development. The irregular shape of the hematoma can hinder the uniform distribution of urokinase and efficient drainage, ultimately compromising HE. Remarkably, our meticulous investigation has uncovered that the existence of a connection between the hematoma edge and the ventricle unexpectedly impedes HE, thus challenging the established clinical understanding. We offer a plausible hypothesis to explain this observation: the infiltration of cerebrospinal fluid into the hematoma cavity potentially dilutes the concentration of urokinase, subsequently limiting the dissolution of blood clots and ultimately compromising the efficiency of HE. In clinical practice, urokinase is often used to dissolve blood clots and transform solid hematoma into liquid hematoma. Studies have shown that urokinase can be used as a safe and effective alternative to alteplase [35], but the optimal dose of urokinase remains uncertain. In this study, all patients received urokinase in combination, but the effect of stereotactic aspiration combined with urokinase thrombolysis on long-term functional prognosis and the optimal dose of urokinase needed further study. Drawing upon the aforementioned conclusions, we have developed an innovative predictive model. Within the framework of the early HE rates prediction model, patients scoring of 6 or 7 demonstrate a significantly elevated likelihood of achieving HE rates of 70% or higher. Similarly, within the context of the prediction model for HE rates prior to catheter removal, individuals scoring 2 or 3 exhibit superior HE outcomes. Our findings are similar to the results of Kim JH et al. that a positive blend sign can promote the removal of hematoma in minimally invasive hematoma aspiration and catheter drainage [36]. This study was a single-center retrospective study, and our results further support this conclusion. In previous studies [36,37], the volume of the hematoma was calculated using the traditional formula [ABC/2], where A is the maximum diameter of the hematoma on the axial CT image, B is the diameter at 90° from A, and C is the number of CT sections in the hematoma multiplied by the section thickness. Although this method is simple and feasible, its accuracy is inadequate. Moreover, the CT value is measured only at the largest level of hematoma, which cannot reflect the average CT value of the whole hematoma. We have made modifications to our measurement methods. The accuracy of 3Dslicer in calculating the volume of hematoma is better than that of [ABC/2] [38], and 3Dslicer can automatically calculate the average CT value of the entire hematoma, indirectly reflecting the density of the entire hematoma. Wang T et al. believed that the CT value reflected the viscosity of the hematoma, the lower CT value was conducive to the removal of hematoma 35 . However, our results are the same as those of Kim JH et al. that hematoma CT values do not affect the HE. The reason may be different from the parameters set by CT instruments in different centers, resulting in bias in the results. In the future, unified CT parameters should be used to further determine the role of image features in predicting the removal of hematoma. There are still some potential limitations in our study. Although it comes from a prospective, multicenter cerebral hemorrhage registry database, the sample size is small, which may affect the conclusion of the study and its universality. In addition, the predictive model employed in this study has yet to undergo external validation, necessitating future endeavors to thoroughly assess its performance on independent datasets. Conclusion The predictive model for HE rates possesses the advantages of simplicity and convenience, providing clinicians with a reference for enhancing the efficiency of minimally invasive surgical procedures. For patients with sICH undergoing stereotactic aspiration and catheter drainage, we may potentially deduce the HE efficiency of this surgical procedure from relevant preoperative CT imaging data. Abbreviations AUC area under the curve CT computed tomography CI Confidence interval DICOM Digital Imaging and Communications in Medicine GCS Glasgow coma scale HE hematoma evacuation HU Hounsfield Unit mRS modified Rankin Scale NCCT non-contrast computed tomography OR odds ratio ROC receiver operating characteristic ROI region of interest sICH spontaneous intracerebral hemorrhage. Declarations Funding : This work was funded by the Fujian Science and Technology Innovation Joint Fund Project (2019Y9118), the Fujian Provincial Health Commission (2022ZD01003), and the Stroke Prevention and Treatment Project of the National Health Commission— Research and Popularization of Appropriate Intervention Technology for the Stroke High Risk Group in China (GN2018R002). Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. In the article, we confirmed that we used the STROBE checklist. Author Contributions: All persons who meet authorship criteria are listed as authors. WHF and DZK proposed research concepts and designs, provided support, and supervised the study. DZK obtained the funding. XQL and KMS prepared the draft manuscript and coordinated its finalization. XQL and LYZ performed statistical analyses; XQL and FXL drew and prepared the tables and figures. ZYG, QH, YZ, XQL, KBL and HCSG gathered pertinent data, performed database management and data cleaning. YXL and FXL revised the manuscript. All authors have seen and approved the final manuscript. Ethics approval: Institutional review board approval obtained as noted in the article. Declaration: We declare that none of the material related to this manuscript has been published or is under consideration for publication elsewhere and it complies the instructions for all authors. References Qureshi AI, Mendelow AD, Hanley DF. Intracerebral haemorrhage. Lancet . 2009;373(9675):1632–1644. https://doi.org/10.1016/S0140-6736(09)60371-8 Al-Shahi Salman R, Labovitz DL, Stapf C. Spontaneous intracerebral haemorrhage. BMJ . 2009;339:b2586. Published 2009 Jul 24. doi:10.1136/bmj.b2586 https://doi.org/10.1136/bmj.b2586 Kim JY, Bae HJ. Spontaneous Intracerebral Hemorrhage: Management. J Stroke . 2017;19(1):28–39. doi:10.5853/jos.2016.01935 https://doi.org/10.5853/jos.2016.01935 Tsai CF, Thomas B, Sudlow CL. Epidemiology of stroke and its subtypes in Chinese vs white populations: a systematic review. Neurology . 2013;81(3):264–272. https://doi.org/10.1212/WNL.0b013e31829bfde3 Diringer MN. Intracerebral hemorrhage: pathophysiology and management. Crit Care Med . 1993;21(10):1591–1603. https://doi.org/10.1097/00003246-199310000-00032 van Etten ES, Kaushik K, Jolink WMT, et al. Trigger Factors for Spontaneous Intracerebral Hemorrhage: A Case-Crossover Study. Stroke . 2022;53(5):1692–1699. https://doi.org/10.1161/STROKEAHA.121.036233 Rzepliński R, Sługocki M, Tarka S, et al. Mechanism of Spontaneous Intracerebral Hemorrhage Formation: An Anatomical Specimens-Based Study. Stroke . 2022;53(11):3474–3480. https://doi.org/10.1161/STROKEAHA.122.040143 Wan J, Ren H, Wang J. Iron toxicity, lipid peroxidation and ferroptosis after intracerebral haemorrhage. Stroke Vasc Neurol . 2019;4(2):93–95. https://doi.org/10.1136/svn-2018-000205 de Oliveira Manoel AL. Surgery for spontaneous intracerebral hemorrhage. Crit Care . 2020;24(1):45. https://doi.org/10.1186/s13054-020-2749-2 Hanley DF, Thompson RE, Rosenblum M, et al. Efficacy and safety of minimally invasive surgery with thrombolysis in intracerebral haemorrhage evacuation (MISTIE III): a randomised, controlled, open-label, blinded endpoint phase 3 trial. Lancet . 2019;393(10175):1021–1032. https://doi.org/10.1016/S0140-6736(19)30195-3 Pradilla G, Ratcliff JJ, Hall AJ, et al. Trial of Early Minimally Invasive Removal of Intracerebral Hemorrhage. N Engl J Med . 2024;390(14):1277–1289. https://doi.org/10.1056/NEJMoa2308440 Awad IA, Polster SP, Carrión-Penagos J, et al. Surgical Performance Determines Functional Outcome Benefit in the Minimally Invasive Surgery Plus Recombinant Tissue Plasminogen Activator for Intracerebral Hemorrhage Evacuation (MISTIE) Procedure. Neurosurgery . 2019;84(6):1157–1168. https://doi.org/10.1093/neuros/nyz077 Guo W, Liu H, Tan Z, et al. Comparison of endoscopic evacuation, stereotactic aspiration, and craniotomy for treatment of basal ganglia hemorrhage. J Neurointerv Surg . 2020;12(1):55–61. https://doi.org/10.1136/neurintsurg-2019-014962 Wang JW, Li JP, Song YL, et al. Stereotactic aspiration versus craniotomy for primary intracerebral hemorrhage: a meta-analysis of randomized controlled trials. PLoS One . 2014;9(9):e107614. https://doi.org/10.1371/journal.pone.0107614 Kumar S, Madhariya SN, Singh D, et al. Comparison of Craniotomy and Stereotactic Aspiration Plus Thrombolysis in Isolated Capsulo-Ganglionic Hematoma: A Retrospective Analyses. Neurol India . 2022;70(2):535–542. https://doi.org/10.4103/0028-3886.344635 Lin F, He Q, Tong Y, et al. Early Deterioration and Long-Term Prognosis of Patients With Intracerebral Hemorrhage Along With Hematoma Volume More Than 20 ml: Who Needs Surgery?. Front Neurol . 2022;12:789060. https://doi.org/10.3389/fneur.2021.789060 Feng H, Wang X, Wang W, Zhao X. Risk factors and a prediction model for the prognosis of intracerebral hemorrhage using cerebral microhemorrhage and clinical factors. Front Neurol . 2023;14:1268627. https://doi.org/10.3389/fneur.2023.1268627 Hemphill JC 3rd, Greenberg SM, Anderson CS, et al. Guidelines for the Management of Spontaneous Intracerebral Hemorrhage: A Guideline for Healthcare Professionals From the American Heart Association/American Stroke Association. Stroke . 2015;46(7):2032–2060. https://doi.org/10.1161/STR.0000000000000069 Greenberg SM, Ziai WC, Cordonnier C, et al. 2022 Guideline for the Management of Patients With Spontaneous Intracerebral Hemorrhage: A Guideline From the American Heart Association/American Stroke Association. Stroke . 2022;53(7):e282-e361. https://doi.org/10.1161/STR.0000000000000407 Urday S, Kimberly WT, Beslow LA, et al. Targeting secondary injury in intracerebral haemorrhage–perihaematomal oedema. Nat Rev Neurol . 2015;11(2):111–122. https://doi.org/10.1038/nrneurol.2014.264 Li Q, Zhang G, Xiong X, et al. Black Hole Sign: Novel Imaging Marker That Predicts Hematoma Growth in Patients With Intracerebral Hemorrhage. Stroke . 2016;47(7):1777–1781. https://doi.org/10.1161/STROKEAHA.116.013186 Li Q, Liu QJ, Yang WS, et al. Island Sign: An Imaging Predictor for Early Hematoma Expansion and Poor Outcome in Patients With Intracerebral Hemorrhage. Stroke . 2017;48(11):3019–3025. https://doi.org/10.1161/STROKEAHA.117.017985 Yu Z, Zheng J, Ali H, et al. Significance of satellite sign and spot sign in predicting hematoma expansion in spontaneous intracerebral hemorrhage. Clin Neurol Neurosurg . 2017;162:67–71. https://doi.org/10.1016/j.clineuro.2017.09.008 Li Q, Zhang G, Huang YJ, et al. Blend Sign on Computed Tomography: Novel and Reliable Predictor for Early Hematoma Growth in Patients With Intracerebral Hemorrhage. Stroke . 2015;46(8):2119–2123. https://doi.org/10.1161/STROKEAHA.115.009185 Sato S, Delcourt C, Zhang S, et al. Determinants and Prognostic Significance of Hematoma Sedimentation Levels in Acute Intracerebral Hemorrhage. Cerebrovasc Dis . 2016;41(1–2):80–86. https://doi.org/10.1159/000442532 Barras CD, Tress BM, Christensen S, et al. Density and shape as CT predictors of intracerebral hemorrhage growth. Stroke . 2009;40(4):1325–1331. https://doi.org/10.1161/STROKEAHA.108.536888 Mendelow AD, Gregson BA, Fernandes HM, et al. Early surgery versus initial conservative treatment in patients with spontaneous supratentorial intracerebral haematomas in the International Surgical Trial in Intracerebral Haemorrhage (STICH): a randomised trial. Lancet . 2005;365(9457):387–397. https://doi.org/10.1016/S0140-6736(05)17826-X Demchuk AM, Dowlatshahi D, Rodriguez-Luna D, et al. Prediction of haematoma growth and outcome in patients with intracerebral haemorrhage using the CT-angiography spot sign (PREDICT): a prospective observational study. Lancet Neurol . 2012;11(4):307–314. https://doi.org/10.1016/S1474-4422(12)70038-8 Malinova V, Schlegel A, Rohde V, Mielke D. Catheter placement for lysis of spontaneous intracerebral hematomas: does a catheter position in the core of the hematoma allow more effective and faster hematoma lysis?. Neurosurg Rev . 2017;40(3):397–402. https://doi.org/10.1007/s10143-016-0792-x Polster SP, Carrión-Penagos J, Lyne SB, et al. Thrombolysis for Evacuation of Intracerebral and Intraventricular Hemorrhage: A Guide to Surgical Protocols With Practical Lessons Learned From the MISTIE and CLEAR Trials. Oper Neurosurg (Hagerstown) . 2020;20(1):98–108. https://doi.org/10.1093/ons/opaa306 Nowinski WL, Gomolka RS, Qian G, et al. Characterization of intraventricular and intracerebral hematomas in non-contrast CT. Neuroradiol J . 2014;27(3):299–315. https://doi.org/10.15274/NRJ-2014-10042 New PF, Aronow S. Attenuation measurements of whole blood and blood fractions in computed tomography. Radiology . 1976;121(3 Pt. 1):635–640. https://doi.org/10.1148/121.3.635 Mendelow AD, Gregson BA, Rowan EN, et al. Early surgery versus initial conservative treatment in patients with spontaneous supratentorial lobar intracerebral haematomas (STICH II): a randomised trial. Lancet . 2013;382(9890):397–408. https://doi.org/10.1016/S0140-6736(13)60986-1 Hanley DF, Thompson RE, Muschelli J, et al. Safety and efficacy of minimally invasive surgery plus alteplase in intracerebral haemorrhage evacuation (MISTIE): a randomised, controlled, open-label, phase 2 trial. Lancet Neurol . 2016;15(12):1228–1237. https://doi.org/10.1016/S1474-4422(16)30234-4 Zhang X, Zhou S, Zhang Q, et al. Stereotactic aspiration for hypertensive intracerebral haemorrhage in a Chinese population: a retrospective cohort study. Stroke Vasc Neurol . 2019;4(1):14–21. https://doi.org/10.1136/svn-2018-000200 Kim JH, Lee HS, Ahn JH, et al. Clinical and radiographic factors involved in achieving a hematoma evacuation rate of more than 70% through minimally invasive catheter drainage for spontaneous intracerebral hemorrhage. J Clin Neurosci . 2021;92:103–109. https://doi.org/10.1016/j.jocn.2021.07.038 Wang T, Guan Y, Du J, et al. Factors affecting the evacuation rate of intracerebral hemorrhage in basal ganglia treated by minimally invasive craniopuncture. Clin Neurol Neurosurg . 2015;134:104–109. https://doi.org/10.1016/j.clineuro.2015.04.020 Xu HZ, Guo J, Wang C, et al. A Novel Stereotactic Aspiration Technique for Intracerebral Hemorrhage. World Neurosurg . 2023;170:e28-e36. https://doi.org/10.1016/j.wneu.2022.10.051 Additional Declarations No competing interests reported. Supplementary Files SupplementaryData.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4576662","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":321293725,"identity":"a806cfef-9666-4169-b422-51638c3d7bbf","order_by":0,"name":"Xinqun Luo","email":"","orcid":"","institution":"Department of Neurosurgery, Neurosurgery Research Institute, The First Affiliated Hospital, Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xinqun","middleName":"","lastName":"Luo","suffix":""},{"id":321293728,"identity":"db38f49b-910b-42bd-bcfa-ef198f0d7bf4","order_by":1,"name":"Keming Song","email":"","orcid":"","institution":"Department of Neurosurgery, Shunchang County General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Keming","middleName":"","lastName":"Song","suffix":""},{"id":321293733,"identity":"6354e292-71a6-4dae-9f32-f41ba015bbcf","order_by":2,"name":"Lingyun Zhuo","email":"","orcid":"","institution":"Department of Neurosurgery, Neurosurgery Research Institute, The First Affiliated Hospital, Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lingyun","middleName":"","lastName":"Zhuo","suffix":""},{"id":321293735,"identity":"2595d340-9037-49dc-9168-77b95ce0deec","order_by":3,"name":"Fuxin Lin","email":"","orcid":"","institution":"Department of Neurosurgery, Neurosurgery Research Institute, The First Affiliated Hospital, Fujian Medical 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Fang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYBAC9gYow4CBgfEBUVp4DiC0MBuQrIVNgjgt7GcPv2Bss8kzl0h+VvGmjEGeX+wAAS08eWkWjG1pxZYz0sxuzjnHYDhzdgJ+LfYMOWYGjNsOJ264kWB2m7eNIcHgNgEtPPxvQFr+A7WkfysmTotEjvEDxm0HgFpyzJiJ1PLGjCHxX3LihjNviiXnnJMg7Bce/hzjDx/O2CVuOJ6+8cObMht5fmkCWhhA0QFXw0Nk1DB/QNjJRpSOUTAKRsEoGGEAAJVMQqxwAwv9AAAAAElFTkSuQmCC","orcid":"","institution":"Department of Neurosurgery, Neurosurgery Research Institute, The First Affiliated Hospital, Fujian Medical University","correspondingAuthor":true,"prefix":"","firstName":"Wenhua","middleName":"","lastName":"Fang","suffix":""}],"badges":[],"createdAt":"2024-06-13 13:55:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4576662/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4576662/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":59598112,"identity":"ac7a3caa-0890-45f5-be52-15d71046c484","added_by":"auto","created_at":"2024-07-03 16:17:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":108093,"visible":true,"origin":"","legend":"\u003cp\u003eFlow Diagram for Patients with Intracerebral Hemorrhage Enrolled in the Study\u003c/p\u003e\n\u003cp\u003eAbbreviations: HE, Hematoma Evacuation; Pre-op, Preoperative; Post-op, Postoperative.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4576662/v1/7d9efa033ff6f1aa0cebe6f4.png"},{"id":59598758,"identity":"1c59f6eb-e346-4549-83ca-43358678918a","added_by":"auto","created_at":"2024-07-03 16:25:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1128595,"visible":true,"origin":"","legend":"\u003cp\u003e(A) The distribution of hematoma volumes prior to surgery, within 24 hours pre-op, and prior to extubation. (B) The hematoma volume distribution across three distinct factors, both preoperatively and before extubation. (C) ROC curve analysis between pre-op hematoma volume and Day 1 post-op HE rates ≥ 70%, AUC was 0.724, with a cutoff point of 45.46ml (purple line). ROC curve analysis between catheter position score and Day 1 post-op HE rates ≥ 70%, AUC was 0.729, with a cutoff point of 2.5 (yellow line). ROC curve analysis for the predictive model of Day 1 post-op HE rates ≥ 70%, AUC was 0.871, with a cutoff point of 5.5 (red line). (D) ROC curve analysis for the predictive model of HE rates ≥ 70% prior to extubation, AUC was 0.753, with a cutoff point of 1.5.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4576662/v1/dc7eecc2182cf7ab7c476c96.png"},{"id":59598114,"identity":"5913222a-dfd0-42c9-a965-7566028753ef","added_by":"auto","created_at":"2024-07-03 16:17:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":380506,"visible":true,"origin":"","legend":"\u003cp\u003eThe distribution range of different hematoma evacuation rates based on factors such as the blend sign positive or negative, hematoma shape, and whether hematoma edge linked to the ventricle.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4576662/v1/371908b0d60024767041676c.png"},{"id":59598115,"identity":"935c6649-09ba-4769-ab2d-70472462d836","added_by":"auto","created_at":"2024-07-03 16:17:45","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1252386,"visible":true,"origin":"","legend":"\u003cp\u003eThis is a case of a 26-year-old male patient with a left basal ganglia hemorrhage of 38.26ml. Preoperative CT scans showed a positive blend sign, with a regular hematoma morphology, and did not rupture into the ventricles (A). A follow-up CT scan within 24 hours after surgery showed a reduction in hematoma volume to 8.85ml, achieving HE rates of 76.9% (B). Prior to extubation, the hematoma volume further reduced to 4.58ml, achieving an overall HE rates of 88.0% (C).\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4576662/v1/2622cfcc6fe5235134e8b620.png"},{"id":67581343,"identity":"40746825-ab64-42a0-9974-2dcef0a8568b","added_by":"auto","created_at":"2024-10-27 14:46:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4078835,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4576662/v1/95af9e89-76ee-473e-8689-86fcd302b2d0.pdf"},{"id":59598113,"identity":"a85f4bba-623a-4393-abb0-ac8b87d3d813","added_by":"auto","created_at":"2024-07-03 16:17:44","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":25577,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryData.docx","url":"https://assets-eu.researchsquare.com/files/rs-4576662/v1/1a149d254597c7f963dd43f6.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analysis of associated factors affecting hematoma evacuation rates in spontaneous intracerebral hemorrhage with stereotactic aspiration combined with catheter drainage","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSpontaneous intracerebral hemorrhage (sICH) is one of the most serious types of stroke, with high mortality and disability rates worldwide [1,2,3], and is the second leading cause of death globally [4]. The pathological mechanism usually involve the non-traumatic rupture of intracranial arteries, veins, or capillaries, leading to blood flow into the brain parenchyma, resulting in a mass effect and subsequent secondary damage from red blood cell lysis [5,6,7,8]. The treatment of sICH is still controversial [9]. In recent years, with the continuous development of surgical treatment of cerebral hemorrhage, minimally invasive surgery combined with catheterization thrombolysis has been proved to be safe and effective [10,11]. If the residual volume of hematoma is controlled within 15ml or the evacuation rates of hematoma are more than 70%, it is promising to improve the neurological status one year after operation [10,12]. Compared with traditional craniotomy, stereotactic surgery has less trauma, faster postoperative recovery, shorter hospital stays, and a lower economic burden on patients [13,14,15], so it is suitable for grassroots hospitals. Hematoma volume is the most important prognostic factor in patients with sICH [16,17]. However, due to blood clot coagulation, this surgical technique cannot completely remove the hematoma at one time. It is of great significance to improve the hematoma evacuation (HE) rates of stereotactic hematoma puncture aspiration and catheter drainage.\u003c/p\u003e \u003cp\u003eHowever, there are still few studies focusing on the factors affecting the evacuation rates of intracerebral hematoma after stereotactic surgery. Therefore, this study evaluated the clinical and imaging characteristics of sICH patients who underwent stereotactic aspiration and catheter drainage from a prospective, multicenter intracerebral hemorrhage database and analyzed the factors affecting the evacuation rates of hematoma.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003eWe recruited sICH patients from our prospectively maintained ICH patient database (Risa-MIS-ICH, ClinicalTrials.gov Identifier: NCT03862729) between July 2019 and July 2023. The criteria for inclusion are as follows: (1) Patients underwent stereotactic hematoma puncture aspiration and catheter drainage without other surgical treatment (such as endoscopic hematoma resection, craniotomy hematoma removal, or only external ventricular drainage, etc.); (2) Supratentorial cerebral parenchymal hemorrhage was diagnosed by ccomputed tomography (CT) before surgery, and complete CT image data were available for postoperative follow-up; (3) There are no metal artifacts on CT images, which can accurately calculate the volume of hematoma. The surgical operations are executed with precision, adhering strictly to standardized protocols. While the stereotactic equipment may vary among different medical centers due to disparities in resources, the fundamental approach remains consistent: minimally invasive hematoma puncture aspiration and catheter placement for drainage. All related treatments adhere to the guideline of the American Heart Association / American Stroke Association (AHA/ASA) for the management of spontaneous intracerebral hemorrhage [18,19]. This study was approved by the Ethics Committee of the First Affiliated Hospital of Fujian Medical University (Ethical Approval Number: MRCTA, ECFAH of FMU [2018] 082\u0026thinsp;\u0026minus;\u0026thinsp;1) and complied with relevant laws and regulations.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eInitial and follow-up CT image data for all patients were archived in the Digital Imaging and Communications in Medicine (DICOM) format. The hematoma was detected layer by layer on the axial section through 3Dslicer (version No. 5.2.2), and the threshold value of hematoma was set between 44 and 100 Hounsfield Unit (HU) [20]. The specific value could fluctuate depending on the actual situation; the upper limit should not exceed 100 HU, and the lower limit should not be lower than 35 HU. The region of interest (ROI) was delineated, and the volume and CT value of hematoma were calculated by automatic segmentation. In order to prevent retrograde infection, the drainage tube is usually left in place for 3 to 5 days. The primary endpoint of this study aimed to evaluate the HE rates prior to the removal of the drainage tube following stereotactic aspiration coupled with catheter thrombolysis. Additionally, the secondary endpoint encompassed the initial HE rates achieved within 24 hours following the surgical intervention. The calculation of HE efficacy was meticulously determined as follows: HE rates = [(Hematoma volume recorded within six hours before surgery) \u0026ndash; (Hematoma volume measured either within 24 hours or prior to extubation postoperatively)] / (Hematoma volume within the six hours prior to surgery).\u003c/p\u003e \u003cp\u003eGeneral information of patients was extracted from the database, such as gender, age, current or previous smoking and drinking, history of diabetes and hypertension, medication history (such as anticoagulant drugs, antiplatelet drugs), admission GCS score, duration of hospitalization, total hospitalization expenses, and routine laboratory assay, such as white blood cell count, hemoglobin and platelet counts, erythrocyte ratio, blood glucose, blood calcium, coagulation function, and D-dimer. In addition, surgery-related information was collected, such as the number of drainage tubes, simultaneous insertion of external ventricular drainage, use of urokinase, and duration of catheter placement. Functional status at discharge and functional prognosis during the 3 to 12 months follow-up period were evaluated using the modified Rankin Scale (mRS). The mRS score of 0 to 3 was defined as favorable functional outcomes.\u003c/p\u003e \u003cp\u003eBaseline non-contrast CT (NCCT) markers were examined by two independent investigators in all patients. The researchers did not know the patients' clinical history and follow-up CT results. The detected NCCT markers and their definitions are as follows: (1) Black hole sign: a relatively hypoattenuating area encapsulated with a hyperattenuating hematoma. The boundary is clear, and there is a density difference of at least 28 HU between the two regions. The low-density area can be round, oval, or strip but should not be connected to the adjacent brain tissue [21]; (2) Island sign: at the same level, there are \u0026ge;\u0026thinsp;3 scattered small hematoma separated from the main hematoma, or \u0026ge;\u0026thinsp;4 small hematoma, some or all of which are connected to the main hematoma [22]; (3) Satellite sign: there is a high-density small hematoma separated from the main hematoma on at least one level. The maximum diameter of the small hematoma is less than 10mm, the maximum distance between the small hematoma and the main hematoma is less than 20mm, and intraventricular involvement or subarachnoid extension of the main hematoma are excluded [23]; (4) Blend sign: a relatively high-density area next to a low-density area of the hematoma. The boundary between the two regions is well-defined and can be identified by the naked eye, and the CT value difference is more than 18HU. The hypoattenuating area is not surrounded by the hyperattenuating area [24]; (5) Fluid level: there is at least one distinct ICH region with a low CT attenuation region above (hypodense compared to brain) and a high CT attenuation region below (hyperdense compared to brain), separated by a clear horizontal line [25]. Furthermore, irregular hematoma morphology is defined by the Barras shape scale as two or more irregular shapes on the margins of the hematoma, which is assessed on the axial slice [26]. The definition of the hematoma edge linked to the ventricle is that the parenchymal hematoma breaks into the ventricle, and the hematoma edge is tangent to the ventricle. Other definitions include deep hematoma: hematoma greater than 1cm from the cortex [27]; Postoperative hematoma enlargement: manifesting as an augmentation of 6 ml or a 33% expansion relative to its preoperative dimensions [28]. Based on previous experience [29,30], we employed the following catheter position scoring system: a score of 3 was awarded for lateral catheter holes positioned precisely at the center of the hematoma, a score of 2 was given for those located within the cavity of the hematoma but outside the center, a score of 1 was designated for holes situated at the hematoma's edge, and a score of 0 was assigned to those positioned completely outside the hematoma, signifying an inadequate location. Discrepancies about the occurrence of CT markers were settled by joint discussion of the two readers.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eContinuous variables consistent with normal distribution were presented as mean and standard deviations, while those consistent with non-normal distribution were presented as median (interquartile range). Categorical data are presented as proportions. The demographic characteristics, clinical data, and CT features between patients with and without HE rates exceeding 70% were compared using the Pearson chi-square test, Fisher exact test, Student\u0026rsquo;s t-test, or the Mann\u0026ndash;Whitney U test, as appropriate. Univariable logistic regression preliminarily determined the potential factors affecting HE. Baseline variables that were considered relevant or candidate variables with a \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1 on univariable analysis were entered into a multivariate logistics regression model. The receiver operating characteristic (ROC) curve and the area under the curve (AUC) determine the predictive efficiency of the model. IBM SPSS Statistics 27 (Version 27.0. Armonk, NY: IBM Corp) were used for analysis, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Result","content":"\u003cp\u003eA total of 894 patients with ICH were screened, of which only 140 patients underwent stereotactic aspiration and catheter drainage for sICH, the following situations were excluded: 22 cases due to the absence of preoperative imaging data, 7 cases of patients with the hard channel puncture tube resulting in significant artifacts in the follow-up images, 1 case of surgery performed after 17 days of onset, 4 cases of emergency decompressive craniectomy necessitated by postoperative hematoma enlargement. Finally, 106 patients with sICH across 12 medical centers were included in this analysis. In the entire cohort, 101 patients had complete CT imaging data within 24 hours postoperatively (group A), while imaging data were obtainable for 82 patients prior to extubation (group B) (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eBaseline characteristics\u003c/h2\u003e\n \u003cp\u003eIn the entire cohort who received stereotactic aspiration and catheter drainage, the average age was 59.0\u0026thinsp;\u0026plusmn;\u0026thinsp;15.1 years old, including 82 men (77.4%) and 24 women (22.6%). The median interval from onset to stereotactic surgery was 1(1\u0026ndash;2) days. The preoperative median hematoma volume was 39.01(31.16\u0026ndash;53.95) ml (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. A), and the median CT value was 61.02(58.43\u0026ndash;64.17) HU. The preoperative CT showed that 68 patients (64.2%) had deep cerebral hemorrhage, including thalamus and basal ganglia, 38 patients (35.8%) had lobar hemorrhage and 52 patients (49.1%) had intraventricular hemorrhage, but did not lead to obstructive hydrocephalus. Two drainage tubes were placed in 6 patients, which were perpendicular to the long axis and short axis of the hematoma, respectively. Among the patients with 2 drainage tubes, 5 cases had hematoma evacuation rate\u0026thinsp;\u0026ge;\u0026thinsp;70%. Based on morphologic features of hematoma on preoperative CT, the number of patients exhibiting the black hole sign, island sign, satellite sign, blend sign and the fluid level were 40 (37.7%), 25 (23.6%), 26 (24.5%), 33 (31.1%), and 4 (3.8%), respectively. According to the Barras shape scale, hematoma morphology was divided into regular and irregular, irregular hematoma was found in 35 patients (33.0%). Based on the scoring system pertaining to the catheter\u0026rsquo;s placement, we observed 50(47.2%) patients scoring 3, 25(23.6%) scoring 2, and 29(27.4%) scoring 1. Notably, only 2(1.9%) patients exhibited unsatisfactory catheter positioning, necessitating readjustment. The median duration of indwelling drainage tube placement was 4(2\u0026ndash;5) days (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). As for the available data, within 24 hours postoperatively, 10 patients (9.9%) exhibited HE rates of 70% or higher. Prior to extubation, the number of patients with HE rates\u0026thinsp;\u0026ge;\u0026thinsp;70% reached 39 (47.6%).\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eClinical characteristics of patients undergoing stereotactic aspiration and catheter drainage\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eClinical characteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber (proportion, %)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePatients number\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge, year, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59.0\u0026thinsp;\u0026plusmn;\u0026thinsp;15.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82(77.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24(22.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent or ever smoker, n(%)a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23(21.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRegular drinkers, n(%)a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20(18.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedical history\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11(10.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypertension, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67(63.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedication history\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnticoagulation therapy, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(1.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAntiplatelet therapy, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5(4.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdmission GCS scores, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.0(9.0\u0026ndash;14.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003elaboratory assay\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeucocyte,10^9/L, median(IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.36(7.55\u0026ndash;13.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeutrophilic,10^9/L, median(IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.35(4.81\u0026ndash;11.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLymphocyte,10^9/L, median(IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.26(0.81\u0026ndash;1.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHemoglobin, g/L, median(IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e143.00(129.00-156.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThrombocyte,10^9/L, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e210.39\u0026thinsp;\u0026plusmn;\u0026thinsp;59.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAPTT, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.1(22.8\u0026ndash;27.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eINR, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0(0.9\u0026ndash;1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe time from onset to operation, day, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(1\u0026ndash;2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePre-op radiological features\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHematoma location\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDeep, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68(64.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLobe, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38(35.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePre-op hematoma volume, ml, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.01(31.16\u0026ndash;53.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePre-op mean hematoma CT value, HU, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.02(58.43\u0026ndash;64.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlack hole sign, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40(37.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIsland sign, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25(23.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSatellite sign, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26(24.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlend sign, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33(31.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eContinued\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eClinical characteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber (proportion, %)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFluid level, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(3.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHematoma shape\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eregular, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71(67.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIrregular, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35(33.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntraventricular hemorrhage, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52(49.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHematoma edges linked to ventricles, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28(26.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExternal ventricular drainage, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9(8.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCatheter position score, n(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(1.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29(27.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25(23.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50(47.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTwo drainage tubes, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6(5.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntubation time, day, median (IQR) a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(2\u0026ndash;5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003ea, missing data; APTT, activated partial thromboplastin time; GCS, Glasgow Coma Scale; IQR, interquartile range; INR, international normalized ratio; Pre-op, preoperative; SD, standard deviation.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eFactors associated with Day 1 post-op HE rates\u0026thinsp;\u0026ge;\u0026thinsp;70%\u003c/h2\u003e\n \u003cp\u003eA meticulous multivariate logistic regression analysis revealed that preoperative hematoma volume (OR, 0.913; 95% CI, 0.836\u0026ndash;0.997; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.042), the presence of blend sign (OR, 9.457; 95% CI, 0.999\u0026ndash;89.508; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.050), and the catheter position score (OR, 5.551; 95% CI, 1.231\u0026ndash;25.019; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026) emerged as significant factors in influencing HE on the first postoperative day (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The cutoff values for hematoma volume and catheter position score were determined to be 45.46ml and 2.5 points, respectively. We have developed a logistic regression model, formulated as follows: Y\u0026thinsp;=\u0026thinsp;2.911\u0026ndash;1.634*X1\u0026thinsp;+\u0026thinsp;0.287*X2\u0026ndash;0.091*X3\u0026ndash;0.095*X4\u0026thinsp;+\u0026thinsp;2.247*X5\u0026thinsp;+\u0026thinsp;0.299*X6\u0026thinsp;+\u0026thinsp;1.714*X7. In this model, Y represents the predicted log-odds, and the probability, P, of the outcome is calculated using the formula P\u0026thinsp;=\u0026thinsp;1/[1\u0026thinsp;+\u0026thinsp;e^(-Y)]. The predictors included in this model encompassing X1, which represents lymphocyte count; X2, indicating the time elapsed from onset to surgery; X3, preoperative hematoma volume; X4, preoperative mean hematoma CT value; X5, the presence of blend sign; X6, intraventricular hemorrhage; and X7, catheter position score. This sophisticated model allows us to accurately estimate the predictive probability of the HE rates exceeding 70% within 24 hours postoperatively. To enhance clinical applicability, according to the regression coefficients, we categorized patients into two groups based on preoperative hematoma volume: those with a volume exceeding 45.46ml were assigned a score of 0, while those with a volume of 45.46ml or less received a score of 3. For catheter position, the corresponding score is identical to the score for catheter location. Regarding the blend sign, a negative manifestation was assigned a score of 0, while a positive manifestation received a score of 1. Utilizing these criteria, we developed a predictive model with a score from 0 to 7 aimed at estimating HE rates\u0026thinsp;\u0026ge;\u0026thinsp;70% within 24 hours postoperatively (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Notably, the ROC of our model exhibited exceptional predictive capacity, with an AUC of 0.871 and a cutoff point 5.5. This predictive prowess surpasses that of relying solely on the catheter position score (AUC\u0026thinsp;=\u0026thinsp;0.729) or preoperative hematoma volume (AUC\u0026thinsp;=\u0026thinsp;0.724) as predictors (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. C).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eUnivariate logistic and multiple logistic regression of correlation between pre-op CT or clinical features and Day 1 post-op HE rates\u0026thinsp;\u0026ge;\u0026thinsp;70%\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eUnivariate logistic regression\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eMultiple logistic regression\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.608\u0026ndash;9.211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.964\u0026ndash;1.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLymphocyte\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.093\u0026ndash;1.190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.035\u0026ndash;1.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe time from onset to operation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.975\u0026ndash;2.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.767\u0026ndash;2.313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.309\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdmission GCS scores\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.939\u0026ndash;1.576\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePre-op hematoma volume\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.940\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.887\u0026ndash;0.997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.836\u0026ndash;0.997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePre-op mean hematoma CT value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.774\u0026ndash;0.993\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.772\u0026ndash;1.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.254\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlack hole sign\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.382\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.077\u0026ndash;1.902\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlend sign\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.634\u0026ndash;8.861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.457\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.999\u0026ndash;89.508\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIrregular shape\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.461\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.092\u0026ndash;2.302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntraventricular hemorrhage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.256\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.051\u0026ndash;1.270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.151\u0026ndash;12.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.789\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHematoma edge linked to ventricle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.739\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.146\u0026ndash;3.735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.715\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCatheter position score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.090-14.753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.551\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.231\u0026ndash;25.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTwo drainage tubes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.306\u0026ndash;34.686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eCI, confidence interval; CT, computed tomography; GCS, Glasgow Coma Scale; HE, hematoma evacuation; OR, odds ratio; pre-op, preoperative; post-op, postoperative.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eFactors associated with HE rates\u0026thinsp;\u0026ge;\u0026thinsp;70% prior to extubation\u003c/h2\u003e\n \u003cp\u003eOur multivariate logistic regression analysis has demonstrated that positive blend sign (OR, 4.120; 95% CI, 1.344\u0026ndash;12.630; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013), absence of irregular hematoma morphology (OR, 0.291; 95% CI, 0.095\u0026ndash;0.893; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.031), and the absence of the hematoma edge linked to the ventricle (OR, 0.185; 95% CI, 0.036\u0026ndash;0.950; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.043) are independent predictors for achieving HE rates of at least 70% (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. B, Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). We have crafted a logistic regression model, defined as Y\u0026thinsp;=\u0026thinsp;0.197\u0026thinsp;+\u0026thinsp;1.416*X1\u0026ndash;1.234*X2\u0026ndash;0.133*X3\u0026ndash;1.685*X4, with the predictive probability P calculated as 1/[1\u0026thinsp;+\u0026thinsp;e^(-Y)]. In this framework, X1 captures the presence of the blend sign, X2 characterizes irregular shape, X3 reflects intraventricular hemorrhage, and X4 denotes the linkage of the hematoma edge to the ventricle. Similarly, to enhance clinical applicability, according to the regression coefficients, we assigned preoperative CT blend sign a score of 0 for absence and 1 for presence. Hematoma morphology received a score of 0 for irregularity and 1 for regularity. Additionally, we assigned a score of 0 when the hematoma edge was linked to the ventricle, and a score of 1 when they were not linked. Using these criteria, we developed a predictive model with a score from 0 to 3 aimed at estimating HE rates\u0026thinsp;\u0026ge;\u0026thinsp;70% prior to extubation (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Our ROC curve analysis revealed solid predictive efficacy with an AUC of 0.753 and a cutoff point 1.5 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. D).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"char\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eUnivariate logistic and multiple logistic regression of correlation between pre-op CT or clinical features and prior to extubation HE rates\u0026thinsp;\u0026ge;\u0026thinsp;70%\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eUnivariate logistic regression\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eMultiple logistic regression\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.556\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.169\u0026ndash;1.830\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.965\u0026ndash;1.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.054\u0026ndash;1.429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.477\u0026ndash;2.859\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLymphocyte\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.730\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.436\u0026ndash;1.223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe time from onset to operation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.880\u0026ndash;2.377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdmission GCS scores\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.962\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.830\u0026ndash;1.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePre-op hematoma volume\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.982\u0026ndash;1.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.624\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePre-op mean hematoma CT value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.970\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.902\u0026ndash;1.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.407\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlack hole sign\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.393\u0026ndash;2.325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlend sign\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.217\u0026ndash;8.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.344\u0026ndash;12.630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIrregular shape\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.122\u0026ndash;0.871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.095\u0026ndash;0.893\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntraventricular hemorrhage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.129\u0026ndash;0.796\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.258\u0026ndash;2.969\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.831\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHematoma edge linked to ventricle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.058\u0026ndash;0.644\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.036\u0026ndash;0.950\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCatheter position score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.680\u0026ndash;1.854\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.652\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTwo drainage tubes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.688\u0026ndash;55.416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eCI, confidence interval; CT, computed tomography; GCS, Glasgow Coma Scale; pre-op; HE, hematoma evacuation; OR, odds ratio; preoperative; post-op, postoperative.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe predictive model of HE rates\u0026thinsp;\u0026ge;\u0026thinsp;70%\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eDay 1 post-op HE rates\u0026thinsp;\u0026ge;\u0026thinsp;70%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003ePrior to extubation HE rates\u0026thinsp;\u0026ge;\u0026thinsp;70%\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePre-op hematoma\u003c/p\u003e\n \u003cp\u003evolume\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFeature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScore\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;45.46ml\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIrregular shape P/N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0/1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;45.46ml\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlend sign P/N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1/0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlend sign P/N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1/0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHematoma edge linked to ventricle P/N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0/1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCatheter position score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eHE, hematoma evacuation; N, negative; pre-op, preoperative; post-op, postoperative; P, positive.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe analyzed the clinical and imaging data of patients with supratentorial sICH who underwent stereotactic aspiration and catheter drainage, aiming to identify factors conducive to HE and improve the effect of minimally invasive surgery.\u003c/p\u003e \u003cp\u003eThe presence of a positive NCCT blend sign prior to surgery significantly enhances HE on both the first postoperative day and before tube removal. Conversely, a favorable catheter position score only promotes HE on the initial postoperative day, exhibiting no significant correlation with HE at the later time point. Furthermore, our analysis demonstrates that a larger hematoma volume is deleterious to HE on the first postoperative day, with an optimal cutoff value identified as 45.46ml. Notably, the presence of irregular hematoma morphology and a hematoma edge contiguous with the ventricle are adverse factors for HE prior to extubation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePrevious scholarly investigations have demonstrated that the blend sign can serve as an indicator of active bleeding and reflect various stages of hematoma development [24]. Notably, the attenuation values observed on CT scans are intimately linked to the hemoglobin content within the hematoma [31]. In the initial phase of hemorrhage, red blood cells remain intact, resulting in a relatively low concentration of hemoglobin within the hematoma. Consequently, this manifests as low attenuation on CT images. Subsequently, as the red blood cells undergo lysis, hemoglobin is released, and the clot undergoes shrinkage, leading to the separation of serum [32]. This process significantly elevates the hemoglobin concentration, ultimately resulting in a high attenuation signal on CT. Consequently, a positive blend sign suggests the presence of a mobile liquid hematoma, which is more amenable to drainage, thereby facilitating HE.\u003c/p\u003e \u003cp\u003eAnalysis of preoperative hematoma volume indicates that a larger hematoma is disadvantageous for HE within 24 hours postoperatively, with a threshold value of 45.46ml. The traditional surgical indication for cerebral hemorrhage is a hematoma volume of \u0026ge;\u0026thinsp;30ml, and the standard surgical approach involves craniotomy for hematoma evacuation, optionally with decompressive craniectomy [18,19]. The Surgical Treatment for Intracerebral Hemorrhage (STICH) study suggested that craniotomy does not significantly improve the prognosis of patients [27,33]. Consequently, minimally invasive surgical techniques have emerged as viable alternatives. Nevertheless, given their inherent limitations, in cases involving substantial hematoma volumes, direct visualization of hematoma removal, such as through endoscopic or microscopic approaches, may still be necessary to enhance HE rates.\u003c/p\u003e \u003cp\u003eIn addition, for sICH patients, more accurate catheterization may be needed. MISTIE II taught us that efficacy of the surgical task is directly related to satisfactory catheter placement [34]. Our profound research has also revealed that the precise placement of the catheter can effectively facilitate the early elimination of intracranial hematoma. Catheters that are accurately positioned within the hematoma enables surgeons to effectively aspirate the blood and clot, reducing the size of the hematoma and relieving pressure on surrounding tissues. Furthermore, as an NCCT marker, the irregular shape of hematoma is associated with hematoma expansion [24]. This may reflect the multi-source bleeding in the hematoma, along the multi-path development. The irregular shape of the hematoma can hinder the uniform distribution of urokinase and efficient drainage, ultimately compromising HE. Remarkably, our meticulous investigation has uncovered that the existence of a connection between the hematoma edge and the ventricle unexpectedly impedes HE, thus challenging the established clinical understanding. We offer a plausible hypothesis to explain this observation: the infiltration of cerebrospinal fluid into the hematoma cavity potentially dilutes the concentration of urokinase, subsequently limiting the dissolution of blood clots and ultimately compromising the efficiency of HE. In clinical practice, urokinase is often used to dissolve blood clots and transform solid hematoma into liquid hematoma. Studies have shown that urokinase can be used as a safe and effective alternative to alteplase [35], but the optimal dose of urokinase remains uncertain. In this study, all patients received urokinase in combination, but the effect of stereotactic aspiration combined with urokinase thrombolysis on long-term functional prognosis and the optimal dose of urokinase needed further study.\u003c/p\u003e \u003cp\u003eDrawing upon the aforementioned conclusions, we have developed an innovative predictive model. Within the framework of the early HE rates prediction model, patients scoring of 6 or 7 demonstrate a significantly elevated likelihood of achieving HE rates of 70% or higher. Similarly, within the context of the prediction model for HE rates prior to catheter removal, individuals scoring 2 or 3 exhibit superior HE outcomes.\u003c/p\u003e \u003cp\u003eOur findings are similar to the results of Kim JH et al. that a positive blend sign can promote the removal of hematoma in minimally invasive hematoma aspiration and catheter drainage [36]. This study was a single-center retrospective study, and our results further support this conclusion. In previous studies [36,37], the volume of the hematoma was calculated using the traditional formula [ABC/2], where A is the maximum diameter of the hematoma on the axial CT image, B is the diameter at 90\u0026deg; from A, and C is the number of CT sections in the hematoma multiplied by the section thickness. Although this method is simple and feasible, its accuracy is inadequate. Moreover, the CT value is measured only at the largest level of hematoma, which cannot reflect the average CT value of the whole hematoma. We have made modifications to our measurement methods. The accuracy of 3Dslicer in calculating the volume of hematoma is better than that of [ABC/2] [38], and 3Dslicer can automatically calculate the average CT value of the entire hematoma, indirectly reflecting the density of the entire hematoma. Wang T et al. believed that the CT value reflected the viscosity of the hematoma, the lower CT value was conducive to the removal of hematoma\u003csup\u003e35\u003c/sup\u003e. However, our results are the same as those of Kim JH et al. that hematoma CT values do not affect the HE. The reason may be different from the parameters set by CT instruments in different centers, resulting in bias in the results. In the future, unified CT parameters should be used to further determine the role of image features in predicting the removal of hematoma.\u003c/p\u003e \u003cp\u003eThere are still some potential limitations in our study. Although it comes from a prospective, multicenter cerebral hemorrhage registry database, the sample size is small, which may affect the conclusion of the study and its universality. In addition, the predictive model employed in this study has yet to undergo external validation, necessitating future endeavors to thoroughly assess its performance on independent datasets.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe predictive model for HE rates possesses the advantages of simplicity and convenience, providing clinicians with a reference for enhancing the efficiency of minimally invasive surgical procedures. For patients with sICH undergoing stereotactic aspiration and catheter drainage, we may potentially deduce the HE efficiency of this surgical procedure from relevant preoperative CT imaging data.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003earea under the curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecomputed tomography\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDICOM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDigital Imaging and Communications in Medicine\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGCS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlasgow coma scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehematoma evacuation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHU\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHounsfield Unit\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003emRS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emodified Rankin Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNCCT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003enon-contrast computed tomography\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eodds ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ereceiver operating characteristic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eregion of interest\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003esICH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003espontaneous intracerebral hemorrhage.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: This work was funded by the Fujian Science and Technology Innovation Joint Fund Project (2019Y9118), the Fujian Provincial Health Commission (2022ZD01003), and the Stroke Prevention and Treatment Project of the National Health Commission\u0026mdash; Research and Popularization of Appropriate Intervention Technology for the Stroke High Risk Group in China (GN2018R002).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u0026nbsp;\u003c/strong\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. In the article, we confirmed that we used the STROBE checklist.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e All persons who meet authorship criteria are listed as authors. WHF and DZK proposed research concepts and designs, provided support, and supervised the study. DZK obtained the funding. XQL and KMS prepared the draft manuscript and coordinated its finalization. XQL and LYZ performed statistical analyses; XQL and FXL drew and prepared the tables and figures. ZYG, QH, YZ, XQL, KBL and HCSG gathered pertinent data, performed database management and data cleaning. YXL and FXL revised the manuscript. All authors have seen and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval:\u003c/strong\u003e Institutional review board approval obtained as noted in the article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration:\u0026nbsp;\u003c/strong\u003eWe declare that none of the material related to this manuscript has been published or is under consideration for publication elsewhere and it complies the instructions for all authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e Qureshi AI, Mendelow AD, Hanley DF. Intracerebral haemorrhage. \u003cem\u003eLancet\u003c/em\u003e. 2009;373(9675):1632\u0026ndash;1644. https://doi.org/10.1016/S0140-6736(09)60371-8\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Al-Shahi Salman R, Labovitz DL, Stapf C. Spontaneous intracerebral haemorrhage. \u003cem\u003eBMJ\u003c/em\u003e. 2009;339:b2586. Published 2009 Jul 24. doi:10.1136/bmj.b2586 https://doi.org/10.1136/bmj.b2586\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Kim JY, Bae HJ. Spontaneous Intracerebral Hemorrhage: Management. \u003cem\u003eJ Stroke\u003c/em\u003e. 2017;19(1):28\u0026ndash;39. doi:10.5853/jos.2016.01935 https://doi.org/10.5853/jos.2016.01935\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Tsai CF, Thomas B, Sudlow CL. Epidemiology of stroke and its subtypes in Chinese vs white populations: a systematic review. \u003cem\u003eNeurology\u003c/em\u003e. 2013;81(3):264\u0026ndash;272. https://doi.org/10.1212/WNL.0b013e31829bfde3\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Diringer MN. Intracerebral hemorrhage: pathophysiology and management. \u003cem\u003eCrit Care Med\u003c/em\u003e. 1993;21(10):1591\u0026ndash;1603. https://doi.org/10.1097/00003246-199310000-00032\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e van Etten ES, Kaushik K, Jolink WMT, et al. Trigger Factors for Spontaneous Intracerebral Hemorrhage: A Case-Crossover Study. \u003cem\u003eStroke\u003c/em\u003e. 2022;53(5):1692\u0026ndash;1699. https://doi.org/10.1161/STROKEAHA.121.036233\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Rzepliński R, Sługocki M, Tarka S, et al. Mechanism of Spontaneous Intracerebral Hemorrhage Formation: An Anatomical Specimens-Based Study. \u003cem\u003eStroke\u003c/em\u003e. 2022;53(11):3474\u0026ndash;3480. https://doi.org/10.1161/STROKEAHA.122.040143\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Wan J, Ren H, Wang J. Iron toxicity, lipid peroxidation and ferroptosis after intracerebral haemorrhage. \u003cem\u003eStroke Vasc Neurol\u003c/em\u003e. 2019;4(2):93\u0026ndash;95. https://doi.org/10.1136/svn-2018-000205\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e de Oliveira Manoel AL. Surgery for spontaneous intracerebral hemorrhage. \u003cem\u003eCrit Care\u003c/em\u003e. 2020;24(1):45. https://doi.org/10.1186/s13054-020-2749-2\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Hanley DF, Thompson RE, Rosenblum M, et al. Efficacy and safety of minimally invasive surgery with thrombolysis in intracerebral haemorrhage evacuation (MISTIE III): a randomised, controlled, open-label, blinded endpoint phase 3 trial. \u003cem\u003eLancet\u003c/em\u003e. 2019;393(10175):1021\u0026ndash;1032. https://doi.org/10.1016/S0140-6736(19)30195-3\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Pradilla G, Ratcliff JJ, Hall AJ, et al. Trial of Early Minimally Invasive Removal of Intracerebral Hemorrhage. \u003cem\u003eN Engl J Med\u003c/em\u003e. 2024;390(14):1277\u0026ndash;1289. https://doi.org/10.1056/NEJMoa2308440\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Awad IA, Polster SP, Carri\u0026oacute;n-Penagos J, et al. Surgical Performance Determines Functional Outcome Benefit in the Minimally Invasive Surgery Plus Recombinant Tissue Plasminogen Activator for Intracerebral Hemorrhage Evacuation (MISTIE) Procedure. \u003cem\u003eNeurosurgery\u003c/em\u003e. 2019;84(6):1157\u0026ndash;1168. https://doi.org/10.1093/neuros/nyz077\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Guo W, Liu H, Tan Z, et al. Comparison of endoscopic evacuation, stereotactic aspiration, and craniotomy for treatment of basal ganglia hemorrhage. \u003cem\u003eJ Neurointerv Surg\u003c/em\u003e. 2020;12(1):55\u0026ndash;61. https://doi.org/10.1136/neurintsurg-2019-014962\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Wang JW, Li JP, Song YL, et al. Stereotactic aspiration versus craniotomy for primary intracerebral hemorrhage: a meta-analysis of randomized controlled trials. \u003cem\u003ePLoS One\u003c/em\u003e. 2014;9(9):e107614. https://doi.org/10.1371/journal.pone.0107614\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Kumar S, Madhariya SN, Singh D, et al. Comparison of Craniotomy and Stereotactic Aspiration Plus Thrombolysis in Isolated Capsulo-Ganglionic Hematoma: A Retrospective Analyses. \u003cem\u003eNeurol India\u003c/em\u003e. 2022;70(2):535\u0026ndash;542. https://doi.org/10.4103/0028-3886.344635\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Lin F, He Q, Tong Y, et al. Early Deterioration and Long-Term Prognosis of Patients With Intracerebral Hemorrhage Along With Hematoma Volume More Than 20 ml: Who Needs Surgery?. \u003cem\u003eFront Neurol\u003c/em\u003e. 2022;12:789060. https://doi.org/10.3389/fneur.2021.789060\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Feng H, Wang X, Wang W, Zhao X. Risk factors and a prediction model for the prognosis of intracerebral hemorrhage using cerebral microhemorrhage and clinical factors. \u003cem\u003eFront Neurol\u003c/em\u003e. 2023;14:1268627. https://doi.org/10.3389/fneur.2023.1268627\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Hemphill JC 3rd, Greenberg SM, Anderson CS, et al. Guidelines for the Management of Spontaneous Intracerebral Hemorrhage: A Guideline for Healthcare Professionals From the American Heart Association/American Stroke Association. \u003cem\u003eStroke\u003c/em\u003e. 2015;46(7):2032\u0026ndash;2060. https://doi.org/10.1161/STR.0000000000000069\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Greenberg SM, Ziai WC, Cordonnier C, et al. 2022 Guideline for the Management of Patients With Spontaneous Intracerebral Hemorrhage: A Guideline From the American Heart Association/American Stroke Association. \u003cem\u003eStroke\u003c/em\u003e. 2022;53(7):e282-e361. https://doi.org/10.1161/STR.0000000000000407\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Urday S, Kimberly WT, Beslow LA, et al. Targeting secondary injury in intracerebral haemorrhage\u0026ndash;perihaematomal oedema. \u003cem\u003eNat Rev Neurol\u003c/em\u003e. 2015;11(2):111\u0026ndash;122. https://doi.org/10.1038/nrneurol.2014.264\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Li Q, Zhang G, Xiong X, et al. Black Hole Sign: Novel Imaging Marker That Predicts Hematoma Growth in Patients With Intracerebral Hemorrhage. \u003cem\u003eStroke\u003c/em\u003e. 2016;47(7):1777\u0026ndash;1781. https://doi.org/10.1161/STROKEAHA.116.013186\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Li Q, Liu QJ, Yang WS, et al. Island Sign: An Imaging Predictor for Early Hematoma Expansion and Poor Outcome in Patients With Intracerebral Hemorrhage. \u003cem\u003eStroke\u003c/em\u003e. 2017;48(11):3019\u0026ndash;3025. https://doi.org/10.1161/STROKEAHA.117.017985\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Yu Z, Zheng J, Ali H, et al. Significance of satellite sign and spot sign in predicting hematoma expansion in spontaneous intracerebral hemorrhage. \u003cem\u003eClin Neurol Neurosurg\u003c/em\u003e. 2017;162:67\u0026ndash;71. https://doi.org/10.1016/j.clineuro.2017.09.008\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Li Q, Zhang G, Huang YJ, et al. Blend Sign on Computed Tomography: Novel and Reliable Predictor for Early Hematoma Growth in Patients With Intracerebral Hemorrhage. \u003cem\u003eStroke\u003c/em\u003e. 2015;46(8):2119\u0026ndash;2123. https://doi.org/10.1161/STROKEAHA.115.009185\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Sato S, Delcourt C, Zhang S, et al. Determinants and Prognostic Significance of Hematoma Sedimentation Levels in Acute Intracerebral Hemorrhage. \u003cem\u003eCerebrovasc Dis\u003c/em\u003e. 2016;41(1\u0026ndash;2):80\u0026ndash;86. https://doi.org/10.1159/000442532\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Barras CD, Tress BM, Christensen S, et al. Density and shape as CT predictors of intracerebral hemorrhage growth. \u003cem\u003eStroke\u003c/em\u003e. 2009;40(4):1325\u0026ndash;1331. https://doi.org/10.1161/STROKEAHA.108.536888\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Mendelow AD, Gregson BA, Fernandes HM, et al. Early surgery versus initial conservative treatment in patients with spontaneous supratentorial intracerebral haematomas in the International Surgical Trial in Intracerebral Haemorrhage (STICH): a randomised trial. \u003cem\u003eLancet\u003c/em\u003e. 2005;365(9457):387\u0026ndash;397. https://doi.org/10.1016/S0140-6736(05)17826-X\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Demchuk AM, Dowlatshahi D, Rodriguez-Luna D, et al. Prediction of haematoma growth and outcome in patients with intracerebral haemorrhage using the CT-angiography spot sign (PREDICT): a prospective observational study. \u003cem\u003eLancet Neurol\u003c/em\u003e. 2012;11(4):307\u0026ndash;314. https://doi.org/10.1016/S1474-4422(12)70038-8\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Malinova V, Schlegel A, Rohde V, Mielke D. Catheter placement for lysis of spontaneous intracerebral hematomas: does a catheter position in the core of the hematoma allow more effective and faster hematoma lysis?. \u003cem\u003eNeurosurg Rev\u003c/em\u003e. 2017;40(3):397\u0026ndash;402. https://doi.org/10.1007/s10143-016-0792-x\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Polster SP, Carri\u0026oacute;n-Penagos J, Lyne SB, et al. Thrombolysis for Evacuation of Intracerebral and Intraventricular Hemorrhage: A Guide to Surgical Protocols With Practical Lessons Learned From the MISTIE and CLEAR Trials. \u003cem\u003eOper Neurosurg (Hagerstown)\u003c/em\u003e. 2020;20(1):98\u0026ndash;108. https://doi.org/10.1093/ons/opaa306\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Nowinski WL, Gomolka RS, Qian G, et al. Characterization of intraventricular and intracerebral hematomas in non-contrast CT. \u003cem\u003eNeuroradiol J\u003c/em\u003e. 2014;27(3):299\u0026ndash;315. https://doi.org/10.15274/NRJ-2014-10042\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e New PF, Aronow S. Attenuation measurements of whole blood and blood fractions in computed tomography. \u003cem\u003eRadiology\u003c/em\u003e. 1976;121(3 Pt. 1):635\u0026ndash;640. https://doi.org/10.1148/121.3.635\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Mendelow AD, Gregson BA, Rowan EN, et al. Early surgery versus initial conservative treatment in patients with spontaneous supratentorial lobar intracerebral haematomas (STICH II): a randomised trial. \u003cem\u003eLancet\u003c/em\u003e. 2013;382(9890):397\u0026ndash;408. https://doi.org/10.1016/S0140-6736(13)60986-1\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Hanley DF, Thompson RE, Muschelli J, et al. Safety and efficacy of minimally invasive surgery plus alteplase in intracerebral haemorrhage evacuation (MISTIE): a randomised, controlled, open-label, phase 2 trial. \u003cem\u003eLancet Neurol\u003c/em\u003e. 2016;15(12):1228\u0026ndash;1237. https://doi.org/10.1016/S1474-4422(16)30234-4\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Zhang X, Zhou S, Zhang Q, et al. Stereotactic aspiration for hypertensive intracerebral haemorrhage in a Chinese population: a retrospective cohort study. \u003cem\u003eStroke Vasc Neurol\u003c/em\u003e. 2019;4(1):14\u0026ndash;21. https://doi.org/10.1136/svn-2018-000200\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Kim JH, Lee HS, Ahn JH, et al. Clinical and radiographic factors involved in achieving a hematoma evacuation rate of more than 70% through minimally invasive catheter drainage for spontaneous intracerebral hemorrhage. \u003cem\u003eJ Clin Neurosci\u003c/em\u003e. 2021;92:103\u0026ndash;109. https://doi.org/10.1016/j.jocn.2021.07.038\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Wang T, Guan Y, Du J, et al. Factors affecting the evacuation rate of intracerebral hemorrhage in basal ganglia treated by minimally invasive craniopuncture. \u003cem\u003eClin Neurol Neurosurg\u003c/em\u003e. 2015;134:104\u0026ndash;109. https://doi.org/10.1016/j.clineuro.2015.04.020\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Xu HZ, Guo J, Wang C, et al. A Novel Stereotactic Aspiration Technique for Intracerebral Hemorrhage. \u003cem\u003eWorld Neurosurg\u003c/em\u003e. 2023;170:e28-e36. https://doi.org/10.1016/j.wneu.2022.10.051\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"hematoma evacuation rates, predictors, spontaneous intracerebral hemorrhage, stereotactic aspiration","lastPublishedDoi":"10.21203/rs.3.rs-4576662/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4576662/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe aim of this study was to delve into the relevant factors affecting hematoma evacuation (HE) rates during the treatment of sICH with stereotactic aspiration combined with catheter drainage. We pooled individual data from our prospective ICH databas, encompassing patients who underwent stereotactic aspiration and catheter drainage between July 2019 and July 2023. The primary outcome was HE rates prior to extubation, the secondary outcome was HE rates within 24 hours postoperatively. Logistic regression was employed to assess relevant clinical and radiological characteristics to establish a predictive model for achieving HE rates ≥ 70%. The model was validated by the ROC curve. Of the 894 patients with sICH enrolled in our database, 106 were eligible for this analysis. Factors affecting HE on the initial postoperative day were determined as preoperative hematoma volume (OR, 0.913; 95% CI, 0.836-0.997; \u003cem\u003eP\u003c/em\u003e=0.042), blend sign (OR, 9.457; 95% CI, 0.999-89.508; \u003cem\u003eP\u003c/em\u003e=0.050), and the catheter position score (OR, 5.551; 95% CI, 1.231-25.019; \u003cem\u003eP\u003c/em\u003e=0.026). The positive blend sign (OR, 4.120; 95% CI, 1.344-12.630; \u003cem\u003eP\u003c/em\u003e=0.013), absence of irregular hematoma morphology (OR, 0.291; 95% CI, 0.095-0.893; \u003cem\u003eP\u003c/em\u003e=0.031), and hematoma edge not linked to the ventricle (OR, 0.185; 95% CI, 0.036-0.950; \u003cem\u003eP\u003c/em\u003e=0.043) emerged as independent predictors for achieving HE rates ≥ 70% prior to extubation. Then, we developed two predictive models: one for early HE rates≥ 70%with a score from 0 to 7, and another for prior to extubation, scoring from 0 to 3. The ROC curve revealed AUC values of 0.871 and 0.753 for each model, respectively, and cutoff values of 5.5 and 1.5, accordingly. The predictive model of HE rates ≥ 70% within 24 hours postoperatively and prior to extuation has demonstrated remarkable predictive capability, holds the potential to assist clinicians in optimizing surgical efficiency.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial registration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinicalTrials.gov Identifier NCT03862729.\u003c/p\u003e","manuscriptTitle":"Analysis of associated factors affecting hematoma evacuation rates in spontaneous intracerebral hemorrhage with stereotactic aspiration combined with catheter drainage","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-03 16:17:40","doi":"10.21203/rs.3.rs-4576662/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e806ec76-8c88-49c0-afa2-3989f2a72ec0","owner":[],"postedDate":"July 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-27T14:38:45+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-03 16:17:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4576662","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4576662","identity":"rs-4576662","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}