Prediction of early postoperative functional outcomes in patients with aneurysmal subarachnoid hemorrhage: a Chinese bicenter retrospective study

Prediction of early postoperative functional outcomes in patients with aneurysmal subarachnoid hemorrhage: a Chinese bicenter retrospective study | 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 Prediction of early postoperative functional outcomes in patients with aneurysmal subarachnoid hemorrhage: a Chinese bicenter retrospective study Liang Chu, Ming Qi, Yingying Ding, Kuan Jiang, Yunpeng Lu, Kan Cao, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4465305/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 Objectives This study aims to analyze the early functional outcomes of patients with aneurysmal subarachnoid hemorrhage (aSAH) undergoing endovascular coiling or surgical clipping, and to construct predictive models based on both treatment modalities. Materials and Methods Patients diagnosed with aSAH were recruited from two Chinese hospitals between 1st January ,2015 and 31st December,2022. These patients were categorized into two groups: the endovascular coiling group and the surgical clipping group. Independent risk factors were determined using Least Absolute Shrinkage and Selection Operator regression and multivariate logistic regression. The relative weights of these significant factors were computed, and nomograms were developed accordingly. Subsequent validation analyses were conducted to assess the performance of the nomograms. Results Multifactorial analyses revealed that Hunt-Hess grade, GCS score, mFS, D-dimer, age, and body temperature can predict early functional outcomes of endovascular coiling (all P values < 0.05), while Hunt-Hess grade, GCS score, mFS, and D-dimer can predict early functional outcomes of surgical clipping (all P values < 0.05). Further computation of weights showed that the contributions of Hunt-Hess grade, mFS, GCS score and D-dimer were 32.78%, 31.99%, 4.63% and 13.73%, respectively, for endovascular coiling, and 33.55%, 38.02%, 8.44% and 19.99% for surgical clipping. Nomograms were constructed for the endovascular coiling and surgical clipping groups, and their discriminative ability and clinical utility were validated using ROC curves, calibration curves, and DCA curves, demonstrating good performance. Conclusion This study developed predictive nomogram models for early functional outcomes of patients with aSAH undergoing endovascular coiling or surgical clipping. It underscores the significance of scoring systems and clinical parameters (such as D-dimer), showing strong clinical utility. aneurysmal subarachnoid hemorrhage early functional outcomes endovascular coiling surgical clipping nomogram Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Aneurysmal subarachnoid hemorrhage (aSAH) is a severely disabling and fatal disease worldwide. The global incidence of aSAH is about 6.1 cases per 100,000 person-years[1], with China having an estimated incidence of about 2.0 cases per 100,000 person-years[2]. Recent studies have shown a positive correlation between the incidence of aSAH and advancing age. Consequently, with the aging of society, an increasing number of individuals are at risk of sudden aSAH[3]. Despite significant advancements in managing patients with subarachnoid hemorrhage, the mortality rate remains high. The case-fatality rate for aSAH ranges from 32% to 67%, with approximately one-third of survivors experiencing long-term disability or cognitive impairment[4]. Due to the rapid progression of the disease, timely diagnosis and early treatment are of paramount importance. For individuals with ruptured aneurysms in either the anterior or posterior circulation, endovascular coiling offers a more favorable prognosis compared to surgical clipping [5, 6]. However, surgical clipping still holds unique advantages in patients with large intracerebral hematomas or middle cerebral artery aneurysms[7]. A follow-up study of the International Subarachnoid Aneurysm Trial (ISAT)[8] found that compared to the surgical clipping group, the endovascular coiling group experienced a 7% reduction in mortality risk at 1 year. Nonetheless, the long-term risk of rebleeding increased. In clinical practice, evaluating the postoperative status following surgical clipping and endovascular coiling primarily relies on the Hunt-Hess grading scale and the Glasgow Coma Scale. However, these evaluations do not fully incorporate the personalized clinical parameters of patients, which could also impact the outcome[9]. This study aims to develop new nomogram models based on commonly used clinical scoring systems and some clinical parameters to evaluate the early functional outcomes of these two different surgical approaches. Materials and Methods Study population During the period from 1st July 2023 to 30st September 2023, we visited the hospital's information system to collect relevant data.This study retrospectively and continuously collected patients with aSAH who were hospitalized and treated at the Affiliated Yixing Hospital of Jiangsu University and the Affiliated Zhenjiang First Hospital of Jiangsu University from 1st January ,2015 to 31st December,2022. Diagnosis, surgery, and perioperative management strictly adhered to the latest clinical practice guidelines of the Chinese Medical Association[ 10 ]. The study adhered to the Helsinki Declaration and obtained approval from the ethics committees of two hospitals, with approval numbers 2023043 and K-20230086-w. All participants or their authorized representatives were required to sign an informed consent form during hospitalization, ensuring their full understanding of the purpose, methods, risks, and benefits of the surgery and study, and indicating their voluntary participation. Inclusion criteria include: (1) age 18 years or older; (2) confirmed as aSAH through laboratory and imaging examinations; (3) patients must undergo either endovascular coiling or surgical clipping treatment. Exclusion criteria include: (1) previous hospitalization for aSAH; (2) subarachnoid hemorrhage caused by other reasons, including vascular malformation, cerebral atherosclerosis, trauma, etc.; (3) the presence of severe cardiovascular, liver, kidney or other vital organ diseases; (4) loss of clinical parameters such as platelet count and D-dimer. The study variables and outcomes The study variables include demographic information (age and sex), physical examination data (body temperature and mean arterial pressure), laboratory tests (D-dimer, and platelet count), imaging examinations (length, width, height, position, etc.), past medical history (hypertension and diabetes), clinical scoring tools (GCS, Hunt-Hess, mFS), treatment modalities (endovascular coiling and surgical clipping), and the use of stents. Patients were stratified into the endovascular coiling group and the surgical clipping group based on treatment methods. The primary endpoint of this study was the early functional outcomes of patients one month after surgery. Considering that the modified Rankin Scale (mRS) is recommended for assessing neurological recovery status, this study utilized the mRS score at one month post-surgery as the primary endpoint, with scores ranging from 0 to 3 indicating a favorable prognosis and scores from 4 to 6 indicating an unfavorable prognosis[ 11 ]. Statistical analysis Chi-square tests were used for categorical variables, while non-parametric tests were employed for continuous variables. Univariate and multivariate logistic regression analyses were conducted to determine independent predictive factors and calculate their relative weights on the outcome variable. Two nomogram models were constructed. Receiver operating characteristic curves (ROC) were plotted, and the area under the curve (AUC) was calculated to assess the discriminative ability of the nomogram. Calibration curves were generated to illustrate the consistency between predicted probabilities and observed outcomes across various predicted probabilities. Decision curve analysis (DCA) was conducted to evaluate the clinical utility of the nomogram. Finally, risk stratification was performed. All statistical analyses were conducted using SPSS version 26.0 and R version 4.3.1, with statistical significance defined at the conventional threshold of P < 0.05 (two-tailed). Results Characteristics of patients in the endovascular coiling and surgical clipping groups A total of 962 patients diagnosed with aSAH were included in the final analysis cohort ( Figure 1 ). Overall, the proportion of early adverse outcomes in the surgical clipping group (32.7%) was higher than that in the endovascular coiling group (15.0%) ( Figure 2 ). In patients undergoing endovascular coiling, those with poor functional outcomes tended to be older, have higher D-dimer levels, higher Hunt-Hess grades, GCS scores of 3-8, higher mFS grades, larger aneurysm volumes, were more likely to be located in the anterior cerebral artery and posterior circulation. Among patients undergoing surgical clipping, those with poor functional outcomes tended to be older, have higher Hunt-Hess grades, GCS scores of 3-8, higher mFS grades, higher D-dimer levels, and lower platelet counts ( Table 1 ; Table 2 ). Table1 : Baseline characteristics of patients undergoing endovascular coiling. Total (n=638) Good functional outcome (n=542) Poor functional outcome (n=96) P-value Age, years 60.0 (52.0-68.0) 60.0 (51.8-67.0) 64.0 (53.0-72.0) 0.004 Gender 0.703 Female 413 (64.7%) 353 (65.1%) 60 (62.5%) Male 225 (35.3%) 189 (34.9%) 36 (37.5%) Hunt-Hess grade <0.001 0-3 566 (88.7%) 514 (94.8%) 52 (54.2%) 4-5 72 (11.3%) 28 (5.2%) 44 (45.8%) Hypertension 0.809 NO 263(41.2%) 225 (41.5%) 38(39.6%) YES 375 (58.8%) 317 (58.5%) 58 (60.4%) Diabetes mellitus 0.293 NO 597 (93.6%) 510 (94.1%) 87 (90.6%) YES 41 (6.4%) 32 (5.9%) 9 (9.4%) Body temperature, ℃ 36.8 (36.5-37.0) 36.8 (36.5-37.0) 36.8 (36.3-37.0) 0.068 MAP, mmHg 106.67(96.67%-117.33) 106.67(96.67%-116.67) 108.67(98.67%-124.00) 0.105 GCS score <0.001 3-8 61 (9.6%) 19 (3,5%) 42 (43.8%) 9-12 50 (7.8%) 35 (6.5%) 15 (15.6%) 12-15 527 (82.6%) 488(90.0%) 39 (40.6%) mFS group <0.001 0-2 245 (38.4%) 234 (43.2%) 11 (11.5%) 3-4 393 (61.6%) 308 (56.8%) 85 (88.5%) Length, mm 4.0 (3.00-6.00) 4.0 (3.00-6.00) 4.58(3.00-7.00) 0.063 Width, mm 4.0 (3.00-5.21) 4.0 (3.00-5.00) 4.5 (3.00-6.00) 0.191 Height, mm 4.0 (3.00-5.00) 3.88 (2.80-5.00) 4.00 (3.00-6.00) 0.038 Volume, mm3 33.51 (14.22-66.76) 31.42 (14.14-65.45) 41.89 (18.43-89.54) 0.027 Aneurysm location 0.018 Anterior cerebral artery 190 (29.8%) 154 (28.4%) 36 (37.5%) Middle cerebral artery 53 (8.3%) 49 (9.0%) 4 (4.2%) Internal carotid artery 135 (21.2%) 121 (22.3%) 14 (14.6%) Posterior communicating artery 200 (31.3%) 173 (31.9%) 27 (28.1%) Posterior circulation 60 (9.4%) 45 (8.3%) 15 (15.6%) Presence of hematoma 0.375 No 628 (98.4%) 535 (98.7%) 93 (96.9%) Yes 10 (1.6%) 7 (1.3%) 3 (3.1%) Use of stents 0.724 No 325 (50.9%) 274 (50.6%) 51 (53.1%) Yes 313 (49.1%) 268 (49.4%) 45 (46.9%) D-dimer, mg/L 0.81 (0.40-1.85) 0.72 (0.33-1.54) 1.86 (0.94-4.00) <0.001 PLT, ×109/L 208.0 (169.25-261.00) 207.5 (170.25-260.3) 211.50 (158.25-267.00) 0.858 Abbreviations: MAP, mean arterial pressure; GCS, Glasgow Coma Scale; PLT, platelet. Table2 : Baseline characteristics of patients undergoing neurosurgical clipping. Total (n=324) Good functional outcome (n=218) Poor functional outcome (n=106) P-value Age, years 60.0 (51.75-68.00) 58.5 (51.00-67.00) 63.0 (53.25-69.00) 0.020 Gender 0.720 Female 208 (64.2%) 138 (63.3%) 70 (66.0%) Male 116 (35.8%) 80 (36.7%) 36 (34.0%) Hunt-Hess grade <0.001 0-3 200 (61.7%) 167 (76.6%) 33 (31.1.2%) 4-5 124 (38.3%) 51 (23.4%) 73 (68.9%) Hypertension 1.000 NO 91 (28.1%) 61(28.0%) 30(28.3%) YES 233 (71.9%) 157 (72.0%) 76 (71.7%) Diabetes mellitus 1.000 NO 308(95.1%) 207 (95.0%) 101 (95.3%) YES 16 (4.9%) 11 (5.0%) 5 (4.7%) Body temperature, ℃ 36.80 (36.5-37.0) 36.80 (36.5-37.0) 36.80 (36.5-37.1) 0.975 MAP, mmHg 111.00(98.67-122.00) 111.00 (98.75-123.00) 111.50(97.75-120.67) 0.741 GCS score <0.001 3-8 95 (29.3%) 34 (15.6%) 61 (57.5%) 9-12 23 (7.1%) 17 (7.8%) 6 (5.7%) 12-15 206 (63..6%) 167 (76.6%) 39 (36.8%) mFS group <0.001 0-2 136 (42.0%) 121 (55.5%) 15 (14.2%) 3-4 188 (58.0%) 97 (44.5%) 91 (85.8%) Length, mm 4.00 (3.00-6.00) 4.00 (3.00-5.20) 4.00 (3.00-6.00) 0.651 Width, mm 4.00 (3.00-5.00) 4.00 (3.00-5.00) 4.00 (3.00-5.00) 0.088 Height, mm 4.00 (3.00-5.00) 4.00 (3.00-5.00) 4.00 (3.00-5.00) 0.093 Aneurysm volume, mm3 33.51 (14.14-65.45) 31.42 (14.14-62.83) 33.51 (14.14-89.54) 0.186 Aneurysm location 0.231 Anterior cerebral artery 106 (32.7%) 75 (34.4%) 31 (29.2%) Middle cerebral artery 126 (38.9%) 81 (37.2%) 45 (42.5%) Internal carotid artery 40 (12.3%) 26 (11.9%) 14 (13.2%) Posterior communicating artery 44 (13.6%) 33 (15.1%) 11 (10.4%) Posterior circulation 8 (2.5%) 3 (1.4%) 5 (4.7%) Presence of hematoma 0.791 No 276 (85.2%) 187 (85.8%) 89 (84.0%) Yes 48 (14.8%) 31 (14.2%) 17 (16.0%) D-dimer, mg/L 1.64 (0.73-2.93) 1.33 (0.66-2.48) 2.33 (1.34-3.99) <0.001 PLT, ×109/L 260.00(160.75-349.25) 286.00 (192.75-362.50) 194.50 (119.25-291.75) <0.001 Abbreviations: MAP, mean arterial pressure; GCS, Glasgow Coma Scale; PLT, platelet. Univariate and multivariate logistic regression analysis We utilized univariate and multivariate logistic regression models to assess the impact of clinical variables and scores on outcomes. The results revealed that, in the endovascular coiling group, age (adjusted odds ratio [OR]: 1.033, 95% confidence interval [CI]: 1.007-1.06, P =0.015), Hunt-Hess grade (adjusted OR: 2.727, 95% CI: 1.172-6.154, P =0.017), GCS score (9-12: adjusted OR: 0.308, 95% CI: 0.11-0.835, P =0.022; 12-15: adjusted OR: 0.098, 95% CI: 0.039-0.24, P <0.001), mFS (adjusted OR: 2.769, 95% CI: 1.382-5.959, P =0.006), D-dimer (adjusted OR: 1.248, 95% CI: 1.106-1.412, P <0.001), and body temperature (adjusted OR: 0.469, 95% CI: 0.274-0.784, P =0.005) were significantly associated with adverse functional outcomes ( Table 3 ). Table 3. Logistic analysis of functional outcomes after endovascular coiling. Variables Univariablea Multivariableb OR (95% CI) P value OR (95% CI) P value Sex Female Male 1.121(0.71-1.748) 0.619 Age 1.031(1.01-1.053) 0.004 1.033(1.007-1.06) 0.015 Hunt_Hess 0-3 4-5 15.533(9.002-27.292) <0.001 2.727(1.172-6.154) 0.017 Body temperature 0.613(0.379-0.979) 0.043 0.469(0.274-0.784) 0.005 GCS 3-8 9-12 0.194(0.084-0.429) <0.001 0.308(0.11-0.835) 0.022 12-15 0.036(0.019-0.067) <0.001 0.098(0.039-0.24) <0.001 PLT 0.999(0.996-1.002) 0.574 D_dimer 1.375(1.248-1.522) <0.001 1.248(1.106-1.412) <0.001 mFSgroup 0-2 3-4 5.871(3.193-11.875) <0.001 2.769(1.382-5.959) 0.006 Hypertension No Yes 1.083(0.698-1.698) 0.723 Diabetes No Yes 1.649(0.719-3.44) 0.205 Length 1.032(0.976-1.086) 0.239 Width 1.018(0.952-1.08) 0.565 Height 1.044(0.966-1.12) 0.252 Volume 1(0.999-1) 0.985 MAP 1.014(1.001-1.027) 0.028 1.015(1.000-1.031) 0.051 Stent No Yes 0.902(0.583-1.393) 0.642 location Anterior cerebral artery Middle cerebral artery 0.349(0.101-0.928) 0.057 Internal carotid artery 0.495(0.248-0.94) 0.037 Posterior communicating artery 0.668(0.385-1.147) 0.146 Posterior circulation 1.426(0.702-2.8) 0.312 a Univariable logistic regression models. b Multivariable logistic regression model adjusting for age at diagnosis- Hunt-Hess grade- body temperature- MAP- GCS score and D-dimer. Abbreviations: OR,odd ratio; CI, confidence interval; MAP, mean arterial pressure; GCS, Glasgow Coma Scale. Whereas, in the surgical clipping group, Hunt-Hess grade (adjusted OR: 2.369, 95% CI: 1.051-5.408, P =0.038), GCS score (12-15: adjusted OR: 0.274, 95% CI: 0.121-0.606, P =0.002), mFS (adjusted OR: 2.918, 95% CI:1.245-6.994, P =0.014), and D-dimer (adjusted OR: 1.508, 95% CI: 1.304-1.766, P <0.001) were independent predictors of functional outcomes ( Table 4 ). Table 4. Logistic analysis of functional outcomes after surgical clipping. Variables Univariablea Multivariableb OR (95% CI) P value OR (95% CI) P value Sex Female Male 0.887(0.542-1.439) 0.630 Age 1.026(1.003-1.049) 0.027 1.021(0.994-1.05) 0.128 Hunt_Hess 0-3 4-5 7.244(4.358-12.281) <0.001 2.369(1.051-5.408) 0.038 Body temperature 1.025(0.644-1.613) 0.916 GCS 3-8 9-12 0.197(0.066-0.522) 0.002 0.376(0.111-1.173) 0.099 12-15 0.13(0.075-0.223) <0.001 0.274(0.121-0.606) 0.002 PLT 0.997(0.995-0.999) 0.001 0.998(0.996-1.000) 0.060 D_dimer 1.266(1.132-1.423) <0.001 1.508(1.304-1.766) <0.001 mFSgroup 0-2 3-4 7.568(4.228-14.364) <0.001 2.918(1.245-6.994) 0.014 Hypertension No Yes 0.984(0.591-1.662) 0.952 Diabetes No Yes 0.932(0.287-2.635) 0.898 Length 1.058(0.994-1.128) 0.076 Width 1.064(0.991-1.151) 0.095 Height 1.09(1.004-1.197) 0.049 1.057(0.958-1.178) 0.283 Volume 1(1.000-1.001) 0.284 MAP 0.994(0.981-1.007) 0.360 Aneurysm_volume 1(1-1.001) 0.284 Anterior cerebral artery Middle cerebral artery 1.344(0.774-2.354) 0.296 Internal carotid artery 1.303(0.592-2.801) 0.502 Posterior communicating artery 0.806(0.351-1.761) 0.598 Posterior circulation 4.032(0.933-20.641) 0.067 a Univariable logistic regression models. b Multivariable logistic regression model adjusting for age at diagnosis- blood group- GCS score- aneurysm width- aneurysm height- D-dimer and PLT. Abbreviations: OR,odd ratio; CI, confidence interval; MAP, mean arterial pressure; GCS, Glasgow Coma Scale. The forest plot displays the significant multivariable factors ( Figure 3A and B ). The relative weights of impact on the outcome We further calculated the predictive relative weights of significant variables in the multivariate logistic regression analysis. In the endovascular coiling group, the proportions of significant variables were as follows: Hunt-Hess grade accounted for 32.78%, mFS for 31.99%, D-dimer for 13.73%, age for 11.61%, body temperature for 5.26%, and GCS for 4.63%. In the surgical clipping group, mFS accounted for 38.02%, Hunt-Hess grade for 33.55%, D-dimer for 19.99%, and GCS for 8.44% ( Figure 4A and B ). Nomogram for functional outcome and validation Based on the multivariate logistic regression model, significant predictors were identified to construct the nomogram model, visualizing individual risks of adverse functional outcomes one month post-surgery ( Figure 5A and 6A ). In the endovascular coiling group, the ROC curve demonstrated good predictive accuracy, with an AUC value of 0.847 (95% CI: 0.800-0.894) ( Figure 5B ), the calibration curve showed close alignment between the model and predictions ( Figure 5C ), while DCA curve indicated good clinical predictive value across different threshold probabilities ( Figure 5D ). Similarly, in the surgical clipping group, the ROC curve displayed good predictive accuracy, with an AUC value of 0.841 (95% CI: 0.796-0.886) ( Figure 6B ), the calibration curve showed basic alignment with predictions ( Figure 6C ), and DCA curve similarly demonstrated good clinical predictive value across different threshold probabilities ( Figure 6D ). Nomogram predictive risk stratification Based on the risk scores predicted by the Nomogram, patients were categorized into three groups: low risk, medium risk, and high risk. Utilizing logistic regression, we observed significant disparities in adverse functional outcomes across these risk groups. In the endovascular coiling group, individuals in the medium-risk group faced a 2.791-fold higher risk compared to those in the low-risk category, while those in the high-risk group experienced a substantially elevated risk of 17.506 times compared to the low-risk group ( Table 5 ). Similarly, within the surgical clipping group, participants classified in the medium-risk tier were at a 5.291-fold increased risk relative to the low-risk counterparts, with those in the high-risk category encountering an even greater risk, elevated by a substantial 26.641 times compared to the low-risk group ( Table 5 ). These differences were statistically significant, highlighting the clinical significance of our nomogram model incorporating clinical scores and parameters. Table 5. Nomogram risk stratification . Variables endovascular coiling surgical clipping OR (95% CI) P value OR (95% CI) P value Low-risk group Medium-risk group 2.791(1.134,7.868) 0.034 5.291(2.326,13.671) <0.001 High-risk group 17.506(8.014,46.109) <0.001 26.641(11.913,68.537) <0.001 Discussion The study suggests that factors such as gender, hypertension, alcohol consumption, and smoking[12-14], as well as aneurysm-related factors such as size and location[15-17] influence the occurrence and development of aSAH. Additionally, proteomic analysis has revealed that proteins associated with focal adhesion and extracellular matrix-receptor interaction pathways also play a significant role in the occurrence and development of intracranial aneurysms[18]. Currently, endovascular coiling and neurosurgical clipping have become the main treatment methods for aSAH. In our study, the rate of adverse functional outcomes in patients receiving endovascular coiling was significantly lower than that in patients receiving neurosurgical clipping treatment, which is consistent with current guidelines[19, 20]. However, endovascular coiling treatment increases the economic burden during the patient's hospitalization[21], and poses risks such as low complete occlusion rates and high risks of delayed rebleeding[22-24]. Based on this, we have established two new nomogram models, which were based on commonly used clinical scoring systems (such as the Hunt-Hess grading scale and the Glasgow Coma Scale) and some clinical parameters, to evaluate the early functional outcomes of these two different surgical methods. This study found that among patients treated with endovascular coiling, those who exhibited early poor functional outcomes were typically older and showed elevated mFS scores, increased Hunt-Hess grades, and lower GCS scores, consistent with prior research[25].Additionally, we also found that such patients often had elevated D-dimer levels, larger aneurysm sizes, and aneurysms located in the anterior cerebral arteries. In a retrospective study by Li et al.[21], it was observed that among patients undergoing surgical clipping, those with early poor functional outcomes were typically older and exhibited higher Hunt-Hess and mFS scores. These findings were corroborated by our study. Additionally, our research indicated that patients with a poor prognosis after surgical clipping demonstrated lower GCS scores, elevated D-dimer levels, and decreased platelet counts. To further investigate, we conducted univariate and multivariate logistic regression analyses. Through multifactorial logistic analysis, we identified age, body temperature, D-dimer, mFS, Hunt-Hess grade, and Glasgow Coma Scale (GCS) score as independent risk factors influencing the prognosis of the endovascular coiling group. For surgical clipping treatment, we determined Hunt-Hess grade, GCS score, mFS, and D-dimer level as independent factors influencing the prognosis. We found that D-dimer levels can predict outcomes in both surgical methods, possibly because: (1) In neurosurgical procedures, stress can impair patients' hemostatic and fibrinolytic functions; (2) Changes in D-dimer levels can reflect alterations in coagulation status, and abnormal D-dimer levels may indicate postoperative bleeding and thrombotic complications[26-28]. Interestingly, hypothermia may lead to poorer outcomes in patients undergoing endovascular coiling treatment, possibly due to its adverse effects on resuscitation and potential myocardial damage[29]. These findings underscore the importance of individualized perioperative management strategies[30]. For the first time, this study introduces two innovative predictive models specifically designed for aSAH patient treatment strategies. These models can effectively predict early functional outcomes by stratifying patients according to risk levels. Additionally, we found for the first time that, regardless of whether patients undergo endovascular coiling or surgical clipping, D-dimer levels emerge as the significant variable affecting early functional outcomes, second only to the Hunt-Hess grade and mFS grading system, and superior to the GCS score. This discovery provides new insights for personalized clinical treatment approaches. The study has some unavoidable limitations. Firstly, being a retrospective study, there might be confounding factors that could impact the stability of the results. Secondly, the assessment of patients' functional outcomes was conducted only one month post-surgery, necessitating further long-term follow-up to evaluate extended outcomes. In summary, the customized predictive models tailored to endovascular coiling and neurosurgical clipping highlight the importance of scoring systems and clinical parameters (such as D-dimer), demonstrating significant clinical utility. Declarations Acknowledgements Not applicable Funding This study was supported by the Key Project of the Medical-Educational Collaborative Innovation Fund at Jiangsu University for the year 2023 (Project N0.JDY2023011). Availability of data and materials Not applicable Ethical Approval The study followed the ethical principles of the Helsinki Declaration and its amendments, obtaining approval from the hospital ethics committee, with ethical approval numbers 2023043 and K-20230086-w. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article. Consent for Publication Not applicable. Conflict of Interest The authors declared that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Author Contributions DW designed the study. LC, MQ, and YYD collected clinical data from hospitals in China and drafted the initial manuscript. KC, KJ, and YPL analyzed the data. DW, LC, KC, MQ, and YYD contributed to the revision of the manuscript. All authors read and approved the final manuscript. References Etminan N, Chang HS, Hackenberg K, et al. Worldwide Incidence of Aneurysmal Subarachnoid Hemorrhage According to Region, Time Period, Blood Pressure, and Smoking Prevalence in the Population: A Systematic Review and Meta-analysis. JAMA Neurol. 2019;76:588–97. Ingall T, Asplund K, Mähönen M, Bonita R. A multinational comparison of subarachnoid hemorrhage epidemiology in the WHO MONICA stroke study. Stroke. 2000;31:1054–61. Bakker MK, Kanning JP, Abraham G, et al. Genetic Risk Score for Intracranial Aneurysms: Prediction of Subarachnoid Hemorrhage and Role in Clinical Heterogeneity. Stroke. 2023;54:810–8. Hop JW, Rinkel GJ, Algra A, van Gijn J. Case-fatality rates and functional outcome after subarachnoid hemorrhage: a systematic review. Stroke. 1997;28:660–4. Liu A, Huang J. Treatment of Intracranial Aneurysms: Clipping Versus Coiling. Curr Cardiol Rep. 2015;17:628. Lindgren A, Vergouwen MD, van der Schaaf I, et al. Endovascular coiling versus neurosurgical clipping for people with aneurysmal subarachnoid haemorrhage. Cochrane Database Syst Rev. 2018;8:Cd003085. Güresir E, Beck J, Vatter H, et al. Subarachnoid hemorrhage and intracerebral hematoma: incidence, prognostic factors, and outcome. Neurosurgery. 2008;63:1088–93. discussion 1093 – 1084. Molyneux AJ, Birks J, Clarke A, et al. The durability of endovascular coiling versus neurosurgical clipping of ruptured cerebral aneurysms: 18 year follow-up of the UK cohort of the International Subarachnoid Aneurysm Trial (ISAT). Lancet. 2015;385:691–7. Zhu W, Ling X, Petersen JD, et al. Clipping versus coiling for aneurysmal subarachnoid hemorrhage: a systematic review and meta-analysis of prospective studies. Neurosurg Rev. 2022;45:1291–302. Lou M, Ding J, Hu B, et al. Chinese Stroke Association guidelines for clinical management of cerebrovascular disorders: executive summary and 2019 update on organizational stroke management. Stroke Vasc Neurol. 2020;5:260–9. Dekker L, Venema E, Pirson FAV, et al. Endovascular treatment in anterior circulation stroke beyond 6.5 hours after onset or time last seen well: results from the MR CLEAN Registry. Stroke Vasc Neurol. 2021;6:572–80. Bor AS, Koffijberg H, Wermer MJ, Rinkel GJ. Optimal screening strategy for familial intracranial aneurysms: a cost-effectiveness analysis. Neurology. 2010;74:1671–9. Broderick JP, Brown RD Jr., Sauerbeck L, et al. Greater rupture risk for familial as compared to sporadic unruptured intracranial aneurysms. Stroke. 2009;40:1952–7. Larsson SC, Männistö S, Virtanen MJ, et al. Dietary fiber and fiber-rich food intake in relation to risk of stroke in male smokers. Eur J Clin Nutr. 2009;63:1016–24. Lall RR, Eddleman CS, Bendok BR, Batjer HH. Unruptured intracranial aneurysms and the assessment of rupture risk based on anatomical and morphological factors: sifting through the sands of data. Neurosurg Focus. 2009;26:E2. Lindner SH, Bor AS, Rinkel GJ. Differences in risk factors according to the site of intracranial aneurysms. J Neurol Neurosurg Psychiatry. 2010;81:116–8. Linn FH, Rinkel GJ, Algra A, van Gijn J. Incidence of subarachnoid hemorrhage: role of region, year, and rate of computed tomography: a meta-analysis. Stroke. 1996;27:625–9. Liu Y, Song Y, Liu P, et al. Comparative bioinformatics analysis between proteomes of rabbit aneurysm model and human intracranial aneurysm with label-free quantitative proteomics. CNS Neurosci Ther. 2021;27:101–12. Connolly ES Jr., Rabinstein AA, Carhuapoma JR, et al. Guidelines for the management of aneurysmal subarachnoid hemorrhage: a guideline for healthcare professionals from the American Heart Association/american Stroke Association. Stroke. 2012;43:1711–37. Steiner T, Juvela S, Unterberg A, et al. European Stroke Organization guidelines for the management of intracranial aneurysms and subarachnoid haemorrhage. Cerebrovasc Dis. 2013;35:93–112. Li R, Lin F, Chen Y et al. In-hospital complication-related risk factors for discharge and 90-day outcomes in patients with aneurysmal subarachnoid hemorrhage after surgical clipping and endovascular coiling: a propensity score-matched analysis. J Neurosurg 2021; 1–12. Bakker NA, Metzemaekers JD, Groen RJ, et al. International subarachnoid aneurysm trial 2009: endovascular coiling of ruptured intracranial aneurysms has no significant advantage over neurosurgical clipping. Neurosurgery. 2010;66:961–2. Molyneux AJ, Kerr RS, Yu LM, et al. International subarachnoid aneurysm trial (ISAT) of neurosurgical clipping versus endovascular coiling in 2143 patients with ruptured intracranial aneurysms: a randomised comparison of effects on survival, dependency, seizures, rebleeding, subgroups, and aneurysm occlusion. Lancet. 2005;366:809–17. Risselada R, Lingsma HF, Bauer-Mehren A, et al. Prediction of 60 day case-fatality after aneurysmal subarachnoid haemorrhage: results from the International Subarachnoid Aneurysm Trial (ISAT). Eur J Epidemiol. 2010;25:261–6. Zhuang D, Ren Z, Sheng J, et al. A dynamic nomogram for predicting unfavorable prognosis after aneurysmal subarachnoid hemorrhage. Ann Clin Transl Neurol. 2023;10:1058–71. Liu JH, Li XK, Chen ZB, et al. D-dimer may predict poor outcomes in patients with aneurysmal subarachnoid hemorrhage: a retrospective study. Neural Regen Res. 2017;12:2014–20. Raatikainen E, Kiiski H, Kuitunen A, et al. Increased blood coagulation is associated with poor neurological outcome in aneurysmal subarachnoid hemorrhage. J Neurol Sci. 2024;458:122943. Fang F, Wang P, Yao W, et al. Association between D-dimer levels and long-term mortality in patients with aneurysmal subarachnoid hemorrhage. Neurosurg Focus. 2022;52:E8. Zhao H, Shang F, Qi M et al. Related Factors and a Threshold of the Maximum Neuron-Specific Enolase Value Affecting the Prognosis of Patients with Aneurysmal Subarachnoid Hemorrhage. Appl Bionics Biomech. 2022; 2022: 7596426. Sharma D. Perioperative Management of Aneurysmal Subarachnoid Hemorrhage. Anesthesiology. 2020;133:1283–305. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4465305","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":310240406,"identity":"60935b4a-db0d-405e-abcc-e1d351eb7a5f","order_by":0,"name":"Liang Chu","email":"","orcid":"","institution":"The Affiliated Yixing Hospital of Jiangsu University","correspondingAuthor":false,"prefix":"","firstName":"Liang","middleName":"","lastName":"Chu","suffix":""},{"id":310240407,"identity":"9e71f25d-f55d-4d6c-ae2e-9d37fb4cef6b","order_by":1,"name":"Ming Qi","email":"","orcid":"","institution":"The Affiliated Yixing Hospital of Jiangsu University","correspondingAuthor":false,"prefix":"","firstName":"Ming","middleName":"","lastName":"Qi","suffix":""},{"id":310240408,"identity":"01761453-20f7-4ec3-a623-a8f80e7e028f","order_by":2,"name":"Yingying Ding","email":"","orcid":"","institution":"The Affiliated Yixing Hospital of Jiangsu University","correspondingAuthor":false,"prefix":"","firstName":"Yingying","middleName":"","lastName":"Ding","suffix":""},{"id":310240409,"identity":"20370c42-777c-43a7-b06b-ea44623e379e","order_by":3,"name":"Kuan Jiang","email":"","orcid":"","institution":"The Affiliated Yixing Hospital of Jiangsu University","correspondingAuthor":false,"prefix":"","firstName":"Kuan","middleName":"","lastName":"Jiang","suffix":""},{"id":310240410,"identity":"68c22446-67c1-4eb7-a00d-ccf4e1c64acb","order_by":4,"name":"Yunpeng Lu","email":"","orcid":"","institution":"The Affiliated Yixing Hospital of Jiangsu University","correspondingAuthor":false,"prefix":"","firstName":"Yunpeng","middleName":"","lastName":"Lu","suffix":""},{"id":310240411,"identity":"4e30a773-01f3-4a16-a38f-a1ed5145cf64","order_by":5,"name":"Kan Cao","email":"","orcid":"","institution":"The Affiliated Yixing Hospital of Jiangsu University","correspondingAuthor":false,"prefix":"","firstName":"Kan","middleName":"","lastName":"Cao","suffix":""},{"id":310240412,"identity":"7bf660a3-e107-430a-aaee-36366c8fcbe0","order_by":6,"name":"Da Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAv0lEQVRIiWNgGAWjYHACA2YgwcPA3tj48ANpWngONxtLkKKFgUEivU2Ahyj1N5I3fi7cYydjLvmwjUGCwU5Ot4GglrRi6RnPknksZye2PShgSDY2O0BAi9mNHANpngPMPAa3E9sNJBgOJG4jQovxb54D9TwGNw+2SfAQqcUMaMthHoMbjERqsT/zrMx6xoHjPAZnEoGBbECEXyTbkzffLjhQbW9w/PjDhx8q7OQIakEDBqQpHwWjYBSMglGAAwAADrFB/SQNSQEAAAAASUVORK5CYII=","orcid":"","institution":"The Affiliated Yixing Hospital of Jiangsu University","correspondingAuthor":true,"prefix":"","firstName":"Da","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2024-05-23 08:15:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4465305/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4465305/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58146090,"identity":"365faf7f-48e6-4e17-9268-27bbae6b34b0","added_by":"auto","created_at":"2024-06-11 18:34:45","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":476577,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of eligible patients diagnosed with aSAH.\u003c/p\u003e","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4465305/v1/1935d902625d4eadadcc78c9.jpg"},{"id":58146086,"identity":"acd4f770-5e8a-4202-a98d-ad06ed95fd4d","added_by":"auto","created_at":"2024-06-11 18:34:45","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":203340,"visible":true,"origin":"","legend":"\u003cp\u003e(A)Ruptured aneurysm of the left middle cerebral artery with subarachnoid hemorrhage,\u003c/p\u003e\n\u003cp\u003e(B)DSA showing left middle cerebral artery aneurysm,(C)Post-embolization CT of left middle cerebral artery aneurysm,(D)Post-embolization angiogram of left middle cerebral artery aneurysm,(E) Left cerebral middle artery aneurysm rupture with subarachnoid hemorrhage and temporal lobe hematoma,(F) Left cerebral middle artery aneurysm CTA imaging,(G) Post-clipping of left cerebral middle artery aneurysm and clearance of temporal lobe hematoma,(H) Post-clipping of left cerebral middle artery aneurysm.\u003c/p\u003e","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4465305/v1/ef92fdff4706cb6520f0f7be.jpg"},{"id":58146087,"identity":"5fe8004d-2b46-42a1-9056-b937ba8eb981","added_by":"auto","created_at":"2024-06-11 18:34:45","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":97346,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot based on multivariate analysis in the endovascular coiling (A) and surgical clipping groups (B).\u003c/p\u003e","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4465305/v1/c5588f0a6361cc1951c78b7f.jpg"},{"id":58147457,"identity":"e0c78e22-7de4-42e8-bb34-9989bccb4a32","added_by":"auto","created_at":"2024-06-11 18:42:45","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":56121,"visible":true,"origin":"","legend":"\u003cp\u003eThe relative weights of variables influencing early functional outcomes in the endovascular coiling (A) and surgical clipping groups (B).\u003c/p\u003e","description":"","filename":"Fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4465305/v1/98ee0308e1fff0b0db132d8f.jpg"},{"id":58146088,"identity":"8f74dd6d-8fa3-442a-9793-3815da246a17","added_by":"auto","created_at":"2024-06-11 18:34:45","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":101248,"visible":true,"origin":"","legend":"\u003cp\u003eConstructing a nomogram(A) to forecast functional outcomes in patients undergoing endovascular coiling, and validating it through ROC curves (B), calibration plots (C), and DCA curves (D).\u003c/p\u003e","description":"","filename":"Fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4465305/v1/808da787342c95aa7c0c3a51.jpg"},{"id":58146091,"identity":"1dc7ab77-7bce-4e9c-b650-e65afdc34fe9","added_by":"auto","created_at":"2024-06-11 18:34:45","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":86283,"visible":true,"origin":"","legend":"\u003cp\u003eConstructing a nomogram(A) to forecast functional outcomes in patients undergoing surgical clipping, and validating it through ROC curves (B), calibration plots (C), and DCA curves (D).\u003c/p\u003e","description":"","filename":"Fig6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4465305/v1/f5730403a5d2563f8e096e4a.jpg"},{"id":58148056,"identity":"8c4e1083-ddf9-4237-823a-3fa28c8b7b5d","added_by":"auto","created_at":"2024-06-11 18:50:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1925078,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4465305/v1/409671a3-e716-4c60-a330-efdc69d11369.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prediction of early postoperative functional outcomes in patients with aneurysmal subarachnoid hemorrhage: a Chinese bicenter retrospective study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAneurysmal subarachnoid hemorrhage (aSAH) is a severely disabling and fatal disease worldwide. The global incidence of aSAH is about 6.1 cases per 100,000 person-years[1], with China having an estimated incidence of about 2.0 cases per 100,000 person-years[2]. Recent studies have shown a positive correlation between the incidence of aSAH and advancing age. Consequently, with the aging of society, an increasing number of individuals are at risk of sudden aSAH[3]. Despite significant advancements in managing patients with subarachnoid hemorrhage, the mortality rate remains high. The case-fatality rate for aSAH ranges from 32% to 67%, with approximately one-third of survivors experiencing long-term disability or cognitive impairment[4]. Due to the rapid progression of the disease, timely diagnosis and early treatment are of paramount importance.\u003c/p\u003e\n\u003cp\u003eFor individuals with ruptured aneurysms in either the anterior or posterior circulation, endovascular coiling offers a more favorable prognosis compared to\u0026nbsp;surgical\u0026nbsp;clipping\u0026nbsp;[5, 6]. However,\u0026nbsp;surgical\u0026nbsp;clipping still holds unique advantages in patients with large intracerebral hematomas or middle cerebral artery aneurysms[7]. A follow-up study of the International Subarachnoid Aneurysm Trial (ISAT)[8]\u0026nbsp;found that compared to the\u0026nbsp;surgical clipping\u0026nbsp;group, the endovascular coiling group experienced a 7% reduction in mortality risk at 1 year. Nonetheless, the long-term risk of rebleeding increased.\u003c/p\u003e\n\u003cp\u003eIn clinical practice, evaluating the postoperative status following surgical clipping and endovascular coiling primarily relies on the Hunt-Hess grading scale and the Glasgow Coma Scale. However, these evaluations do not fully incorporate the personalized clinical parameters of patients, which could also impact the outcome[9]. This study aims to develop new nomogram models based on commonly used clinical scoring systems and some clinical parameters to evaluate the early functional outcomes of these two different surgical approaches.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eDuring the period from 1st July 2023 to 30st September 2023, we visited the hospital's information system to collect relevant data.This study retrospectively and continuously collected patients with aSAH who were hospitalized and treated at the Affiliated Yixing Hospital of Jiangsu University and the Affiliated Zhenjiang First Hospital of Jiangsu University from 1st January ,2015 to 31st December,2022. Diagnosis, surgery, and perioperative management strictly adhered to the latest clinical practice guidelines of the Chinese Medical Association[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The study adhered to the Helsinki Declaration and obtained approval from the ethics committees of two hospitals, with approval numbers 2023043 and K-20230086-w. All participants or their authorized representatives were required to sign an informed consent form during hospitalization, ensuring their full understanding of the purpose, methods, risks, and benefits of the surgery and study, and indicating their voluntary participation.\u003c/p\u003e \u003cp\u003eInclusion criteria include: (1) age 18 years or older; (2) confirmed as aSAH through laboratory and imaging examinations; (3) patients must undergo either endovascular coiling or surgical clipping treatment. Exclusion criteria include: (1) previous hospitalization for aSAH; (2) subarachnoid hemorrhage caused by other reasons, including vascular malformation, cerebral atherosclerosis, trauma, etc.; (3) the presence of severe cardiovascular, liver, kidney or other vital organ diseases; (4) loss of clinical parameters such as platelet count and D-dimer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eThe study variables and outcomes\u003c/h2\u003e \u003cp\u003eThe study variables include demographic information (age and sex), physical examination data (body temperature and mean arterial pressure), laboratory tests (D-dimer, and platelet count), imaging examinations (length, width, height, position, etc.), past medical history (hypertension and diabetes), clinical scoring tools (GCS, Hunt-Hess, mFS), treatment modalities (endovascular coiling and surgical clipping), and the use of stents. Patients were stratified into the endovascular coiling group and the surgical clipping group based on treatment methods.\u003c/p\u003e \u003cp\u003eThe primary endpoint of this study was the early functional outcomes of patients one month after surgery. Considering that the modified Rankin Scale (mRS) is recommended for assessing neurological recovery status, this study utilized the mRS score at one month post-surgery as the primary endpoint, with scores ranging from 0 to 3 indicating a favorable prognosis and scores from 4 to 6 indicating an unfavorable prognosis[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eChi-square tests were used for categorical variables, while non-parametric tests were employed for continuous variables. Univariate and multivariate logistic regression analyses were conducted to determine independent predictive factors and calculate their relative weights on the outcome variable. Two nomogram models were constructed. Receiver operating characteristic curves (ROC) were plotted, and the area under the curve (AUC) was calculated to assess the discriminative ability of the nomogram. Calibration curves were generated to illustrate the consistency between predicted probabilities and observed outcomes across various predicted probabilities. Decision curve analysis (DCA) was conducted to evaluate the clinical utility of the nomogram. Finally, risk stratification was performed. All statistical analyses were conducted using SPSS version 26.0 and R version 4.3.1, with statistical significance defined at the conventional threshold of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (two-tailed).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eCharacteristics of patients in the endovascular coiling and surgical clipping groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 962 patients diagnosed with aSAH were included in the final analysis cohort (\u003cstrong\u003eFigure 1\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOverall, the proportion of\u0026nbsp;early\u0026nbsp;adverse outcomes in the surgical clipping group (32.7%) was higher than that in the endovascular coiling group (15.0%) (\u003cstrong\u003eFigure 2\u003c/strong\u003e). In patients undergoing endovascular coiling, those with poor functional outcomes tended to be older, have higher D-dimer levels, higher Hunt-Hess grades, GCS scores of 3-8, higher mFS grades, larger aneurysm volumes, were more likely to be located in the anterior cerebral artery and posterior circulation. Among patients undergoing surgical clipping, those with poor functional outcomes tended to be older, have higher Hunt-Hess grades, GCS scores of 3-8, higher mFS grades, higher D-dimer levels, and lower platelet counts (\u003cstrong\u003eTable 1\u003c/strong\u003e; \u003cstrong\u003eTable 2\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eTable1 : Baseline characteristics of patients undergoing endovascular coiling.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"665\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003eTotal (n=638)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003eGood functional outcome (n=542)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003ePoor functional outcome (n=96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003eAge, years \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e60.0 (52.0-68.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e60.0 (51.8-67.0) \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e64.0 (53.0-72.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e0.703\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e413 (64.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e353 (65.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e60 (62.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e225 (35.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e189 (34.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e36 (37.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003eHunt-Hess grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003e0-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e566 (88.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e514 (94.8%) \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e52 (54.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003e4-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e72 (11.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e28 (5.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e44 (45.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e0.809\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e263(41.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e225 (41.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e38(39.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e375 (58.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e317 (58.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e58 (60.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003eDiabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e0.293\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e597 (93.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e510 (94.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e87 (90.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e41 (6.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e32 (5.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e9 (9.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003eBody temperature, ℃\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e36.8 (36.5-37.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e36.8 (36.5-37.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e36.8 (36.3-37.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003eMAP, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e106.67(96.67%-117.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e106.67(96.67%-116.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e108.67(98.67%-124.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003eGCS score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003e3-8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e61 (9.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e19 (3,5%) \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e42 (43.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003e9-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e50 (7.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e35 (6.5%) \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e15 (15.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003e12-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e527 (82.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e488(90.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e39 (40.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003emFS group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003e0-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e245 (38.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e234 (43.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e11 (11.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003e3-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e393 (61.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e308 (56.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e85 (88.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003eLength, mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e4.0 (3.00-6.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e4.0 (3.00-6.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e4.58(3.00-7.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003eWidth, mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e4.0 (3.00-5.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e4.0 (3.00-5.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e4.5 (3.00-6.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e0.191\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003eHeight, mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e4.0 (3.00-5.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e3.88 (2.80-5.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e4.00 (3.00-6.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003eVolume, mm3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e33.51 (14.22-66.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e31.42 (14.14-65.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e41.89 (18.43-89.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003eAneurysm location\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003eAnterior cerebral artery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e190 (29.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e154 (28.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e36 (37.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003eMiddle cerebral artery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e53 (8.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e49 (9.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e4 (4.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003eInternal carotid artery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e135 (21.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e121 (22.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e14 (14.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003ePosterior communicating artery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e200 (31.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e173 (31.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e27 (28.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003ePosterior circulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e60 (9.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e45 (8.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e15 (15.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003ePresence of hematoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e0.375\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e628 (98.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e535 (98.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e93 (96.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e10 (1.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e7 (1.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e3 (3.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003eUse of stents\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e0.724\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e325 (50.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e274 (50.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e51 (53.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e313 (49.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e268 (49.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e45 (46.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003eD-dimer, mg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e0.81 (0.40-1.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e0.72 (0.33-1.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e1.86 (0.94-4.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003ePLT, \u0026times;109/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e208.0 (169.25-261.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e207.5 (170.25-260.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e211.50 (158.25-267.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e0.858\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: MAP, mean arterial pressure; GCS, Glasgow Coma Scale; PLT, platelet.\u003c/p\u003e\n\u003cp\u003eTable2 : Baseline characteristics of patients undergoing neurosurgical clipping.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"665\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003eTotal (n=324)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003eGood functional outcome (n=218)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003ePoor functional outcome (n=106)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003eAge, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e60.0 (51.75-68.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e58.5 (51.00-67.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e63.0 (53.25-69.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e0.720\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e208 (64.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e138 (63.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e70 (66.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e116 (35.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e80 (36.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e36 (34.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003eHunt-Hess grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003e0-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e200 (61.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e167 (76.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e33 (31.1.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003e4-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e124 (38.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e51 (23.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e73 (68.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e91 (28.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e61(28.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e30(28.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e233 (71.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e157 (72.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e76 (71.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003eDiabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e308(95.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e207 (95.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e101 (95.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e16 (4.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e11 (5.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e5 (4.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003eBody temperature, ℃\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e36.80 (36.5-37.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e36.80 (36.5-37.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e36.80 (36.5-37.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e0.975\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003eMAP, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e111.00(98.67-122.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e111.00 (98.75-123.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e111.50(97.75-120.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e0.741\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003eGCS score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003e3-8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e95 (29.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e34 (15.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e61 (57.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003e9-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e23 (7.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e17 (7.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e6 (5.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003e12-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e206 (63..6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e167 (76.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e39 (36.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003emFS group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003e0-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e136 (42.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e121 (55.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e15 (14.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003e3-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e188 (58.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e97 (44.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e91 (85.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003eLength, mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e4.00 (3.00-6.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e4.00 (3.00-5.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e4.00 (3.00-6.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e0.651\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003eWidth, mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e4.00 (3.00-5.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e4.00 (3.00-5.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e4.00 (3.00-5.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003eHeight, mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e4.00 (3.00-5.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e4.00 (3.00-5.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e4.00 (3.00-5.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003eAneurysm volume, mm3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e33.51 (14.14-65.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e31.42 (14.14-62.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e33.51 (14.14-89.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e0.186\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003eAneurysm location\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e0.231\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003eAnterior cerebral artery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e106 (32.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e75 (34.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e31 (29.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003eMiddle cerebral artery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e126 (38.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e81 (37.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e45 (42.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003eInternal carotid artery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e40 (12.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e26 (11.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e14 (13.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003ePosterior communicating artery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e44 (13.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e33 (15.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e11 (10.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003ePosterior circulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e8 (2.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e3 (1.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e5 (4.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003ePresence of hematoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e0.791\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e276 (85.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e187 (85.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e89 (84.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e48 (14.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e31 (14.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e17 (16.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003eD-dimer, mg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e1.64 (0.73-2.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e1.33 (0.66-2.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e2.33 (1.34-3.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.353383458646615%\" valign=\"top\"\u003e\n \u003cp\u003ePLT, \u0026times;109/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.157894736842106%\"\u003e\n \u003cp\u003e260.00(160.75-349.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.007518796992482%\"\u003e\n \u003cp\u003e286.00 (192.75-362.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.55639097744361%\"\u003e\n \u003cp\u003e194.50 (119.25-291.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.924812030075188%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: MAP, mean arterial pressure; GCS, Glasgow Coma Scale; PLT, platelet.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUnivariate and multivariate logistic regression analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe utilized univariate and multivariate logistic regression models to assess the impact of clinical variables and scores on outcomes. The results revealed that, in the endovascular coiling group, age (adjusted odds ratio [OR]: 1.033, 95% confidence interval [CI]: 1.007-1.06, \u003cem\u003eP\u003c/em\u003e=0.015), Hunt-Hess grade (adjusted OR: 2.727, 95% CI: 1.172-6.154, \u003cem\u003eP\u003c/em\u003e=0.017), GCS score (9-12: adjusted OR: 0.308, 95% CI: 0.11-0.835, \u003cem\u003eP\u003c/em\u003e=0.022; 12-15: adjusted OR: 0.098, 95% CI: 0.039-0.24, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001), mFS (adjusted OR: 2.769, 95% CI: 1.382-5.959, \u003cem\u003eP\u003c/em\u003e=0.006), D-dimer (adjusted OR: 1.248, 95% CI: 1.106-1.412, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001), and body temperature (adjusted OR: 0.469, 95% CI: 0.274-0.784, \u003cem\u003eP\u003c/em\u003e=0.005) were significantly associated with adverse functional outcomes (\u003cstrong\u003eTable 3\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3. \u0026nbsp;Logistic analysis of functional outcomes after endovascular coiling.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"558\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.74910394265233%\" rowspan=\"2\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.55913978494624%\" colspan=\"2\"\u003e\n \u003cp\u003eUnivariablea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.691756272401435%\" colspan=\"2\"\u003e\n \u003cp\u003eMultivariableb\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.714285714285715%\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.632653061224488%\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.3265306122449%\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.74910394265233%\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.089605734767026%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.469534050179211%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.469534050179211%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.74910394265233%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.089605734767026%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.469534050179211%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.469534050179211%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.74910394265233%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.089605734767026%\"\u003e\n \u003cp\u003e1.121(0.71-1.748)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.469534050179211%\"\u003e\n \u003cp\u003e0.619\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.469534050179211%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.74910394265233%\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.089605734767026%\"\u003e\n \u003cp\u003e1.031(1.01-1.053)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.469534050179211%\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n 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\u003c/td\u003e\n \u003ctd width=\"11.469534050179211%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.469534050179211%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.74910394265233%\"\u003e\n \u003cp\u003e4-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.089605734767026%\"\u003e\n \u003cp\u003e15.533(9.002-27.292)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.469534050179211%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\"\u003e\n \u003cp\u003e2.727(1.172-6.154)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.469534050179211%\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.74910394265233%\"\u003e\n \u003cp\u003eBody temperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.089605734767026%\"\u003e\n \u003cp\u003e0.613(0.379-0.979)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.469534050179211%\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\"\u003e\n \u003cp\u003e0.469(0.274-0.784)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.469534050179211%\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.74910394265233%\"\u003e\n \u003cp\u003eGCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.089605734767026%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.469534050179211%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.469534050179211%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.74910394265233%\"\u003e\n \u003cp\u003e3-8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.089605734767026%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.469534050179211%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.469534050179211%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.74910394265233%\" valign=\"bottom\"\u003e\n \u003cp\u003e9-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.089605734767026%\"\u003e\n \u003cp\u003e0.194(0.084-0.429)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.469534050179211%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\"\u003e\n \u003cp\u003e0.308(0.11-0.835)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.469534050179211%\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.74910394265233%\"\u003e\n \u003cp\u003e12-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.089605734767026%\"\u003e\n \u003cp\u003e0.036(0.019-0.067)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.469534050179211%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\"\u003e\n \u003cp\u003e0.098(0.039-0.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.469534050179211%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.74910394265233%\" valign=\"bottom\"\u003e\n \u003cp\u003ePLT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.089605734767026%\"\u003e\n 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width=\"25.089605734767026%\"\u003e\n \u003cp\u003e1.083(0.698-1.698)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.469534050179211%\"\u003e\n \u003cp\u003e0.723\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.469534050179211%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.74910394265233%\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.089605734767026%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.469534050179211%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.469534050179211%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.74910394265233%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.089605734767026%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.469534050179211%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.469534050179211%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.74910394265233%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.089605734767026%\"\u003e\n \u003cp\u003e1.649(0.719-3.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.469534050179211%\"\u003e\n \u003cp\u003e0.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.469534050179211%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.74910394265233%\"\u003e\n \u003cp\u003eLength\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.089605734767026%\"\u003e\n \u003cp\u003e1.032(0.976-1.086)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.469534050179211%\"\u003e\n \u003cp\u003e0.239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.469534050179211%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.74910394265233%\"\u003e\n \u003cp\u003eWidth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.089605734767026%\"\u003e\n \u003cp\u003e1.018(0.952-1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.469534050179211%\"\u003e\n \u003cp\u003e0.565\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.469534050179211%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.74910394265233%\"\u003e\n \u003cp\u003eHeight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.089605734767026%\"\u003e\n \u003cp\u003e1.044(0.966-1.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.469534050179211%\"\u003e\n \u003cp\u003e0.252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.469534050179211%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.74910394265233%\"\u003e\n \u003cp\u003eVolume\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.089605734767026%\"\u003e\n \u003cp\u003e1(0.999-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.469534050179211%\"\u003e\n \u003cp\u003e0.985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.469534050179211%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.74910394265233%\"\u003e\n \u003cp\u003eMAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.089605734767026%\"\u003e\n \u003cp\u003e1.014(1.001-1.027)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.469534050179211%\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\"\u003e\n \u003cp\u003e1.015(1.000-1.031)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.469534050179211%\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.74910394265233%\"\u003e\n \u003cp\u003eStent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.089605734767026%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.469534050179211%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.469534050179211%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.74910394265233%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.089605734767026%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.469534050179211%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.469534050179211%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.74910394265233%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.089605734767026%\"\u003e\n \u003cp\u003e0.902(0.583-1.393)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.469534050179211%\"\u003e\n \u003cp\u003e0.642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.469534050179211%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.74910394265233%\"\u003e\n \u003cp\u003elocation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.089605734767026%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.469534050179211%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.469534050179211%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.74910394265233%\"\u003e\n \u003cp\u003eAnterior cerebral artery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.089605734767026%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.469534050179211%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.469534050179211%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.74910394265233%\"\u003e\n \u003cp\u003e\u0026nbsp;Middle cerebral artery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.089605734767026%\"\u003e\n \u003cp\u003e0.349(0.101-0.928)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.469534050179211%\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.469534050179211%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.74910394265233%\"\u003e\n \u003cp\u003eInternal carotid artery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.089605734767026%\"\u003e\n \u003cp\u003e0.495(0.248-0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.469534050179211%\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.469534050179211%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.74910394265233%\"\u003e\n \u003cp\u003ePosterior communicating artery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.089605734767026%\"\u003e\n \u003cp\u003e0.668(0.385-1.147)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.469534050179211%\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.469534050179211%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.74910394265233%\"\u003e\n \u003cp\u003ePosterior circulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.089605734767026%\"\u003e\n \u003cp\u003e1.426(0.702-2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.469534050179211%\"\u003e\n \u003cp\u003e0.312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.469534050179211%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\"\u003e\n \u003cp\u003ea Univariable logistic regression models.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\"\u003e\n \u003cp\u003eb Multivariable logistic regression model adjusting for age at diagnosis- Hunt-Hess grade- body temperature- MAP- GCS score and D-dimer.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\"\u003e\n \u003cp\u003eAbbreviations: OR,odd ratio; CI, confidence interval; MAP, mean arterial pressure; GCS, Glasgow Coma Scale.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;Whereas, in the surgical clipping group, Hunt-Hess grade (adjusted OR: 2.369, 95% CI: 1.051-5.408,\u003cem\u003e\u0026nbsp;P\u003c/em\u003e=0.038), GCS score (12-15: adjusted OR: 0.274, 95% CI: 0.121-0.606, \u003cem\u003eP\u003c/em\u003e=0.002), mFS (adjusted OR: 2.918, 95% CI:1.245-6.994, \u003cem\u003eP\u003c/em\u003e=0.014), and D-dimer (adjusted OR: 1.508, 95% CI: 1.304-1.766, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001) were independent predictors of functional outcomes (\u003cstrong\u003eTable 4\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4. \u0026nbsp;Logistic analysis of functional outcomes after surgical clipping.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"565\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.19148936170213%\" rowspan=\"2\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.234042553191486%\" colspan=\"2\"\u003e\n \u003cp\u003eUnivariablea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.57446808510638%\" colspan=\"2\"\u003e\n \u003cp\u003eMultivariableb\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.04938271604938%\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.802469135802468%\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.34567901234568%\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.802469135802468%\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.19148936170213%\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.886524822695037%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.22695035460993%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.19148936170213%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.886524822695037%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.22695035460993%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.19148936170213%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.886524822695037%\"\u003e\n \u003cp\u003e0.887(0.542-1.439)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\n \u003cp\u003e0.630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.22695035460993%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.19148936170213%\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.886524822695037%\"\u003e\n \u003cp\u003e1.026(1.003-1.049)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.22695035460993%\"\u003e\n \u003cp\u003e1.021(0.994-1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\n \u003cp\u003e0.128\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.19148936170213%\"\u003e\n \u003cp\u003eHunt_Hess\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.886524822695037%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n 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\u003cp\u003e4-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.886524822695037%\"\u003e\n \u003cp\u003e7.244(4.358-12.281)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.22695035460993%\"\u003e\n \u003cp\u003e2.369(1.051-5.408)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.19148936170213%\"\u003e\n \u003cp\u003eBody temperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.886524822695037%\"\u003e\n \u003cp\u003e1.025(0.644-1.613)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\n \u003cp\u003e0.916\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.22695035460993%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.19148936170213%\"\u003e\n \u003cp\u003eGCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.886524822695037%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.22695035460993%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.19148936170213%\"\u003e\n \u003cp\u003e3-8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.886524822695037%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.22695035460993%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.19148936170213%\" valign=\"bottom\"\u003e\n \u003cp\u003e9-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.886524822695037%\"\u003e\n \u003cp\u003e0.197(0.066-0.522)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.22695035460993%\"\u003e\n \u003cp\u003e0.376(0.111-1.173)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\n \u003cp\u003e0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.19148936170213%\"\u003e\n \u003cp\u003e12-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.886524822695037%\"\u003e\n 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\u003ctd width=\"28.19148936170213%\" valign=\"bottom\"\u003e\n \u003cp\u003eD_dimer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.886524822695037%\"\u003e\n \u003cp\u003e1.266(1.132-1.423)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.22695035460993%\"\u003e\n \u003cp\u003e1.508(1.304-1.766)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.19148936170213%\"\u003e\n \u003cp\u003emFSgroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.886524822695037%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.22695035460993%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.19148936170213%\" valign=\"bottom\"\u003e\n \u003cp\u003e0-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.886524822695037%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.22695035460993%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.19148936170213%\"\u003e\n \u003cp\u003e3-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.886524822695037%\"\u003e\n \u003cp\u003e7.568(4.228-14.364)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.22695035460993%\"\u003e\n \u003cp\u003e2.918(1.245-6.994)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.19148936170213%\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.886524822695037%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.22695035460993%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.19148936170213%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.886524822695037%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.22695035460993%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.19148936170213%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.886524822695037%\"\u003e\n \u003cp\u003e0.984(0.591-1.662)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\n \u003cp\u003e0.952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.22695035460993%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n 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width=\"11.347517730496454%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.19148936170213%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.886524822695037%\"\u003e\n \u003cp\u003e0.932(0.287-2.635)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\n \u003cp\u003e0.898\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.22695035460993%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.19148936170213%\"\u003e\n \u003cp\u003eLength\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.886524822695037%\"\u003e\n \u003cp\u003e1.058(0.994-1.128)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.22695035460993%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.19148936170213%\"\u003e\n \u003cp\u003eWidth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.886524822695037%\"\u003e\n \u003cp\u003e1.064(0.991-1.151)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.22695035460993%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.19148936170213%\"\u003e\n \u003cp\u003eHeight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.886524822695037%\"\u003e\n \u003cp\u003e1.09(1.004-1.197)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.22695035460993%\"\u003e\n \u003cp\u003e1.057(0.958-1.178)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\n \u003cp\u003e0.283\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.19148936170213%\"\u003e\n \u003cp\u003eVolume\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.886524822695037%\"\u003e\n \u003cp\u003e1(1.000-1.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\n \u003cp\u003e0.284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.22695035460993%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.19148936170213%\"\u003e\n \u003cp\u003eMAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.886524822695037%\"\u003e\n \u003cp\u003e0.994(0.981-1.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\n \u003cp\u003e0.360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.22695035460993%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.19148936170213%\"\u003e\n \u003cp\u003eAneurysm_volume\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.886524822695037%\"\u003e\n \u003cp\u003e1(1-1.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\n \u003cp\u003e0.284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.22695035460993%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.19148936170213%\"\u003e\n \u003cp\u003eAnterior cerebral artery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.886524822695037%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.22695035460993%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.19148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;Middle cerebral artery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.886524822695037%\"\u003e\n \u003cp\u003e1.344(0.774-2.354)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\n \u003cp\u003e0.296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.22695035460993%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.19148936170213%\"\u003e\n \u003cp\u003eInternal carotid artery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.886524822695037%\"\u003e\n \u003cp\u003e1.303(0.592-2.801)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\n \u003cp\u003e0.502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.22695035460993%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.19148936170213%\"\u003e\n \u003cp\u003ePosterior communicating artery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.886524822695037%\"\u003e\n \u003cp\u003e0.806(0.351-1.761)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\n \u003cp\u003e0.598\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.22695035460993%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.19148936170213%\"\u003e\n \u003cp\u003ePosterior circulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.886524822695037%\"\u003e\n \u003cp\u003e4.032(0.933-20.641)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.22695035460993%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.347517730496454%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\"\u003e\n \u003cp\u003ea Univariable logistic regression models.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\"\u003e\n \u003cp\u003eb Multivariable logistic regression model adjusting for age at diagnosis- blood group- GCS score- aneurysm width- aneurysm height- D-dimer and PLT.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\"\u003e\n \u003cp\u003eAbbreviations: OR,odd ratio; CI, confidence interval; MAP, mean arterial pressure; GCS, Glasgow Coma Scale.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;The forest plot displays the significant multivariable factors (\u003cstrong\u003eFigure 3A\u003c/strong\u003e and \u003cstrong\u003eB\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe relative weights of impact on the outcome\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe further calculated the predictive relative weights of significant variables in the multivariate logistic regression analysis. In the endovascular coiling group, the proportions of significant variables were as follows: Hunt-Hess grade accounted for 32.78%, mFS for 31.99%, D-dimer for 13.73%, age for 11.61%, body temperature for 5.26%, and GCS for 4.63%. In the surgical clipping group, mFS accounted for 38.02%, Hunt-Hess grade for 33.55%, D-dimer for 19.99%, and GCS for 8.44% (\u003cstrong\u003eFigure 4A\u003c/strong\u003e and \u003cstrong\u003eB\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNomogram for functional outcome and validation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the multivariate logistic regression model,\u0026nbsp;significant\u0026nbsp;predictors were identified to construct the nomogram model, visualizing individual risks of adverse functional outcomes one month post-surgery\u0026nbsp;(\u003cstrong\u003eFigure 5A\u0026nbsp;\u003c/strong\u003eand\u003cstrong\u003e\u0026nbsp;6A\u003c/strong\u003e). In the endovascular coiling group, the ROC curve demonstrated good predictive accuracy, with an AUC value of 0.847 (95% CI: 0.800-0.894)\u0026nbsp;(\u003cstrong\u003eFigure 5B\u003c/strong\u003e),\u0026nbsp;the calibration curve showed close alignment between the model and predictions\u0026nbsp;(\u003cstrong\u003eFigure 5C\u003c/strong\u003e), while DCA curve indicated good clinical predictive value across different threshold probabilities\u0026nbsp;(\u003cstrong\u003eFigure 5D\u003c/strong\u003e). Similarly, in the surgical clipping group, the ROC curve displayed good predictive accuracy, with an AUC value of 0.841 (95% CI: 0.796-0.886)\u0026nbsp;(\u003cstrong\u003eFigure 6B\u003c/strong\u003e), the calibration curve showed basic alignment with predictions\u0026nbsp;(\u003cstrong\u003eFigure 6C\u003c/strong\u003e), and DCA curve similarly demonstrated good clinical predictive value across different threshold probabilities\u0026nbsp;(\u003cstrong\u003eFigure 6D\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNomogram predictive risk stratification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the risk scores predicted by the Nomogram, patients were categorized into three groups: low risk, medium risk, and high risk. Utilizing logistic regression, we observed significant disparities in adverse functional outcomes across these risk groups. In the endovascular coiling group, individuals in the medium-risk group faced a 2.791-fold higher risk compared to those in the low-risk category, while those in the high-risk group experienced a substantially elevated risk of 17.506 times compared to the low-risk group (\u003cstrong\u003eTable 5\u003c/strong\u003e). Similarly, within the surgical clipping group, participants classified in the medium-risk tier were at a 5.291-fold increased risk relative to the low-risk counterparts, with those in the high-risk category encountering an even greater risk, elevated by a substantial 26.641 times compared to the low-risk group (\u003cstrong\u003eTable 5\u003c/strong\u003e). These differences were statistically significant, highlighting the clinical significance of our nomogram model incorporating clinical scores and parameters.\u003c/p\u003e\n\u003cp\u003eTable 5. \u0026nbsp;Nomogram risk stratification .\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"565\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.141592920353983%\" rowspan=\"2\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.75221238938053%\" colspan=\"2\"\u003e\n \u003cp\u003eendovascular coiling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.10619469026549%\" colspan=\"2\"\u003e\n \u003cp\u003esurgical clipping\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.23645320197044%\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.517241379310345%\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.960591133004925%\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.141592920353983%\"\u003e\n \u003cp\u003eLow-risk group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.601769911504423%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.150442477876107%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.84070796460177%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.265486725663717%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.141592920353983%\"\u003e\n \u003cp\u003eMedium-risk group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.601769911504423%\"\u003e\n \u003cp\u003e2.791(1.134,7.868)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.150442477876107%\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.84070796460177%\"\u003e\n \u003cp\u003e5.291(2.326,13.671)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.265486725663717%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.141592920353983%\"\u003e\n \u003cp\u003eHigh-risk group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.601769911504423%\"\u003e\n \u003cp\u003e17.506(8.014,46.109)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.150442477876107%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.84070796460177%\"\u003e\n \u003cp\u003e26.641(11.913,68.537)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.265486725663717%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe study suggests that factors such as gender, hypertension, alcohol consumption, and smoking[12-14], as well as aneurysm-related factors such as size and location[15-17]\u0026nbsp;influence the occurrence and development of aSAH. Additionally, proteomic analysis has revealed that proteins associated with focal adhesion and extracellular matrix-receptor interaction pathways also play a significant role in the occurrence and development of intracranial aneurysms[18]. Currently, endovascular coiling and neurosurgical clipping have become the main treatment methods for aSAH. In our study, the rate of adverse functional outcomes in patients receiving endovascular coiling was significantly lower than that in patients receiving neurosurgical clipping treatment, which is consistent with current guidelines[19, 20]. However, endovascular coiling treatment increases the economic burden during the patient\u0026apos;s hospitalization[21], and poses risks such as low complete occlusion rates and high risks of delayed rebleeding[22-24]. Based on this, we have established\u0026nbsp;two\u0026nbsp;new nomogram models, which\u0026nbsp;were\u0026nbsp;based on commonly used clinical scoring systems (such as the Hunt-Hess grading scale and the Glasgow Coma Scale) and some clinical parameters, to evaluate the early functional outcomes of these two different surgical methods.\u003c/p\u003e\n\u003cp\u003eThis study found that among patients treated with endovascular coiling, those who exhibited early poor functional outcomes were typically older and showed elevated mFS scores, increased Hunt-Hess grades, and lower GCS scores, consistent with prior research[25].Additionally, we also found that such patients often had elevated D-dimer levels, larger aneurysm sizes, and aneurysms located in the anterior cerebral arteries. In a retrospective study by Li et al.[21], it was observed that among patients undergoing surgical clipping, those with early poor functional outcomes were typically older and exhibited higher Hunt-Hess and mFS scores. These findings were corroborated by our study. Additionally, our research indicated that patients with a poor prognosis after surgical clipping demonstrated lower GCS scores, elevated D-dimer levels, and decreased platelet counts. To further investigate, we conducted univariate and multivariate logistic regression analyses.\u003c/p\u003e\n\u003cp\u003eThrough multifactorial logistic analysis, we identified age, body temperature, D-dimer, mFS, Hunt-Hess grade, and Glasgow Coma Scale (GCS) score as independent risk factors influencing the prognosis of the endovascular coiling group. For surgical clipping treatment, we determined Hunt-Hess grade, GCS score, mFS, and D-dimer level as independent factors influencing the prognosis. We found that D-dimer levels can predict outcomes in both surgical methods, possibly because: (1) In neurosurgical procedures, stress can impair patients\u0026apos; hemostatic and fibrinolytic functions; (2) Changes in D-dimer levels can reflect alterations in coagulation status, and abnormal D-dimer levels may indicate postoperative bleeding and thrombotic complications[26-28]. Interestingly, hypothermia may lead to poorer outcomes in patients undergoing endovascular coiling treatment, possibly due to its adverse effects on resuscitation and potential myocardial damage[29]. These findings underscore the importance of individualized perioperative management strategies[30].\u003c/p\u003e\n\u003cp\u003eFor the first time, this study introduces two innovative predictive models specifically designed for aSAH patient treatment strategies. These models can effectively predict early functional outcomes by stratifying patients according to risk levels. Additionally, we found for the first time that, regardless of whether patients undergo endovascular coiling or surgical clipping, D-dimer levels emerge as the significant variable affecting early functional outcomes, second only to the Hunt-Hess grade and mFS grading system, and superior to the GCS score. This discovery provides new insights for personalized clinical treatment approaches.\u003c/p\u003e\n\u003cp\u003eThe study has some unavoidable limitations. Firstly, being a retrospective study, there might be confounding factors that could impact the stability of the results. Secondly, the assessment of patients\u0026apos; functional outcomes was conducted only one month post-surgery, necessitating further long-term follow-up to evaluate extended outcomes.\u003c/p\u003e\n\u003cp\u003eIn summary, the customized predictive models tailored to endovascular coiling and neurosurgical clipping highlight the importance of scoring systems and clinical parameters (such as D-dimer), demonstrating significant clinical utility.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Key Project of the Medical-Educational Collaborative Innovation Fund at Jiangsu University for the year 2023 (Project N0.JDY2023011).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study followed the ethical principles of the Helsinki Declaration and its amendments, obtaining approval from the hospital ethics committee, with ethical approval numbers 2023043 and K-20230086-w. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declared\u0026nbsp;that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDW designed the study. LC, MQ, and YYD collected clinical data from hospitals in China and drafted the initial manuscript. KC, KJ, and YPL analyzed the data. DW, LC, KC, MQ, and YYD contributed to the revision of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eEtminan N, Chang HS, Hackenberg K, et al. Worldwide Incidence of Aneurysmal Subarachnoid Hemorrhage According to Region, Time Period, Blood Pressure, and Smoking Prevalence in the Population: A Systematic Review and Meta-analysis. JAMA Neurol. 2019;76:588\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIngall T, Asplund K, M\u0026auml;h\u0026ouml;nen M, Bonita R. A multinational comparison of subarachnoid hemorrhage epidemiology in the WHO MONICA stroke study. Stroke. 2000;31:1054\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBakker MK, Kanning JP, Abraham G, et al. Genetic Risk Score for Intracranial Aneurysms: Prediction of Subarachnoid Hemorrhage and Role in Clinical Heterogeneity. Stroke. 2023;54:810\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHop JW, Rinkel GJ, Algra A, van Gijn J. Case-fatality rates and functional outcome after subarachnoid hemorrhage: a systematic review. Stroke. 1997;28:660\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu A, Huang J. Treatment of Intracranial Aneurysms: Clipping Versus Coiling. Curr Cardiol Rep. 2015;17:628.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLindgren A, Vergouwen MD, van der Schaaf I, et al. Endovascular coiling versus neurosurgical clipping for people with aneurysmal subarachnoid haemorrhage. Cochrane Database Syst Rev. 2018;8:Cd003085.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eG\u0026uuml;resir E, Beck J, Vatter H, et al. Subarachnoid hemorrhage and intracerebral hematoma: incidence, prognostic factors, and outcome. Neurosurgery. 2008;63:1088\u0026ndash;93. discussion 1093\u0026thinsp;\u0026ndash;\u0026thinsp;1084.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMolyneux AJ, Birks J, Clarke A, et al. The durability of endovascular coiling versus neurosurgical clipping of ruptured cerebral aneurysms: 18 year follow-up of the UK cohort of the International Subarachnoid Aneurysm Trial (ISAT). Lancet. 2015;385:691\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu W, Ling X, Petersen JD, et al. Clipping versus coiling for aneurysmal subarachnoid hemorrhage: a systematic review and meta-analysis of prospective studies. Neurosurg Rev. 2022;45:1291\u0026ndash;302.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLou M, Ding J, Hu B, et al. Chinese Stroke Association guidelines for clinical management of cerebrovascular disorders: executive summary and 2019 update on organizational stroke management. Stroke Vasc Neurol. 2020;5:260\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDekker L, Venema E, Pirson FAV, et al. Endovascular treatment in anterior circulation stroke beyond 6.5 hours after onset or time last seen well: results from the MR CLEAN Registry. Stroke Vasc Neurol. 2021;6:572\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBor AS, Koffijberg H, Wermer MJ, Rinkel GJ. Optimal screening strategy for familial intracranial aneurysms: a cost-effectiveness analysis. Neurology. 2010;74:1671\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBroderick JP, Brown RD Jr., Sauerbeck L, et al. Greater rupture risk for familial as compared to sporadic unruptured intracranial aneurysms. Stroke. 2009;40:1952\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLarsson SC, M\u0026auml;nnist\u0026ouml; S, Virtanen MJ, et al. Dietary fiber and fiber-rich food intake in relation to risk of stroke in male smokers. Eur J Clin Nutr. 2009;63:1016\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLall RR, Eddleman CS, Bendok BR, Batjer HH. Unruptured intracranial aneurysms and the assessment of rupture risk based on anatomical and morphological factors: sifting through the sands of data. Neurosurg Focus. 2009;26:E2.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLindner SH, Bor AS, Rinkel GJ. Differences in risk factors according to the site of intracranial aneurysms. J Neurol Neurosurg Psychiatry. 2010;81:116\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLinn FH, Rinkel GJ, Algra A, van Gijn J. Incidence of subarachnoid hemorrhage: role of region, year, and rate of computed tomography: a meta-analysis. Stroke. 1996;27:625\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Y, Song Y, Liu P, et al. Comparative bioinformatics analysis between proteomes of rabbit aneurysm model and human intracranial aneurysm with label-free quantitative proteomics. CNS Neurosci Ther. 2021;27:101\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eConnolly ES Jr., Rabinstein AA, Carhuapoma JR, et al. Guidelines for the management of aneurysmal subarachnoid hemorrhage: a guideline for healthcare professionals from the American Heart Association/american Stroke Association. Stroke. 2012;43:1711\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSteiner T, Juvela S, Unterberg A, et al. European Stroke Organization guidelines for the management of intracranial aneurysms and subarachnoid haemorrhage. Cerebrovasc Dis. 2013;35:93\u0026ndash;112.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi R, Lin F, Chen Y et al. In-hospital complication-related risk factors for discharge and 90-day outcomes in patients with aneurysmal subarachnoid hemorrhage after surgical clipping and endovascular coiling: a propensity score-matched analysis. J Neurosurg 2021; 1\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBakker NA, Metzemaekers JD, Groen RJ, et al. International subarachnoid aneurysm trial 2009: endovascular coiling of ruptured intracranial aneurysms has no significant advantage over neurosurgical clipping. Neurosurgery. 2010;66:961\u0026ndash;2.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMolyneux AJ, Kerr RS, Yu LM, et al. International subarachnoid aneurysm trial (ISAT) of neurosurgical clipping versus endovascular coiling in 2143 patients with ruptured intracranial aneurysms: a randomised comparison of effects on survival, dependency, seizures, rebleeding, subgroups, and aneurysm occlusion. Lancet. 2005;366:809\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRisselada R, Lingsma HF, Bauer-Mehren A, et al. Prediction of 60 day case-fatality after aneurysmal subarachnoid haemorrhage: results from the International Subarachnoid Aneurysm Trial (ISAT). Eur J Epidemiol. 2010;25:261\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhuang D, Ren Z, Sheng J, et al. A dynamic nomogram for predicting unfavorable prognosis after aneurysmal subarachnoid hemorrhage. Ann Clin Transl Neurol. 2023;10:1058\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu JH, Li XK, Chen ZB, et al. D-dimer may predict poor outcomes in patients with aneurysmal subarachnoid hemorrhage: a retrospective study. Neural Regen Res. 2017;12:2014\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaatikainen E, Kiiski H, Kuitunen A, et al. Increased blood coagulation is associated with poor neurological outcome in aneurysmal subarachnoid hemorrhage. J Neurol Sci. 2024;458:122943.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFang F, Wang P, Yao W, et al. Association between D-dimer levels and long-term mortality in patients with aneurysmal subarachnoid hemorrhage. Neurosurg Focus. 2022;52:E8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao H, Shang F, Qi M et al. Related Factors and a Threshold of the Maximum Neuron-Specific Enolase Value Affecting the Prognosis of Patients with Aneurysmal Subarachnoid Hemorrhage. Appl Bionics Biomech. 2022; 2022: 7596426.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharma D. Perioperative Management of Aneurysmal Subarachnoid Hemorrhage. Anesthesiology. 2020;133:1283\u0026ndash;305.\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":"aneurysmal subarachnoid hemorrhage, early functional outcomes, endovascular coiling, surgical clipping, nomogram","lastPublishedDoi":"10.21203/rs.3.rs-4465305/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4465305/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eThis study aims to analyze the early functional outcomes of patients with aneurysmal subarachnoid hemorrhage (aSAH) undergoing endovascular coiling or surgical clipping, and to construct predictive models based on both treatment modalities.\u003c/p\u003e\u003ch2\u003eMaterials and Methods\u003c/h2\u003e \u003cp\u003ePatients diagnosed with aSAH were recruited from two Chinese hospitals between 1st January ,2015 and 31st December,2022. These patients were categorized into two groups: the endovascular coiling group and the surgical clipping group. Independent risk factors were determined using Least Absolute Shrinkage and Selection Operator regression and multivariate logistic regression. The relative weights of these significant factors were computed, and nomograms were developed accordingly. Subsequent validation analyses were conducted to assess the performance of the nomograms.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eMultifactorial analyses revealed that Hunt-Hess grade, GCS score, mFS, D-dimer, age, and body temperature can predict early functional outcomes of endovascular coiling (all \u003cem\u003eP\u003c/em\u003e values\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while Hunt-Hess grade, GCS score, mFS, and D-dimer can predict early functional outcomes of surgical clipping (all \u003cem\u003eP\u003c/em\u003e values\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Further computation of weights showed that the contributions of Hunt-Hess grade, mFS, GCS score and D-dimer were 32.78%, 31.99%, 4.63% and 13.73%, respectively, for endovascular coiling, and 33.55%, 38.02%, 8.44% and 19.99% for surgical clipping. Nomograms were constructed for the endovascular coiling and surgical clipping groups, and their discriminative ability and clinical utility were validated using ROC curves, calibration curves, and DCA curves, demonstrating good performance.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study developed predictive nomogram models for early functional outcomes of patients with aSAH undergoing endovascular coiling or surgical clipping. It underscores the significance of scoring systems and clinical parameters (such as D-dimer), showing strong clinical utility.\u003c/p\u003e","manuscriptTitle":"Prediction of early postoperative functional outcomes in patients with aneurysmal subarachnoid hemorrhage: a Chinese bicenter retrospective study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-11 18:34:40","doi":"10.21203/rs.3.rs-4465305/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":"35ed8707-82b0-4ce7-a9ac-98b1538a91b7","owner":[],"postedDate":"June 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-06-18T11:51:29+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-11 18:34:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4465305","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4465305","identity":"rs-4465305","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}