A nomogram for predicting the risk of tracheostomy following surgical procedures treatment of aneurysmal subarachnoid hemorrhage.

A nomogram for predicting the risk of tracheostomy following surgical procedures treatment of aneurysmal subarachnoid hemorrhage. | 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 A nomogram for predicting the risk of tracheostomy following surgical procedures treatment of aneurysmal subarachnoid hemorrhage. Dongyuan Zhang, Liangsheng Peng, Bohong Wang, Jiahao Liu, Xi Zhang, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5766955/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Objective: Effective and timely airway management is particularly crucial for recovery in patients with aneurysmal subarachnoid hemorrhage following surgical procedures treatment. This study aimed to develop a stable nomogram model to predict the likelihood of postoperative tracheostomy in these patients. Methods: The clinical data and imaging findings of 249 patients with aneurysmal subarachnoid hemorrhage (aSAH) by microsurgical clipping or endovascular treatment on admission from January 2021 to October 2023 were retrospectively analyzed. Least absolute shrinkage and selection operator (LASSO), logistic regression analyses, and a nomogram were used to develop the prognostic models. Receiver operating characteristic (ROC) curves and Hosmer–Lemeshow tests were used to assess discrimination and calibration. The bootstrap method (1,000 repetitions) was used for internal validation. Decision curve analysis (DCA) was conducted to evaluate the clinical validity of the nomogram. Results: The following four independent influencing factors were selected by LASSO-Logistic regression: the GCS score, preoperative pulmonary infection, operation method, and mechanical ventilation. The area under curve (AUC) was 0.928 in the training set and 0.878 in the internal validation set. Calibration curves and Hosmer–Lemeshow tests indicated that the nomogram demonstrated strong calibration ability. Additionally, the DCA curve revealed enhanced clinical utility of the nomogram. Conclusion: This study introduces a reliable and valuable nomogram model that is both applicable and user-friendly, facilitating accurate predictions of tracheostomy risk following surgical interventions for aneurysmal subarachnoid hemorrhage. This model aids clinicians in making timely and informed decisions, thereby significantly enhancing patient outcomes. Ruptured intracranial aneurysms aSAH surgery LASSO Logistic tracheostomy nomogram. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Approximately 85% of non-traumatic subarachnoid hemorrhage cases result from aneurysm rupture 1 , 2 . Despite a steady decline in the mortality rate of acute subarachnoid hemorrhage in recent years, nevertheless, around one-third of individuals remain permanently dependent on care, with only 30% achieving independent living 3 . Furthermore, 10–25% of aneurysmal subarachnoid hemorrhage (aSAH) patients die immediately after bleeding or before reaching the hospital. The advantages of tracheotomy over continued translaryngeal intubation are well documented, including the facilitation of pulmonary hygiene and the reduction of laryngeal and upper airway damage. Additional benefits encompass easier ventilator weaning, decreased need for sedation, enhanced nutrition and communication, improved oral hygiene, and subjective improvements in patient satisfaction 4 . Patients with aSAH who are unable to breathe and adequately protect their airway require the use of an endotracheal tube. However, compared with an endotracheal, a tracheotomy has the advantage of securing the airway with less physiological distress, allowing for a more rapid awakening of the aSAH patient 5 . The necessity, timing, potential benefits, and risks of tracheostomy for patients with severe aSAH remain challenging and controversial 6 . Recent studies have successfully predicted pulmonary infections following traumatic brain injury and severe intracranial hemorrhage, while also analyzing the efficacy of airway management strategies 7 – 9 . Unfortunately, there is a paucity of research focusing on the role of admission and baseline characteristics in predicting the risk of postoperative tracheotomy in patients with aSAH. This study employs Lasso and Logistic regression to develop nomograms and retrospectively analyze the relevant factors affecting postoperative tracheostomy in patients with aSAH. Furthermore, this research aims to investigate the determinants of postoperative airway management in patients with aSAH and to create predictive models that facilitate the early identification of high-risk individuals, thereby improving preoperative baseline conditions and optimizing clinical outcomes. Risk prediction models can forecast patient outcomes by utilizing highly influential indicators derived from medical history, physical examinations, laboratory tests, imaging studies, and surgical factors, thereby playing a crucial role in clinical management decision-making. Among various classification or regression studies, the least absolute shrinkage and selection operator (LASSO) has been identified as one of the most effective algorithms for predicting clinical outcomes (RT. Regression shrinkage and selection via LASSO) 10 , 11 . Materials and methods The study presents a retrospective analysis based on our institutional database containing all consecutive patients with aSAH treated between January 2021 and October 2023. The study protocol was approved by the institutional ethics review board of Shanxi Bethune Hospital. All patients or their relatives signed informed consent forms before treatment. Inclusion criteria are as follows: (1) Age ≥ 18 years; (2) Patients diagnosed with ruptured intracranial aneurysm resulting in SAH confirmed by CT angiography (CTA) or digital subtraction angiography (DSA) upon admission; (3) Surgical procrdures on the intracranial aneurysms, including microsurgical clipping and endovascular treatment. Exclusion criteria are as follows: (1) Age < 18 years; (2) Diagnosis of traumatic subarachnoid hemorrhage; (3) Lack of surgical procrdures treatment; (4) Prooperative multiple organ failure. The decision in favor of clipping or coiling was determined by a multidisciplinary team of neurosurgeons, interventional neuroradiologists, and anesthesiologists based on the clinical situation and consent of the family members. All tracheotomies were performed by traditional open surgical technique (otolaryngology and surgical specialty services) either at the bedside in the neurosurgical intensive care unit (NICU) or the operating room, with all procedures conducted with informed consent from the patient or their family and under clinical standards. There are four main indications for tracheotomy: long-term mechanical ventilation, weaning failure, upper airway obstruction, and copious secretions 12 . Relative indications for performing tracheotomy in the operating room rather than bedside included perceived anatomic difficulty, history of previous tracheotomy, hemodynamic instability, coagulopathy, the need for a concomitant surgical procedure, and attending preference. All patients routinely underwent comprehensive chest CT examination upon admission. Diagnostic criteria for pulmonary infection: Diagnosis is confirmed through clinical symptoms, laboratory indicators, chest X-rays, and other imaging and microbiological examinations, while excluding non-infectious pulmonary infiltrates such as pulmonary tuberculosis, lung cancer, and pulmonary embolism. Chest CT scans showing exudative changes consistent with lung infection were also used for diagnosis 13 . The following clinical and imaging characteristics of patients were collected: (1) Preoperative clinical data, including age, gender, medical history (Hypertension, Diabetes, Coronary Heart Disease, Smoking, Drinking), Hunt-Hess grade, modified Fisher grade, Glasgow Coma Scale (GCS) score, preoperative pulmonary infection, aneurysm location, and selected laboratory test results. (2) Surgical data: type of surgery, time of surgery, and whether additional procedures were performed; (3) For patients with respiratory failure, routine use of invasive mechanical ventilation was implemented, along with the total length of stay. Statistical Analysis The data analysis was performed using R statistical software (R version 4.3.1, R Project, www.r-project.org ). Data were tested for normality using the Shapiro-Wilk test. All continuous variables are presented by the median ± standard deviation when they conform to the normal distribution and are compared using the t-test, otherwise they are presented by the median (interquartile range) and compared using the Mann-Whitney U test. Categorical variables are described as frequency (percentage), with intergroup comparisons conducted using a chi-square test. All available baseline, imaging, and surgical variables were included in univariable logistic regression analysis to determine their association with postoperative tracheostomy. Variables with an univariable association of p < 0.10 were included for LASSO regression analysis and cross-validated to screen for variables with the most predictive value. The nomogram model is established according to multiple logistic regression, and the predictive values of models were evaluated by receiver operating characteristic (ROC) curve analysis and presented with the area under the curve (AUC). Evaluating model accuracy by plotting calibration curves based on Bootstrap 1000 times self-service resampling. Decision curve analysis (DCA) was used to assess the clinical validity of the predictive model. Statistical significance was set at P-value < 0.05 unless otherwise specified. Results Baseline characteristics This study included a total of 249 patients with intracranial aneurysms following surgical treatment, of whom 66 underwent tracheostomy postoperatively, while 183 did not receive tracheostomy. The median age of the patients was 57 [19.0, 81.0], with 105 males (42.2%) and 144 females (57.8%). As shown in the baseline characteristics table (Table 1 ), there were no significant differences between the tracheostomy group and the non-tracheostomy group in terms of gender, length of stay, hypertension, diabetes, coronary heart disease, smoking, drinking, LY count, and aneurysm location. However, there were statistically significant differences between the two groups in age, WBC count, NEUT count, Hunt-Hess grade, GCS score, preoperative pulmonary infection (Pre-op PI), use of mechanical ventilation, operation method, modified Fisher grade, and the need for external ventricular drainage, intracranial hematoma evacuation, decompressive craniectomy, and time of surgery (P < 0.05). Table 1 Baseline characteristics. NO YES OVERALL. P.value (N = 183) (N = 66) (N = 249) Gender Male 75 (41.0%) 30 (45.5%) 105 (42.2%) 0.82 Female 108 (59.0%) 36 (54.5%) 144 (57.8%) Age (years) Mean (SD) 55.5 (12.2) 59.8 (10.2) 56.6 (11.8) 0.0405 Median [Min, Max] 56.0 [19.0, 81.0] 63.0 [33.0, 78.0] 57.0 [19.0, 81.0] Length of stay (days) Mean (SD) 15.1 (6.88) 17.0 (9.48) 15.6 (7.68) 0.682 Median [Min, Max] 15.0 [1.00, 42.0] 15.0 [3.00, 56.0] 15.0 [1.00, 56.0] Hypertension NO 78 (42.6%) 21 (31.8%) 99 (39.8%) 0.307 YES 105 (57.4%) 45 (68.2%) 150 (60.2%) Diabetes NO 169 (92.3%) 57 (86.4%) 226 (90.8%) 0.355 YES 14 (7.7%) 9 (13.6%) 23 (9.2%) CHD NO 176 (96.2%) 59 (89.4%) 235 (94.4%) 0.122 YES 7 (3.8%) 7 (10.6%) 14 (5.6%) Smoke NO 130 (71.0%) 38 (57.6%) 168 (67.5%) 0.135 YES 53 (29.0%) 28 (42.4%) 81 (32.5%) Drink NO 146 (79.8%) 48 (72.7%) 194 (77.9%) 0.496 YES 37 (20.2%) 18 (27.3%) 55 (22.1%) WBC Mean (SD) 11.6 (4.41) 14.4 (4.48) 12.4 (4.58) < 0.001 Median [Min, Max] 11.2 [4.10, 28.0] 13.7 [5.60, 27.3] 11.7 [4.10, 28.0] NEUT Mean (SD) 9.93 (4.44) 12.5 (4.48) 10.6 (4.59) < 0.001 Median [Min, Max] 9.66 [2.29, 25.7] 12.3 [2.71, 24.5] 9.99 [2.29, 25.7] LY Mean (SD) 1.49 (5.06) 1.47 (2.28) 1.49 (4.49) 0.995 Median [Min, Max] 0.990 [0.250, 69.0] 1.01 [0.240, 18.0] 0.990 [0.240, 69.0] Hunt-Hess (Grade) 1 42 (23.0%) 2 (3.0%) 44 (17.7%) < 0.001 2 80 (43.7%) 6 (9.1%) 86 (34.5%) 3 46 (25.1%) 20 (30.3%) 66 (26.5%) 4 11 (6.0%) 26 (39.4%) 37 (14.9%) 5 4 (2.2%) 12 (18.2%) 16 (6.4%) GCS Mean (SD) 13.3 (2.67) 8.14 (3.80) 12.0 (3.78) < 0.001 Median [Min, Max] 14.0 [3.00, 15.0] 7.00 [3.00, 15.0] 14.0 [3.00, 15.0] Pre-op PI NO 67 (36.6%) 4 (6.1%) 71 (28.5%) < 0.001 YES 116 (63.4%) 62 (93.9%) 178 (71.5%) Mechanical Ventilation NO 157 (85.8%) 21 (31.8%) 178 (71.5%) < 0.001 YES 26 (14.2%) 45 (68.2%) 71 (28.5%) Operation clip 85 (46.4%) 51 (77.3%) 136 (54.6%) < 0.001 coil 98 (53.6%) 15 (22.7%) 113 (45.4%) Modified Fisher (Grade) 1 89 (48.6%) 7 (10.6%) 96 (38.6%) < 0.001 2 56 (30.6%) 15 (22.7%) 71 (28.5%) 3 16 (8.7%) 6 (9.1%) 22 (8.8%) 4 22 (12.0%) 38 (57.6%) 60 (24.1%) Aneurysm Location anterior cerebral circulation 166 (90.7%) 61 (92.4%) 227 (91.2%) 0.915 postetior cerebral circulation 17 (9.3%) 5 (7.6%) 22 (8.8%) External Ventricular Drainage NO 138 (75.4%) 32 (48.5%) 170 (68.3%) < 0.001 YES 45 (24.6%) 34 (51.5%) 79 (31.7%) Hematoma Aspiration NO 173 (94.5%) 47 (71.2%) 220 (88.4%) < 0.001 YES 10 (5.5%) 19 (28.8%) 29 (11.6%) Decompressive Craniectomy NO 177 (96.7%) 54 (81.8%) 231 (92.8%) < 0.001 YES 6 (3.3%) 12 (18.2%) 18 (7.2%) Time (min) Mean (SD) 188 (102) 247 (106) 204 (106) < 0.001 Median [Min, Max] 170 [55.0, 655] 244 [49.0, 545] 190 [49.0, 655] Abbreviations: CHD coronary heart disease, WBC white blood cell, NEUT neutrophils, LY lymphocytes, GCS glasgow coma scale, Pre-op PI preoperative pulmonary infection Screening of predictors The correlation heatmap between all variables is shown in Fig. 1 . The ROC curves and AUC values for all variables are shown in Fig. 2 + 3 . In the LASSO regression analysis, the optimal penalty coefficient (λ) was confirmed in the model by a tenfold cross-validation of the minimum criterion. The model is optimal when λ increases to a standard error (lambda.1SE), and variables with non-zero coefficients were screened out as potential predictors (Fig. 4 + 5 ). Univariate logistic regression analysis was performed for the total population ( Table 2 ) . To miss as few valuable variables as possible, we included variables with p less than 0.2 in the univariate logistic regression analysis in the Lasso regression analyses and cross-validated them. Table 2 Univariate logistic regression analysis. OR 95%CI P Gender 0.83 0.47–1.47 0.53 Age 1.03 1.01–1.06 0.01 Length of stay 1.03 0.99–1.07 0.09 Hypertension 1.59 0.88–2.89 0.13 Diabetes 1.91 0.78–4.64 0.16 CHD 2.98 1-8.86 0.05 Smoke 1.81 1.01–3.24 0.05 Drink 1.48 0.77–2.84 0.24 WBC 1.14 1.07–1.21 < 0.001 NEUT 1.13 1.06–1.21 < 0.001 LY 1 0.94–1.06 0.97 Hunt-Hess 3.91 2.7–5.66 < 0.001 GCS 0.68 0.62–0.75 < 0.001 Aneurysm Location 0.8 0.28–2.26 0.67 Modified Fisher 2.67 2.02–3.53 < 0.001 Pre-op PI 8.95 3.12–25.71 < 0.001 Operation 0.26 0.13–0.49 < 0.001 External Ventricular Drainage 3.26 1.81–5.87 < 0.001 Hematoma Aspiration 6.99 3.05–16.05 < 0.001 Decompressive Craniectomy 6.56 2.35–18.29 < 0.001 Time 1.01 1-1.01 < 0.001 Mechanical ventilation 12.94 6.66–25.13 < 0.001 Abbreviations: CHD coronary heart disease, WBC white blood cell, NEUT neutrophils, LY lymphocytes, GCS glasgow coma scale, Pre-op PI preoperative pulmonary infection After effective selection using LASSO-Logistic regression analysis, five potential predictive factors were identified: GCS score (OR: 0.756, 95%CI: 0.654–0.863, P < 0.001), modified Fisher grade (OR: 1.341, 95%CI: 0.877–2.029, P = 0.166), use of mechanical ventilation (OR: 4.952, 95%CI: 1.99-12.635, P < 0.001), operation approach (OR: 0.239, 95%CI: 0.085–0.608, P = 0.004), preoperative pulmonary infection (OR: 5.146, 95%CI: 1.451–23.566, P = 0.019) (Table 3 ). Table 3 Multifactor logistic regression analysis. B Wald OR(95%CI) P GCS -0.28 15.849 0.756(0.654 ~ 0.863) < 0.001 mFS 0.293 1.916 1.341(0.877 ~ 2.029) 0.166 Mechanical ventilation 1.600 11.681 4.952(1.99 ~ 12.635) < 0.001 Pre-op PI 1.638 5.492 5.146(1.451 ~ 23.566) 0.019 Operation -1.433 8.313 0.239(0.085 ~ 0.608) 0.004 Abbreviations: GCS glasgow coma scale, mFS modified Fisher Scale, Pre-op PI preoperative pulmonary infection Construction of nomogram prediction model According to the influencing factors screened by Lasso-logistic regression, multivariate logistic regression was carried out, to establish a prediction model for tracheotomy in aSAH patients in the present study and plotted nomogram (Fig. 6). Each important variable in the graph is assigned a weighted score from 0 to 100, and a total score is calculated by summing the scores for each risk factor in the nomogram to accurately predict the risk of tracheotomy in aSAH patients. The higher the total score, the higher the risk of tracheotomy. If a patient diagnosed with aSAH has a GCS score of 4 at the time of admission, along with concomitant pulmonary infection, undergoes surgical clipping of a cerebral aneurysm, and is on mechanical ventilation postoperatively, the probability of requiring tracheostomy is estimated to be approximately 90%. Validation and performance of nomogram Receiver operating characteristic (ROC) curve analysis was conducted to assess the discrimination of the prediction model based on a risk nomogram. As shown in Fig. 7 + 8 , The AUC of the training set was 0.928 (95% CI: 0.890–0.959), and the sensitivity and specificity of the prediction model were 0.918 and 0.814, respectively, when the critical value was the maximum value of the Youden index. The AUC of the validation set was 0.878 (95% CI: 0.717–0.963), and the sensitivity and specificity of the prediction model were 0.863 and 0.746, respectively, when the critical value was the maximum value of the Youden index. It indicates that the prediction model showed excellent prediction ability in both groups of patients. The Hosmer–Lemeshow test results for the prediction model in the training and validation sets are X² = 6.961 (P = 0.541 > 0.05) and X² = 3.633 (P = 0.821 > 0.05), which demonstrate that the model has good goodness-of-fit in both datasets. An internally validated bootstrap sampling method (1,000 times) was used to verify the nomogram model. The C-index value for the nomogram model was 0.927, implying that the model had good discriminatory and predictive power. The calibration curves plotted are all close to the reference line, indicating good agreement between predicted and observed outcomes (Fig. 9 ) . A DCA curve was used to evaluate the clinical utility of the model. The results are shown in Fig. 10 . The abscissa and ordinate represent threshold probability and net benefit, respectively. The lines marked “None” and “All” represent the two extreme cases. The further away the model curve is from these two lines, the better the clinical benefit of the nomogram. When the risk threshold was > 0.02, the risk nomogram model predicting a poor prognosis exhibited better clinical utilization in the DCA curve. Discussion Intracranial aneurysm (IA) is a complex disease characterized by pathological dilatations of the cerebral arteries, and the rupture of an IA leads to SAH 14 . A patient with an aSAH may present to the emergency department with a range of neurological symptoms, making airway management particularly critical 15 . Previous studies have shown that pulmonary complications frequently arise following aSAH and are associated with poor prognosis 16 , 17 . In patients with more severe conditions, prolonged intubation not only impedes the recovery of consciousness but also heightens the risk of airway injury, subsequently leading to pulmonary complications. A randomized clinical trial and a systematic review have demonstrated that early tracheostomy can reduce the length of hospital stay, duration of ICU stay, time on invasive mechanical ventilation, sedation time, and short-term mortality in critically ill patients 18 , 19 . Therefore, the early identification of patients requiring prolonged mechanical ventilation or those at risk of weaning failure, followed by tracheostomy, is essential for effective clinical management and prognosis 12 . In this study, we collected samples from patients with aSAH and established a predictive model incorporating four influencing factors: GCS score, invasive mechanical ventilation, preoperative pulmonary infection, and operation method. Through internal validation, we found that the nomogram demonstrated good calibration, predictive capability, and clinical applicability. The GCS is widely utilized in clinical practice to classify the severity of head injuries. A GCS score of less than 8 is associated with severe brain injury 20 . Damage to the central nervous system can disrupt the regulation of the respiratory center, thereby affecting respiratory rate and depth, airway tone, and normal neurological reflexes, such as the cough and swallowing reflexs, as well as apnea and breathing facilitation. Consequently, these patients often require intubation for airway protection 21 , 22 . For patients with prolonged expected recovery of consciousness postoperatively, early tracheostomy may be necessary to enhance ventilation. Recent studies have shown that the one-year mortality rate for patients with aSAH and a GCS score of less than 6 is significantly high, with a mortality rate of up to 100% for those who did not undergo surgical intervention 23 . Our study suggests that the GCS score can serve as a predictive factor for tracheostomy in patients with aSAH, aligning with the findings of Chen et al. 24 . We found that preoperative pulmonary infection and the use of mechanical ventilation can predict postoperative tracheostomy, and their relationship is inseparable, which is consistent with the findings of Chen et al. 24 . Pulmonary infection can lead to severe complications, including acute respiratory distress syndrome and respiratory failure. A study on acute respiratory distress syndrome (ARDS) indicated that pneumonia significantly prolongs the duration of mechanical ventilation and increases the incidence of ventilator-associated pneumonia 25 . However, Artigas A et al. found that diaphragmatic dysfunction in critically ill patients occurs primarily through two mechanisms: ventilator-induced diaphragmatic dysfunction and sepsis-induced dysfunction, which may ultimately lead to failure to wean off mechanical ventilation 26 . A single-center retrospective analysis indicated that successful weaning from mechanical ventilation and performing tracheostomy are critical for improving long-term survival outcomes for patients on prolonged mechanical ventilation 27 . For patients admitted with a diagnosis of aSAH who are intubated or receiving mechanical ventilation, if pneumonia is also present, attention must be given to airway management, and the early and appropriate use of antibiotics is essential. This study found that the choice of surgical approach significantly impacts postoperative airway management. For patients with ruptured intracranial aneurysms, the primary goal of surgical procedures treatment is to reduce the risk of rebleeding. Compared to endovascular treatment, craniotomy, and clipping increase surgical invasiveness, including craniotomy, scalp incision, and the inevitable intraoperative damage to brain tissue 28 , this also accounts for the longer operative duration of aneurysm clipping. We propose that patients undergoing craniotomy and clipping may require a longer recovery time, which in turn affects close postoperative airway management and increases the necessity for tracheostomy. A retrospective analysis and a systematic review indicate that, relative to endovascular treatment, craniotomy and clipping are associated with prolonged hospital stays 29 , 30 . This predictive model may assist clinicians in evaluating the risk of postoperative tracheostomy based on initial GCS scores upon admission, the presence of pulmonary infection, the choice of surgical approach, and the need for invasive mechanical ventilation. However, in this investigation, age, the location of the aneurysm, and the Hunt-Hess grade were ultimately not selected. In a similar test with the same sample size, we found that the size of the aneurysm was also not considered a predictive factor for postoperative tracheostomy. We speculate that this may be due to biases resulting from the small sample size or that these factors may have a more significant correlation with the functional prognosis of the patients. In addition, the modified Fisher scale (mFS) was identified as an independent predictor for tracheostomy after aSAH. However, due to its stronger potential association with post-aSAH vascular spasm or other neurologic complications, it was not included in the final predictive model 31 . Recent research has focused on predicting tracheostomy requirements in critically ill patients and those undergoing nasopharyngeal surgeries. The innovation of this study lies in its application of LASSO regression to analyze the factors influencing postoperative tracheostomy in patients with aSAH, ultimately establishing a predictive model and conducting internal validation. Furthermore, to our knowledge, this study is the first to develop an accurate predictive model based on GCS, preoperative pulmonary infection, operation method, and mechanical ventilation. This study has several limitations that warrant attention. First, although the risk model demonstrated excellent accuracy following internal validation, it still lacks external validation. Second, this study is a retrospective analysis, and the conclusions drawn need to be validated and refined in prospective cohort studies. Third, given the urgency of addressing hemorrhagic complications arising from ruptured intracranial aneurysms, our study does not include a detailed classification or stratified discussion of the severity of preoperative pulmonary infections in patients. Finally, this is a case-control study with a relatively small sample size and incomplete or missing medical history records, which may lead to potential bias in the results. Conclusion In this study, we identified four risk factors that influence the likelihood of postoperative tracheostomy in patients with aSAH through LASSO, univariate, and multivariate logistic regression analyses. The identified factors are the GCS score, preoperative pulmonary infection, operation method, and mechanical ventilation, all of which exhibit interactions among these clinical characteristics. Additionally, we constructed a nomogram using these four predictive factors. Internal validation of this model demonstrates good accuracy and clinical utility, assisting surgeons in evaluating the risk of early tracheostomy, ultimately benefiting patient outcomes. Abbreviations IA, intracranial aneurysm SAH, subarachnoid hemorrhage aSAH, aneurysmal subarachnoid hemorrhage CTA, CT angiography DSA, digital subtraction angiography NICU, neurosurgical intensive care unit CHD, Coronary Heart Disease GCS, Glasgow Coma Scale WBC, white blood cell NEUT, neutrophils LY, lymphocytes Pre-op PI, preoperative pulmonary infection mFS, modified Fisher Scale DCI, delayed cerebral ischemia ARDS, acute respiratory distress syndrome Declarations Human Ethics and Consent to Participate declarations Informed consent was obtained from all patients. This study was approved by the Ethics Committee of the third hospital of Shanxi Medical University and was conducted according to the Declaration of Helsinki. Competing interests: The authors declare no competing interests. Funding: This project is supported by the Scientific Research Initiation Fund for Talent Introduction of Shanxi Bethune Hospital (Project No.2021RC006) . Author Contribution Conception and design: Dongyuan Zhang, Liangsheng Peng, Xinmin Ding. Acquisition of data: Liangsheng Peng, Jiahao Liu, Xi Zhang, Ziyuan Liu, Bohong Wang. Analysis and interpretation of data: Dongyuan Zhang, Liangsheng Peng. Drafting the article: Dongyuan Zhang. Critically revising the article: Dongyuan Zhang, Liangsheng Peng, Xinmin Ding. Statistical analysis: Dongyuan Zhang, Liangsheng Peng, Bohong Wang. Administrative/technical/material support: Xiaolong Wang, Li Han, Ying Zhang. Study supervision: Xinmin Ding.All authors reviewed the manuscript. Availability of data and materials: The data presented in this study are available on request from the correspond ing author. References Thompson BG, Brown RD, Jr., Amin-Hanjani S, et al. 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Associations among Genetic Variants and Intracranial Aneurysm in a Chinese Population. Yonsei Med J. 2019;60(7): 651-658. https://doi.org/10.3349/ymj.2019.60.7.651. Grimm JW. Aneurysmal Subarachnoid Hemorrhage: A Potentially Lethal Neurological Disease. J Emerg Nurs. 2015;41(4): 281-284. https://doi.org/10.1016/j.jen.2014.12.018. Friedman JA, Pichelmann MA, Piepgras DG, et al. Pulmonary complications of aneurysmal subarachnoid hemorrhage. Neurosurgery. 2003;52(5): 1025-1031; discussion 1031-1022. Claassen J, Vu A, Kreiter KT, et al. Effect of acute physiologic derangements on outcome after subarachnoid hemorrhage. Crit Care Med. 2004;32(3): 832-838. https://doi.org/10.1097/01.ccm.0000114830.48833.8a. Diaz-Prieto A, Mateu A, Gorriz M, et al. A randomized clinical trial for the timing of tracheotomy in critically ill patients: factors precluding inclusion in a single center study. Crit Care. 2014;18(5): 585. https://doi.org/10.1186/s13054-014-0585-y. Liu X, Wang HC, Xing YW, He YL, Zhang ZF, Wang T. The effect of early and late tracheotomy on outcomes in patients: a systematic review and cumulative meta-analysis. Otolaryngol Head Neck Surg. 2014;151(6): 916-922. https://doi.org/10.1177/0194599814552415. Roppolo LP, Walters K. Airway management in neurological emergencies. Neurocrit Care. 2004;1(4): 405-414. https://doi.org/10.1385/ncc:1:4:405. Dunham CM, Barraco RD, Clark DE, et al. Guidelines for emergency tracheal intubation immediately after traumatic injury. J Trauma. 2003;55(1): 162-179. https://doi.org/10.1097/01.ta.0000083335.93868.2c. Bianchi AL, Gestreau C. The brainstem respiratory network: an overview of a half century of research. Respir Physiol Neurobiol. 2009;168(1-2): 4-12. https://doi.org/10.1016/j.resp.2009.04.019. Lashkarivand A, Sorteberg W, Rosseland LA, Sorteberg A. Survival and outcome in patients with aneurysmal subarachnoid hemorrhage in Glasgow coma score 3-5. Acta Neurochir (Wien). 2020;162(3): 533-544. https://doi.org/10.1007/s00701-019-04190-y. Chen XH, Zhao JJ, Chen C, Yao L. Establishment and validation of a predictive model for tracheotomy in critically ill patients and analysis of the impact of different tracheotomy timing on patient prognosis. BMC Anesthesiol. 2024;24(1): 175. https://doi.org/10.1186/s12871-024-02558-x. Hsu PC, Lin YT, Kao KC, et al. Risk factors for prolonged mechanical ventilation in critically ill patients with influenza-related acute respiratory distress syndrome. Respir Res. 2024;25(1): 9. https://doi.org/10.1186/s12931-023-02648-3. Artigas A, Bernard GR, Carlet J, et al. The American-European Consensus Conference on ARDS, part 2. Ventilatory, pharmacologic, supportive therapy, study design strategies and issues related to recovery and remodeling. Intensive Care Med. 1998;24(4): 378-398. https://doi.org/10.1007/s001340050585. Huang C. The Survival Outcomes of Patients Requiring Prolonged Mechanical Ventilation. Medicina (Kaunas). 2023;59(3). https://doi.org/10.3390/medicina59030614. Campos JK, Lien BV, Wang AS, Lin LM. Advances in endovascular aneurysm management: coiling and adjunctive devices. Stroke Vasc Neurol. 2020;5(1): 14-21. https://doi.org/10.1136/svn-2019-000303. Bekelis K, Gottlieb DJ, Su Y, et al. Comparison of clipping and coiling in elderly patients with unruptured cerebral aneurysms. J Neurosurg. 2017;126(3): 811-818. https://doi.org/10.3171/2016.1.Jns152028. Zhang X, Tang H, Huang Q, Hong B, Xu Y, Liu J. Total Hospital Costs and Length of Stay of Endovascular Coiling Versus Neurosurgical Clipping for Unruptured Intracranial Aneurysms: Systematic Review and Meta-Analysis. World Neurosurg. 2018;115: 393-399. https://doi.org/10.1016/j.wneu.2018.04.028. Frontera JA, Claassen J, Schmidt JM, et al. Prediction of symptomatic vasospasm after subarachnoid hemorrhage: the modified fisher scale. Neurosurgery. 2006;59(1): 21-27; discussion 21-27. https://doi.org/10.1227/01.neu.0000243277.86222.6c. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 08 Jan, 2025 Editor assigned by journal 07 Jan, 2025 Submission checks completed at journal 07 Jan, 2025 First submitted to journal 05 Jan, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5766955","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":399540786,"identity":"271df2e0-d1df-4715-963e-425c383fb19a","order_by":0,"name":"Dongyuan Zhang","email":"","orcid":"","institution":"Third Hospital of Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Dongyuan","middleName":"","lastName":"Zhang","suffix":""},{"id":399540787,"identity":"968613a2-e89a-41b0-86b7-c482a57b0720","order_by":1,"name":"Liangsheng Peng","email":"","orcid":"","institution":"Third Hospital of Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Liangsheng","middleName":"","lastName":"Peng","suffix":""},{"id":399540788,"identity":"d91d235a-5974-4b1d-b0c5-bb1f9452cc73","order_by":2,"name":"Bohong Wang","email":"","orcid":"","institution":"Third Hospital of Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Bohong","middleName":"","lastName":"Wang","suffix":""},{"id":399540789,"identity":"12d1fab1-f046-4fe3-9243-28430c758e33","order_by":3,"name":"Jiahao Liu","email":"","orcid":"","institution":"Third Hospital of Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jiahao","middleName":"","lastName":"Liu","suffix":""},{"id":399540790,"identity":"8139ff13-db2f-4df5-b93e-be2934578e2c","order_by":4,"name":"Xi Zhang","email":"","orcid":"","institution":"Third Hospital of Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xi","middleName":"","lastName":"Zhang","suffix":""},{"id":399540791,"identity":"a15b8c30-b3aa-4322-8588-82e7325669bd","order_by":5,"name":"Ziyuan Liu","email":"","orcid":"","institution":"Third Hospital of Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ziyuan","middleName":"","lastName":"Liu","suffix":""},{"id":399540793,"identity":"14e74a10-5a69-4903-9693-2cad6c35bbaa","order_by":6,"name":"Ying Zhang","email":"","orcid":"","institution":"Third Hospital of Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Zhang","suffix":""},{"id":399540795,"identity":"882b0526-f426-40c2-9cf5-4767ad7c2ceb","order_by":7,"name":"Xiaolong Wang","email":"","orcid":"","institution":"Shanxi Bethune Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiaolong","middleName":"","lastName":"Wang","suffix":""},{"id":399540796,"identity":"9e60ef15-901b-4e4c-88da-55b5fa219942","order_by":8,"name":"Li Han","email":"","orcid":"","institution":"Shanxi Bethune Hospital","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Han","suffix":""},{"id":399540797,"identity":"504e6954-69ab-4476-83b0-1657d519392f","order_by":9,"name":"XinMin Ding","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvklEQVRIiWNgGAWjYDCCAwkggoGBn5n58APStEi2s6UZkKbF4DyPggRROviO5z78XHDGJnHzYR4GA4Yam2iCWiTPPDeWnnEjLXHbYd4DDxiOpeU2ENJicCONQZrnw2GgFr4EA8aGw0RpYf7N8+F/4uZmHgMJYrWwSfPcOJC4gZlYLZJnnrFZ85xJNp5xGBjICcT4he94GvNtnmN2sv39hw8/+FBjQ1gLDDiCVSYQqxwE7ElRPApGwSgYBSMMAAAj50UF8ocC6AAAAABJRU5ErkJggg==","orcid":"","institution":"Third Hospital of Shanxi Medical University","correspondingAuthor":true,"prefix":"","firstName":"XinMin","middleName":"","lastName":"Ding","suffix":""}],"badges":[],"createdAt":"2025-01-05 09:08:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5766955/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5766955/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73672153,"identity":"35397a31-3a1d-498a-8d58-d2b4f657e49c","added_by":"auto","created_at":"2025-01-13 12:47:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":141292,"visible":true,"origin":"","legend":"\u003cp\u003eThe heatmap of features correlation. Abbreviations: TT tracheotomy, LOS length of stay, HP hypertension, DM diabetes mellitus, CHD coronary heart disease, WBC white blood cell, NEUT neutrophils, LY lymphocytes, GCS glasgow coma scale, LOA location of aneurysm, mFS modified Fisher grade, Pre-op PI preoperative pulmonary infection, EVD external ventricular drainage, DC decompressive craniectomy, HA hematoma aspiration, IMV invasive mechanical ventilation\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5766955/v1/6ad9782f2fa380b1539b3da5.png"},{"id":73672151,"identity":"11bfd2de-2e78-4ddf-a1e7-652240a65a48","added_by":"auto","created_at":"2025-01-13 12:47:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":184710,"visible":true,"origin":"","legend":"\u003cp\u003eThe ROC curves for 22 variables.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5766955/v1/b0677b0dbfb03b4bdf4dd019.png"},{"id":73674103,"identity":"28d70132-eabd-4ec6-bc44-2fc790224e38","added_by":"auto","created_at":"2025-01-13 12:55:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":57795,"visible":true,"origin":"","legend":"\u003cp\u003eThe AUC values of 22 variables.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5766955/v1/602311ff18222699d0fe9a60.png"},{"id":73674105,"identity":"7f4e02a7-b3af-466c-a314-ee4eb3034f9a","added_by":"auto","created_at":"2025-01-13 12:55:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":51765,"visible":true,"origin":"","legend":"\u003cp\u003eSelection of potential predictors using a LASSO regression model. The optimal parameter (lambda) in the LASSO model was confirmed in the model by tenfold cross-validation of the minimum criterion. A dashed vertical line is drawn at the optimal value by using the smallest criterion (left dashed line) and one standard error of the smallest criterion (lambda.1SE) (right dashed line). It effectively decreased the 22 influencing factors to 5 as potential predictors.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5766955/v1/67dd0620b031ca032d18820d.png"},{"id":73672154,"identity":"9b7a7602-6685-489e-9a77-96935bdae3df","added_by":"auto","created_at":"2025-01-13 12:47:06","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":86069,"visible":true,"origin":"","legend":"\u003cp\u003eFeature selection using the LASSO logistic regression model. The LASSO coefficient profiles of the 22 features. A coefficient profile plot was produced against the log (lambda) sequence.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5766955/v1/0d3e4e0c5dc8cab680556bbb.png"},{"id":73672164,"identity":"1cce0fea-af2d-4cd9-b334-c9ac308adfdb","added_by":"auto","created_at":"2025-01-13 12:47:06","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":31963,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram. Each variable is scored from 0 to 100. A vertical line is drawn on the axis as a score according to the particular state the variable is in. The scores for each variable are summed to give a total score, based on which to assess the patient’s risk of tracheotomy. Pre-op.PI: 1 = YES, 2 = NO. Mechanical ventilation: 1 = YES, 2 = NO. Operation: 1 = microsurgical clipping, 2 = endovascular treatment.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-5766955/v1/e9bc05c8e6fe43b9421984ef.png"},{"id":73672157,"identity":"e2572b94-db13-46e6-b43c-d65d33e6f0ae","added_by":"auto","created_at":"2025-01-13 12:47:06","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":48294,"visible":true,"origin":"","legend":"\u003cp\u003eThe ROC curves of the nomogram for training sets was 0.928 (0.890-0.959).\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-5766955/v1/3fee418dbbfa14af1c821d3a.png"},{"id":73672162,"identity":"c560cf59-759c-47f7-8855-1c7cb0f88d22","added_by":"auto","created_at":"2025-01-13 12:47:06","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":53859,"visible":true,"origin":"","legend":"\u003cp\u003eThe ROC curves of the nomogram for validation sets was 0.878 (0.717-0.963).\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-5766955/v1/c2b1cb4d509f3c0310c77f57.png"},{"id":73674110,"identity":"58b791e6-713a-46f9-ae95-5f97bbcefd57","added_by":"auto","created_at":"2025-01-13 12:55:06","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":45891,"visible":true,"origin":"","legend":"\u003cp\u003eThe Calibration curves of the nomogram in the training and internal validation sets. Calibration of our model for predicting tracheostomy rate after surgical procedures. The x-axis represents the nomogram-predicted probability, and the y-axis represents the actual probability. The dotted line represents the entire cohort (n = 249) and the solid line is bias-corrected by bootstrapping, indicating the observed nomogram performance. Perfect prediction would correspond to the diagonal dashed line.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-5766955/v1/5da5aa125fa56c8323d0052b.png"},{"id":73672167,"identity":"25e6b12f-93c5-42c2-867a-96c3ef23b245","added_by":"auto","created_at":"2025-01-13 12:47:06","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":44727,"visible":true,"origin":"","legend":"\u003cp\u003eClinical decision curve analysis of prediction model. The decision curve analysis (DCA) for the nomogram. Solid line: Assume the intervention-none, and the net benefit is zero; Grey line: assume intervention-all-patients; Red line: intervention if the proposed model exceeds a threshold (ranging from approximately 3% to 80%). For example, if your personal threshold probability is 5% , then the proposed nomogram model can be beneficial for making the decision to undergo intervention.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-5766955/v1/ae9df397cc4adf597d994e71.png"},{"id":73675316,"identity":"a3a00deb-bb98-4d6b-b5be-7fdca995e884","added_by":"auto","created_at":"2025-01-13 13:03:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1565478,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5766955/v1/182fb21c-9e4d-42ab-8a14-91fc49990353.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A nomogram for predicting the risk of tracheostomy following surgical procedures treatment of aneurysmal subarachnoid hemorrhage.","fulltext":[{"header":"Introduction","content":"\u003cp\u003eApproximately 85% of non-traumatic subarachnoid hemorrhage cases result from aneurysm rupture \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Despite a steady decline in the mortality rate of acute subarachnoid hemorrhage in recent years, nevertheless, around one-third of individuals remain permanently dependent on care, with only 30% achieving independent living \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Furthermore, 10\u0026ndash;25% of aneurysmal subarachnoid hemorrhage (aSAH) patients die immediately after bleeding or before reaching the hospital. The advantages of tracheotomy over continued translaryngeal intubation are well documented, including the facilitation of pulmonary hygiene and the reduction of laryngeal and upper airway damage. Additional benefits encompass easier ventilator weaning, decreased need for sedation, enhanced nutrition and communication, improved oral hygiene, and subjective improvements in patient satisfaction \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Patients with aSAH who are unable to breathe and adequately protect their airway require the use of an endotracheal tube. However, compared with an endotracheal, a tracheotomy has the advantage of securing the airway with less physiological distress, allowing for a more rapid awakening of the aSAH patient \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. The necessity, timing, potential benefits, and risks of tracheostomy for patients with severe aSAH remain challenging and controversial \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRecent studies have successfully predicted pulmonary infections following traumatic brain injury and severe intracranial hemorrhage, while also analyzing the efficacy of airway management strategies \u003csup\u003e\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Unfortunately, there is a paucity of research focusing on the role of admission and baseline characteristics in predicting the risk of postoperative tracheotomy in patients with aSAH. This study employs Lasso and Logistic regression to develop nomograms and retrospectively analyze the relevant factors affecting postoperative tracheostomy in patients with aSAH. Furthermore, this research aims to investigate the determinants of postoperative airway management in patients with aSAH and to create predictive models that facilitate the early identification of high-risk individuals, thereby improving preoperative baseline conditions and optimizing clinical outcomes.\u003c/p\u003e \u003cp\u003eRisk prediction models can forecast patient outcomes by utilizing highly influential indicators derived from medical history, physical examinations, laboratory tests, imaging studies, and surgical factors, thereby playing a crucial role in clinical management decision-making. Among various classification or regression studies, the least absolute shrinkage and selection operator (LASSO) has been identified as one of the most effective algorithms for predicting clinical outcomes (RT. Regression shrinkage and selection via LASSO) \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eThe study presents a retrospective analysis based on our institutional database containing all consecutive patients with aSAH treated between January 2021 and October 2023. The study protocol was approved by the institutional ethics review board of Shanxi Bethune Hospital. All patients or their relatives signed informed consent forms before treatment.\u003c/p\u003e\n\u003cp\u003eInclusion criteria are as follows: (1) Age\u0026thinsp;\u0026ge;\u0026thinsp;18 years; (2) Patients diagnosed with ruptured intracranial aneurysm resulting in SAH confirmed by CT angiography (CTA) or digital subtraction angiography (DSA) upon admission; (3) Surgical procrdures on the intracranial aneurysms, including microsurgical clipping and endovascular treatment. Exclusion criteria are as follows: (1) Age\u0026thinsp;\u0026lt;\u0026thinsp;18 years; (2) Diagnosis of traumatic subarachnoid hemorrhage; (3) Lack of surgical procrdures treatment; (4) Prooperative multiple organ failure.\u003c/p\u003e\n\u003cp\u003eThe decision in favor of clipping or coiling was determined by a multidisciplinary team of neurosurgeons, interventional neuroradiologists, and anesthesiologists based on the clinical situation and consent of the family members. All tracheotomies were performed by traditional open surgical technique (otolaryngology and surgical specialty services) either at the bedside in the neurosurgical intensive care unit (NICU) or the operating room, with all procedures conducted with informed consent from the patient or their family and under clinical standards. There are four main indications for tracheotomy: long-term mechanical ventilation, weaning failure, upper airway obstruction, and copious secretions \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Relative indications for performing tracheotomy in the operating room rather than bedside included perceived anatomic difficulty, history of previous tracheotomy, hemodynamic instability, coagulopathy, the need for a concomitant surgical procedure, and attending preference. All patients routinely underwent comprehensive chest CT examination upon admission. Diagnostic criteria for pulmonary infection: Diagnosis is confirmed through clinical symptoms, laboratory indicators, chest X-rays, and other imaging and microbiological examinations, while excluding non-infectious pulmonary infiltrates such as pulmonary tuberculosis, lung cancer, and pulmonary embolism. Chest CT scans showing exudative changes consistent with lung infection were also used for diagnosis \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe following clinical and imaging characteristics of patients were collected: (1) Preoperative clinical data, including age, gender, medical history (Hypertension, Diabetes, Coronary Heart Disease, Smoking, Drinking), Hunt-Hess grade, modified Fisher grade, Glasgow Coma Scale (GCS) score, preoperative pulmonary infection, aneurysm location, and selected laboratory test results. (2) Surgical data: type of surgery, time of surgery, and whether additional procedures were performed; (3) For patients with respiratory failure, routine use of invasive mechanical ventilation was implemented, along with the total length of stay.\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical Analysis\u003c/h2\u003e\n \u003cp\u003eThe data analysis was performed using R statistical software (R version 4.3.1, R Project, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.r-project.org\u003c/span\u003e\u003c/span\u003e). Data were tested for normality using the Shapiro-Wilk test. All continuous variables are presented by the median\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation when they conform to the normal distribution and are compared using the t-test, otherwise they are presented by the median (interquartile range) and compared using the Mann-Whitney U test. Categorical variables are described as frequency (percentage), with intergroup comparisons conducted using a chi-square test. All available baseline, imaging, and surgical variables were included in univariable logistic regression analysis to determine their association with postoperative tracheostomy. Variables with an univariable association of p\u0026thinsp;\u0026lt;\u0026thinsp;0.10 were included for LASSO regression analysis and cross-validated to screen for variables with the most predictive value. The nomogram model is established according to multiple logistic regression, and the predictive values of models were evaluated by receiver operating characteristic (ROC) curve analysis and presented with the area under the curve (AUC). Evaluating model accuracy by plotting calibration curves based on Bootstrap 1000 times self-service resampling. Decision curve analysis (DCA) was used to assess the clinical validity of the predictive model. Statistical significance was set at P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 unless otherwise specified.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics\u003c/h2\u003e \u003cp\u003eThis study included a total of 249 patients with intracranial aneurysms following surgical treatment, of whom 66 underwent tracheostomy postoperatively, while 183 did not receive tracheostomy. The median age of the patients was 57 [19.0, 81.0], with 105 males (42.2%) and 144 females (57.8%). As shown in the baseline characteristics table (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), there were no significant differences between the tracheostomy group and the non-tracheostomy group in terms of gender, length of stay, hypertension, diabetes, coronary heart disease, smoking, drinking, LY count, and aneurysm location. However, there were statistically significant differences between the two groups in age, WBC count, NEUT count, Hunt-Hess grade, GCS score, preoperative pulmonary infection (Pre-op PI), use of mechanical ventilation, operation method, modified Fisher grade, and the need for external ventricular drainage, intracranial hematoma evacuation, decompressive craniectomy, and time of surgery (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOVERALL.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP.value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;183)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;249)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75 (41.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (45.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e105 (42.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e108 (59.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (54.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e144 (57.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.5 (12.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.8 (10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56.6 (11.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0405\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian [Min, Max]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.0 [19.0, 81.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.0 [33.0, 78.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57.0 [19.0, 81.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLength of stay (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.1 (6.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.0 (9.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.6 (7.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.682\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian [Min, Max]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.0 [1.00, 42.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.0 [3.00, 56.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.0 [1.00, 56.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78 (42.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (31.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99 (39.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.307\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e105 (57.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45 (68.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e150 (60.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e169 (92.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57 (86.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e226 (90.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.355\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (7.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (13.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23 (9.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e176 (96.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59 (89.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e235 (94.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (3.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (10.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (5.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e130 (71.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (57.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e168 (67.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53 (29.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (42.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81 (32.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrink\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e146 (79.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48 (72.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e194 (77.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.496\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37 (20.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (27.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55 (22.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.6 (4.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.4 (4.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.4 (4.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian [Min, Max]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.2 [4.10, 28.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.7 [5.60, 27.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.7 [4.10, 28.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNEUT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.93 (4.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.5 (4.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.6 (4.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian [Min, Max]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.66 [2.29, 25.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.3 [2.71, 24.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.99 [2.29, 25.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.49 (5.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.47 (2.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.49 (4.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.995\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian [Min, Max]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.990 [0.250, 69.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.01 [0.240, 18.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.990 [0.240, 69.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHunt-Hess (Grade)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42 (23.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (3.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44 (17.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80 (43.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (9.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86 (34.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46 (25.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (30.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66 (26.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (6.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (39.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37 (14.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (18.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (6.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.3 (2.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.14 (3.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.0 (3.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian [Min, Max]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.0 [3.00, 15.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.00 [3.00, 15.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.0 [3.00, 15.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-op PI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67 (36.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (6.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71 (28.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e116 (63.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62 (93.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e178 (71.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMechanical Ventilation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e157 (85.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (31.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e178 (71.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (14.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45 (68.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71 (28.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOperation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eclip\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85 (46.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51 (77.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e136 (54.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecoil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98 (53.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (22.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e113 (45.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModified Fisher (Grade)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89 (48.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (10.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96 (38.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56 (30.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (22.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71 (28.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (8.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (9.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (8.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (12.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (57.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60 (24.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAneurysm Location\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eanterior cerebral circulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e166 (90.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61 (92.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e227 (91.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.915\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epostetior cerebral circulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (9.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (7.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (8.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExternal Ventricular Drainage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e138 (75.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (48.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e170 (68.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45 (24.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (51.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79 (31.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHematoma Aspiration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e173 (94.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47 (71.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e220 (88.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (5.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (28.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (11.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecompressive Craniectomy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e177 (96.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54 (81.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e231 (92.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (18.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (7.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime (min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e188 (102)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e247 (106)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e204 (106)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian [Min, Max]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e170 [55.0, 655]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e244 [49.0, 545]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e190 [49.0, 655]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations: CHD coronary heart disease, WBC white blood cell, NEUT neutrophils, LY lymphocytes, GCS glasgow coma scale, Pre-op PI preoperative pulmonary infection\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eScreening of predictors\u003c/h3\u003e\n\u003cp\u003eThe correlation heatmap between all variables is shown in \u003cb\u003eFig.\u0026nbsp;1\u003c/b\u003e. The ROC curves and AUC values for all variables are shown in \u003cb\u003eFig.\u0026nbsp;2\u0026thinsp;+\u0026thinsp;3\u003c/b\u003e. In the LASSO regression analysis, the optimal penalty coefficient (λ) was confirmed in the model by a tenfold cross-validation of the minimum criterion. The model is optimal when λ increases to a standard error (lambda.1SE), and variables with non-zero coefficients were screened out as potential predictors (Fig.\u0026nbsp;4\u0026thinsp;\u003cb\u003e+\u0026thinsp;5\u003c/b\u003e). Univariate logistic regression analysis was performed for the total population \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. To miss as few valuable variables as possible, we included variables with p less than 0.2 in the univariate logistic regression analysis in the Lasso regression analyses and cross-validated them.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate logistic regression analysis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.47\u0026ndash;1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.01\u0026ndash;1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLength of stay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.99\u0026ndash;1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.88\u0026ndash;2.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.78\u0026ndash;4.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1-8.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.01\u0026ndash;3.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrink\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.77\u0026ndash;2.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.07\u0026ndash;1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNEUT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.06\u0026ndash;1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.94\u0026ndash;1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHunt-Hess\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.7\u0026ndash;5.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.62\u0026ndash;0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAneurysm Location\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.28\u0026ndash;2.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModified Fisher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.02\u0026ndash;3.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-op PI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.12\u0026ndash;25.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOperation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.13\u0026ndash;0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExternal Ventricular Drainage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.81\u0026ndash;5.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHematoma Aspiration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.05\u0026ndash;16.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecompressive Craniectomy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.35\u0026ndash;18.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1-1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMechanical ventilation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.66\u0026ndash;25.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eAbbreviations: CHD coronary heart disease, WBC white blood cell, NEUT neutrophils, LY lymphocytes, GCS glasgow coma scale, Pre-op PI preoperative pulmonary infection\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAfter effective selection using LASSO-Logistic regression analysis, five potential predictive factors were identified: GCS score (OR: 0.756, 95%CI: 0.654\u0026ndash;0.863, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), modified Fisher grade (OR: 1.341, 95%CI: 0.877\u0026ndash;2.029, P\u0026thinsp;=\u0026thinsp;0.166), use of mechanical ventilation (OR: 4.952, 95%CI: 1.99-12.635, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), operation approach (OR: 0.239, 95%CI: 0.085\u0026ndash;0.608, P\u0026thinsp;=\u0026thinsp;0.004), preoperative pulmonary infection (OR: 5.146, 95%CI: 1.451\u0026ndash;23.566, P\u0026thinsp;=\u0026thinsp;0.019) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultifactor logistic regression analysis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWald\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.756(0.654\u0026thinsp;~\u0026thinsp;0.863)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emFS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.916\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.341(0.877\u0026thinsp;~\u0026thinsp;2.029)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMechanical ventilation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.952(1.99\u0026thinsp;~\u0026thinsp;12.635)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-op PI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.146(1.451\u0026thinsp;~\u0026thinsp;23.566)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOperation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.239(0.085\u0026thinsp;~\u0026thinsp;0.608)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations: GCS glasgow coma scale, mFS modified Fisher Scale, Pre-op PI preoperative pulmonary infection\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eConstruction of nomogram prediction model\u003c/h3\u003e\n\u003cp\u003eAccording to the influencing factors screened by Lasso-logistic regression, multivariate logistic regression was carried out, to establish a prediction model for tracheotomy in aSAH patients in the present study and plotted nomogram (Fig.\u0026nbsp;6). Each important variable in the graph is assigned a weighted score from 0 to 100, and a total score is calculated by summing the scores for each risk factor in the nomogram to accurately predict the risk of tracheotomy in aSAH patients. The higher the total score, the higher the risk of tracheotomy. If a patient diagnosed with aSAH has a GCS score of 4 at the time of admission, along with concomitant pulmonary infection, undergoes surgical clipping of a cerebral aneurysm, and is on mechanical ventilation postoperatively, the probability of requiring tracheostomy is estimated to be approximately 90%.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eValidation and performance of nomogram\u003c/h2\u003e \u003cp\u003eReceiver operating characteristic (ROC) curve analysis was conducted to assess the discrimination of the prediction model based on a risk nomogram. As shown in \u003cb\u003eFig.\u0026nbsp;7\u0026thinsp;+\u0026thinsp;8\u003c/b\u003e, The AUC of the training set was 0.928 (95% CI: 0.890\u0026ndash;0.959), and the sensitivity and specificity of the prediction model were 0.918 and 0.814, respectively, when the critical value was the maximum value of the Youden index. The AUC of the validation set was 0.878 (95% CI: 0.717\u0026ndash;0.963), and the sensitivity and specificity of the prediction model were 0.863 and 0.746, respectively, when the critical value was the maximum value of the Youden index. It indicates that the prediction model showed excellent prediction ability in both groups of patients. The Hosmer\u0026ndash;Lemeshow test results for the prediction model in the training and validation sets are X\u0026sup2; = 6.961 (P\u0026thinsp;=\u0026thinsp;0.541\u0026thinsp;\u0026gt;\u0026thinsp;0.05) and X\u0026sup2; = 3.633 (P\u0026thinsp;=\u0026thinsp;0.821\u0026thinsp;\u0026gt;\u0026thinsp;0.05), which demonstrate that the model has good goodness-of-fit in both datasets.\u003c/p\u003e \u003cp\u003eAn internally validated bootstrap sampling method (1,000 times) was used to verify the nomogram model. The C-index value for the nomogram model was 0.927, implying that the model had good discriminatory and predictive power. The calibration curves plotted are all close to the reference line, indicating good agreement between predicted and observed outcomes (Fig.\u0026nbsp;9\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eA DCA curve was used to evaluate the clinical utility of the model. The results are shown in \u003cb\u003eFig.\u0026nbsp;10\u003c/b\u003e. The abscissa and ordinate represent threshold probability and net benefit, respectively. The lines marked \u0026ldquo;None\u0026rdquo; and \u0026ldquo;All\u0026rdquo; represent the two extreme cases. The further away the model curve is from these two lines, the better the clinical benefit of the nomogram. When the risk threshold was \u0026gt;\u0026thinsp;0.02, the risk nomogram model predicting a poor prognosis exhibited better clinical utilization in the DCA curve.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIntracranial aneurysm (IA) is a complex disease characterized by pathological dilatations of the cerebral arteries, and the rupture of an IA leads to SAH \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. A patient with an aSAH may present to the emergency department with a range of neurological symptoms, making airway management particularly critical \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Previous studies have shown that pulmonary complications frequently arise following aSAH and are associated with poor prognosis \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. In patients with more severe conditions, prolonged intubation not only impedes the recovery of consciousness but also heightens the risk of airway injury, subsequently leading to pulmonary complications. A randomized clinical trial and a systematic review have demonstrated that early tracheostomy can reduce the length of hospital stay, duration of ICU stay, time on invasive mechanical ventilation, sedation time, and short-term mortality in critically ill patients \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Therefore, the early identification of patients requiring prolonged mechanical ventilation or those at risk of weaning failure, followed by tracheostomy, is essential for effective clinical management and prognosis \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. In this study, we collected samples from patients with aSAH and established a predictive model incorporating four influencing factors: GCS score, invasive mechanical ventilation, preoperative pulmonary infection, and operation method. Through internal validation, we found that the nomogram demonstrated good calibration, predictive capability, and clinical applicability.\u003c/p\u003e \u003cp\u003eThe GCS is widely utilized in clinical practice to classify the severity of head injuries. A GCS score of less than 8 is associated with severe brain injury \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Damage to the central nervous system can disrupt the regulation of the respiratory center, thereby affecting respiratory rate and depth, airway tone, and normal neurological reflexes, such as the cough and swallowing reflexs, as well as apnea and breathing facilitation. Consequently, these patients often require intubation for airway protection \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. For patients with prolonged expected recovery of consciousness postoperatively, early tracheostomy may be necessary to enhance ventilation. Recent studies have shown that the one-year mortality rate for patients with aSAH and a GCS score of less than 6 is significantly high, with a mortality rate of up to 100% for those who did not undergo surgical intervention \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Our study suggests that the GCS score can serve as a predictive factor for tracheostomy in patients with aSAH, aligning with the findings of Chen et al. \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe found that preoperative pulmonary infection and the use of mechanical ventilation can predict postoperative tracheostomy, and their relationship is inseparable, which is consistent with the findings of Chen et al. \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Pulmonary infection can lead to severe complications, including acute respiratory distress syndrome and respiratory failure. A study on acute respiratory distress syndrome (ARDS) indicated that pneumonia significantly prolongs the duration of mechanical ventilation and increases the incidence of ventilator-associated pneumonia \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. However, Artigas A et al. found that diaphragmatic dysfunction in critically ill patients occurs primarily through two mechanisms: ventilator-induced diaphragmatic dysfunction and sepsis-induced dysfunction, which may ultimately lead to failure to wean off mechanical ventilation \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. A single-center retrospective analysis indicated that successful weaning from mechanical ventilation and performing tracheostomy are critical for improving long-term survival outcomes for patients on prolonged mechanical ventilation \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. For patients admitted with a diagnosis of aSAH who are intubated or receiving mechanical ventilation, if pneumonia is also present, attention must be given to airway management, and the early and appropriate use of antibiotics is essential.\u003c/p\u003e \u003cp\u003eThis study found that the choice of surgical approach significantly impacts postoperative airway management. For patients with ruptured intracranial aneurysms, the primary goal of surgical procedures treatment is to reduce the risk of rebleeding. Compared to endovascular treatment, craniotomy, and clipping increase surgical invasiveness, including craniotomy, scalp incision, and the inevitable intraoperative damage to brain tissue \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, this also accounts for the longer operative duration of aneurysm clipping. We propose that patients undergoing craniotomy and clipping may require a longer recovery time, which in turn affects close postoperative airway management and increases the necessity for tracheostomy. A retrospective analysis and a systematic review indicate that, relative to endovascular treatment, craniotomy and clipping are associated with prolonged hospital stays \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. This predictive model may assist clinicians in evaluating the risk of postoperative tracheostomy based on initial GCS scores upon admission, the presence of pulmonary infection, the choice of surgical approach, and the need for invasive mechanical ventilation.\u003c/p\u003e \u003cp\u003eHowever, in this investigation, age, the location of the aneurysm, and the Hunt-Hess grade were ultimately not selected. In a similar test with the same sample size, we found that the size of the aneurysm was also not considered a predictive factor for postoperative tracheostomy. We speculate that this may be due to biases resulting from the small sample size or that these factors may have a more significant correlation with the functional prognosis of the patients. In addition, the modified Fisher scale (mFS) was identified as an independent predictor for tracheostomy after aSAH. However, due to its stronger potential association with post-aSAH vascular spasm or other neurologic complications, it was not included in the final predictive model \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRecent research has focused on predicting tracheostomy requirements in critically ill patients and those undergoing nasopharyngeal surgeries. The innovation of this study lies in its application of LASSO regression to analyze the factors influencing postoperative tracheostomy in patients with aSAH, ultimately establishing a predictive model and conducting internal validation. Furthermore, to our knowledge, this study is the first to develop an accurate predictive model based on GCS, preoperative pulmonary infection, operation method, and mechanical ventilation.\u003c/p\u003e \u003cp\u003eThis study has several limitations that warrant attention. First, although the risk model demonstrated excellent accuracy following internal validation, it still lacks external validation. Second, this study is a retrospective analysis, and the conclusions drawn need to be validated and refined in prospective cohort studies. Third, given the urgency of addressing hemorrhagic complications arising from ruptured intracranial aneurysms, our study does not include a detailed classification or stratified discussion of the severity of preoperative pulmonary infections in patients. Finally, this is a case-control study with a relatively small sample size and incomplete or missing medical history records, which may lead to potential bias in the results.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this study, we identified four risk factors that influence the likelihood of postoperative tracheostomy in patients with aSAH through LASSO, univariate, and multivariate logistic regression analyses. The identified factors are the GCS score, preoperative pulmonary infection, operation method, and mechanical ventilation, all of which exhibit interactions among these clinical characteristics. Additionally, we constructed a nomogram using these four predictive factors. Internal validation of this model demonstrates good accuracy and clinical utility, assisting surgeons in evaluating the risk of early tracheostomy, ultimately benefiting patient outcomes.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eIA, intracranial aneurysm\u003c/p\u003e\n\u003cp\u003eSAH, subarachnoid hemorrhage\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eaSAH, aneurysmal subarachnoid hemorrhage\u003c/p\u003e\n\u003cp\u003eCTA, CT angiography\u003c/p\u003e\n\u003cp\u003eDSA, digital subtraction angiography\u003c/p\u003e\n\u003cp\u003eNICU, neurosurgical intensive care unit\u003c/p\u003e\n\u003cp\u003eCHD, Coronary Heart Disease\u003c/p\u003e\n\u003cp\u003eGCS, Glasgow Coma Scale\u003c/p\u003e\n\u003cp\u003eWBC, white blood cell\u003c/p\u003e\n\u003cp\u003eNEUT, neutrophils\u003c/p\u003e\n\u003cp\u003eLY, lymphocytes\u003c/p\u003e\n\u003cp\u003ePre-op PI, preoperative pulmonary infection\u003c/p\u003e\n\u003cp\u003emFS, modified Fisher Scale\u003c/p\u003e\n\u003cp\u003eDCI, delayed cerebral ischemia\u003c/p\u003e\n\u003cp\u003eARDS, acute respiratory distress syndrome\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eHuman Ethics and Consent to Participate declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all patients. This study was approved by the Ethics Committee of the third hospital of Shanxi Medical University and was conducted according to the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding:\u003c/h2\u003e\n\u003cp\u003eThis project is supported by the Scientific Research Initiation Fund for Talent Introduction of Shanxi Bethune Hospital (Project No.2021RC006) .\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eConception and design: Dongyuan Zhang, Liangsheng Peng, Xinmin Ding. Acquisition of data: Liangsheng Peng, Jiahao Liu, Xi Zhang, Ziyuan Liu, Bohong Wang. Analysis and interpretation of data: Dongyuan Zhang, Liangsheng Peng. Drafting the article: Dongyuan Zhang. Critically revising the article: Dongyuan Zhang, Liangsheng Peng, Xinmin Ding. Statistical analysis: Dongyuan Zhang, Liangsheng Peng, Bohong Wang. Administrative/technical/material support: Xiaolong Wang, Li Han, Ying Zhang. Study supervision: Xinmin Ding.All authors reviewed the manuscript.\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials:\u003c/h2\u003e\n\u003cp\u003eThe data presented in this study are available on request from the correspond ing author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eThompson BG, Brown RD, Jr., Amin-Hanjani S, et al. Guidelines for the Management of Patients With Unruptured Intracranial Aneurysms: A Guideline for Healthcare Professionals From the American Heart Association/American Stroke Association. \u003cem\u003eStroke.\u003c/em\u003e 2015;46(8): 2368-2400. https://doi.org/10.1161/str.0000000000000070.\u003c/li\u003e\n\u003cli\u003eMarcolini E, Hine J. 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Prediction of symptomatic vasospasm after subarachnoid hemorrhage: the modified fisher scale. \u003cem\u003eNeurosurgery.\u003c/em\u003e 2006;59(1): 21-27; discussion 21-27. https://doi.org/10.1227/01.neu.0000243277.86222.6c.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-neurology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurl","sideBox":"Learn more about [BMC Neurology](http://bmcneurol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurl","title":"BMC Neurology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Ruptured intracranial aneurysms, aSAH, surgery, LASSO, Logistic, tracheostomy, nomogram.","lastPublishedDoi":"10.21203/rs.3.rs-5766955/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5766955/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e Effective and timely airway management is particularly crucial for recovery in patients with aneurysmal subarachnoid hemorrhage following surgical procedures treatment. This study aimed to develop a stable nomogram model to predict the likelihood of postoperative tracheostomy in these patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThe clinical data and imaging findings of 249 patients with aneurysmal subarachnoid hemorrhage (aSAH) by microsurgical clipping or endovascular treatment on admission from January 2021 to October 2023 were retrospectively analyzed. Least absolute shrinkage and selection operator (LASSO), logistic regression analyses, and a nomogram were used to develop the prognostic models. Receiver operating characteristic (ROC) curves and Hosmer–Lemeshow tests were used to assess discrimination and calibration. The bootstrap method (1,000 repetitions) was used for internal validation. Decision curve analysis (DCA) was conducted to evaluate the clinical validity of the nomogram.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe following four independent influencing factors were selected by LASSO-Logistic regression: the GCS score, preoperative pulmonary infection, operation method, and mechanical ventilation. The area under curve (AUC) was 0.928 in the training set and 0.878 in the internal validation set. Calibration curves and Hosmer–Lemeshow tests indicated that the nomogram demonstrated strong calibration ability. Additionally, the DCA curve revealed enhanced clinical utility of the nomogram.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e This study introduces a reliable and valuable nomogram model that is both applicable and user-friendly, facilitating accurate predictions of tracheostomy risk following surgical interventions for aneurysmal subarachnoid hemorrhage. This model aids clinicians in making timely and informed decisions, thereby significantly enhancing patient outcomes.\u003c/p\u003e","manuscriptTitle":"A nomogram for predicting the risk of tracheostomy following surgical procedures treatment of aneurysmal subarachnoid hemorrhage.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-13 12:47:01","doi":"10.21203/rs.3.rs-5766955/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-01-08T16:24:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-01-07T13:12:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-01-07T13:11:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Neurology","date":"2025-01-05T08:54:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-neurology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurl","sideBox":"Learn more about [BMC Neurology](http://bmcneurol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurl","title":"BMC Neurology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a98b0cbd-d49c-41d5-b7bc-20f241813c58","owner":[],"postedDate":"January 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-01-13T12:47:01+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-13 12:47:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5766955","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5766955","identity":"rs-5766955","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}