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Objective: This study aimed to identify risk factors for intracranial aneurysms (IAs) recurrence and establish a predictive model to aid evaluation., Methods: A total of 302 patients with 312 IAs undergoing coil embolization between September 2017 and October 2022 were divided into two groups based on digital subtraction angiography follow-up. Clinical characteristics, operation-related factors, and morphologies were measured. Cox proportional hazard regression was used to identify the risk factors. Hazard ratios (HRs) were used to score points, and a predictive model was established. The test cohorts consisted of 51 IAs. Receiver operating characteristic curves were generated to determine the cutoff values and area under the curves (AUCs). A Delong test was performed to compare the AUCs., Results: Diameter maximum (D max) ( p < 0.001, HR = 1.221), Raymond-Roy occlusion classification (RROC) II or III ( p = 0.004, HR = 2.852), and ruptured status ( p < 0.001, HR = 7.782) were independent risk factors for the recurrence of IAs. A predictive model was established: D max + 2 * RROC (II or III; yes = 1, no = 0) + 6 * ruptured status (yes = 1; no = 0). The AUC of the predictive model (0.818) was significantly higher than those of D max (0.704), RROC (II or III) (0.645), and rupture status (0.683), respectively (Delong test, p < 0.05). The cutoff values of the predictive model and D max were 9.75 points and 6.65 mm, respectively., Conclusion: The D max, RROC (II or III), and ruptured status could independently predict the recurrence of IAs after coil embolization. Our model could aid in practical evaluations., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 He, Chen and Chen.)

Objectives: To identify risk factors for hospital-acquired pneumonia (HAP) in patients with aneurysmal subarachnoid hemorrhage (aSAH) and establish a predictive model to aid evaluation., Methods: The cohorts of 253 aSAH patients were divided into the HAP group ( n = 64) and the non-HAP group ( n = 189). Univariate and multivariate logistic regression were performed to identify risk factors. A logistic model (Model-Logit) was established based on the independent risk factors. We used risk factor categories to develop a model (Model-Cat). Receiver operating characteristic curves were generated to determine the cutoff values. Areas under the curves (AUCs) were calculated to assess the accuracy of models and single factors. The Delong test was performed to compare the AUCs., Results: The multivariate logistic analysis showed that the age [ p = 0.012, odds ratio (OR) = 1.059, confidence interval (CI) = 1.013-1.107], blood glucose (BG; >7.22 mmol/L; p = 0.011, OR = 2.781, CI = 1.263-6.119), red blood distribution width standard deviation (RDW-SD; p = 0.024, OR = 1.118, CI = 1.015-1.231), and Glasgow coma scale (GCS; p < 0.001, OR = 0.710, CI = 0.633-0.798) were independent risk factors. The Model-Logit was as follows: Logit( P ) = -5.467 + 0.057 * Age + 1.023 * BG (>7.22 mmol/L, yes = 1, no = 0) + 0.111 * RDW-SD-0.342 * GCS. The AUCs values of the Model-Logit, GCS, age, BG (>7.22 mmol/L), and RDW-SD were 0.865, 0.819, 0.634, 0.698, and 0.625, respectively. For clinical use, the Model-Cat was established. In the Model-Cat, the AUCs for GCS, age, BG, and RDW-SD were 0.850, 0.760, 0.700, 0.641, and 0.564, respectively. The AUCs of the Model-Logit were insignificantly higher than the Model-Cat (Delong test, p = 0.157). The total points from -3 to 4 and 5 to 14 were classified as low- and high-risk levels, respectively., Conclusions: Age, BG (> 7.22 mmol/L), GCS, and RDW-SD were independent risk factors for HAP in aSAH patients. The Model-Cat was convenient for practical evaluation. The aSAH patients with total points from 5 to 14 had a high risk for HAP, suggesting the need for more attention during treatment., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Hu, Hu, Chen and Yu.)

Objectives: To identify hemodynamic risk factors for intracranial aneurysm rupture and establish a predictive model to aid evaluation., Methods: We analyzed the hemodynamic parameters of 91 pairs of ruptured mirror aneurysms. A conditional univariate analysis was used for the continuous variables. A conditional multivariate logistic regression analysis was performed to identify the independent risk factors. Differences where p < 0.05 were statistically significant. A predictive model was established based on independent risk factors. Odds ratios (ORs) were used to score points. The validation cohort consisted of 189 aneurysms. Receiver operating characteristic curves were generated to determine the cutoff values and area under the curves (AUCs) of the predictive model and independent risk factors., Results: The conditional multivariate logistic analysis showed that the low shear area (LSA) (OR = 70.322, p = 0.044, CI = 1.112-4,445.256), mean combined hemodynamic parameter (CHP) (>0.087) (OR = 3.171, p = 0.034, CI = 1.089-9.236), and wall shear stress gradient (WSSG) ratio (>893.180) (OR = 5.740, p = 0.003, CI = 1.950-16.898) were independent risk factors. A prediction model was established: 23 * LSA + 1 * CHP mean (>0.087: yes = 1, no = 0) + 2 * WSSG ratio (>893.180: yes = 1, no = 0). The AUC values of the predictive model, LSA, mean CHP (>0.087), and WSSG ratio (>893.180) were 0.748, 0.700, 0.654, and 0.703, respectively. The predictive model and LSA cutoff values were 1.283 and 0.016, respectively. In the validation cohort, the predictive model, LSA, CHP (>0.087), and WSSG ratio (>893.180) were 0.736, 0.702, 0.689, and 0.706, respectively., Conclusions: LSA, CHP (>0.087), and WSSG ratio (>893.180) were independent risk factors for aneurysm rupture. Our predictive model could aid practical evaluation., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Hu, Chen, Xu, Li and Yu.)

Objective: The aim of this study was to explore the correlation between the mean of 24-h venous blood glucose (BG) and in-hospital mortality and all-cause mortality (ACM) in patients with subarachnoid hemorrhage (SAH)., Methods: Detailed clinical information was acquired from the Medical Information Mart for Intensive IV (MIMIC-IV) database. The best cutoff value of mean BG was calculated using the X-tile program. Univariate and multivariate logistic regressive analyses were utilized to analyze the prognosis significance of mean BG, and survival curves were drawn using the Kaplan-Meier (K-M) approach. To improve the reliability of results and balance the impact of underlying confounders, the 1:1 propensity score matching (PSM) approach was utilized., Results: An overall of 1,230 subjects were selected herein. The optimal cutoff value of the mean BG for in-hospital mortality was 152.25. In addition, 367 pairs of score-matched subjects were acquired after PSM analysis, and nearly all variables' differences were balanced. K-M analysis showed that patients with mean BG ≥ 152.25 mg/dl had significantly higher in-hospital, 3-month, and 6-month mortalities compared with patients with mean BG < 152.25 mg/dl ( p < 0.001). The multivariable logistic regressive analyses revealed that patients with mean BG ≥ 152.25 mg/dl had significantly increased in-hospital mortality compared with patients with mean BG < 152.25 mg/dl after the adjustment for possible confounders (OR = 1.994, 95% CI: 1.321-3.012, p = 0.001). Similar outcomes were discovered in the PSM cohort., Conclusion: Our data suggested that mean BG was related to ACM of patients with SAH. More studies are needed to further analyze the role of the mean of 24-h venous BG in patients with SAH., (Copyright © 2022 Wang, Li, Yang, Chen and Yu.)