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Background: Determining the rupture risk of unruptured intracranial aneurysm is crucial for treatment strategy. The purpose of this study was to predict the rupture risk of middle cerebral artery (MCA) aneurysms using a machine learning technique. Methods: We retrospectively reviewed 403 MCA aneurysms and randomly partitioned them into the training and testing datasets with a ratio of 8:2. A generalized linear model with logit link was developed using training dataset to predict the aneurysm rupture risk based on the clinical variables and morphological features manually measured from computed tomography angiography. To facilitate the clinical application, we further constructed an easy-to-use nomogram based on the developed model. Results: Ruptured MCA aneurysm had larger aneurysm size, aneurysm height, perpendicular height, aspect ratio, size ratio, bottleneck factor, and height-width ratio. Presence of a daughter-sac was more common in ruptured than in unruptured MCA aneurysms. Six features, including aneurysm multiplicity, lobulations, size ratio, bottleneck factor, height-width ratio, and aneurysm angle, were adopted in the model after feature selection. The model achieved a relatively good performance with areas under the receiver operating characteristic curves of 0.77 in the training dataset and 0.76 in the testing dataset. The nomogram provided a visual interpretation of our model, and the rupture risk probability of MCA aneurysms can be directly read from it. Conclusion: Our model can be used to predict the rupture risk of MCA aneurysm. [ABSTRACT FROM AUTHOR]

Background: Body composition is a crucial factor associated with the incidence of type 2 diabetes mellitus. However, no study on this relationship has been performed in the Chinese population. This study aimed to investigate the relationship between body composition indicators and risk of type 2 diabetes mellitus among Chinese adults undergoing medical examination.Methods: Between January 2018 and July 2018, a retrospective cross-sectional study was performed on 3367 (2307 male and 1060 female) participants aged ≥18 years undergoing medical examination in Zhengzhou. Logistic regression analysis was performed to explore the relationship between body composition indicators and risk of type 2 diabetes mellitus. A receiver operating characteristic curve was used to calculate cutoff points and the predictive power of each indicator.Results: Among the 3367 participants, 12.53% were diagnosed with type 2 diabetes mellitus. Multivariate logistic analysis indicated that male participants (odds ratio [OR] = 1.68, 95% confidence interval [CI]: 1.29-2.19), older participants (OR = 1.05, 95% CI: 1.04-1.06), participants with a waist-to-hip ratio above the reference value (OR = 1.56, 95% CI: 1.18-2.07), participants with body fat percentage above the reference value (OR = 1.62, 95% CI: 1.01-2.68), and participant with a large visceral fat area (OR = 1.01, 95% CI: 1.01-1.02) had a high risk of type 2 diabetes mellitus. Waist-to-hip ratio, body fat percentage, and visceral fat area were the best indicators of type 2 diabetes mellitus (P < 0.001) with cutoff values of 0.90, 25.02%, and 92.00 cm2, respectively.Conclusion: This study suggests a predictive relationship between type 2 diabetes mellitus and body composition indicators of waist-to-hip ratio, body fat percentage, and visceral fat area, which are valuable for screening diabetes and providing effective health education and behavioral intervention for high-risk populations. [ABSTRACT FROM AUTHOR]

Background and Purpose: Optic nerve sheath diameter (ONSD) enlargement occurs in patients with intracerebral hemorrhage (ICH). However, the relationship between ONSD and prognosis of ICH is uncertain. This study aimed to investigate the predictive value of ONSD on poor outcome of patients with acute spontaneous ICH. Methods: We studied 529 consecutive patients with acute spontaneous ICH who underwent initial CT within 6 h of symptom onset between October 2016 and February 2019. The ONSDs were measured 3 mm behind the eyeball on initial CT images. Poor outcome was defined as having a Glasgow Outcome Scale (GOS) score of 1–3, and favorable outcome was defined as having a GOS score of 4–5 at discharge. Results: The ONSD of the poor outcome group was significantly greater than that of the favorable outcome group (5.87 ± 0.86 vs. 5.21 ± 0.69 mm, p < 0.001). ONSD was related to hematoma volume (r = 0.475, p < 0.001). Adjusting other meaningful predictors, ONSD (OR: 2.83; 95% CI: 1.94–4.15) was associated with poor functional outcome by multivariable logistic regression analysis. Receiver operating characteristic curve showed that the ONSD improved the accuracy of ultraearly hematoma growth in the prediction of poor outcome (AUC: 0.790 vs. 0.755, p = 0.016). The multivariable logistic regression model with all the meaningful predictors showed a better predictive performance than the model without ONSD (AUC: 0.862 vs. 0.831, p = 0.001). Conclusions: The dilated ONSD measured on initial CT indicated elevated intracranial pressure and poor outcome, so appropriate intervention should be taken in time. [ABSTRACT FROM AUTHOR]