Background and Purpose: Perihematomal edema (PHE) is associated with poor functional outcomes after intracerebral hemorrhage (ICH). Early identification of risk factors associated with PHE growth may allow for targeted therapeutic interventions.Methods: We used data contained in the risk stratification and minimally invasive surgery in acute intracerebral hemorrhage (Risa-MIS-ICH) patients: a prospective multicenter cohort study. Patients' clinical, laboratory, and radiological data within 24 h of admission were obtained from their medical records. The absolute increase in PHE volume from baseline to day 3 was defined as iPHE volume. Poor outcome was defined as modified Rankin Scale (mRS) of 4 to 6 at 90 days. Binary logistic regression was used to assess the relationship between iPHE volume and poor outcome. The receiver operating characteristic curve was used to find the best cutoff. Linear regression was used to identify variables associated with iPHE volume (ClinicalTrials.gov Identifier: NCT03862729).Results: One hundred ninety-seven patients were included in this study. iPHE volume was significantly associated with poor outcome [P = 0.003, odds ratio (OR) 1.049, 95% confidence interval (CI) 1.016–1.082] after adjustment for hematoma volume. The best cutoff point of iPHE volume was 7.98 mL with a specificity of 71.4% and a sensitivity of 47.5%. Diabetes mellitus (P = 0.043, β = 7.66 95% CI 0.26–15.07), black hole sign (P = 0.002, β = 18.93 95% CI 6.84–31.02), and initial ICH volume (P = 0.018, β = 0.20 95% CI 0.03–0.37) were significantly associated with iPHE volume. After adjusting for hematoma expansion, the black hole sign could still independently predict the increase of PHE (P < 0.001, β = 21.62 95% CI 10.10–33.15).Conclusions: An increase of PHE volume >7.98 mL from baseline to day 3 may lead to poor outcome. Patients with diabetes mellitus, black hole sign, and large initial hematoma volume result in more PHE growth, which should garner attention in the treatment.
Abstract. Because of its wide deck, elegant design and reasonable stress, the steel truss arch bridge is suitable for urban bridges. In the steel truss arch bridge, the main arch hinge is an important structure, the local structure and the stress is complex, and it is necessary to analyze the local stress state of the arch hinge. Arch hinge problem belongs to the contact problem, this paper based on Chengdu Tianfu District Shenyang Lu Xi Duan Jin Jiang in bearing steel truss arch bridge design, take the finite element software ANSYS on the main arch hinge is locally analyzed, the arch at the junction of reliable performance test. Studies have shown that half through steel truss arch bridge should be adopted by reasonable cylindrical arch hinge, and Hertz theory is in the analysis of the arch hinge contact does not apply. IntroductionTianfu District Shenyang Lu Xi Duan Jin Jiang Bridge is located in Chengdu City, Sichuan Province, in the design process of the bridge, not only to meet the requirements of bridge design, but also have certain landscape value. After careful design than the selection, the final use of a steel truss arch bridge. The middle deck steel truss arch bridge is a kind of complex space bridge structure, which is composed of the middle steel truss and the main beam. In this system, the main force structure is the main arch and the main beam. Under the action of live loads, the main arch under motorized vehicles and non motor vehicle lanes and sidewalks load, dead load and live load through the tie bar transfer to the main arch ring, and then transmitted to the foundation, finally force transmission to the ground, the structure of force is very clear. There are several transverse beams on the main arch, the whole has a strong lateral stiffness, with good stability [1]. The steel truss arch bridge is suitable for the urban bridges with high landscape requirements and wide deck.The main bridge of the bridge is 230m; the whole bridge width is 43.5m, the motor vehicle lane width is 22.5m, and both sides of the sidewalk width is 3.5m.
BackgroundEarly hematoma growth is associated with poor functional outcomes in patients with intracerebral hemorrhage (ICH). We aimed to explore whether quantitative hematoma heterogeneity in non-contrast computed tomography (NCCT) can predict early hematoma growth.MethodsWe used data from the Risk Stratification and Minimally Invasive Surgery in Acute Intracerebral Hemorrhage (Risa-MIS-ICH) trial. Our study included patients with ICH with a time to baseline NCCT <12 h and a follow-up CT duration <72 h. To get a Hounsfield unit histogram and the coefficient of variation (CV) of Hounsfield units (HUs), the hematoma was segmented by software using the auto-segmentation function. Quantitative hematoma heterogeneity is represented by the CV of hematoma HUs. Multivariate logistic regression was utilized to determine hematoma growth parameters. The discriminant score predictive value was assessed using the area under the ROC curve (AUC). The best cutoff was determined using ROC curves. Hematoma growth was defined as a follow-up CT hematoma volume increase of >6 mL or a hematoma volume increase of 33% compared with the baseline NCCT.ResultsA total of 158 patients were enrolled in the study, of which 31 (19.6%) had hematoma growth. The multivariate logistic regression analysis revealed that time to initial baseline CT (P = 0.040, odds ratio [OR]: 0.824, 95 % confidence interval [CI]: 0.686–0.991), “heterogeneous” in the density category (P = 0.027, odds ratio [OR]: 5.950, 95 % confidence interval [CI]: 1.228–28.828), and CV of hematoma HUs (P = 0.018, OR: 1.301, 95 % CI: 1.047–1.617) were independent predictors of hematoma growth. By evaluating the receiver operating characteristic curve, the CV of hematoma HUs (AUC = 0.750) has a superior predictive value for hematoma growth than for heterogeneous density (AUC = 0.638). The CV of hematoma HUs had an 18% cutoff, with a specificity of 81.9 % and a sensitivity of 58.1 %.ConclusionThe CV of hematoma HUs can serve as a quantitative hematoma heterogeneity index that predicts hematoma growth in patients with early ICH independently.
BackgroundStroke-associated pneumonia (SAP) contributes to high mortality rates in spontaneous intracerebral hemorrhage (sICH) populations. Accurate prediction and early intervention of SAP are associated with prognosis. None of the previously developed predictive scoring systems are widely accepted. We aimed to derive and validate novel supervised machine learning (ML) models to predict SAP events in supratentorial sICH populations.MethodsThe data of eligible supratentorial sICH individuals were extracted from the Risa-MIS-ICH database and split into training, internal validation, and external validation datasets. The primary outcome was SAP during hospitalization. Univariate and multivariate analyses were used for variable filtering, and logistic regression (LR), Gaussian naïve Bayes (GNB), random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM), extreme gradient boosting (XGB), and ensemble soft voting model (ESVM) were adopted for ML model derivations. The accuracy, sensitivity, specificity, and area under the curve (AUC) were adopted to evaluate the predictive value of each model with internal/cross-/external validations.ResultsA total of 468 individuals with sICH were included in this work. Six independent variables [nasogastric feeding, airway support, unconscious onset, surgery for external ventricular drainage (EVD), larger sICH volume, and intensive care unit (ICU) stay] for SAP were identified and selected for ML prediction model derivations and validations. The internal and cross-validations revealed the superior and robust performance of the GNB model with the highest AUC value (0.861, 95% CI: 0.793–0.930), while the LR model had the highest AUC value (0.867, 95% CI: 0.812–0.923) in external validation. The ESVM method combining the other six methods had moderate but robust abilities in both cross-validation and external validation and achieved an AUC of 0.843 (95% CI: 0.784–0.902) in external validation.ConclusionThe ML models could effectively predict SAP in sICH populations, and our novel ensemble model demonstrated reliable robust performance outcomes despite the populational and algorithmic differences. This attempt indicated that ML application may benefit in the early identification of SAP.
Background: Stroke-associated pneumonia (SAP) contributes to high mortality rates in spontaneous intracerebral hemorrhage (sICH) populations. The accurate prediction and early intervention of SAP are associated with prognosis. Although various predictive scoring systems have been previously developed, none are widely accepted. We aimed to derive and validate novel supervised machine learning (ML) models to predict SAP events in supratentorial sICH populations.Methods: In this work, the data of eligible supratentorial sICH individuals were extracted from the database of the Risa-MIS-ICH study, and the participants were split into training, internal validation, and external validation datasets. The primary outcome was SAP during hospitalization. Univariate and multivariate analyses were used for variable filtrations, and logistic regression (LR), Gaussian naïve Bayes (GNB), random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM), extreme gradient boosting (XGB), and ensemble soft voting model (ESVM) were adopted for ML model derivations. The metrics of accuracy, sensitivity, specificity, and area under the curve (AUC) were adopted to evaluate the predictive value of each model with internal/cross-/external validations.Results: After screening 909 individuals with sICH, a total of 468 were included in this work. Six independent variables [nasogastric feeding, airway support, unconscious onset, surgery of external ventricular drainage (EVD), sICH volume, and intensive care unit (ICU) stay] for SAP were identified and selected for seven ML prediction model derivations and validations. The internal and cross-validations revealed the superior and robust performance of the GNB model with the highest AUC value (0.861, 95% CI: 0.793-0.930), while the LR model had the highest AUC value (0.867, 95% CI: 0.812-0.923) in external validation. The ESVM method combining the other six methods had moderate but robust abilities in both cross- and external validations and achieved an AUC of 0.843 (95% CI: 0.784, 0.902) in the external validation.Conclusion: The ML models could effectively predict SAP events in sICH populations, and our novel ensemble models demonstrated reliable robust performance outcomes despite the populational and algorithmic differences.Registration: URL: https://www.clinicaltrials.gov. Unique Identifier: NCT03862729
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