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Background: Corneal edema (CE) affects the outcome of phacoemulsification. Effective ways to predict the CE after phacoemulsification are needed. Methods: On the basis of data from patients conforming to the protocol of the AGSPC trial, 17 variables were selected to predict CE after phacoemulsification by constructing a CE nomogram through multivariate logistic regression, which was improved via variable selection with copula entropy. The prediction models were evaluated using predictive accuracy, the area under the receiver operating characteristic curve (AUC), and decision curve analysis (DCA). Results: Data from 178 patients were used to construct prediction models. After copula entropy variable selection, which shifted the variables used for prediction in the CE nomogram from diabetes, best corrected visual acuity (BCVA), lens thickness and cumulative dissipated energy (CDE) to CDE and BCVA in the Copula nomogram, there was no significant change in predictive accuracy (0.9039 vs. 0.9098). There was also no significant difference in AUCs between the CE nomogram and the Copula nomogram (0.9637, 95% CI 0.9329–0.9946 vs. 0.9512, 95% CI 0.9075–0.9949; p = 0.2221). DCA suggested that the Copula nomogram has clinical application. Conclusions: This study obtained a nomogram with good performance to predict CE after phacoemulsification, and showed the improvement of copula entropy for nomogram models. [ABSTRACT FROM AUTHOR]

The fluidics system is an indispensable and primary component of phacoemulsification. Both the gravity-fluidics system and active-fluidics system are commonly used in practice. The irrigation pressure of the gravity-fluidics system is determined by the bottle height, which is relatively constant, while the active-fluidics system is paired with a cassette that contains pressure sensors to monitor intraocular pressure changes. The active-fluidics system allows surgeons to preset a target intraocular pressure value, and it replenishes the fluids proactively; thus, the intraocular pressure is consistently maintained near the target value. Under such circumstances, stable intraocular pressure and anterior chamber volume values could be acquired. Research on surgical safety, efficiency and results have reported several strengths of the active-fluidics system. It is also advantageous in some complicated cataract surgeries. However, the system is not widely used at present, mainly due to its low penetration rate and high equipment cost. Some of its updates such as the new Active Sentry handpiece showed potential superiority in laboratory studies recently, but there is still further research to be conducted. This article gives an overview of the mechanism and performance of the active-fluidics system, and it is expected to provide clues for future research. [ABSTRACT FROM AUTHOR]

Background: The ability to accurately detect ischemic stroke and predict its neurological recovery is of great clinical value. This study intended to evaluate the performance of whole-brain dynamic radiomics features (DRF) for ischemic stroke detection, neurological impairment assessment, and outcome prediction. Methods: The supervised feature selection (Lasso) and unsupervised feature-selection methods (five-feature dimension-reduction algorithms) were used to generate four experimental groups with DRF in different combinations. Ten machine learning models were used to evaluate their performance by ten-fold cross-validation. Results: In experimental group_A, the best AUCs (0.873 for stroke detection, 0.795 for NIHSS assessment, and 0.818 for outcome prediction) were obtained by outstanding DRF selected by Lasso, and the performance of significant DRF was better than the five-feature dimension-reduction algorithms. The selected outstanding dimension-reduction DRF in experimental group_C obtained a better AUC than dimension-reduction DRF in experimental group_A but were inferior to the outstanding DRF in experimental group_A. When combining the outstanding DRF with each dimension-reduction DRF (experimental group_B), the performance can be improved in ischemic stroke detection (best AUC = 0.899) and NIHSS assessment (best AUC = 0.835) but failed in outcome prediction (best AUC = 0.806). The performance can be further improved when combining outstanding DRF with outstanding dimension-reduction DRF (experimental group_D), achieving the highest AUC scores in all three evaluation items (0.925 for stroke detection, 0.853 for NIHSS assessment, and 0.828 for outcome prediction). By the method in this study, comparing the best AUC of Ft-test in experimental group_A and the best_AUC in experimental group_D, the AUC in stroke detection increased by 19.4% (from 0.731 to 0.925), the AUC in NIHSS assessment increased by 20.1% (from 0.652 to 0.853), and the AUC in prognosis prediction increased by 14.9% (from 0.679 to 0.828). This study provided a potential clinical tool for detailed clinical diagnosis and outcome prediction before treatment. [ABSTRACT FROM AUTHOR]