Objective
To investigate the characteristics of fundus angiography with fluorescein and indocyanine green in reattachment of rhegmatogenous retinal detachment．
Methods
Fundus fluorescein angiography(FFA)and indocyanine green angiography(ICGA)were performed on 21 cases with simple rhegmatogenous retinal detachment postoperatively．
Results
(1) On the artificial ridge of 21 cases,window defect hyperfluorescence was found in FFA,choroidal vessel dilatation and choroidal filling defect of 14 cases were showed in ICGA;(2) In 6 cases cryocoagulation,fluorescein leakage was showed in FFA and ICGA,leakage of choroid was observed in late stage of ICGA;(3) In 6 cases photocoagulation,a few fluorescein leakage was demonstrated in FFA and none of this feature was found in ICGA;(4) ICGA showed slow filling of choroid of perioptic disc in 2 case,and temporo-inferiorly in 2 case.
Conclusion
Window defect hyperfluorescence of FFA and choroidal vessel dilatation of ICGA on the artificial ridge were the main characteristics in reattachment of rhegmatogenous retinal detachment.
(Chin J Ocul Fundus Dis,20000,16:17-19)
ObjectiveWhen using multi-center data to construct clinical prediction models, the independence assumption of data will be violated, and there is an obvious clustering effect among research objects. In order to fully consider the clustering effect, this study intends to compare the model performance of the random intercept logistic regression model (RI) and the fixed effects model (FEM) considering the clustering effect with the standard logistic regression model (SLR) and the random forest algorithm (RF) without considering the clustering effect under different scenarios. MethodsIn the process of forecasting model establishment, the prediction performance of different models at the center level was simulated when there were different degrees of clustering effects, including the difference of discrimination and calibration in different scenarios, and the change trend of this difference at different event rates was compared. ResultsAt the center level, different models, except RF, showed little difference in the discrimination of different scenarios under the clustering effect, and the mean of their C-index changed very little. When using multi-center highly clustered data for forecasting, the marginal forecasts (M.RI, SLR and RF) had calibrated intercepts slightly less than 0 compared with the conditional forecasts, which overestimated the average probability of prediction. RF performed well in intercept calibration under the condition of multi-center and large samples, which also reflected the advantage of machine learning algorithm for processing large sample data. When there were few multiple patients in the center, the FEM made conditional predictions, the calibrated intercept was greater than 0, and the predicted mean probability was underestimated. In addition, when the multi-center large sample data were used to develop the prediction model, the slopes of the three conditional forecasts (FEM, A.RI, C.RI) were well calibrated, while the calibrated slopes of the marginal forecasts (M.RI and SLR) were greater than 1, which led to the problem of underfitting, and the underfitting problem became more prominent with the increase in the central aggregation effect. In particular, when there were few centers and few patients, overfitting of the data could mask the difference in calibration performance between marginal and conditional forecasts. Finally, the lower the event rate the central clustering effect at the central level had a more pronounced impact on the forecasting performance of the different models. ConclusionThe highly clustered multi-center data are used to construct the model and apply it to the prediction in a specific environment. RI and FEM can be selected for conditional prediction when the number of centers is small or the difference between centers is large due to different incidence rates. When the number of hearts is large and the sample size is large, RI can be selected for conditional prediction or RF for edge prediction.
ObjectivesTo systematically review the prognostic value of plasma soluble triggering receptor expressed on myeloid cells-1 (sTREM-1) level in predicting 28-day mortality in sepsis.MethodsPubMed, The Cochrane Library, EMbase, Web of Science, CBM, CNKI, VIP and WanFang Data databases were electronically searched to collect studies about the prognostic value of plasma sTREM-1 in early 28-day mortality in sepsis from inception to April 16th, 2018. Two reviewers independently screened literature, extracted data and assessed the risk of bias of included studies, then, meta-analysis was performed by using Stata 14.0 software.ResultsA total of 13 studies involving 1 115 patients were included. The results of meta-analysis showed that the sensitivity and specificity were 79% and 77%, respectively. The positive likelihood ratio and the negative likelihood ratio were 3.4 and 0.28, respectively. The diagnostic ratio was 12. The overall area under the summary receiver operator characteristic (SROC) curve was 0.80.ConclusionsCurrent evidence shows that plasma sTREM-1, as a single index, may play a prognostic role in the early 28-day mortality of sepsis in patients. Due to limited quality and quantity of the included studies, more high quality studies are needed to verify the above conclusion.
