ABSTRACT
BACKGROUND@#The basis of individualized treatment should be individualized mortality risk predictive information. The present study aimed to develop an online individual mortality risk predictive tool for acute-on-chronic liver failure (ACLF) patients based on a random survival forest (RSF) algorithm.@*METHODS@#The current study retrospectively enrolled ACLF patients from the Department of Infectious Diseases of The First People's Hospital of Foshan, Shunde Hospital of Southern Medical University, and Jiangmen Central Hospital. Two hundred seventy-six consecutive ACLF patients were included in the present study as a model cohort (n = 276). Then the current study constructed a validation cohort by drawing patients from the model dataset based on the resampling method (n = 276). The RSF algorithm was used to develop an individual prognostic model for ACLF patients. The Brier score was used to evaluate the diagnostic accuracy of prognostic models. The weighted mean rank estimation method was used to compare the differences between the areas under the time-dependent ROC curves (AUROCs) of prognostic models.@*RESULTS@#Multivariate Cox regression identified hepatic encephalopathy (HE), age, serum sodium level, acute kidney injury (AKI), red cell distribution width (RDW), and international normalization index (INR) as independent risk factors for ACLF patients. A simplified RSF model was developed based on these previous risk factors. The AUROCs for predicting 3-, 6-, and 12-month mortality were 0.916, 0.916, and 0.905 for the RSF model and 0.872, 0.866, and 0.848 for the Cox model in the model cohort, respectively. The Brier scores were 0.119, 0.119, and 0.128 for the RSF model and 0.138, 0.146, and 0.156 for the Cox model, respectively. The nonparametric comparison suggested that the RSF model was superior to the Cox model for predicting the prognosis of ACLF patients.@*CONCLUSIONS@#The current study developed a novel online individual mortality risk predictive tool that could predict individual mortality risk predictive curves for individual patients. Additionally, the current online individual mortality risk predictive tool could further provide predicted mortality percentages and 95% confidence intervals at user-defined time points.
Subject(s)
Humans , Acute-On-Chronic Liver Failure , Prognosis , Proportional Hazards Models , ROC Curve , Retrospective StudiesABSTRACT
<p><b>OBJECTIVE</b>To provide novel spectral domain optical coherence tomography (SD OCT) findings of Vogt-Koyanagi-Harada (VKH) disease as well as new insights into the pathogenesis of this disease.</p><p><b>METHODS</b>Detailed SD OCT and fluorescein angiography (FA) findings of 18 consecutive VKH patients (11 women and 7 men) from December 2007 to April 2009 who were in acute uveitic stage at presentation were reviewed. All the patients had been followed up for at least 6 months with reevaluation(s) of SD OCT performed in 10 patients.</p><p><b>RESULTS</b>Intraretinal cysts were found to be located in various layers of the outer retina. In addition to the photoreceptor layer, they could also be found between the outer plexiform layer and the outer nuclear layer, or spanning the external limiting membrane. On FA, intraretinal cysts could be hypofluorescent, normofluorescent, or hyperfluorescent. Some intraretinal cysts had a characteristic FA pattern, in which a small round hypofluorescent area was surrounded by a ring of hyperfluorescence (donut-shaped dye pooling). Subretinal fibrinoid deposit appeared in acute uveitic stage in two severe VKH patients and seemed to develop from subretinal exudates and evolved into typical subretinal fibrosis. Gradual transfiguration/migration and progressive proliferation/pigmentation of the subretinal fibrinoid deposit/subretinal fibrosis was observed in one patient.</p><p><b>CONCLUSIONS</b>Intraretinal cysts could form in various layers of the outer retina and may result from extension of choroidal inflammation. Subretinal fibrosis may develop from subretinal exudates in VKH patients and may cause substantial visual impairment.</p>