Development of a prediction tool for kidney function decline in children with chronic kidney disease.

Background: A paucity of literature exists on the development of predictive tools for the decline of kidney function in pediatric chronic kidney disease (CKD). The objective of this study is to develop and internally validate a tool for the short-term prediction of a kidney function decline in pediatric patients with CKD.

Methods: A total of 539 patients participating in the KNOW-PedCKD (KoreaN cohort study for Outcomes in patients With Pediatric Chronic Kidney Disease) were evaluated for 48 variables related to sociodemographic characteristics, laboratory data, and treatment use.

A novel approach to the relation of multi-pollutant effect and kidney dysfunction: data analysis from the Korean National Environmental Health Survey Cycle 3 (2015-2017).

Background: Traditional statistical models for estimating the impact of multiple environmental chemicals on kidney outcomes have limitations. This study aimed to evaluate the risk prediction of kidney disease in the general population using innovative methodologies.

Methods: Serum persistent organic pollutant (POP), urinary chemical, serum creatinine, and urinary albumin levels were measured in a subpopulation of adults (n = 1,266) drawn from the Korean National Environmental Health Survey Cycle 3 (n = 3,787).

Predicting early mortality in hemodialysis patients: a deep learning approach using a nationwide prospective cohort in South Korea.

Early mortality after hemodialysis (HD) initiation significantly impacts the longevity of HD patients. This study aimed to quantify the effect sizes of risk factors on mortality using various machine learning approaches. A cohort of 3284 HD patients from the CRC-ESRD (2008-2014) was analyzed.

Predicting outcomes of continuous renal replacement therapy using body composition monitoring: a deep-learning approach.

Fluid balance is a critical prognostic factor for patients with severe acute kidney injury (AKI) requiring continuous renal replacement therapy (CRRT). This study evaluated whether repeated fluid balance monitoring could improve prognosis in this clinical population. This was a multicenter retrospective study that included 784 patients (mean age, 67.

Tackling the Challenges in Scene Graph Generation With Local-to-Global Interactions.

In this work, we seek new insights into the underlying challenges of the scene graph generation (SGG) task. Quantitative and qualitative analysis of the visual genome (VG) dataset implies: 1) ambiguity: even if interobject relationship contains the same object (or predicate), they may not be visually or semantically similar; 2) asymmetry: despite the nature of the relationship that embodied the direction, it was not well addressed in previous studies; and 3) higher-order contexts: leveraging the identities of certain graph elements can help generate accurate scene graphs. Motivated by the analysis, we design a novel SGG framework, Local-to-global interaction networks (LOGINs).

Recalibration and validation of the Charlson Comorbidity Index in an Asian population: the National Health Insurance Service-National Sample Cohort study.

Weights assigned to comorbidities in predicting mortality may vary based on the type of index disease and advances in the management of comorbidities. We aimed to develop a modified version of the Charlson Comorbidity Index (CCI) using an Asian nationwide database (mCCI-A), enabling the precise prediction of mortality rates in this population. The main data source used in this study was the National Health Insurance Service-National Sample Cohort (NHIS-NSC) obtained from the National Health Insurance database, which includes health insurance claims filed between January 1, 2002, and December 31, 2013, in Korea.

Prediction of the Mortality Risk in Peritoneal Dialysis Patients using Machine Learning Models: A Nation-wide Prospective Cohort in Korea.

Herein, we aim to assess mortality risk prediction in peritoneal dialysis patients using machine-learning algorithms for proper prognosis prediction. A total of 1,730 peritoneal dialysis patients in the CRC for ESRD prospective cohort from 2008 to 2014 were enrolled in this study. Classification algorithms were used for prediction of N-year mortality including neural network.

A Machine Learning Approach Using Survival Statistics to Predict Graft Survival in Kidney Transplant Recipients: A Multicenter Cohort Study.

Accurate prediction of graft survival after kidney transplant is limited by the complexity and heterogeneity of risk factors influencing allograft survival. In this study, we applied machine learning methods, in combination with survival statistics, to build new prediction models of graft survival that included immunological factors, as well as known recipient and donor variables. Graft survival was estimated from a retrospective analysis of the data from a multicenter cohort of 3,117 kidney transplant recipients.

Superior outcomes of kidney transplantation compared with dialysis: An optimal matched analysis of a national population-based cohort study between 2005 and 2008 in Korea.

Data regarding kidney transplantation (KT) and dialysis outcomes are rare in Asian populations. In the present study, we evaluated the clinical outcomes associated with KT using claims data from the Korean national public health insurance program. Among the 35,418 adult patients with incident dialysis treated between 2005 and 2008 in Korea, 1539 underwent KT.