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Ajou University

Can we predict when to start renal replacement therapy in chronic kidney disease patient by using 6 months clinical data?

Abstract

dc:description

BACKGROUND/AIMS: The purpose of this study was to develop a model of Chronic Kidney Disease (CKD) progression for predicting the probability and time to progression from various CKD stage to renal replacement therapy (RRT), using 6 months clinical data variables routinely measured in healthcare centers. METHODS: The data were derived from the electronic medical records (EMR) at Ajou University Hospital, Suwon, South Korea from October 1997 to September 2012. We included patients who were diagnosed with CKD (eGFR <60 mL·min–1·1.73 m–2 for ≥3 months) and followed up for at least 6 months. Study population was divided into a training set and a test set in random. RESULTS: There were 4,509 patients with reasonable diagnostic criteria. We divided patients into two groups at random, and after excluding the patients with missing values, the training and test set included 1,625 and 1,618 patients, respectively. The integral mean showed most powerful explanatory (R2 = 0.404) among the 8 modified values. Eleven variables (age, sex, Diabetes mellitus (DM), Polycystic kidney disease (PKD), serum albumin, serum hemoglobin, serum calcium, serum phosphorus, serum potassium, eGFR (MDRD), and urine protein) were included final risk prediction model (R2 = 0.403). The calculated risk index(RI) was –0.011 x age – 0.468 x albumin - 0.069 - hemoglobin – 0.226 x calcium + 0.223 x phosphorus + 0.266 x potassium – 0.045 x eGFR (MDRD) + 4.203 – 0.405 (if female) + 0.402 (if DM) + 1.096 (if PKD) + 0.908 (if urine protein 1+) + 1.195 (if urine protein 2+) + 1.360 (if urine protein 3+) +1.658 (if urine protein 4+). The Equation for the probability of not starting RRT at some point (t, years) is as follows. S(t) = S0(t)exp (RI) CONCLUSIONS: we made prediction model with 11 variables by using integral means. From the result of brier score (BS) and area under the curve (AUC), we consider that our model have significant explanatory power to predict the probability and interval time to start RRT.

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • 박, 주한
Contributors dc:contributor
  • 김, 흥수
  • 신, 규태
  • 박, 래웅
  • 대학원 의학과
  • 107339

Subjects

dc:subject × 8

Rights

Language dc:language
en

Identifiers

dc:identifier.*
OAI identifier oai:identifier
oai:repository.ajou.ac.kr:201003/10890

Chain of custody

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Harvested from
Ajou University
Base URL
repository.ajou.ac.kr/oai/request
Last updated
2026-07-24
Source record
OAI-PMH GetRecord
citation

박, 주한. Can we predict when to start renal replacement therapy in chronic kidney disease patient by using 6 months clinical data?. 2014. http://repository.ajou.ac.kr/handle/201003/10890