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논문분류	춘계학술대회 초록집
제목	Can we predict when to start renal replacement therapy in chronic kidney disease patient by using 6months clinical data?
저자	Min-Jeong Lee*, Joo-Han Park1, Jong Cheol Jeong1, Dukyong Yoon2, Rae Woong Park2, Gyu-Tae Shin1, Heungsoo Kim1
출판정보	2016; 2016(1):
키워드	chronic kidney disease, renal replacement therapy
초록	Background: 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 ´ age – 0.468 ´ albumin - 0.069 ´ hemoglobin – 0.226 ´ calcium + 0.223 ´ phosphorus + 0.266 ´ potassium – 0.045 ´ 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. Conclusion: 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.
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