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만성 신질환자에서 6개월간의 임상자료를 이용하여 신대체요법 시작 시점을 추정 할 수 있는가?
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | 김흥수, 신규태, 박래웅 | - |
| dc.contributor.author | Park, Joo Han | - |
| dc.date.accessioned | 2019-10-21T07:22:38Z | - |
| dc.date.available | 2019-10-21T07:22:38Z | - |
| dc.date.issued | 2014-02 | - |
| dc.identifier.other | 16109 | - |
| dc.identifier.uri | https://dspace.ajou.ac.kr/handle/2018.oak/18348 | - |
| dc.description | 학위논문(석사)--아주대학교 일반대학원 :의학과,2014. 2 | - |
| dc.language.iso | eng | - |
| dc.publisher | The Graduate School, Ajou University | - |
| dc.rights | 아주대학교 논문은 저작권에 의해 보호받습니다. | - |
| dc.title | 만성 신질환자에서 6개월간의 임상자료를 이용하여 신대체요법 시작 시점을 추정 할 수 있는가? | - |
| dc.title.alternative | Joo-Han Park | - |
| dc.type | Thesis | - |
| dc.contributor.affiliation | 아주대학교 일반대학원 | - |
| dc.contributor.alternativeName | Joo-Han Park | - |
| dc.contributor.department | 일반대학원 의학과 | - |
| dc.date.awarded | 2014. 2 | - |
| dc.description.degree | Master | - |
| dc.identifier.localId | 608165 | - |
| dc.identifier.url | http://dcoll.ajou.ac.kr:9080/dcollection/jsp/common/DcLoOrgPer.jsp?sItemId=000000016109 | - |
| dc.subject.keyword | Chronic kidney disease | - |
| dc.subject.keyword | renal replacement therapy | - |
| dc.subject.keyword | progression | - |
| dc.subject.keyword | equation | - |
| dc.description.alternativeAbstract | 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 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. S(t)= 〖S_0 (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. | - |
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- Graduate School of Ajou University > Department of Medicine > 3. Theses(Master)
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