Cited 16 times in
Which Biomarker is the Best for Predicting Mortality in Incident Peritoneal Dialysis Patients: NT-ProBNP, Cardiac TnT, or hsCRP?: A Prospective Observational Study
DC Field | Value | Language |
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dc.contributor.author | 강신욱 | - |
dc.contributor.author | 권영은 | - |
dc.contributor.author | 박경숙 | - |
dc.contributor.author | 박정탁 | - |
dc.contributor.author | 오형중 | - |
dc.contributor.author | 유태현 | - |
dc.contributor.author | 이미정 | - |
dc.contributor.author | 한승혁 | - |
dc.date.accessioned | 2018-03-26T16:42:35Z | - |
dc.date.available | 2018-03-26T16:42:35Z | - |
dc.date.issued | 2015 | - |
dc.identifier.issn | 0025-7974 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/156738 | - |
dc.description.abstract | Although numerous previous studies have explored various biomarkers for their ability to predict mortality in end-stage renal disease (ESRD) patients, these studies have been limited by retrospective analyses, mostly prevalent dialysis patients, and the measurement of only 1 or 2 biomarkers. This prospective study was aimed to evaluate the association between 3 biomarkers and mortality in incident 335 ESRD patients starting continuous ambulatory peritoneal dialysis (CAPD) in Korea. According to the baseline NT-proBNP, cTnT, and hsCRP levels, the patients were stratified into tertiles, and cardiovascular (CV) and all-cause mortalities were compared. Additionally, time-dependent ROC curves were constructed, and the net reclassification index (NRI) and integrated discrimination improvement (IDI) of the models with various biomarkers were calculated. We found the upper tertile of NT-proBNP was significantly associated with increased risk of both CV and all-cause mortalities. However, the upper tertile of hsCRP was significantly related only to the high risk of all-cause mortality even after adjustment for age, sex, and white blood cell counts. Moreover, NT-proBNP had the highest predictive power for CV mortality, whereas hsCRP was the best prognostic marker for all-cause mortality among these biomarkers. In conclusions, NT-proBNP is a more significant prognostic factor for CV mortality than cTnT and hsCRP, whereas hsCRP is a more significant predictor than NT-proBNP and cTnT for all-cause mortality in incident peritoneal dialysis patients. | - |
dc.description.statementOfResponsibility | open | - |
dc.language | English | - |
dc.publisher | Lippincott Williams & Wilkins | - |
dc.relation.isPartOf | MEDICINE | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.rights | https://creativecommons.org/licenses/by-nc-nd/2.0/kr/ | - |
dc.subject.MESH | Biomarkers/blood | - |
dc.subject.MESH | C-Reactive Protein/metabolism* | - |
dc.subject.MESH | Female | - |
dc.subject.MESH | Humans | - |
dc.subject.MESH | Kidney Failure, Chronic/blood* | - |
dc.subject.MESH | Kidney Failure, Chronic/mortality | - |
dc.subject.MESH | Kidney Failure, Chronic/therapy | - |
dc.subject.MESH | Male | - |
dc.subject.MESH | Middle Aged | - |
dc.subject.MESH | Natriuretic Peptide, Brain/blood* | - |
dc.subject.MESH | Peptide Fragments/blood* | - |
dc.subject.MESH | Peritoneal Dialysis, Continuous Ambulatory* | - |
dc.subject.MESH | Prognosis | - |
dc.subject.MESH | Prospective Studies | - |
dc.subject.MESH | Protein Precursors | - |
dc.subject.MESH | ROC Curve | - |
dc.subject.MESH | Republic of Korea/epidemiology | - |
dc.subject.MESH | Troponin T | - |
dc.title | Which Biomarker is the Best for Predicting Mortality in Incident Peritoneal Dialysis Patients: NT-ProBNP, Cardiac TnT, or hsCRP?: A Prospective Observational Study | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine | - |
dc.contributor.department | Dept. of Internal Medicine | - |
dc.contributor.googleauthor | Hyung Jung Oh | - |
dc.contributor.googleauthor | Mi Jung Lee | - |
dc.contributor.googleauthor | Young Eun Kwon | - |
dc.contributor.googleauthor | Kyoung Sook Park | - |
dc.contributor.googleauthor | Jung Tak Park | - |
dc.contributor.googleauthor | Seung Hyeok Han | - |
dc.contributor.googleauthor | Tae-Hyun Yoo | - |
dc.contributor.googleauthor | Yong-Lim Kim | - |
dc.contributor.googleauthor | Yon Su Kim | - |
dc.contributor.googleauthor | Chul Woo Yang | - |
dc.contributor.googleauthor | Nam-Ho Kim | - |
dc.contributor.googleauthor | Shin-Wook Kang | - |
dc.identifier.doi | 10.1097/MD.0000000000001636 | - |
dc.contributor.localId | A00053 | - |
dc.contributor.localId | A00232 | - |
dc.contributor.localId | A01423 | - |
dc.contributor.localId | A01654 | - |
dc.contributor.localId | A02417 | - |
dc.contributor.localId | A02526 | - |
dc.contributor.localId | A02773 | - |
dc.contributor.localId | A04304 | - |
dc.relation.journalcode | J02214 | - |
dc.identifier.eissn | 1536-5964 | - |
dc.identifier.pmid | 26554763 | - |
dc.contributor.alternativeName | Kang, Shin Wook | - |
dc.contributor.alternativeName | Kwon, Young Eun | - |
dc.contributor.alternativeName | Park, Kyoung Sook | - |
dc.contributor.alternativeName | Park, Jung Tak | - |
dc.contributor.alternativeName | Oh, Hyung Jung | - |
dc.contributor.alternativeName | Yoo, Tae Hyun | - |
dc.contributor.alternativeName | Lee, Mi Jung | - |
dc.contributor.alternativeName | Han, Seung Hyeok | - |
dc.contributor.affiliatedAuthor | Kang, Shin Wook | - |
dc.contributor.affiliatedAuthor | Kwon, Young Eun | - |
dc.contributor.affiliatedAuthor | Park, Kyoung Sook | - |
dc.contributor.affiliatedAuthor | Park, Jung Tak | - |
dc.contributor.affiliatedAuthor | Oh, Hyung Jung | - |
dc.contributor.affiliatedAuthor | Yoo, Tae Hyun | - |
dc.contributor.affiliatedAuthor | Lee, Mi Jung | - |
dc.contributor.affiliatedAuthor | Han, Seung Hyeok | - |
dc.citation.volume | 94 | - |
dc.citation.number | 44 | - |
dc.citation.startPage | e1636 | - |
dc.identifier.bibliographicCitation | MEDICINE, Vol.94(44) : e1636, 2015 | - |
dc.identifier.rimsid | 39868 | - |
dc.type.rims | ART | - |
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