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Individualized prediction of mortality using multiple inflammatory markers in patients on dialysis

This study aimed to evaluate whether the combination of inflammatory markers could provide predictive powers for mortality in individual patients on dialysis and develop a predictive model for mortality according to dialysis modality. Data for inflammatory markers were obtained at the time of enroll...

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Autores principales: Jung, Hee-Yeon, Kim, Su Hee, Jang, Hye Min, Lee, Sukyung, Kim, Yon Su, Kang, Shin-Wook, Yang, Chul Woo, Kim, Nam-Ho, Choi, Ji-Young, Cho, Jang-Hee, Kim, Chan-Duck, Park, Sun-Hee, Kim, Yong-Lim
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5832435/
https://www.ncbi.nlm.nih.gov/pubmed/29494637
http://dx.doi.org/10.1371/journal.pone.0193511
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author Jung, Hee-Yeon
Kim, Su Hee
Jang, Hye Min
Lee, Sukyung
Kim, Yon Su
Kang, Shin-Wook
Yang, Chul Woo
Kim, Nam-Ho
Choi, Ji-Young
Cho, Jang-Hee
Kim, Chan-Duck
Park, Sun-Hee
Kim, Yong-Lim
author_facet Jung, Hee-Yeon
Kim, Su Hee
Jang, Hye Min
Lee, Sukyung
Kim, Yon Su
Kang, Shin-Wook
Yang, Chul Woo
Kim, Nam-Ho
Choi, Ji-Young
Cho, Jang-Hee
Kim, Chan-Duck
Park, Sun-Hee
Kim, Yong-Lim
author_sort Jung, Hee-Yeon
collection PubMed
description This study aimed to evaluate whether the combination of inflammatory markers could provide predictive powers for mortality in individual patients on dialysis and develop a predictive model for mortality according to dialysis modality. Data for inflammatory markers were obtained at the time of enrollment from 3,309 patients on dialysis from a prospective multicenter cohort. Net reclassification index (NRI) and integrated discrimination improvement (IDI) were calculated. Cox proportional hazards regression analysis was used to derive a prediction model of mortality and the integrated area under the curve (iAUC) was calculated to compare the predictive accuracy of the models. The incremental additions of albumin, high-sensitive C-reactive protein (hsCRP), white blood count (WBC), and ferritin to the conventional risk factors showed the highest predictive powers for all-cause mortality in the entire population (NRI, 21.0; IDI, 0.045) and patients on peritoneal dialysis (NRI, 25.7; IDI, 0.061). The addition of albumin and hsCRP to the conventional risk factors markedly increased predictive powers for all-cause mortality in HD patients (NRI, 19.0; IDI, 0.035). The prediction model for all-cause mortality using conventional risk factors and combination of inflammatory markers with highest NRI value (iAUC, 0.741; 95% CI, 0.722–0.761) was the most accurate in the entire population compared with a model including conventional risk factors alone (iAUC, 0.719; 95% CI, 0.700–0.738) or model including only significant conventional risk factors and inflammatory markers (iAUC, 0.734; 95% CI, 0.714–0.754). Using multiple inflammatory markers practically available in a clinic can provide higher predictive power for all-cause mortality in patients on dialysis. The predictive model for mortality based on combinations of inflammatory markers enables a stratified risk assessment. However, the optimal combination for the predictive model was different in each dialysis modality.
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spelling pubmed-58324352018-03-23 Individualized prediction of mortality using multiple inflammatory markers in patients on dialysis Jung, Hee-Yeon Kim, Su Hee Jang, Hye Min Lee, Sukyung Kim, Yon Su Kang, Shin-Wook Yang, Chul Woo Kim, Nam-Ho Choi, Ji-Young Cho, Jang-Hee Kim, Chan-Duck Park, Sun-Hee Kim, Yong-Lim PLoS One Research Article This study aimed to evaluate whether the combination of inflammatory markers could provide predictive powers for mortality in individual patients on dialysis and develop a predictive model for mortality according to dialysis modality. Data for inflammatory markers were obtained at the time of enrollment from 3,309 patients on dialysis from a prospective multicenter cohort. Net reclassification index (NRI) and integrated discrimination improvement (IDI) were calculated. Cox proportional hazards regression analysis was used to derive a prediction model of mortality and the integrated area under the curve (iAUC) was calculated to compare the predictive accuracy of the models. The incremental additions of albumin, high-sensitive C-reactive protein (hsCRP), white blood count (WBC), and ferritin to the conventional risk factors showed the highest predictive powers for all-cause mortality in the entire population (NRI, 21.0; IDI, 0.045) and patients on peritoneal dialysis (NRI, 25.7; IDI, 0.061). The addition of albumin and hsCRP to the conventional risk factors markedly increased predictive powers for all-cause mortality in HD patients (NRI, 19.0; IDI, 0.035). The prediction model for all-cause mortality using conventional risk factors and combination of inflammatory markers with highest NRI value (iAUC, 0.741; 95% CI, 0.722–0.761) was the most accurate in the entire population compared with a model including conventional risk factors alone (iAUC, 0.719; 95% CI, 0.700–0.738) or model including only significant conventional risk factors and inflammatory markers (iAUC, 0.734; 95% CI, 0.714–0.754). Using multiple inflammatory markers practically available in a clinic can provide higher predictive power for all-cause mortality in patients on dialysis. The predictive model for mortality based on combinations of inflammatory markers enables a stratified risk assessment. However, the optimal combination for the predictive model was different in each dialysis modality. Public Library of Science 2018-03-01 /pmc/articles/PMC5832435/ /pubmed/29494637 http://dx.doi.org/10.1371/journal.pone.0193511 Text en © 2018 Jung et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Jung, Hee-Yeon
Kim, Su Hee
Jang, Hye Min
Lee, Sukyung
Kim, Yon Su
Kang, Shin-Wook
Yang, Chul Woo
Kim, Nam-Ho
Choi, Ji-Young
Cho, Jang-Hee
Kim, Chan-Duck
Park, Sun-Hee
Kim, Yong-Lim
Individualized prediction of mortality using multiple inflammatory markers in patients on dialysis
title Individualized prediction of mortality using multiple inflammatory markers in patients on dialysis
title_full Individualized prediction of mortality using multiple inflammatory markers in patients on dialysis
title_fullStr Individualized prediction of mortality using multiple inflammatory markers in patients on dialysis
title_full_unstemmed Individualized prediction of mortality using multiple inflammatory markers in patients on dialysis
title_short Individualized prediction of mortality using multiple inflammatory markers in patients on dialysis
title_sort individualized prediction of mortality using multiple inflammatory markers in patients on dialysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5832435/
https://www.ncbi.nlm.nih.gov/pubmed/29494637
http://dx.doi.org/10.1371/journal.pone.0193511
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