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

Hee Yeon Jung, Su Hee Kim, Hye Min Jang, Sukyung Lee, Yon Su Kim, Shin-Wook Kang, Chul Woo Yang, Nam Ho Kim, Ji Young Choi, Jang Hee Cho, Chan Duck Kim, Sun Hee Park, Yong Lim Kim

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article numbere0193511
JournalPloS one
Volume13
Issue number3
DOIs
Publication statusPublished - 2018 Mar 1

Fingerprint

Dialysis
dialysis
taxonomic revisions
prediction
Mortality
risk factors
Area Under Curve
C-reactive protein
C-Reactive Protein
albumins
Albumins
ferritin
Peritoneal Dialysis
Ferritins
Regression analysis
Risk assessment
Population
risk assessment
Hazards
Blood

All Science Journal Classification (ASJC) codes

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)

Cite this

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. In: PloS one. 2018 ; Vol. 13, No. 3.
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title = "Individualized prediction of mortality using multiple inflammatory markers in patients on dialysis",
abstract = "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.",
author = "Jung, {Hee Yeon} and Kim, {Su Hee} and Jang, {Hye Min} and Sukyung Lee and Kim, {Yon Su} and Shin-Wook Kang and Yang, {Chul Woo} and Kim, {Nam Ho} and Choi, {Ji Young} and Cho, {Jang Hee} and Kim, {Chan Duck} and Park, {Sun Hee} and Kim, {Yong Lim}",
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Jung, HY, Kim, SH, Jang, HM, Lee, S, Kim, YS, Kang, S-W, Yang, CW, Kim, NH, Choi, JY, Cho, JH, Kim, CD, Park, SH & Kim, YL 2018, 'Individualized prediction of mortality using multiple inflammatory markers in patients on dialysis', PloS one, vol. 13, no. 3, e0193511. https://doi.org/10.1371/journal.pone.0193511

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.

In: PloS one, Vol. 13, No. 3, e0193511, 01.03.2018.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Individualized prediction of mortality using multiple inflammatory markers in patients on dialysis

AU - Jung, Hee Yeon

AU - Kim, Su Hee

AU - Jang, Hye Min

AU - Lee, Sukyung

AU - Kim, Yon Su

AU - Kang, Shin-Wook

AU - Yang, Chul Woo

AU - Kim, Nam Ho

AU - Choi, Ji Young

AU - Cho, Jang Hee

AU - Kim, Chan Duck

AU - Park, Sun Hee

AU - Kim, Yong Lim

PY - 2018/3/1

Y1 - 2018/3/1

N2 - 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.

AB - 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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