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

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)
  • General

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 Kang, {Shin Wook} 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, SW, 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, 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

Y1 - 2018/3

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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