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PLoS One. 2018 Mar 1;13(3):e0193511. doi: 10.1371/journal.pone.0193511. eCollection 2018.

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

Jung HY1,2, Kim SH1,2, Jang HM2,3, Lee S1,2, Kim YS2,4, Kang SW2,5, Yang CW2,6, Kim NH2,7, Choi JY1,2, Cho JH1,2, Kim CD1,2, Park SH1,2, Kim YL1,2,8.

Author information

Department of Internal Medicine, Kyungpook National University Hospital and School of Medicine, Kyungpook National University, Daegu, Korea.
Clinical Research Center for End Stage Renal Disease, Daegu, Korea.
Department of Statistics, Kyungpook National University, Daegu, Korea.
Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea.
Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea.
Department of Internal Medicine, The Catholic University of Korea College of Medicine, Seoul, Korea.
Department of Internal Medicine, Chonnam National University Medical School, Gwangju, Korea.
Bk21 Plus KNU Biomedical Convergence Program, Department of Biomedical Science, Kyungpook National University, Daegu, Korea.


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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