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.
Bibliographical noteFunding Information:
This research was supported by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), which is funded by the Ministry of Health & Welfare, Republic of Korea (HI15C0001 and HC15C1129 to YLK and HI13C1232 to CDK). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
© 2018 Jung et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
All Science Journal Classification (ASJC) codes
- Biochemistry, Genetics and Molecular Biology(all)
- Agricultural and Biological Sciences(all)