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

Author

Listed:
  • 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

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.

Suggested Citation

  • 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, 2018. "Individualized prediction of mortality using multiple inflammatory markers in patients on dialysis," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-13, March.
  • Handle: RePEc:plo:pone00:0193511
    DOI: 10.1371/journal.pone.0193511
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    Cited by:

    1. Shinya Suzuki & Takeshi Yamashita & Tsuyoshi Sakama & Takuto Arita & Naoharu Yagi & Takayuki Otsuka & Hiroaki Semba & Hiroto Kano & Shunsuke Matsuno & Yuko Kato & Tokuhisa Uejima & Yuji Oikawa & Minor, 2019. "Comparison of risk models for mortality and cardiovascular events between machine learning and conventional logistic regression analysis," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-14, September.

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