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.
Highlights
There have been recent advances in management of end-stage renal disease (ESRD), mortality rates in patients with ESRD remain high [1]
Considering that the risks for patients on dialysis are different from other populations, conventional risk factors for mortality cannot be applied to patients with ESRD
This study aimed to investigate 1) whether multiple inflammatory markers measured in routine clinical practice could improve risk prediction for mortality in patients on dialysis, using reclassification and discrimination analyses, and 2) whether the predictive model is different in each dialysis modality
Summary
There have been recent advances in management of end-stage renal disease (ESRD), mortality rates in patients with ESRD remain high [1]. Early risk stratification and prompt therapy of patients at high mortality risk are crucial elements of ESRD care. Framingham risk factors were considered as accurate predictors for coronary heart disease in the general population [2]. Considering that the risks for patients on dialysis are different from other populations, conventional risk factors for mortality cannot be applied to patients with ESRD. Multiple studies have attempted to discover additional biomarkers, which improve the predictive power of mortality beyond the established risk factors in patients on dialysis [3]. We studied the additional predictive powers of inflammatory biomarkers above those of conventional risk factors
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