Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Multicenter Study
. 2015 Jun 9;10(6):e0129180.
doi: 10.1371/journal.pone.0129180. eCollection 2015.

Risk Score to Predict 1-Year Mortality After Haemodialysis Initiation in Patients With Stage 5 Chronic Kidney Disease Under Predialysis Nephrology Care

Free PMC article
Multicenter Study

Risk Score to Predict 1-Year Mortality After Haemodialysis Initiation in Patients With Stage 5 Chronic Kidney Disease Under Predialysis Nephrology Care

Toshiki Doi et al. PLoS One. .
Free PMC article


Background: Few risk scores are available for predicting mortality in chronic kidney disease (CKD) patients undergoing predialysis nephrology care. Here, we developed a risk score using predialysis nephrology practice data to predict 1-year mortality following the initiation of haemodialysis (HD) for CKD patients.

Methods: This was a multicenter cohort study involving CKD patients who started HD between April 2006 and March 2011 at 21 institutions with nephrology care services. Patients who had not received predialysis nephrology care at an estimated glomerular filtration rate (eGFR) of approximately 10 mL/min per 1.73 m2 were excluded. Twenty-nine candidate predictors were selected, and the final model for 1-year mortality was developed via multivariate logistic regression and was internally validated by a bootstrapping technique.

Results: A total of 688 patients were enrolled, and 62 (9.0%) patients died within one year of HD initiation. The following variables were retained in the final model: eGFR, serum albumin, calcium, Charlson Comorbidity Index excluding diabetes and renal disease (modified CCI), performance status (PS), and usage of erythropoiesis-stimulating agent (ESA). Their β-coefficients were transformed into integer scores: three points were assigned to modified CCI≥3 and PS 3-4; two to calcium>8.5 mg/dL, modified CCI 1-2, and no use of ESA; and one to albumin<3.5 g/dL, eGFR>7 mL/min per 1.73 m2, and PS 1-2. Predicted 1-year mortality risk was 2.5% (score 0-4), 5.5% (score 5-6), 15.2% (score 7-8), and 28.9% (score 9-12). The area under the receiver operating characteristic curve was 0.83 (95% confidence interval, 0.79-0.89).

Conclusions: We developed a simple 6-item risk score predicting 1-year mortality after the initiation of HD that might help nephrologists make a shared decision with patients and families regarding the initiation of HD.

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.


Fig 1
Fig 1. Process of the multiple imputation and derivation of the prediction rule.
(1) Five multiply imputed datasets were created using original data. (2) Backward elimination was separately applied to each of the five imputed datasets, resulting in five sets of selected predictors. (3) Predictors that were selected in all of the five data sets were chosen as the final set of selected predictors, with exclusion of some predictors based on balance between number of candidate predictors with number of outcomes (deaths) and discussion according to clinical relevance. (4) The logistic regression with the selected six predictors was separately applied to each of the five imputed data sets, giving five sets of β-coefficients of the six predictors. (5) To avoid overfitting, each of five sets of β-coefficients of the six predictors were shrunken using heuristic shrinkage factor. Then, the mean for each of the five estimates for β-coefficients of the final model were taken and variances of the five estimates were pooled according to Rubin’s rules. (6) The shrunken β-coefficients of the predictors in the final model divided by two-fifths of the two small β-coefficients in the model and rounded up to the nearest integer to give a simple point score.
Fig 2
Fig 2. Agreement between the predicted mortality risks and the observed proportions.
The short-dashed line (“Apparent”) indicates the agreement between predicted mortality risks and observed proportions of the original model. The sold line (“Bias-corrected”) indicates the agreement between predicted mortality risks and observed proportions of the bootstrap model.
Fig 3
Fig 3. Predicted mortality risks and observed proportions for ranges of total scores.
Prognostic score calculated form the following six items well predicts 1-year mortality for patients initiating haemodialysis: high eGFR level (>7 mL/min per 1.73 m2), low serum albumin levels, high calcium levels, high modified Charlson Comorbidity Index, low performance status, and no use of ESA. The modified Charlson Comorbidity Index was excluded items related to diabetes and renal disease from the original Charlson Comorbidity Index in the present study.

Similar articles

See all similar articles

Cited by 9 articles

See all "Cited by" articles


    1. Couchoud C, Labeeuw M, Moranne O, Allot V, Esnault V, Frimat L, et al. A clinical score to predict 6-month prognosis in elderly patients starting dialysis for end-stage renal disease. Nephrol Dial Transplant. 2009;24: 1553–1561. 10.1093/ndt/gfn698 - DOI - PMC - PubMed
    1. Susantitaphong P, Altamimi S, Ashkar M, Balk EM, Stel VS, Wright S, et al. GFR at initiation of dialysis and mortality in CKD: a meta-analysis. Am J Kidney Dis. 2012;59: 829–840. 10.1053/j.ajkd.2012.01.015 - DOI - PMC - PubMed
    1. Yamagata K, Nakai S, Masakane I, Hanafusa N, Iseki K, Tsubakihara Y, et al. Ideal timing and predialysis nephrology care duration for dialysis initiation: from analysis of Japanese dialysis initiation survey. Ther Apher Dial. 2012;16: 54–62. 10.1111/j.1744-9987.2011.01005.x - DOI - PubMed
    1. Wright S, Klausner D, Baird B, Williams ME, Steinman T, Tang H, et al. Timing of dialysis initiation and survival in ESRD. Clin J Am Soc Nephrol. 2010;5: 1828–1835. 10.2215/CJN.06230909 - DOI - PMC - PubMed
    1. van Diepen M, Schroijen MA, Dekkers OM, Rotmans JI, Krediet RT, Boeschoten EW, et al. Predicting mortality in patients with diabetes starting dialysis. PLoS One. 2014;9: e89744 10.1371/journal.pone.0089744 - DOI - PMC - PubMed

Publication types

Grant support

This work was supported by grants from the Institute for Health Outcomes & Process Evaluation research. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.