NDT Advance Access originally published online on December 8, 2005
Nephrology Dialysis Transplantation 2006 21(4):945-956; doi:10.1093/ndt/gfi326
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© The Author [2005]. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Original Articles: Clinical Nephrology
The ERA-EDTA cohort study—comparison of methods to predict survival on renal replacement therapy
1 Renal Unit, Western Infirmary, 2 Renal Unit, Glasgow Royal Infirmary, 3 Engineering Department, University of Strathclyde, Glasgow, 4 Renal Unit, Royal Infirmary of Edinburgh, UK, 5 ERA-EDTA Registry, Department of Medical Informatics, and 6 Department of Clinical Epidemiology and Biostatistics, Academic Medical Centre, Amsterdam, The Netherlands
Correspondence and offprint requests to: Dr Colin C. Geddes, Consultant Nephrologist, Renal Unit, Level 7, Western Infirmary, Dumbarton Road, Glasgow G11 6NT, UK. Email: colin.geddes.wg{at}northglasgow.scot.nhs.uk
Background. Accurate prediction of patient survival from the time of starting renal replacement therapy (RRT) is desirable, but previously published predictive models have low accuracy. We have attempted to overcome limitations of previous studies by conducting an ambidirectional inception cohort study in patients on RRT from centres throughout Europe. A conventional multivariate regression (MVR) model, a self-learning rule-based model (RBM) and a simple co-morbidity score [the Charlson score modified for renal disease (MCS)] were compared.
Methods. In 1996, all 3640 dialysis centres registered with the ERA-EDTA were invited to identify all patients on RRT for end-stage renal failure (ESRF) who died during the 28 days of February 1997 (training cohort) and all patients who started RRT in the same period (validation cohort). Fifty-four clinical and laboratory variables from the time of starting RRT were collected in both cohorts using a two-page questionnaire. The data from the training cohort were given to statisticians at the Amsterdam Academic Medical Centre to create the MVR model and to engineers in Strathclyde University to create the RBM. They were then given the baseline data from patients in the validation cohort to predict how long each patient would survive. Follow-up questionnaires were sent to the centre of each patient in the validation cohort to determine actual survival.
Results. A total of 2310 patients from 793 centres in 37 countries in the ERA-EDTA area were used to construct and validate the models. For predicting 1-year survival, the RBM had the highest positive predictive value (PPV) (84.2%), the MVR model had the highest negative predictive value (NPV) (47%) and the RBM had the highest likelihood ratio (1.59). For predicting 5-year survival, the MCS had the highest PPV (79.4%), the RBM had the highest NPV (74.3%) and the MCS had the highest likelihood ratio (7.0). The proportion of explained variance in survival for MCS, MVR and RBM was 14.6, 12.9 and 3.95%, respectively.
Conclusion. Using the ambidirectional inception cohort design of this ERA-EDTA Registry survey, we have been able to create and validate two novel instruments to predict survival in patients starting RRT and compare them with a simple scoring model. The models tended to predict 5-year survival with more accuracy than 1-year survival. Examples of potential applications include informing clinical decision making about the likely benefit of starting RRT and listing for transplantation, adjusting for baseline risk in comparative studies and identifying specific risk groups to participate in clinical trials.
Keywords: Charlson score; cohort study; Cox model; patient survival; renal replacement therapy; rule-based algorithm
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