The ERA-EDTA cohort study—comparison of methods to predict survival on renal replacement therapy

C.D. Geddes, P. Vandijk, S.D.J. McArthur, W. Metcalfe, K. Jager, A. Zwinderman, M. Mooney, J. Fox, K.H. Simpson

Research output: Contribution to journalArticle

22 Citations (Scopus)

Abstract

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 selflearning rule-based model (RBM) and a simple
co-morbidity score [the Charlson score modified for renal disease (MCS)] were compared. 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. 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. 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.
LanguageEnglish
Pages945-956
Number of pages11
JournalNephrology Dialysis Transplantation
Volume21
Issue number4
DOIs
Publication statusPublished - 2006

Fingerprint

Renal Replacement Therapy
Edetic Acid
Cohort Studies
Survival
Chronic Kidney Failure
Registries
Dialysis
Transplantation
Clinical Trials
Morbidity
Kidney

Keywords

  • nephrology
  • transplantation
  • replacement therapy
  • renal therapy

Cite this

Geddes, C.D. ; Vandijk, P. ; McArthur, S.D.J. ; Metcalfe, W. ; Jager, K. ; Zwinderman, A. ; Mooney, M. ; Fox, J. ; Simpson, K.H. / The ERA-EDTA cohort study—comparison of methods to predict survival on renal replacement therapy. In: Nephrology Dialysis Transplantation. 2006 ; Vol. 21, No. 4. pp. 945-956.
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Geddes, CD, Vandijk, P, McArthur, SDJ, Metcalfe, W, Jager, K, Zwinderman, A, Mooney, M, Fox, J & Simpson, KH 2006, 'The ERA-EDTA cohort study—comparison of methods to predict survival on renal replacement therapy' Nephrology Dialysis Transplantation, vol. 21, no. 4, pp. 945-956. https://doi.org/10.1093/ndt/gfi326

The ERA-EDTA cohort study—comparison of methods to predict survival on renal replacement therapy. / Geddes, C.D.; Vandijk, P.; McArthur, S.D.J.; Metcalfe, W.; Jager, K.; Zwinderman, A.; Mooney, M.; Fox, J.; Simpson, K.H.

In: Nephrology Dialysis Transplantation, Vol. 21, No. 4, 2006, p. 945-956.

Research output: Contribution to journalArticle

TY - JOUR

T1 - The ERA-EDTA cohort study—comparison of methods to predict survival on renal replacement therapy

AU - Geddes, C.D.

AU - Vandijk, P.

AU - McArthur, S.D.J.

AU - Metcalfe, W.

AU - Jager, K.

AU - Zwinderman, A.

AU - Mooney, M.

AU - Fox, J.

AU - Simpson, K.H.

PY - 2006

Y1 - 2006

N2 - Accurate prediction of patient survival from the time of starting renal replacement therapy (RRT) is desirable, but previously published predictivemodels 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 selflearning rule-based model (RBM) and a simpleco-morbidity score [the Charlson score modified for renal disease (MCS)] were compared. 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 clinicaland laboratory variables from the time of starting RRT were collected in both cohorts using a two-page questionnaire. The data from the training cohort weregiven 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 weresent to the centre of each patient in the validation cohort to determine actual survival. 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 predicting1-year survival, the RBM had the highest positive predictive value (PPV) (84.2%), the MVR model had the highest negative predictive value (NPV) (47%) andthe 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. Using the ambidirectional inception cohort design of this ERA-EDTA Registry survey, we have been able to create and validate two novelinstruments to predict survival in patients starting RRT and compare them with a simple scoring model. The models tended to predict 5-year survival with moreaccuracy 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.

AB - Accurate prediction of patient survival from the time of starting renal replacement therapy (RRT) is desirable, but previously published predictivemodels 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 selflearning rule-based model (RBM) and a simpleco-morbidity score [the Charlson score modified for renal disease (MCS)] were compared. 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 clinicaland laboratory variables from the time of starting RRT were collected in both cohorts using a two-page questionnaire. The data from the training cohort weregiven 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 weresent to the centre of each patient in the validation cohort to determine actual survival. 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 predicting1-year survival, the RBM had the highest positive predictive value (PPV) (84.2%), the MVR model had the highest negative predictive value (NPV) (47%) andthe 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. Using the ambidirectional inception cohort design of this ERA-EDTA Registry survey, we have been able to create and validate two novelinstruments to predict survival in patients starting RRT and compare them with a simple scoring model. The models tended to predict 5-year survival with moreaccuracy 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.

KW - nephrology

KW - transplantation

KW - replacement therapy

KW - renal therapy

U2 - 10.1093/ndt/gfi326

DO - 10.1093/ndt/gfi326

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T2 - Nephrology Dialysis Transplantation

JF - Nephrology Dialysis Transplantation

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ER -