Original Investigation
Dialysis
Predicting Mortality in Incident Dialysis Patients: An Analysis of the United Kingdom Renal Registry

https://doi.org/10.1053/j.ajkd.2010.12.023Get rights and content

Background

The risk of death in dialysis patients is high, but varies significantly among patients. No prediction tool is used widely in current clinical practice. We aimed to predict long-term mortality in incident dialysis patients using easily obtainable variables.

Study Design

Prospective nationwide multicenter cohort study in the United Kingdom (UK Renal Registry); models were developed using Cox proportional hazards.

Setting & Participants

Patients initiating hemodialysis or peritoneal dialysis therapy in 2002-2004 who survived at least 3 months on dialysis treatment were followed up for 3 years. Analyses were restricted to participants for whom information for comorbid conditions and laboratory measurements were available (n = 5,447). The data set was divided into data sets for model development (n = 3,631; training) and validation (n = 1,816) using random selection.

Predictors

Basic patient characteristics, comorbid conditions, and laboratory variables.

Outcomes

All-cause mortality censored for kidney transplant, recovery of kidney function, and loss to follow-up.

Results

In the training data set, 1,078 patients (29.7%) died within the observation period. The final model for the training data set included patient characteristics (age, race, primary kidney disease, and treatment modality), comorbid conditions (diabetes, history of cardiovascular disease, and smoking), and laboratory variables (hemoglobin, serum albumin, creatinine, and calcium levels); reached a C statistic of 0.75 (95% CI, 0.73-0.77); and could discriminate accurately among patients with low (6%), intermediate (19%), high (33%), and very high (59%) mortality risk. The model was applied further to the validation data set and achieved a C statistic of 0.73 (95% CI, 0.71-0.76).

Limitations

Number of missing comorbidity data and lack of an external validation data set.

Conclusions

Basic patient characteristics, comorbid conditions, and laboratory variables can predict 3-year mortality in incident dialysis patients with sufficient accuracy. Identification of subgroups of patients according to mortality risk can guide future research and subsequently target treatment decisions in individual patients.

Section snippets

Study Participants of the UK Renal Registry

The detailed organization of the UK Renal Registry (UKRR) has been described previously.19 In brief, the UKRR is operated under the auspices of the UK Renal Association and provides independent audit and analysis of renal care in the United Kingdom. Although the UKRR now receives patient-level data from all UK renal units, during the period of this study, information was collected prospectively electronically from 60 renal units in England and Wales at quarterly intervals for all patients

Study Population

Patient characteristics, comorbid conditions, and laboratory data for study participants in the training and validation data sets are listed in Table 1. Patients were predominantly men, median age was 64 years, and more than two-thirds received HD 3 months after dialysis therapy inception. A third of the patients had diabetes or reported a history of CVD. Approximately three-quarters of the patients were classified as having anemia (79% in the training data set and 73% in the validation data

Discussion

In the UKRR database, we developed a model that predicted 3-year mortality in patients who survived up to 3 months after dialysis inception and were still on dialysis treatment. The model exclusively included easily obtainable and routinely collected patient characteristics and laboratory variables. It achieved sufficient accuracy and was able to discriminate accurately across risk strata.

It is a particular strength of this model that the underlying data were collected prospectively for an

Acknowledgements

We thank the patients and clinicians in renal units that contributed data to the UKRR; Julie Gilg at the UKRR for administrative support; Hocine Tighiouart for providing the SAS macros for calculation of the C statistic and model calibration in Cox proportional hazards models; and Andrew S. Levey and Bertram L. Kasiske for helpful discussions and critical revision of the manuscript.

Support: Dr Wagner receives funding from the fellowship training program of the National Kidney Foundation Center

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