Prognostic model for total mortality in patients with haemodialysis from the Assessments of Survival and Cardiovascular Events (AURORA) study
Article first published online: 2 SEP 2011
© 2011 The Association for the Publication of the Journal of Internal Medicine
Journal of Internal Medicine
Volume 271, Issue 5, pages 463–471, May 2012
How to Cite
Holme, I., Fellström, B. C., Jardin, A. G., Schmieder, R. E., Zannad, F. and Holdaas, H. (2012), Prognostic model for total mortality in patients with haemodialysis from the Assessments of Survival and Cardiovascular Events (AURORA) study. Journal of Internal Medicine, 271: 463–471. doi: 10.1111/j.1365-2796.2011.02435.x
- Issue published online: 23 APR 2012
- Article first published online: 2 SEP 2011
- Accepted manuscript online: 3 AUG 2011 02:10PM EST
- prognostic study;
Abstract. Holme I, Fellström BC, Jardin AG, Schmieder RE, Zannad F, Holdaas H (Oslo University Hospital, Ullevål, Oslo, Norway; British Heart Foundation Glasgow Cardiovascular Research Centre, Glasgow, UK; University Hospital, Erlangen, Germany; Centre d`Investigation Clinique; Centre Hospitalier Universitaire, and Nancy Université, Nancy, France; and Oslo University Hospital, Oslo, Norway). Prognostic model for total mortality in patients with haemodialysis from the Assessments of Survival and Cardiovascular Events (AURORA) study. J Intern Med 2012; 271: 463–471.
Objectives. Risk factors of mortality in patients with haemodialysis (HD) have been identified in several studies, but few prognostic models have been developed with assessments of calibration and discrimination abilities. We used the database of the Assessment of Survival and Cardiovascular Events study to develop a prognostic model of mortality over 3–4 years.
Methods. Five factors (age, albumin, C-reactive protein, history of cardiovascular disease and diabetes) were selected from experience and forced into the regression equation. In a 67% random try-out sample of patients, no further factors amongst 24 candidates added significance (P < 0.01) to mortality outcome as assessed by Cox regression modelling, and individual probabilities of death were estimated in the try-out and test samples. Calibration was explored by calculating the prognostic index with regression coefficients from the try-out sample to patients in the 33% test sample. Discrimination was assessed by receiver operating characteristic (ROC) areas.
Results. The strongest prognostic factor in the try-out sample was age, with small differences between the other four factors. Calibration in the test sample was good when the calculated number of deaths was multiplied by a constant of 1.33. The five-factor model discriminated reasonably well between deceased and surviving patients in both the try-out and test samples with an ROC area of about 0.73.
Conclusions. A model consisting of five factors can be used to estimate and stratify the probability of death for individuals The model is most useful for long-term prognosis in an HD population with survival prospects of more than 1 year.