Journal Article

Predicting future trends in the number of patients on renal replacement therapy in Denmark.

P Vestergaard and H Løkkegaard

in Nephrology Dialysis Transplantation

Volume 12, issue 10, pages 2117-2123
Published in print October 1997 | ISSN: 0931-0509
Published online October 1997 | e-ISSN: 1460-2385 | DOI:
Predicting future trends in the number of patients on renal replacement therapy in Denmark.

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OBJECTIVES: To predict the future prevalence of patients on renal replacement therapy due to chronic renal failure in Denmark. SUBJECTS AND METHODS: Four thousand and nine terminal uraemic patients (median age 50.0 years, 15.2% diabetic) were treated in Denmark with renal replacement therapy in the period 1 January 1991 to 31 December 1995. Incidence rates and rates of transition between the treatment modalities (haemodialysis, peritoneal dialysis, and renal transplantation) were calculated. The prediction was made using a Markov model in three ways: (1) using the average rates (deterministic model), (2) using rates simulated with pseudorandom numbers based on the average rates (stochastic model), and (3) using increasing incidence rates in a deterministic model. RESULTS: Using present rates both model types predicted a significant increase in the prevalence of renal transplant recipients < 60 years (from 1003 in 1995 to about 1465 in 2006) and the prevalence of haemodialysis patients > or = 60 years (from 456 in 1995 to about 903 in 2006) while the prevalence of other treatment modalities would change less dramatically. The overall prevalence proportion would increase from 539 patients per million population (p.m.p.) in 1995 to about 777 p.m.p. in 2006. The stochastic model clearly demonstrated the uncertainties linked to the prognosis in contrast to the deterministic model. The deterministic model with increasing rates predicted a prevalence proportion of 1162 p.m.p. in 2006. CONCLUSION: Even with present rates the prevalence of haemodialysis patients in Denmark will continue to increase. Mathematical models offers a good tool to study future trends and to plan future capacity.

Journal Article.  0 words. 

Subjects: Nephrology

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