Excess mortality attributable to chronic kidney disease. Results from the PIRP project

J Nephrol. 2016 Oct;29(5):663-71. doi: 10.1007/s40620-015-0239-4. Epub 2015 Oct 26.

Abstract

Although chronic kidney disease (CKD) has a high mortality rate, the estimation of CKD mortality burden in the general population may be challenging because CKD is not always listed as a cause of death in mortality registries. To overcome this limitation, relative survival was used to estimate the excess mortality attributable to CKD as compared to the general population using data of patients registered in the Prevenzione Insufficienza Renale Progressiva (PIRP) registry since 2005 and were followed up until 2013. Relative survival was the ratio of survival observed in CKD patients to the expected survival of the general population. Multivariate parametric survival analysis was used to identify factors predicting excess mortality. The relative survival of CKD patients at 9 years was 0.708. Survival was significantly lower in CKD patients with cardiovascular comorbidities, proteinuria, diabetes, anemia and high phosphate levels and in advanced CKD stages, males, older patients and those who underwent dialysis. Relative survival is a viable method to determine mortality attributable to CKD. Study limitations are that patients are representative only of CKD patients followed by nephrologists and that our follow-up duration may be relatively short as a model for mortality.

Keywords: CKD stage; Chronic kidney disease; Excess mortality; Pre-dialysis; Relative survival.

Publication types

  • Multicenter Study

MeSH terms

  • Age Distribution
  • Aged
  • Aged, 80 and over
  • Bayes Theorem
  • Comorbidity
  • Female
  • Humans
  • Italy / epidemiology
  • Logistic Models
  • Male
  • Middle Aged
  • Multivariate Analysis
  • Prognosis
  • Registries
  • Renal Dialysis / mortality
  • Renal Insufficiency, Chronic / diagnosis
  • Renal Insufficiency, Chronic / mortality*
  • Renal Insufficiency, Chronic / therapy
  • Risk Assessment
  • Risk Factors
  • Severity of Illness Index
  • Sex Distribution
  • Survival Analysis
  • Time Factors