title: Comorbidity recording and predictive power of comorbidities in the Australia and New Zealand dialysis and transplant registry compared with administrative data: 2000-2010.
abstract: OBJECTIVE
  To compare comorbidity recording and predictive power of comorbidities for mortality between a clinical renal registry and a state-based hospitalisation dataset.
  
  
  METHODS
  All patients that started renal replacement therapy (dialysis or transplant - RRT) in New South Wales between 1/07/2001 and 31/7/2010 were identified using the Australia and New Zealand Dialysis and Transplant Registry (ANZDATA) and linked to the State Admitted Patient Data Collection (APDC) and the Death Registry. Comorbidities (diabetes mellitus, coronary artery disease (CAD), chronic lung disease, peripheral vascular disease and cerebrovascular disease) were identified at the start of RRT in both datasets and compared using kappa statistics (κ). Survival was calculated using cox proportional hazards models from the start of RRT to death date or end of study (31/07/2011). Four multivariable models were adjusted for age, gender and comorbidities to estimate the predictive power of the comorbidities as recorded in ANZDATA, APDC, either or both datasets RESULTS: We identified 6285 people (23,845 person-years follow-up). Diabetes recording had excellent agreement (94.5%, κ = 0.88), CAD had fair to good agreement (80. 6, κ = 0.56), with poor agreement between the two datasets for the other comorbidities. Deaths totalled 2594 (41.3%). Median follow up time was 3.3 years (IQR 1.7 to 5.4). All five comorbidities were powerful predictors of poor survival in all four models. All models had a similar predictive ability (Harrell's c = 0.71-0.72).
  
  
  CONCLUSIONS
  Variable agreement exists in comorbidity recording between the ANZDATA and APDC. The comorbidities have a similar predictive ability, irrespective of dataset of origin in an End Stage Kidney Disease (ESKD) population.
authors.affiliation: The George Institute for Global Health, University of Sydney. skotwal@georgeinstitute.org.au.
authors.forename: Sradha
authors.initials: S
authors.lastname: Kotwal
authors.affiliation: Sydney School of Public Health, The University of Sydney.|Centre for Transplant and Renal Research, Westmead Hospital, Westmead.
authors.forename: Angela C
authors.initials: AC
authors.lastname: Webster
authors.affiliation: Menzies School of Health Research, Charles Darwin University, Darwin.
authors.forename: Alan
authors.initials: A
authors.lastname: Cass
authors.affiliation: The George Institute for Global Health, University of Sydney.|Concord Clinical School, University of Sydney, Sydney, Australia.
authors.forename: Martin
authors.initials: M
authors.lastname: Gallagher
mesh_terms: D000328:Adult; D001315:Australia / Q000453:epidemiology; D015984:Causality; D015897:Comorbidity; D005260:Female; D006801:Humans; D007676:Kidney Failure, Chronic / Q000401:mortality / Q000628:therapy; D016030:Kidney Transplantation / Q000379:methods / Q000706:statistics & numerical data; D008297:Male; D008875:Middle Aged; D009520:New Zealand / Q000453:epidemiology; D016016:Proportional Hazards Models; D012042:Registries / Q000706:statistics & numerical data; D006435:Renal Dialysis / Q000379:methods / Q000706:statistics & numerical data
keywords: comorbidity; end-stage renal failure; epidemiology and outcomes; medical record linkage; risk adjustment