AIM: 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 (NSW) 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 NSW Admitted Patient Data Collection (APDC) and the NSW 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 with kappa statistics (kappa). Survival was calculated using cox proportional hazards models from the start of RRT to death date or end of study (31/07/2011). We compared four multivariable models 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%, kappa = 0.88), CAD had fair to good agreement (80. 6, kappa = 0.56), with poor agreement for the other comorbidities between the two datasets. 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 and all models had a similar predictive ability (Harrell's c = 0.71-0.72). CONCLUSION: 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 ESKD population. This article is protected by copyright. All rights reserved.
AD - The George Institute for Global Health, University of Sydney, Sydney, Australia.AIM: 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 (NSW) 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 NSW Admitted Patient Data Collection (APDC) and the NSW 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 with kappa statistics (kappa). Survival was calculated using cox proportional hazards models from the start of RRT to death date or end of study (31/07/2011). We compared four multivariable models 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%, kappa = 0.88), CAD had fair to good agreement (80. 6, kappa = 0.56), with poor agreement for the other comorbidities between the two datasets. 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 and all models had a similar predictive ability (Harrell's c = 0.71-0.72). CONCLUSION: 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 ESKD population. This article is protected by copyright. All rights reserved.
PY - 2015 SN - 1440-1797 (Electronic)