Sradha Kotwal1, Angela C Webster2,3, Alan Cass4, Martin Gallagher5,6. 1. The George Institute for Global Health, University of Sydney. skotwal@georgeinstitute.org.au. 2. Sydney School of Public Health, The University of Sydney. 3. Centre for Transplant and Renal Research, Westmead Hospital, Westmead. 4. Menzies School of Health Research, Charles Darwin University, Darwin. 5. The George Institute for Global Health, University of Sydney. 6. Concord Clinical School, University of Sydney, Sydney, Australia.
Abstract
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 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). 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 End Stage Kidney Disease (ESKD) population.
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 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). 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 End Stage Kidney Disease (ESKD) population.
Authors: Juana Serret-Montaya; Jessie N Zurita-Cruz; Miguel A Villasís-Keever; Alejandra Aguilar-Kitsu; Claudia Del Carmen Zepeda-Martinez; Irving Cruz-Anleu; Beatriz C Hernández-Hernández; Sara R Alonso-Flores; Leticia Manuel-Apolinar; Leticia Damasio-Santana; Abigail Hernandez-Cabezza; José C Romo-Vázquez Journal: Pediatr Nephrol Date: 2020-02-10 Impact factor: 3.714