TY - JOUR KW - Female KW - Humans KW - Aged KW - Male KW - Middle Aged KW - Prospective Studies KW - Stroke KW - Australia KW - Aged, 80 and over KW - Hospitals KW - Quality of Health Care KW - Registries KW - Models, Statistical KW - Outcome Assessment (Health Care) KW - Hospital Mortality KW - Risk Adjustment AU - Anderson Craig AU - Lannin Natasha AU - Cadilhac Dominique AU - Kilkenny Monique AU - Levi Christopher AU - Thrift Amanda AU - Kim Joosup AU - Grabsch Brenda AU - Churilov Leonid AU - Dewey Helen AU - Hill Kelvin AU - Faux Steven AU - Grimley Rohan AU - Castley Helen AU - Hand Peter AU - Wong Andrew AU - Herkes Geoffrey AU - Gill Melissa AU - Crompton Douglas AU - Middleton Sandy AU - Donnan Geoffrey AB -

OBJECTIVES: Hospital data used to assess regional variability in disease management and outcomes, including mortality, lack information on disease severity. We describe variance between hospitals in 30-day risk-adjusted mortality rates (RAMRs) for stroke, comparing models that include or exclude stroke severity as a covariate.

DESIGN: Cohort design linking Australian Stroke Clinical Registry data with national death registrations. Multivariable models using recommended statistical methods for calculating 30-day RAMRs for hospitals, adjusted for demographic factors, ability to walk on admission, stroke type, and stroke recurrence.

SETTING: Australian hospitals providing at least 200 episodes of acute stroke care, 2009-2014.

MAIN OUTCOME MEASURES: Hospital RAMRs estimated by different models. Changes in hospital rank order and funnel plots were used to explore variation in hospital-specific 30-day RAMRs; that is, RAMRs more than three standard deviations from the mean.

RESULTS: In the 28 hospitals reporting at least 200 episodes of care, there were 16 218 episodes (15 951 patients; median age, 77 years; women, 46%; ischaemic strokes, 79%). RAMRs from models not including stroke severity as a variable ranged between 8% and 20%; RAMRs from models with the best fit, which included ability to walk and stroke recurrence as variables, ranged between 9% and 21%. The rank order of hospitals changed according to the covariates included in the models, particularly for those hospitals with the highest RAMRs. Funnel plots identified significant deviation from the mean overall RAMR for two hospitals, including one with borderline excess mortality.

CONCLUSIONS: Hospital stroke mortality rates and hospital performance ranking may vary widely according to the covariates included in the statistical analysis.

BT - Med J Aust C1 - https://www.ncbi.nlm.nih.gov/pubmed/28446116?dopt=Abstract IS - 8 J2 - Med. J. Aust. LA - eng N2 -

OBJECTIVES: Hospital data used to assess regional variability in disease management and outcomes, including mortality, lack information on disease severity. We describe variance between hospitals in 30-day risk-adjusted mortality rates (RAMRs) for stroke, comparing models that include or exclude stroke severity as a covariate.

DESIGN: Cohort design linking Australian Stroke Clinical Registry data with national death registrations. Multivariable models using recommended statistical methods for calculating 30-day RAMRs for hospitals, adjusted for demographic factors, ability to walk on admission, stroke type, and stroke recurrence.

SETTING: Australian hospitals providing at least 200 episodes of acute stroke care, 2009-2014.

MAIN OUTCOME MEASURES: Hospital RAMRs estimated by different models. Changes in hospital rank order and funnel plots were used to explore variation in hospital-specific 30-day RAMRs; that is, RAMRs more than three standard deviations from the mean.

RESULTS: In the 28 hospitals reporting at least 200 episodes of care, there were 16 218 episodes (15 951 patients; median age, 77 years; women, 46%; ischaemic strokes, 79%). RAMRs from models not including stroke severity as a variable ranged between 8% and 20%; RAMRs from models with the best fit, which included ability to walk and stroke recurrence as variables, ranged between 9% and 21%. The rank order of hospitals changed according to the covariates included in the models, particularly for those hospitals with the highest RAMRs. Funnel plots identified significant deviation from the mean overall RAMR for two hospitals, including one with borderline excess mortality.

CONCLUSIONS: Hospital stroke mortality rates and hospital performance ranking may vary widely according to the covariates included in the statistical analysis.

PY - 2017 SP - 345 EP - 350 T2 - Med J Aust TI - Risk-adjusted hospital mortality rates for stroke: evidence from the Australian Stroke Clinical Registry (AuSCR). VL - 206 SN - 1326-5377 ER -