@article{22722,
  keywords = {Female, Humans, Aged, Male, Middle Aged, Prospective Studies, Stroke, Australia, Aged, 80 and over, Hospitals, Quality of Health Care, Registries, Models, Statistical, Outcome Assessment (Health Care), Hospital Mortality, Risk Adjustment},
  author = {Anderson Craig and Lannin Natasha and Cadilhac Dominique and Kilkenny Monique and Levi Christopher and Thrift Amanda and Kim Joosup and Grabsch Brenda and Churilov Leonid and Dewey Helen and Hill Kelvin and Faux Steven and Grimley Rohan and Castley Helen and Hand Peter and Wong Andrew and Herkes Geoffrey and Gill Melissa and Crompton Douglas and Middleton Sandy and Donnan Geoffrey},
  title = {Risk-adjusted hospital mortality rates for stroke: evidence from the Australian Stroke Clinical Registry (AuSCR).},
  abstract = {<p>
<b>OBJECTIVES: </b>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.
</p>

<p>
<b>DESIGN: </b>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.
</p>

<p>
<b>SETTING: </b>Australian hospitals providing at least 200 episodes of acute stroke care, 2009-2014.
</p>

<p>
<b>MAIN OUTCOME MEASURES: </b>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.
</p>

<p>
<b>RESULTS: </b>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.
</p>

<p>
<b>CONCLUSIONS: </b>Hospital stroke mortality rates and hospital performance ranking may vary widely according to the covariates included in the statistical analysis.
</p>
},
  year = {2017},
  journal = {Med J Aust},
  volume = {206},
  pages = {345-350},
  issn = {1326-5377},
  language = {eng},
}