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dc.contributor.authorCadilhac, DA
dc.contributor.authorKilkenny, MF
dc.contributor.authorLevi, CR
dc.contributor.authorLannin, NA
dc.contributor.authorThrift, AG
dc.contributor.authorKim, J
dc.contributor.authorGrabsch, B
dc.contributor.authorChurilov, L
dc.contributor.authorDewey, HM
dc.contributor.authorHill, K
dc.contributor.authorFaux, SG
dc.contributor.authorGrimley, R
dc.contributor.authorCastley, H
dc.contributor.authorHand, PJ
dc.contributor.authoret al.
dc.date.accessioned2020-03-18T05:04:32Z
dc.date.available2020-03-18T05:04:32Z
dc.date.issued2017
dc.identifier.issn0025-729X
dc.identifier.doi10.5694/mja16.00525
dc.identifier.urihttp://hdl.handle.net/10072/392431
dc.description.abstractObjectives: 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, 2009e2014. 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.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherWiley Blackwell
dc.relation.ispartofpagefrom345
dc.relation.ispartofpageto350
dc.relation.ispartofissue8
dc.relation.ispartofjournalMedical Journal of Australia
dc.relation.ispartofvolume206
dc.subject.fieldofresearchMedical and Health Sciences
dc.subject.fieldofresearchPsychology and Cognitive Sciences
dc.subject.fieldofresearchcode11
dc.subject.fieldofresearchcode17
dc.titleRisk-Adjusted hospital mortality rates for stroke: Evidence from the Australian Stroke Clinical Registry (AuSCR)
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationCadilhac, DA; Kilkenny, MF; Levi, CR; Lannin, NA; Thrift, AG; Kim, J; Grabsch, B; Churilov, L; Dewey, HM; Hill, K; Faux, SG; Grimley, R; Castley, H; Hand, PJ; Wong, A; Herkes, GK; Gill, M; Crompton, D; Middleton, S; Donnan, GA; Anderson, CS, Risk-Adjusted hospital mortality rates for stroke: Evidence from the Australian Stroke Clinical Registry (AuSCR), Medical Journal of Australia, 2017, 206 (8), pp. 345-350
dcterms.dateAccepted2016-10-11
dc.date.updated2020-03-18T05:02:26Z
gro.hasfulltextNo Full Text
gro.griffith.authorGrimley, Rohan


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