Monique F Kilkenny1,2, Hoang T Phan1,3, Richard I Lindley4,5, Joosup Kim1,2, Derrick Lopez6, Lachlan L Dalli1, Rohan Grimley1,7, Vijaya Sundararajan8, Amanda G Thrift1, Nadine E Andrew1,9, Geoffrey A Donnan10, Dominique A Cadilhac1,2. 1. Stroke and Ageing Research, Department of Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia (M.F.K., H.T.P., J.K., L.L.D., R.G., A.G.T., N.E.A., D.A.C.). 2. The Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria, Australia (M.F.K., J.K., D.A.C.). 3. Menzies Institute for Medical Research, University of Tasmania, Australia (H.T.P.). 4. Westmead Applied Research Centre, University of Sydney, New South Wales, Australia (R.I.L.). 5. George Institute for Global Health, Sydney, New South Wales, Australia (R.I.L.). 6. School of Population and Global Health, The University of Western Australia, Perth, Australia (D.L.). 7. Sunshine Coast Clinical School, Griffith University, Birtinya, Queensland, Australia (R.G.). 8. Department of Public Health, La Trobe University, Bundoora, Victoria, Australia (V.S.). 9. Department of Medicine, Peninsula Clinical School, Monash University, Victoria, Australia (N.E.A.). 10. Melbourne Brain Centre, Royal Melbourne Hospital, University of Melbourne, Parkville, Victoria, Australia (G.A.D.).
Abstract
Background and Purpose: Conditions associated with frailty are common in people experiencing stroke and may explain differences in outcomes. We assessed associations between a published, generic frailty risk score, derived from administrative data, and patient outcomes following stroke/transient ischemic attack; and its accuracy for stroke in predicting mortality compared with other measures of clinical status using coded data. Methods: Patient-level data from the Australian Stroke Clinical Registry (2009–2013) were linked with hospital admissions data. We used International Statistical Classification of Diseases and Related Health Problems, Tenth Revision codes with a 5-year look-back period to calculate the Hospital Frailty Risk Score (termed Frailty Score hereafter) and summarized results into 4 groups: no-risk (0), low-risk (1–5), intermediate-risk (5–15), and high-risk (>15). Multilevel models, accounting for hospital clustering, were used to assess associations between the Frailty Score and outcomes, including mortality (Cox regression) and readmissions up to 90 days, prolonged acute length of stay (>20 days; logistic regression), and health-related quality of life at 90 to 180 days (quantile regression). The performance of the Frailty Score was then compared with the Charlson and Elixhauser Indices using multiple tests (eg, C statistics) for predicting 30-day mortality. Models were adjusted for covariates including sociodemographics and stroke-related factors. Results: Among 15 468 adult patients, 15% died ≤90 days. The frailty scores were 9% no risk; 23% low, 45% intermediate, and 22% high. A 1-point increase in frailty (continuous variable) was associated with greater length of stay (ORadjusted, 1.05 [95% CI, 1.04 to 1.06), 90-day mortality (HRadjusted, 1.04 [95% CI, 1.03 to 1.05]), readmissions (ORadjusted, 1.02 [95% CI, 1.02 to 1.03]; and worse health-related quality of life (median difference, −0.010 [95% CI −0.012 to −0.010]). Adjusting for the Frailty Score provided a slightly better explanation of 30-day mortality (eg, larger C statistics) compared with other indices. Conclusions: Greater frailty was associated with worse outcomes following stroke/transient ischemic attack. The Frailty Score provides equivalent precision compared with the Charlson and Elixhauser indices for assessing risk-adjusted outcomes following stroke/transient ischemic attack.
Background and Purpose: Conditions associated with frailty are common in people experiencing stroke and may explain differences in outcomes. We assessed associations between a published, generic frailty risk score, derived from administrative data, and patient outcomes following stroke/transient ischemic attack; and its accuracy for stroke in predicting mortality compared with other measures of clinical status using coded data. Methods: Patient-level data from the Australian Stroke Clinical Registry (2009–2013) were linked with hospital admissions data. We used International Statistical Classification of Diseases and Related Health Problems, Tenth Revision codes with a 5-year look-back period to calculate the Hospital Frailty Risk Score (termed Frailty Score hereafter) and summarized results into 4 groups: no-risk (0), low-risk (1–5), intermediate-risk (5–15), and high-risk (>15). Multilevel models, accounting for hospital clustering, were used to assess associations between the Frailty Score and outcomes, including mortality (Cox regression) and readmissions up to 90 days, prolonged acute length of stay (>20 days; logistic regression), and health-related quality of life at 90 to 180 days (quantile regression). The performance of the Frailty Score was then compared with the Charlson and Elixhauser Indices using multiple tests (eg, C statistics) for predicting 30-day mortality. Models were adjusted for covariates including sociodemographics and stroke-related factors. Results: Among 15 468 adult patients, 15% died ≤90 days. The frailty scores were 9% no risk; 23% low, 45% intermediate, and 22% high. A 1-point increase in frailty (continuous variable) was associated with greater length of stay (ORadjusted, 1.05 [95% CI, 1.04 to 1.06), 90-day mortality (HRadjusted, 1.04 [95% CI, 1.03 to 1.05]), readmissions (ORadjusted, 1.02 [95% CI, 1.02 to 1.03]; and worse health-related quality of life (median difference, −0.010 [95% CI −0.012 to −0.010]). Adjusting for the Frailty Score provided a slightly better explanation of 30-day mortality (eg, larger C statistics) compared with other indices. Conclusions: Greater frailty was associated with worse outcomes following stroke/transient ischemic attack. The Frailty Score provides equivalent precision compared with the Charlson and Elixhauser indices for assessing risk-adjusted outcomes following stroke/transient ischemic attack.
Authors: Jennifer K Burton; Jennifer Stewart; Mairi Blair; Sinead Oxley; Amy Wass; Martin Taylor-Rowan; Terence J Quinn Journal: Age Ageing Date: 2022-03-01 Impact factor: 12.782
Authors: Andrew B Koo; Aladine A Elsamadicy; Daniela Renedo; Margot Sarkozy; Josiah Sherman; Benjamin C Reeves; John Havlik; Joseph Antonios; Nanthiya Sujijantarat; Ryan Hebert; Ajay Malhotra; Charles Matouk Journal: J Neurointerv Surg Date: 2022-03-15 Impact factor: 8.572