Utility of the Hospital Frailty Risk Score Derived From Administrative Data and the Association With Stroke Outcomes

被引：60

作者：

Kilkenny, Monique F. ^{[1
,2
]}

Phan, Hoang T. ^{[1
,3
]}

Lindley, Richard, I ^{[4
,5
]}

Kim, Joosup ^{[1
,2
]}

Lopez, Derrick ^{[6
]}

Dalli, Lachlan L. ^{[1
]}

Grimley, Rohan ^{[1
,7
]}

Sundararajan, Vijaya ^{[8
]}

Thrift, Amanda G. ^{[1
]}

Andrew, Nadine E. ^{[1
,9
]}

Donnan, Geoffrey A. ^{[10
]}

Cadilhac, Dominique A. ^{[1
,2
]}

机构：

[1] Monash Univ, Monash Hlth, Sch Clin Sci, Stroke & Ageing Res,Dept Med, Clayton, Vic, Australia

[2] Florey Inst Neurosci & Mental Hlth, Heidelberg, Vic, Australia

[3] Univ Tasmania, Menzies Inst Med Res, Hobart, Tas, Australia

[4] Univ Sydney, Westmead Appl Res Ctr, Sydney, NSW, Australia

[5] George Inst Global Hlth, Sydney, NSW, Australia

[6] Univ Western Australia, Sch Populat & Global Hlth, Perth, WA, Australia

[7] Griffith Univ, Sunshine Coast Clin Sch, Birtinya, Qld, Australia

[8] La Trobe Univ, Dept Publ Hlth, Bundoora, Vic, Australia

[9] Monash Univ, Peninsula Clin Sch, Dept Med, Clayton, Vic, Australia

[10] Univ Melbourne, Royal Melbourne Hosp, Melbourne Brain Ctr, Parkville, Vic, Australia

来源：

STROKE | 2021年 / 52卷 / 09期

基金：

英国医学研究理事会; 澳大利亚国家健康与医学研究理事会;

关键词：

hospitalization; ischemic attack; transient; mortality; register; risk factor; QUALITY-OF-LIFE; CHALLENGES; VALIDATION; ADMISSION; EUROQOL;

D O I：

10.1161/STROKEAHA.120.033648

中图分类号：

R74 [神经病学与精神病学];

学科分类号：

摘要：

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.

引用

页码：2874 / 2881

页数：8