Advertisement

Predicting Stroke Complications in Hospital and Functional Status at Discharge by Clustering of Cardiovascular Diseases a Multi-Centre Registry-Based Study of Acute Stroke

      Highlights

      • Four categories from cardiovascular disease Congestive heart failure, Atrial fibrillation, pre-existing Stroke and Hypertension (CASH).were constructed: CASH-0 (no coexisting CVD); CASH-1 (any one coexisting CVD); CASH-2 (any two coexisting CVD); CASH-3 (any three or all four coexisting CVD).
      • Compared to CASH-0, individuals with CASH-3 had twice the risk of in-hospital mortality, prolonged length of stay on hyperacute stroke units, and disability at discharge; two and half to three times the risk of nosocomial infections within seven days of admission.
      • CASH is a novel and simple outcome risk scale which can used to identify patients who are at increased risk of a variety of stroke associated adverse outcomes.

      Abstract

      Objective

      Indicators for outcomes following acute stroke are lacking. We have developed novel evidence-based criteria for identifying outcomes of acute stroke using the presence of clusters of coexisting cardiovascular disease (CVD).

      Materials and methods

      Analysis of prospectively collected data from the Sentinel Stroke National Audit Programme (SSNAP). A total of 1656 men (mean age ±SD=73.1yrs±13.2) and 1653 women (79.3yrs±13.0) were admitted with acute stroke (83.3% ischaemic, 15.7% intracranial haemorrhagic), 1.0% unspecified) in four major UK hyperacute stroke units (HASU) between 2014 and 2016. Four categories from cardiovascular disease Congestive heart failure, Atrial fibrillation, pre-existing Stroke and Hypertension (CASH).were constructed: CASH-0 (no coexisting CVD); CASH-1 (any one coexisting CVD); CASH-2 (any two coexisting CVD); CASH-3 (any three or all four coexisting CVD). These were tested against outcomes, adjusted for age and sex.

      Results

      Compared to CASH-0, individuals with CASH-3 had greatest risks of in-hospital mortality (11.1% vs 24.5%, OR=1.8, 95%CI=1.3-2.7) and disability (modified Rankin Scale score ≥4) at discharge (24.2% vs 46.2%, OR=1.9, 95%CI=1.4-2.7), urinary tract infection (3.8% vs 14.6%, OR= 3.3, 95%CI= 1.9-5.5), and pneumonia (7.1% vs 20.6%, OR= 2.6, 95%CI= 1.7-4.0); length of stay on HASU >14 days (29.8% vs 39.3%, OR=1.8, 95%CI=1.3-2.6); and joint-care planning (20.9% vs 29.8%, OR=1.4, 95%CI=1.0-2.0).

      Conclusions

      We present a simple tool for estimating the risk of adverse outcomes of acute stroke including death, disability at discharge, nosocomial infections, prolonged length of stay, as well as any joint care planning. CASH-0 indicates a low level and CASH-3 indicates a high level of risk of such complications after stroke.

      Key Words

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to Journal of Stroke and Cerebrovascular Diseases
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • Charlson ME
        • Pompei P
        • Ales KL
        • MacKenzie CR.
        A new method of classifying prognostic comorbidity in longitudinal studies: development and validation.
        J Chron Dis. 1987; 40: 373-383
        • Elixhauser A
        • Steiner C
        • Harris DR
        • Coffey RM.
        Comorbidity measures for use with administrative data.
        Med care. 1998; 36: 8-27
        • Li B
        • Evans D
        • Faris P
        • Dean S
        • Quan H.
        Risk adjustment performance of Charlson and Elixhauser comorbidities in ICD-9 and ICD-10 administrative databases.
        BMC Health Serv Res. 2008; 8: 1-7
        • Kelly DM
        • Rothwell PM.
        Impact of multimorbidity on risk and outcome of stroke: Lessons from chronic kidney disease.
        Int J Stroke. 2020; (Online ahead of print)1747493020975250https://doi.org/10.1177/1747493020975250
        • Stirland LE
        • González-Saavedra L
        • Mullin DS
        • Ritchie CW
        • Muniz-Terrera G
        • Russ TC.
        Measuring multimorbidity beyond counting diseases: systematic review of community and population studies and guide to index choice.
        BMJ. 2020; 368: m160
        • Goldstein LB
        • Samsa GP
        • Matchar DB
        • Horner RD.
        Charlson Index comorbidity adjustment for ischemic stroke outcome studies.
        Stroke. 2004; 35: 1941-1945
        • Zhu H
        • Hill MD.
        Stroke: the Elixhauser Index for comorbidity adjustment of in-hospital case fatality.
        Neurology. 2008; 71: 283-287
        • Johnston MC
        • Crilly M
        • Black C
        • Prescott GJ
        • Mercer SW.
        Defining and measuring multimorbidity: a systematic review of systematic reviews.
        Eur J Public Health. 2019; 29: 182-189
        • Jørgensen HS
        • Nakayama H
        • Reith J
        • Raaschou HO
        • Olsen TS.
        Acute stroke with atrial fibrillation: the Copenhagen Stroke Study.
        Stroke. 1996; 27: 1765-1769
        • Han TS
        • Fry CH
        • Fluck D
        • et al.
        Evaluation of anticoagulation status for atrial fibrillation on early ischaemic stroke outcomes: a registry-based, prospective cohort study of acute stroke care in Surrey, UK.
        BMJ Open. 2017; 7e019122
        • Hall RE
        • Porter J
        • Quan H
        • Reeves MJ.
        Developing an adapted Charlson comorbidity index for ischemic stroke outcome studies.
        BMC Health Serv Res. 2019; 19: 930
        • Schnitzler A
        • Woimant F
        • Nicolau J
        • Tuppin P
        • de Peretti C.
        Effect of rehabilitation setting on dependence following stroke: an analysis of the French inpatient database.
        Neurorehabil Neural Repair. 2014; 28: 36-44
        • Berlowitz DR
        • Hoenig H
        • Cowper DC
        • Duncan PW
        • Vogel WB.
        Impact of comorbidities on stroke rehabilitation outcomes: does the method matter?.
        Arch Phys Med Rehabil. 2008; 89: 1903-1906
      1. Royal College of Physicians. Clinical effectiveness and evaluation unit on behalf of the intercollegiate stroke working party. SSNAP January–March 2016. Public Report. https://www.strokeaudit.org/Documents/National/AcuteOrg/2016/2016-AOANationalReport.aspx . Accessed May 09, 2021.

        • Han TS
        • Fry CH
        • Fluck D
        • et al.
        Anticoagulation therapy in patients with stroke and atrial fibrillation: a registry-based study of acute stroke care in Surrey, UK.
        BMJ Open. 2018; 8e022558
        • Han TS
        • Gulli G
        • Affley B
        • Fluck D
        • Fry CH
        • Barrett C
        • et al.
        New evidence-based A1, A2, A3 alarm time zones for transferring thrombolysed patients to hyper-acute stroke units: faster is better.
        Neurol Sci. 2019; 40: 1659-1665
        • Hinton W
        • McGovern A
        • Coyle R
        • et al.
        Incidence and prevalence of cardiovascular disease in English primary care: a cross-sectional and follow-up study of the Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC).
        BMJ Open. 2018; 8e020282
        • Ness J
        • Aronow WS.
        Prevalence of coexistence of coronary artery disease, ischemic stroke, and peripheral arterial disease in older persons, mean age 80 years, in an academic hospital-based geriatrics practice.
        J Am Geriatr Soc. 1999; 47: 1255-1256
        • Schmidt M
        • Jacobsen JB
        • Johnsen SP
        • Bøtker HE
        • Sørensen HT.
        Eighteen-year trends in stroke mortality and the prognostic influence of comorbidity.
        Neurology. 2014; 82: 340-350
        • Gruneir A
        • Griffith LE
        • Fisher K
        • et al.
        Increasing comorbidity and health services utilization in older adults with prior stroke.
        Neurology. 2016; 87: 2091-2098
        • van Swieten JC
        • Koudstaal PJ
        • Visser MC
        • Schouten HJ
        • Van Gijn J.
        Interobserver agreement for the assessment of handicap in stroke patients.
        Stroke. 1988; 19: 604-607
        • Han TS
        • Fry CH
        • Gulli G
        • et al.
        Prestroke disability predicts adverse poststroke outcome: a registry-based prospective cohort study of acute stroke.
        Stroke. 2020; 51: 594-600
        • Kondrup JE
        • Allison SP
        • Elia M
        • Vellas B
        • Plauth M.
        ESPEN guidelines for nutrition screening 2002.
        Clin Nutr. 2003; 22: 415-421
        • Han TS
        • Lisk R
        • Osmani A
        • et al.
        Increased association with malnutrition and malnourishment in older adults admitted with hip fractures who have cognitive impairment and delirium, as assessed by 4AT.
        Nutr Clin Pract. 2021; 36: 1053-1058https://doi.org/10.1002/ncp.10614
        • GBD 2016 Stroke Collaborators
        Global, regional, and national burden of stroke, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016.
        Lancet Neurol. 18. 2019: 439-458
        • Gorelick PB.
        The global burden of stroke: persistent and disabling.
        Lancet Neurol. 2019; 18: 417-418