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Figure 1.  Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Diagram
Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Diagram

mRS indicates modified Rankin Scale; TIA, transient ischemic attack.

Figure 2.  Mean Health Utility Weights by Modified Rankin Scale (mRS) Score for Each Utility Scale, With 95% CIs
Mean Health Utility Weights by Modified Rankin Scale (mRS) Score for Each Utility Scale, With 95% CIs

AQoL-4D indicates Assessment of Quality of Life; EQ-5D, EuroQoL 5-dimension; HRQOLISP, Health-Related Quality of Life in Stroke Patients; Neuro-QoL, Quality of Life in Neurological Disorders; PROMIS-PF, Patient-Reported Outcomes Measurement Information System–Physical Function; SF-36-PF, 36-Item Short Form Survey (SF-36) physical function; SF-36-SF, SF-36 social function; SIS-16, Stroke Impact Scale 16; SIS-SGD, SIS–Stroke Global Disability; and WHO-GBDP, World Health Organization Global Burden of Disease Project. The dashed line represents quality of life equivalent to death; below the dashed line represents quality of life worse than death; shaded areas represent the 95% CIs. Data are presented in eTables 2 and 3 in the Supplement.

Figure 3.  Modified Rankin Scale (mRS) Utility Weights Derived From the EuroQoL 5-Dimension (EQ-5D)
Modified Rankin Scale (mRS) Utility Weights Derived From the EuroQoL 5-Dimension (EQ-5D)

Individual studies (n = 9) are represented by blue circles. The grand means (solid blue circles) and SDs (shaded area) of all included studies are shown. The dashed line represents quality of life equivalent to death; below the dashed line represents quality of life worse than death.

Table.  Reanalysis of Primary Outcomes From 18 Major Acute Stroke Trialsa
Reanalysis of Primary Outcomes From 18 Major Acute Stroke Trialsa
1.
Banks  JL, Marotta  CA.  Outcomes validity and reliability of the modified Rankin scale: implications for stroke clinical trials: a literature review and synthesis.   Stroke. 2007;38(3):1091-1096. doi:10.1161/01.STR.0000258355.23810.c6 PubMedGoogle Scholar
2.
Sulter  G, Steen  C, De Keyser  J.  Use of the Barthel Index and Modified Rankin Scale in acute stroke trials.   Stroke. 1999;30(8):1538-1541. doi:10.1161/01.STR.30.8.1538 PubMedGoogle Scholar
3.
Rankin  J.  Cerebral vascular accidents in patients over the age of 60, II: prognosis.   Scott Med J. 1957;2(5):200-215. doi:10.1177/003693305700200504 PubMedGoogle Scholar
4.
Selby  JV, Beal  AC, Frank  L.  The Patient-Centered Outcomes Research Institute (PCORI) national priorities for research and initial research agenda.   JAMA. 2012;307(15):1583-1584. doi:10.1001/jama.2012.500 PubMedGoogle Scholar
5.
Albers  GW, Goldstein  LB, Hess  DC,  et al; STAIR VII Consortium.  Stroke Treatment Academic Industry Roundtable (STAIR) recommendations for maximizing the use of intravenous thrombolytics and expanding treatment options with intra-arterial and neuroprotective therapies.   Stroke. 2011;42(9):2645-2650. doi:10.1161/STROKEAHA.111.618850 PubMedGoogle Scholar
6.
Nogueira  RG, Jadhav  AP, Haussen  DC,  et al; DAWN Trial Investigators.  Thrombectomy 6 to 24 hours after stroke with a mismatch between deficit and infarct.   N Engl J Med. 2018;378(1):11-21. doi:10.1056/NEJMoa1706442 PubMedGoogle Scholar
7.
ClinicalTrials.gov. Recombinant Factor VIIa (rFVIIa) for Hemorrhagic Stroke Trial (FASTEST). NCT03496883. Accessed February 19, 2020. https://clinicaltrials.gov/ct2/show/NCT03496883
8.
ClinicalTrials.gov. Blood Pressure After Endovascular Stroke Therapy-II (BEST-II). NCT04116112. Accessed February 19, 2020. https://clinicaltrials.gov/ct2/show/NCT04116112
9.
ClinicalTrials.gov. Benefits of Stroke Treatment Delivered Using a Mobile Stroke Unit (BEST-MSU). NCT02190500. Accessed February 19, 2020. https://clinicaltrials.gov/ct2/show/NCT02190500
10.
ClinicalTrials.gov. ENRICH: Early Minimally-invasive Removal of Intracerebral Hemorrhage (ICH) (ENRICH). NCT02880878. Accessed February 19, 2020. https://clinicaltrials.gov/ct2/show/NCT02880878
11.
Feeny  D.  A utility approach to the assessment of health-related quality of life.   Med Care. 2000;38(9)(suppl):II151-II154.PubMedGoogle Scholar
12.
Dorman  P, Slattery  J, Farrell  B, Dennis  M, Sandercock  P.  Qualitative comparison of the reliability of health status assessments with the EuroQol and SF-36 questionnaires after stroke: United Kingdom Collaborators in the International Stroke Trial.   Stroke. 1998;29(1):63-68. doi:10.1161/01.STR.29.1.63 PubMedGoogle Scholar
13.
Whynes  DK, Sprigg  N, Selby  J, Berge  E, Bath  PM; ENOS Investigators.  Testing for differential item functioning within the EQ-5D.   Med Decis Making. 2013;33(2):252-260. doi:10.1177/0272989X12465016 PubMedGoogle Scholar
14.
Ali  M, MacIsaac  R, Quinn  TJ,  et al.  Dependency and health utilities in stroke: data to inform cost-effectiveness analyses.   Eur Stroke J. 2017;2(1):70-76. doi:10.1177/2396987316683780 PubMedGoogle Scholar
15.
Rethnam  V, Bernhardt  J, Dewey  H,  et al; AVERT Trial Collaboration Group.  Utility-weighted modified Rankin Scale: still too crude to be a truly patient-centric primary outcome measure ?  Int J Stroke. Published online February 12, 2019. doi:10.1177/1747493019830583 PubMedGoogle Scholar
16.
Dijkland  SA, Voormolen  DC, Venema  E,  et al; MR CLEAN Investigators.  Utility-weighted modified Rankin Scale as primary outcome in stroke trials: a simulation study.   Stroke. 2018;49(4):965-971. doi:10.1161/STROKEAHA.117.020194 PubMedGoogle Scholar
17.
Oremus  M, Tarride  JE, Clayton  N, Raina  P; Canadian Willingness-to-Pay Study Group.  Health utility scores in Alzheimer’s disease: differences based on calculation with American and Canadian preference weights.   Value Health. 2014;17(1):77-83. doi:10.1016/j.jval.2013.10.009 PubMedGoogle Scholar
18.
Gupta  A, Baradaran  H, Schweitzer  AD,  et al.  Carotid plaque MRI and stroke risk: a systematic review and meta-analysis.   Stroke. 2013;44(11):3071-3077. doi:10.1161/STROKEAHA.113.002551 PubMedGoogle Scholar
19.
Doth  AH, Hansson  PT, Jensen  MP, Taylor  RS.  The burden of neuropathic pain: a systematic review and meta-analysis of health utilities.   Pain. 2010;149(2):338-344. doi:10.1016/j.pain.2010.02.034 PubMedGoogle Scholar
20.
Wyld  M, Morton  RL, Hayen  A, Howard  K, Webster  AC.  A systematic review and meta-analysis of utility-based quality of life in chronic kidney disease treatments.   PLoS Med. 2012;9(9):e1001307. doi:10.1371/journal.pmed.1001307 PubMedGoogle Scholar
21.
Moher  D, Liberati  A, Tetzlaff  J, Altman  DG; PRISMA Group.  Preferred Reporting Items for Systematic Reviews and Meta-Analyses: the PRISMA statement.   PLoS Med. 2009;6(7):e1000097. doi:10.1371/journal.pmed.1000097 PubMedGoogle Scholar
22.
PROSPERO. Systematic Review of Health Utilities Mapped to the Modified Rankin Scale in Stroke Patients. CRD42018099915. Accessed March 14, 2020. https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=99915
23.
Gupta  A, Kesavabhotla  K, Baradaran  H,  et al.  Plaque echolucency and stroke risk in asymptomatic carotid stenosis: a systematic review and meta-analysis.   Stroke. 2015;46(1):91-97. doi:10.1161/STROKEAHA.114.006091 PubMedGoogle Scholar
24.
Gupta  A, Giambrone  AE, Gialdini  G,  et al.  Silent brain infarction and risk of future stroke: a systematic review and meta-analysis.   Stroke. 2016;47(3):719-725. doi:10.1161/STROKEAHA.115.011889 PubMedGoogle Scholar
25.
Rivero-Arias  O, Ouellet  M, Gray  A, Wolstenholme  J, Rothwell  PM, Luengo-Fernandez  R.  Mapping the modified Rankin scale (mRS) measurement into the generic EuroQol (EQ-5D) health outcome.   Med Decis Making. 2010;30(3):341-354. doi:10.1177/0272989X09349961 PubMedGoogle Scholar
26.
Szende  A, Oppe  M, Devlin  N, eds.  EQ-5D Value Sets: Inventory, Comparative Review and User Guide. Springer; 2007. doi:10.1007/1-4020-5511-0
27.
Lai  SM, Perera  S, Duncan  PW, Bode  R.  Physical and social functioning after stroke: comparison of the Stroke Impact Scale and Short Form-36.   Stroke. 2003;34(2):488-493. doi:10.1161/01.STR.0000054162.94998.C0 PubMedGoogle Scholar
28.
Hong  KS, Saver  JL.  Quantifying the value of stroke disability outcomes: WHO Global Burden of Disease Project disability weights for each level of the modified Rankin Scale.   Stroke. 2009;40(12):3828-3833. doi:10.1161/STROKEAHA.109.561365 PubMedGoogle Scholar
29.
Naidech  AM, Beaumont  JL, Berman  M,  et al.  Web-based assessment of outcomes after subarachnoid and intracerebral hemorrhage: a new patient centered option for outcomes assessment.   Neurocrit Care. 2015;23(1):22-27. doi:10.1007/s12028-014-0098-1 PubMedGoogle Scholar
30.
Owolabi  MO.  Psychometric properties of the German version of the Health-Related Quality of Life in Stroke Patients (HRQOLISP) instrument.   NeuroRehabilitation. 2013;33(2):241-250. doi:10.3233/NRE-130951 PubMedGoogle Scholar
31.
Katzan  IL, Fan  Y, Uchino  K, Griffith  SD.  The PROMIS physical function scale: a promising scale for use in patients with ischemic stroke.   Neurology. 2016;86(19):1801-1807. doi:10.1212/WNL.0000000000002652 PubMedGoogle Scholar
32.
Casaubon  LK, Boulanger  JM, Blacquiere  D,  et al; Heart and Stroke Foundation of Canada Canadian Stroke Best Practices Advisory Committee.  Canadian Stroke Best Practice Recommendations: Hyperacute Stroke Care Guidelines, Update 2015.   Int J Stroke. 2015;10(6):924-940. doi:10.1111/ijs.12551 PubMedGoogle Scholar
33.
Powers  WJ, Rabinstein  AA, Ackerson  T,  et al; American Heart Association Stroke Council.  2018 Guidelines for the early management of patients with acute ischemic stroke: a guideline for healthcare professionals from the American Heart Association/American Stroke Association  [published corrections appear in Stroke. 2018;49(3):e138 and 2018;49(6):e233-e234].  Stroke. 2018;49(3):e46-e110. doi:10.1161/STR.0000000000000158 PubMedGoogle Scholar
34.
Powers  WJ, Rabinstein  AA, Ackerson  T,  et al.  Guidelines for the early management of patients with acute ischemic stroke: 2019 update to the 2018 guidelines for the early management of acute ischemic stroke: a guideline for healthcare professionals from the American Heart Association/American Stroke Association  [published correction appears in Stroke. 2019;50(12):e440-e441].  Stroke. 2019;50(12):e344-e418. doi:10.1161/STR.0000000000000211 PubMedGoogle Scholar
35.
Baeten  SA, van Exel  NJA, Dirks  M, Koopmanschap  MA, Dippel  DW, Niessen  LW.  Lifetime health effects and medical costs of integrated stroke services: a non-randomized controlled cluster-trial based life table approach.   Cost Eff Resour Alloc. 2010;8:21. doi:10.1186/1478-7547-8-21 PubMedGoogle Scholar
36.
Golicki  D, Niewada  M, Buczek  J,  et al.  Validity of EQ-5D-5L in stroke.   Qual Life Res. 2015;24(4):845-850. doi:10.1007/s11136-014-0834-1 PubMedGoogle Scholar
37.
Hattori  N, Hirayama  T, Katayama  Y.  Medical care for chronic-phase stroke in Japan.   Neurol Med Chir (Tokyo). 2012;52(4):175-180. doi:10.2176/nmc.52.175 PubMedGoogle Scholar
38.
Carod-Artal  FJ, Coral  LF, Trizotto  DS, Moreira  CM.  The Stroke Impact Scale 3.0: evaluation of acceptability, reliability, and validity of the Brazilian version.   Stroke. 2008;39(9):2477-2484. doi:10.1161/STROKEAHA.107.513671 PubMedGoogle Scholar
39.
Vellone  E, Savini  S, Fida  R,  et al.  Psychometric evaluation of the Stroke Impact Scale 3.0.   J Cardiovasc Nurs. 2015;30(3):229-241. doi:10.1097/JCN.0000000000000145 PubMedGoogle Scholar
40.
Carod-Artal  FJ, Ferreira Coral  L, Stieven Trizotto  D, Menezes Moreira  C.  Self- and proxy-report agreement on the Stroke Impact Scale.   Stroke. 2009;40(10):3308-3314. doi:10.1161/STROKEAHA.109.558031 PubMedGoogle Scholar
41.
Katzan  IL, Lapin  B.  PROMIS GH (Patient-Reported Outcomes Measurement Information System Global Health) scale in stroke: a validation study.   Stroke. 2018;49(1):147-154. doi:10.1161/STROKEAHA.117.018766 PubMedGoogle Scholar
42.
Duncan  PW, Lai  SM, Keighley  J.  Defining post-stroke recovery: implications for design and interpretation of drug trials.   Neuropharmacology. 2000;39(5):835-841. doi:10.1016/S0028-3908(00)00003-4 PubMedGoogle Scholar
43.
Rangaraju  S, Haussen  D, Nogueira  RG, Nahab  F, Frankel  M.  Comparison of 3-month stroke disability and quality of life across Modified Rankin Scale categories.   Interv Neurol. 2017;6(1-2):36-41. doi:10.1159/000452634 PubMedGoogle Scholar
44.
Wang  YL, Pan  YS, Zhao  XQ,  et al; CHANCE Investigators.  Recurrent stroke was associated with poor quality of life in patients with transient ischemic attack or minor stroke: finding from the CHANCE trial.   CNS Neurosci Ther. 2014;20(12):1029-1035. doi:10.1111/cns.12329 PubMedGoogle Scholar
45.
Dewilde  S, Annemans  L, Lloyd  A,  et al.  The combined impact of dependency on caregivers, disability, and coping strategy on quality of life after ischemic stroke.   Health Qual Life Outcomes. 2019;17(1):31. doi:10.1186/s12955-018-1069-6 PubMedGoogle Scholar
46.
King  JT  Jr, Tsevat  J, Roberts  MS.  Measuring preference-based quality of life using the EuroQol EQ-5D in patients with cerebral aneurysms.   Neurosurgery. 2009;65(3):565-572. doi:10.1227/01.NEU.0000350980.01519.D8 PubMedGoogle Scholar
47.
Sallinen  H, Sairanen  T, Strbian  D.  Quality of life and depression 3 months after intracerebral hemorrhage.   Brain Behav. 2019;9(5):e01270. doi:10.1002/brb3.1270 PubMedGoogle Scholar
48.
Duncan  PW, Wallace  D, Lai  SM, Johnson  D, Embretson  S, Laster  LJ.  The Stroke Impact Scale Version 2.0: evaluation of reliability, validity, and sensitivity to change.   Stroke. 1999;30(10):2131-2140. doi:10.1161/01.STR.30.10.2131 PubMedGoogle Scholar
49.
Bracard  S, Ducrocq  X, Mas  JL,  et al; THRACE Investigators.  Mechanical thrombectomy after intravenous alteplase versus alteplase alone after stroke (THRACE): a randomised controlled trial [published correction appears in Lancet Neurol. 2016;15(12):1203].   Lancet Neurol. 2016;15(11):1138-1147. doi:10.1016/S1474-4422(16)30177-6 PubMedGoogle Scholar
50.
Molina  CA, Chamorro  A, Rovira  À,  et al.  REVASCAT: a randomized trial of revascularization with SOLITAIRE FR device vs. best medical therapy in the treatment of acute stroke due to anterior circulation large vessel occlusion presenting within eight-hours of symptom onset.   Int J Stroke. 2015;10(4):619-626. doi:10.1111/ijs.12157 PubMedGoogle Scholar
51.
Delcourt  C, Huang  Y, Wang  J,  et al; INTERACT2 Investigators.  The second (main) phase of an open, randomised, multicentre study to investigate the effectiveness of an intensive blood pressure reduction in acute cerebral haemorrhage trial (INTERACT2).   Int J Stroke. 2010;5(2):110-116. doi:10.1111/j.1747-4949.2010.00415.x PubMedGoogle Scholar
52.
Qureshi  AI, Palesch  YY.  Antihypertensive Treatment of Acute Cerebral Hemorrhage (ATACH) II: design, methods, and rationale.   Neurocrit Care. 2011;15(3):559-576. doi:10.1007/s12028-011-9538-3 PubMedGoogle Scholar
53.
Albers  GW, Lansberg  MG, Kemp  S,  et al.  A multicenter randomized controlled trial of endovascular therapy following imaging evaluation for ischemic stroke (DEFUSE 3).   Int J Stroke. 2017;12(8):896-905. doi:10.1177/1747493017701147 PubMedGoogle Scholar
54.
Hacke  W, Kaste  M, Fieschi  C,  et al; Second European-Australasian Acute Stroke Study Investigators.  Randomised double-blind placebo-controlled trial of thrombolytic therapy with intravenous alteplase in acute ischaemic stroke (ECASS II).   Lancet. 1998;352(9136):1245-1251. doi:10.1016/S0140-6736(98)08020-9 PubMedGoogle Scholar
55.
de Los Ríos la Rosa  F, Khoury  J, Kissela  BM,  et al.  Eligibility for intravenous recombinant tissue-type plasminogen activator within a population: the effect of the European Cooperative Acute Stroke Study (ECASS) III Trial.   Stroke. 2012;43(6):1591-1595. doi:10.1161/STROKEAHA.111.645986 PubMedGoogle Scholar
56.
Anderson  CS, Huang  Y, Lindley  RI,  et al; ENCHANTED Investigators and Coordinators.  Intensive blood pressure reduction with intravenous thrombolysis therapy for acute ischaemic stroke (ENCHANTED): an international, randomised, open-label, blinded-endpoint, phase 3 trial.   Lancet. 2019;393(10174):877-888. doi:10.1016/S0140-6736(19)30038-8 PubMedGoogle Scholar
57.
Goyal  M, Demchuk  AM, Menon  BK,  et al; ESCAPE Trial Investigators.  Randomized assessment of rapid endovascular treatment of ischemic stroke.   N Engl J Med. 2015;372(11):1019-1030. doi:10.1056/NEJMoa1414905 PubMedGoogle Scholar
58.
Saver  JL, Starkman  S, Eckstein  M,  et al; FAST-MAG Investigators and Coordinators.  Prehospital use of magnesium sulfate as neuroprotection in acute stroke.   N Engl J Med. 2015;372(6):528-536. doi:10.1056/NEJMoa1408827 PubMedGoogle Scholar
59.
Broderick  JP, Palesch  YY, Demchuk  AM,  et al; Interventional Management of Stroke (IMS) III Investigators.  Endovascular therapy after intravenous t-PA versus t-PA alone for stroke.  [published correction appears in N Engl J Med. 2013;368(13):1265].  N Engl J Med. 2013;368(10):893-903. doi:10.1056/NEJMoa1214300 PubMedGoogle Scholar
60.
Molyneux  A, Kerr  R, Stratton  I,  et al; International Subarachnoid Aneurysm Trial (ISAT) Collaborative Group.  International Subarachnoid Aneurysm Trial (ISAT) of neurosurgical clipping versus endovascular coiling in 2143 patients with ruptured intracranial aneurysms: a randomized trial.   J Stroke Cerebrovasc Dis. 2002;11(6):304-314. doi:10.1053/jscd.2002.130390 PubMedGoogle Scholar
61.
Ogawa  A, Mori  E, Minematsu  K,  et al; MELT Japan Study Group.  Randomized trial of intraarterial infusion of urokinase within 6 hours of middle cerebral artery stroke: the middle cerebral artery embolism local fibrinolytic intervention trial (MELT) Japan.   Stroke. 2007;38(10):2633-2639. doi:10.1161/STROKEAHA.107.488551 PubMedGoogle Scholar
62.
Berkhemer  OA, Fransen  PS, Beumer  D,  et al; MR CLEAN Investigators.  A randomized trial of intraarterial treatment for acute ischemic stroke.  [published correction appears in N Engl J Med. 2015;372(4):394].  N Engl J Med. 2015;372(1):11-20. doi:10.1056/NEJMoa1411587 PubMedGoogle Scholar
63.
Logallo  N, Novotny  V, Assmus  J,  et al.  Tenecteplase versus alteplase for management of acute ischaemic stroke (NOR-TEST): a phase 3, randomised, open-label, blinded endpoint trial.   Lancet Neurol. 2017;16(10):781-788. doi:10.1016/S1474-4422(17)30253-3 PubMedGoogle Scholar
64.
Furlan  A, Higashida  R, Wechsler  L,  et al.  Intra-arterial prourokinase for acute ischemic stroke. The PROACT II study: a randomized controlled trial. Prolyse in Acute Cerebral Thromboembolism.   JAMA. 1999;282(21):2003-2011. doi:10.1001/jama.282.21.2003 PubMedGoogle Scholar
65.
Saver  JL, Goyal  M, Bonafe  A,  et al; SWIFT PRIME Investigators.  Stent-retriever thrombectomy after intravenous t-PA vs. t-PA alone in stroke.   N Engl J Med. 2015;372(24):2285-2295. doi:10.1056/NEJMoa1415061 PubMedGoogle Scholar
66.
Anderson  CS, Heeley  E, Huang  Y,  et al; INTERACT2 Investigators.  Rapid blood-pressure lowering in patients with acute intracerebral hemorrhage.   N Engl J Med. 2013;368(25):2355-2365. doi:10.1056/NEJMoa1214609 PubMedGoogle Scholar
67.
Jovin  TG, Chamorro  A, Cobo  E,  et al; REVASCAT Trial Investigators.  Thrombectomy within 8 hours after symptom onset in ischemic stroke.   N Engl J Med. 2015;372(24):2296-2306. doi:10.1056/NEJMoa1503780 PubMedGoogle Scholar
68.
Lubetkin  EI, Jia  H, Franks  P, Gold  MR.  Relationship among sociodemographic factors, clinical conditions, and health-related quality of life: examining the EQ-5D in the U.S. general population.   Qual Life Res. 2005;14(10):2187-2196. doi:10.1007/s11136-005-8028-5 PubMedGoogle Scholar
69.
Depaola  SJ, Griffin  M, Young  JR, Neimeyer  RA.  Death anxiety and attitudes toward the elderly among older adults: the role of gender and ethnicity.   Death Stud. 2003;27(4):335-354. doi:10.1080/07481180302904 PubMedGoogle Scholar
70.
Kim  SK, Kim  SH, Jo  MW, Lee  SI.  Estimation of minimally important differences in the EQ-5D and SF-6D indices and their utility in stroke.   Health Qual Life Outcomes. 2015;13(1):32. doi:10.1186/s12955-015-0227-3 PubMedGoogle Scholar
71.
Revicki  DA, Cella  D, Hays  RD, Sloan  JA, Lenderking  WR, Aaronson  NK.  Responsiveness and minimal important differences for patient reported outcomes.   Health Qual Life Outcomes. 2006;4:70. doi:10.1186/1477-7525-4-70 PubMedGoogle Scholar
72.
Post  PN, Stiggelbout  AM, Wakker  PP.  The utility of health states after stroke: a systematic review of the literature.   Stroke. 2001;32(6):1425-1429. doi:10.1161/01.STR.32.6.1425 PubMedGoogle Scholar
73.
Irony  TZ.  The “utility” in composite outcome measures: measuring what is important to patients.   JAMA. 2017;318(18):1820-1821. doi:10.1001/jama.2017.14001 PubMedGoogle Scholar
74.
Mulhern  B, Feng  Y, Shah  K,  et al.  Comparing the UK EQ-5D-3L and English EQ-5D-5L value sets.   Pharmacoeconomics. 2018;36(6):699-713. doi:10.1007/s40273-018-0628-3 PubMedGoogle Scholar
75.
Selivanova  A, Buskens  E, Krabbe  PFM.  Head-to-head comparison of EQ-5D-3L and EQ-5D-5L health values.   Pharmacoeconomics. 2018;36(6):715-725. doi:10.1007/s40273-018-0647-0 PubMedGoogle Scholar
76.
Pan  JH, Song  XY, Lee  SY, Kwok  T.  Longitudinal analysis of quality of life for stroke survivors using latent curve models.   Stroke. 2008;39(10):2795-2802. doi:10.1161/STROKEAHA.108.515460 PubMedGoogle Scholar
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    Original Investigation
    Neurology
    April 29, 2020

    改良兰金量表的健康效用权重: 系统评价和荟萃分析

    Author Affiliations
    • 1Faculty of Medicine, The University of British Columbia, Vancouver, British Columbia, Canada
    • 2Faculty of Medicine, McGill University, Montreal, Quebec, Canada
    • 3Emmes Canada, Vancouver, British Columbia, Canada
    • 4Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
    • 5Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
    • 6Djavad Mowafaghian Centre for Brain Health, The University of British Columbia, Vancouver, British Columbia, Canada
    • 7Vancouver Stroke Program, The University of British Columbia, Vancouver, British Columbia, Canada
    JAMA Netw Open. 2020;3(4):e203767. doi:10.1001/jamanetworkopen.2020.3767
    关键点 español English

    问题  是已有的健康效用加权结局量表更适合用于临床试验,还是针对特定研究的方法更合适?

    结果  这项系统评价和荟萃分析涉及 22,389 名个体和 24 项研究。分析发现,对于报告改良兰金量表效用权重的研究,其研究差异存在统计学意义。当应用于重大急性卒中试验的结果时,不同的研究特定效用权重有时会导致主要结局出现不稳定情况。

    意义  效用权重及其解读取决于用来加权的量表以及研究队列。此外,选择不同的效用加权结局量表可能会改变试验的结局。

    Abstract

    Importance  The utility-weighted modified Rankin Scale (UW-mRS) has been proposed as a patient-centered alternative primary outcome for stroke clinical trials. However, to date, there is no clear consensus on an approach to weighting the mRS.

    Objective  To characterize the between-study variability in utility weighting of the mRS in a population of patients who experienced stroke and its implications when applied to the results of a clinical trial.

    Data Sources  In this systematic review and meta-analysis, MEDLINE, Embase, and PsycINFO were searched from January 1987 through May 2019 using major search terms for stroke, health utility, and mRS.

    Study Selection  Original research articles published in English were reviewed. Included were studies with participants 18 years or older with ischemic or hemorrhagic stroke, transient ischemic attack, or subarachnoid hemorrhage, with mRS scores and utility weights evaluated concurrently. A total of 5725 unique articles were identified. Of these, 283 met criteria for full-text review, and 24 were included in the meta-analysis.

    Data Extraction and Synthesis  PRISMA guidelines for systematic review were followed. Data extraction was performed independently by multiple researchers. Data were pooled using mixed models.

    Main Outcomes and Measures  The mean utility weights and 95% CIs were calculated for each mRS score and health utility scale. Geographic differences in weighting for the EuroQoL 5-dimension (EQ-5D) and Stroke Impact Scale–based UW-mRS were explored using inverse variance–weighted linear models. The results of 18 major acute stroke trials cited in current guidelines were then reanalyzed using the UW-mRS weighting scales identified in the systematic review.

    Results  The meta-analysis included 22 389 individuals; the mean (SD) age of participants was 65.9 (4.0) years, and the mean (SD) proportion of male participants was 58.2% (7.5%). For all health utility scales evaluated, statistically significant differences were observed between the mean utility weights by mRS score. For studies using an EQ-5D–weighted mRS, between-study variance was higher for worse (mRS 2-5) compared with better (mRS 0-1) scores. Of the 18 major acute stroke trials with reanalyzed results, 3 had an unstable outcome when using different UW-mRSs.

    Conclusions and Relevance  Multiple factors, including cohort-specific characteristics and health utility scale selection, can influence mRS utility weighting. If the UW-mRS is selected as a primary outcome, the approach to weighting may alter the results of a clinical trial. Researchers using the UW-mRS should prospectively and concurrently obtain mRS scores and utility weights to characterize study-specific outcomes.

    Introduction

    The modified Rankin Scale (mRS) is an efficient, reliable, and simple functional outcome measure that is widely used as a primary end point for clinical trials in acute stroke.1-3 However, as an ordered categorical scale, it may not reflect potentially unequal differences in perceived quality of life associated with certain 1-point shifts vs others. For example, the ordering of outcomes as scores rated from 0 (no residual symptoms) to 6 (death) does not reflect the fact that some individuals may prefer death (mRS score 6) to being bedridden, incontinent, and completely dependent on others (mRS score 5). To accommodate for an improved focus on patient-centered outcomes in clinical trials, the Stroke Treatment Academic Industry Roundtable (STAIR VII) recommended the development of a utility-weighted mRS (UW-mRS) that weights the mRS against a health utility scale.4,5 The UW-mRS is increasingly used as an end point in clinical stroke trials. Notably, it was a co–primary end point in the DAWN trial,6 and it is the primary outcome in multiple actively enrolling randomized clinical stroke trials.7-10

    Health utility, defined as the desirability of a specific health outcome, allows for comparisons of health-related quality of life across an array of clinical settings.11 Health utility weights, hereafter referred to as utility weights, reflect the spectrum between perfect health (a score of 1) and outcomes worse than death (where death is a score of 0 and negative values indicate an outcome worse than death). Potential challenges in adopting a one-size-fits-all approach to utility weighting for the mRS include differences in elicitation methods (time trade-off or person trade-off techniques),11 selection of an appropriate health utility scale for weighting,12 variations in region-specific norms,13,14 and between-person differences in preference weighting for a given mRS score.15,16 For example, the incorrect application of region-specific norms can substantially alter utility weighting and in turn may influence economic assessments.17

    The literature was systematically reviewed for all studies that concurrently reported mRS scores and utility weights in stroke survivors, with the aim of exploring potential implications in using and interpreting a UW-mRS in the poststroke population. First, differences in utility weighting were examined between studies that used different health utility scales. Next, interstudy variance in utility weighting was compared between studies using the same health utility scale, and the associations of geographically specific tariffs were explored. In addition, EuroQoL 5-dimension (EQ-5D)–weighted UW-mRSs identified by the systematic review were retrospectively applied to major superiority design acute clinical stroke trials to assess how the outcome of each trial might be interpreted.

    Methods
    Search Strategy

    In this systematic review and meta-analysis, MEDLINE, Embase, and PsycINFO were searched from January 1987 through May 2019 using major search terms for stroke, health utility, and modified Rankin Scale. The literature search strategy was based on previous systematic reviews and meta-analyses.18-20 The reference lists of included articles were manually searched for additional studies. The complete search strategy and a full list of search terms are included in the eMethods in the Supplement.

    The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for systematic review were followed.21 The protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO).22

    Eligibility Criteria

    Study eligibility criteria were as follows: (1) participants had an ischemic stroke, hemorrhagic stroke, transient ischemic attack, or subarachnoid hemorrhage; (2) participants were 18 years or older; (3) mRS scores and utility weights were evaluated concurrently; (4) utility weights were mapped to mRS scores; and (5) the scale used to measure health utility was reported. Only original research articles published in English were reviewed. In case of missing data or matters of clarification, the corresponding author was contacted for additional details.

    Data Extraction and Risk of Bias

    Two of us (A.D.R. and Z.R.O.) independently screened titles and abstracts of all articles obtained in the initial database search. Those that met preliminary inclusion criteria were then screened by full-text review against the eligibility criteria by the same 2 authors. Any disagreement was resolved by consensus.

    Data were extracted from eligible articles using a data collection template (eMethods in the Supplement). Extracted were article and study characteristics, participant demographics, clinical characteristics, health utility scale, mRS scores, and utility weights. Studies were evaluated with a risk of bias tool adapted from work by Gupta et al,23,24 which considered selection, detection, reporting, risk of attrition, and confounding biases (eTable 4 in the Supplement).

    Statistical Analysis

    Utility weight was defined as the mean health utility weight reported for a given mRS score. Because previous literature suggested that mRS utility weights remained stable over time,25 we combined UW-mRSs obtained at different times after stroke. All utility weights were converted to a scale of 0 (death) to 1 (perfect health) to simplify interscale comparisons, with a utility weight of 0 assigned to mRS 6 (death).11 Because most studies did not differentiate mRS scores and health utility outcome data by stroke type, we combined all stroke types.

    Data were pooled using mixed models. The EQ-5D 3-Level (EQ-5D-3L) and EQ-5D 5-Level (EQ-5D-5L) models were treated as a single scale (EQ-5D) because we confirmed that there were no statistically significant differences between the mean EQ-5D-3L and EQ-5D-5L health utility weights for each mRS score.26 For the 36-Item Short Form Survey (SF-36), the social function (SF-36-SF) and physical function (SF-36-PF) subcomponents were separated. The mean utility weights and 95% CIs were calculated for each mRS score and health utility scale.

    For the EQ-5D, the only health utility scale for which multiple studies reported the mean and SE of the mean for each mRS score, an inverse variance–weighted linear model was fit with the mean utility weight as the outcome, mRS score as a categorical predictor, and study as a random intercept term. Inverse variance weighting was used to account for differences in variances of each study so that studies with smaller variances for utility weights were more highly weighted in the analysis.

    To assess differences between the mean utility weight by mRS score, an F test was conducted. Tukey tests for pairwise differences in the mean utility weights between mRS scores were conducted. To assess whether there were differences in the EQ-5D by geography, continent was included as a fixed effect in the model, and a type III F test for differences in the mean utility weight by continent was conducted.

    Data were sufficient to evaluate differences in variance between EQ-5D–weighted mRS scores with the Levene test. Then, the Levene test was repeated examining differences in variance using different dichotomized mRS cut points (0-1 vs 2-5, 0-2 vs 3-5, and 0-3 vs 4-5).

    Only 1 study had the necessary data available for hypothesis testing for each of the following instruments: SF-36-PF,27 SF-36-SF,27 World Health Organization Global Burden of Disease Project,28 Patient-Reported Outcomes Measurement Information System–Physical Function,29 Quality of Life in Neurological Disorders,29 Health-Related Quality of Life in Stroke Patients,30 and Assessment of Quality of Life.15 For these health utility scales, an F test was conducted to compare the mean utility weights at each mRS score, and Tukey tests for pairwise differences were conducted to compare pairwise differences at all mRS scores.

    To assess differences in the 1 study31 that reported Stroke Impact Scale (SIS)-16 scores, F tests and Tukey pairwise comparisons were conducted. To model SIS domain values by mRS score, an inverse variance–weighted linear model was fit with the mean domain value as the outcome; mRS score, SIS domain, and the interaction between mRS score and SIS domain as categorical predictors; and study as a random intercept term for all domains other than SIS-16. To test for differences in mRS score by domain, F tests were conducted. For SIS domains other than SIS-16, continent and the interaction between continent and domain were included as categorical fixed effects in the model. To test for differences in the mean domain values by continent, F tests were conducted.

    In addition, different EQ-5D–weighted UW-mRSs identified in the systematic review were applied to the results of major acute stroke trials. This method of reanalyzing clinical trial data using the UW-mRS has been published previously.6 Clinical trials were selected if they reported group results from all 7 mRS scores, used the mRS as their primary outcome, and were considered in Canadian Best Practices32 or American Heart Association/American Stroke Association33,34 guidelines for acute ischemic stroke. We identified 18 eligible major acute stroke trials and converted their primary outcome mRS scores to the EQ-5D–weighted UW-mRS scores identified by the systematic review.

    All data analyses were conducted in SAS (version 9.4; SAS Institute Inc) and MATLAB (version R2019a; MathWorks). Pairwise F tests and Tukey tests were conducted by hand with formulas. Statistical significance was set at 2-sided P < .05.

    Results

    The literature search was last repeated on May 10, 2019. The search strategy initially identified 6619 articles; 910 were duplicates. An additional 16 articles were identified through screening the reference lists. In total, 5725 unique articles underwent formal screening. Based on titles and abstracts, 283 articles met criteria for full-text review. Articles were most frequently excluded during screening (3540 [61.8%]) for failing to mention health utility. Of articles undergoing full-text review, 24 met inclusion criteria and were included in the meta-analysis (Figure 1).

    The meta-analysis included 22 389 patients from 41 countries across 6 continents (North America, South America, Europe, Asia, Africa, and Australia). The mean (SD) age of participants was 65.9 (4.0) years, and the mean (SD) proportion of male participants was 58.2% (7.5%). The median (interquartile range [IQR]) time from stroke onset to outcome determination was 90 (82-180) days. Reported stroke types included combined ischemic and hemorrhagic (14 studies13,15,25,27,28,30,35-42), ischemic (6 studies14,16,31,43-45), and hemorrhagic (3 studies43,46,47). Two studies25,44 included individuals with transient ischemic attack. The median (IQR) sample size was 400 (180-847). The median (IQR) proxy completion rate (eg, by caregiver) was 22.8% (10.6%-34.0%), but 13 studies25,27,28,31,35,38,39,41-43,46,47 did not report proxy completion rates. Demographic and baseline clinical data are summarized in eTable 1 in the Supplement.

    Only studies that reported the sample size in addition to the mean and SD or SE of the mean for each mRS score and utility weights at each mRS score were included in the meta-analysis. Nine studies13,16,25,36,37,43-45,47 using the EQ-5D (n = 9607), 5 studies31,38-40,48 using the SIS (n = 777), and 1 study each using the SF-36-PF (n = 278),27 SF-36-SF (n = 278),27 World Health Organization Global Burden of Disease Project (n = 54),28 Patient-Reported Outcomes Measurement Information System–Physical Function (n = 236),29 Quality of Life in Neurological Disorders (n = 236),29 Health-Related Quality of Life in Stroke Patients (n = 103)30 and Assessment of Quality of Life (n = 1523)15 met these criteria.

    Statistically significant differences were observed between the mean utility weights by mRS score for all health utility scales evaluated (Figure 2). For studies using an EQ-5D–weighted mRS score, between-study variance was higher for worse (mRS 2-5) compared with better (mRS 0-1) scores. Of the 18 major acute stroke trials with reanalyzed results, 3 trials49-51 had an unstable outcome when using different UW-mRSs. With the EQ-5D, there were pairwise differences between all mRS scores. Other health utility scales were variable in distinguishing pairwise differences in utility weights between mRS scores (eTable 2 in the Supplement). For SIS domains, a statistically significant difference was found in the mean domain score by mRS score for every domain except communication (eTable 3 in the Supplement).

    For EQ-5D–generated utility weights, no differences were found in utility weighting between continents. However, this analysis was limited to geographic information from Europe (5 studies), Asia (2 studies), and undifferentiated regions (2 studies). A difference in SIS-generated utility weights by continent was observed for the emotion, social participation, and stroke global disability domains. Estimated SIS utility weights were generally higher for South America compared with Europe.

    Based on the Levene test, heterogeneity of variance (P = .06) between each mRS score for EQ-5D–weighted mRS scores was not statistically significant (Figure 3). In the dichotomized analysis, there was a statistically significant difference in variance between mRS scores of 0-1 vs 2-5 and 0-2 vs 3-5, with no statistically significance difference for 0-3 vs 4-5.

    When EQ-5D–weighted UW-mRSs were used to reanalyze 18 major acute stroke trials, 15 trials6,52-65 had a stable result (ie, positive [P < .05] or neutral [P > .05] result for the primary outcome remained the same) (Table). Three trials, INTERACT2,51 REVASCAT,50 and THRACE,49 had a variable result dependent on which UW-mRS was used to replace the primary outcome. Four UW-mRSs16,37,43,47 had differences in the primary outcome for more than 1 trial. These 4 scales had higher utility weights compared with our calculated mean utility weights for mRS scores 4 and 5.

    Discussion

    The UW-mRS is an increasingly popular primary outcome in randomized clinical trials for acute stroke as a means to incorporate patient preferences. However, despite its emergent role, consensus on the approach to utility weighting is lacking. This work highlights important considerations in using a UW-mRS to reflect a patient-centered approach. First, utility weighting varies based on the cohort and the choice of health utility scale. Second, these differences in weighting may potentially alter the outcome of a clinical trial. Substantial differences were found in utility weighting of the mRS, both when different scales were used and between studies where the same scale was used. As expected, in major acute stroke trials with marginally positive or neutral results,49,66,67 use of different study-specific weighting regimens resulted in instability around the primary outcome.

    Most of the 24 articles identified in the systematic review used the EQ-5D to generate utility weights. Between-study differences were found in utility weighting using the EQ-5D, particularly with worse functional outcomes. Sociocultural and demographic factors, medical comorbidities, and personal values may have a greater influence on perceived quality of life (and perception of death as a more acceptable state than total dependence on others) in more severely disabled patients, which may in part explain this increased variance.14,17,68,69 These differences may contribute to within-cohort heterogeneity in addition to between-study differences. A subanalysis of the MR CLEAN thrombectomy trial showed substantial interindividual variability for EQ-5D weighting of mRS scores and reduced statistical efficiency compared with an ordinal mRS outcome.16 An important consequence of this variability is that different UW-mRSs may alter the outcome of clinical trials. In our analysis, we observed that UW-mRSs with higher utility weighting for severely disabled outcomes when applied to 18 major acute stroke trials were likely to lead to a neutral (ie, non–statistically significant) trial result.

    Although the mRS score is a universally accepted outcome for major acute stroke trials, use of a concurrent health utility scale may more fully capture changes important to survivors, whereas the mRS cannot. In addition to its implicit value statement in ranking death as the worst possible outcome, the mRS may also be insufficiently sensitive to important functional differences altering quality of life. For example, the EQ-5D may be more responsive than the mRS to stroke survivors’ perceptions of functional changes.67 However, these minimally important differences may vary by cohort49,70,71; when choosing a health utility scale, it is important that it reflects the needs and values of a particular study population. For example, in an exclusively minor stroke and transient ischemic attack cohort, where issues with fatigue, cognition, and mood may be most important for quality of life,68 the EQ-5D (which does not capture cognition) may not be an optimal choice for health utility weighting.

    The variability of health utility weighting observed in this study provides evidence to support prior recommendations that clinical trials using a UW-mRS should prospectively and concurrently obtain both the mRS scores and health utility weights to establish trial-specific weighted scales.14,25 However, our findings also raise the broader question of whether a weighted score is the best approach to incorporating patient preferences into trial results; it may be more informative to simply provide the mRS score and a health utility weight separately as co-primary outcomes. Using both outcomes separately allows investigators to report functional differences using the clinically interpretable and reliable mRS alongside a contextually appropriate health utility scale to characterize meaningful differences in patient quality of life.

    Limitations

    This study has limitations. First, the median proxy completion rate was 22.8%, which may have altered the results of our study. Although proxies, such as family members, help to provide health utility from aphasic or severely disabled patients, they may rate the patient’s health utility more negatively than would the patients themselves.72,73 Our high proxy completion rate may have decreased mRS scores, especially for severely disabled patients. Second, combining the EQ-5D-3L and EQ-5D-5L is a potential limitation of this analysis because the EQ-5D-5L generates lower mean utility weights than the EQ-5D-3L, and the EQ-5D-5L has smaller score ranges.74,75 Although this limitation could have potentially caused our combined EQ-5D to overestimate utility weights, we found for our data set that the EQ-5D-3L and EQ-5D-5L utility weights at each mRS score did not differ statistically significantly. Third, in keeping with prior research,25 we assumed that poststroke utility weights remained stable over time. However, a recent subanalysis of the AVERT rehabilitation trial demonstrated statistically significant within-patient variability in utility weights between 3 and 12 months after stroke in those whose mRS score remained stable over that period.15 It is possible and even likely that survivors may become more accepting of their new normal over time or may be experiencing incremental gains not measurable using the mRS.70,76 In addition, we were unable to examine time-specific differences in utility weighting in this study given the limitations of the data, but we believe that this aspect is also worthy of further prospective study.

    Conclusions

    Utility weighting of the mRS depends on multiple factors, including cohort-specific characteristics and the health utility scale used. The choice of weighting may alter the results of a clinical trial. From this study’s findings, it appears that researchers using the UW-mRS should derive a trial-specific score or should consider simply reporting both the mRS score and utility weights as separate co–primary outcomes.

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    Article Information

    Accepted for Publication: February 21, 2020.

    Published: April 29, 2020. doi:10.1001/jamanetworkopen.2020.3767

    Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2020 Rebchuk AD et al. JAMA Network Open.

    Corresponding Author: Thalia S. Field, MD, MHSc, Vancouver Stroke Program, The University of British Columbia, S169-2211 Wesbrook Mall, Vancouver, BC V6T 2B5, Canada (thalia.field@ubc.ca).

    Author Contributions: Mr Rebchuk and Dr Field had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

    Concept and design: Rebchuk, Field.

    Acquisition, analysis, or interpretation of data: All authors.

    Drafting of the manuscript: Rebchuk, O’Neill.

    Critical revision of the manuscript for important intellectual content: Rebchuk, Szefer, Hill, Field.

    Statistical analysis: Rebchuk, Szefer.

    Obtained funding: Rebchuk, Field.

    Administrative, technical, or material support: Rebchuk, O’Neill.

    Supervision: Hill, Field.

    Conflict of Interest Disclosures: Ms Szefer reported being an employee of Emmes Canada, a company under contract to The University of British Columbia Department of Medicine, at the time of the analysis. Dr Hill reported having an advisory relationship from Boehringer Ingelheim (steering committee for the COLUMBUS registry) and receiving grants from Boehringer Ingelheim International GmbH, NoNO Inc, Stryker, and Medtronic LLC. Dr Field reported receiving substantial research grants from the Canadian Institutes of Health Research, the Heart and Stroke Foundation of Canada, the Canadian Stroke Consortium, the Michael Smith Foundation for Health Research, and the Vancouver Coastal Health Research Institute; receiving other substantial research support from Bayer Canada (in-kind study medication); having an advisory relationship with Bayer Canada (2017 advisory board) and Servier (2017 advisory board); and receiving grants and personal fees from Bayer Canada and personal fees from Servier. No other disclosures were reported.

    Funding/Support: This work was supported by the Canadian Institutes of Health Research and the Canadian Stroke Trials for Optimized Results (a joint venture of the Canadian Stroke Consortium and the Canadian Partnership for Stroke Recovery). Mr Rebchuk was supported by the Medical Student Research Scholarship from the American Academy of Neurology. Dr Field was supported by the Heart and Stroke Foundation of Canada, the Michael Smith Foundation for Health Research, and the Vancouver Coastal Health Research Institute.

    Role of the Funder/Sponsor: The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

    Meeting Presentation: This study was presented at the 2020 American Academy of Neurology Annual Meeting; April 29, 2020 (online).

    Additional Contributions: Dean Giustini, MLS, MEd, University of British Columbia Biomedical Branch Library, assisted with the literature search. He was not compensated for his contributions.

    References
    1.
    Banks  JL, Marotta  CA.  Outcomes validity and reliability of the modified Rankin scale: implications for stroke clinical trials: a literature review and synthesis.   Stroke. 2007;38(3):1091-1096. doi:10.1161/01.STR.0000258355.23810.c6 PubMedGoogle Scholar
    2.
    Sulter  G, Steen  C, De Keyser  J.  Use of the Barthel Index and Modified Rankin Scale in acute stroke trials.   Stroke. 1999;30(8):1538-1541. doi:10.1161/01.STR.30.8.1538 PubMedGoogle Scholar
    3.
    Rankin  J.  Cerebral vascular accidents in patients over the age of 60, II: prognosis.   Scott Med J. 1957;2(5):200-215. doi:10.1177/003693305700200504 PubMedGoogle Scholar
    4.
    Selby  JV, Beal  AC, Frank  L.  The Patient-Centered Outcomes Research Institute (PCORI) national priorities for research and initial research agenda.   JAMA. 2012;307(15):1583-1584. doi:10.1001/jama.2012.500 PubMedGoogle Scholar
    5.
    Albers  GW, Goldstein  LB, Hess  DC,  et al; STAIR VII Consortium.  Stroke Treatment Academic Industry Roundtable (STAIR) recommendations for maximizing the use of intravenous thrombolytics and expanding treatment options with intra-arterial and neuroprotective therapies.   Stroke. 2011;42(9):2645-2650. doi:10.1161/STROKEAHA.111.618850 PubMedGoogle Scholar
    6.
    Nogueira  RG, Jadhav  AP, Haussen  DC,  et al; DAWN Trial Investigators.  Thrombectomy 6 to 24 hours after stroke with a mismatch between deficit and infarct.   N Engl J Med. 2018;378(1):11-21. doi:10.1056/NEJMoa1706442 PubMedGoogle Scholar
    7.
    ClinicalTrials.gov. Recombinant Factor VIIa (rFVIIa) for Hemorrhagic Stroke Trial (FASTEST). NCT03496883. Accessed February 19, 2020. https://clinicaltrials.gov/ct2/show/NCT03496883
    8.
    ClinicalTrials.gov. Blood Pressure After Endovascular Stroke Therapy-II (BEST-II). NCT04116112. Accessed February 19, 2020. https://clinicaltrials.gov/ct2/show/NCT04116112
    9.
    ClinicalTrials.gov. Benefits of Stroke Treatment Delivered Using a Mobile Stroke Unit (BEST-MSU). NCT02190500. Accessed February 19, 2020. https://clinicaltrials.gov/ct2/show/NCT02190500
    10.
    ClinicalTrials.gov. ENRICH: Early Minimally-invasive Removal of Intracerebral Hemorrhage (ICH) (ENRICH). NCT02880878. Accessed February 19, 2020. https://clinicaltrials.gov/ct2/show/NCT02880878
    11.
    Feeny  D.  A utility approach to the assessment of health-related quality of life.   Med Care. 2000;38(9)(suppl):II151-II154.PubMedGoogle Scholar
    12.
    Dorman  P, Slattery  J, Farrell  B, Dennis  M, Sandercock  P.  Qualitative comparison of the reliability of health status assessments with the EuroQol and SF-36 questionnaires after stroke: United Kingdom Collaborators in the International Stroke Trial.   Stroke. 1998;29(1):63-68. doi:10.1161/01.STR.29.1.63 PubMedGoogle Scholar
    13.
    Whynes  DK, Sprigg  N, Selby  J, Berge  E, Bath  PM; ENOS Investigators.  Testing for differential item functioning within the EQ-5D.   Med Decis Making. 2013;33(2):252-260. doi:10.1177/0272989X12465016 PubMedGoogle Scholar
    14.
    Ali  M, MacIsaac  R, Quinn  TJ,  et al.  Dependency and health utilities in stroke: data to inform cost-effectiveness analyses.   Eur Stroke J. 2017;2(1):70-76. doi:10.1177/2396987316683780 PubMedGoogle Scholar
    15.
    Rethnam  V, Bernhardt  J, Dewey  H,  et al; AVERT Trial Collaboration Group.  Utility-weighted modified Rankin Scale: still too crude to be a truly patient-centric primary outcome measure ?  Int J Stroke. Published online February 12, 2019. doi:10.1177/1747493019830583 PubMedGoogle Scholar
    16.
    Dijkland  SA, Voormolen  DC, Venema  E,  et al; MR CLEAN Investigators.  Utility-weighted modified Rankin Scale as primary outcome in stroke trials: a simulation study.   Stroke. 2018;49(4):965-971. doi:10.1161/STROKEAHA.117.020194 PubMedGoogle Scholar
    17.
    Oremus  M, Tarride  JE, Clayton  N, Raina  P; Canadian Willingness-to-Pay Study Group.  Health utility scores in Alzheimer’s disease: differences based on calculation with American and Canadian preference weights.   Value Health. 2014;17(1):77-83. doi:10.1016/j.jval.2013.10.009 PubMedGoogle Scholar
    18.
    Gupta  A, Baradaran  H, Schweitzer  AD,  et al.  Carotid plaque MRI and stroke risk: a systematic review and meta-analysis.   Stroke. 2013;44(11):3071-3077. doi:10.1161/STROKEAHA.113.002551 PubMedGoogle Scholar
    19.
    Doth  AH, Hansson  PT, Jensen  MP, Taylor  RS.  The burden of neuropathic pain: a systematic review and meta-analysis of health utilities.   Pain. 2010;149(2):338-344. doi:10.1016/j.pain.2010.02.034 PubMedGoogle Scholar
    20.
    Wyld  M, Morton  RL, Hayen  A, Howard  K, Webster  AC.  A systematic review and meta-analysis of utility-based quality of life in chronic kidney disease treatments.   PLoS Med. 2012;9(9):e1001307. doi:10.1371/journal.pmed.1001307 PubMedGoogle Scholar
    21.
    Moher  D, Liberati  A, Tetzlaff  J, Altman  DG; PRISMA Group.  Preferred Reporting Items for Systematic Reviews and Meta-Analyses: the PRISMA statement.   PLoS Med. 2009;6(7):e1000097. doi:10.1371/journal.pmed.1000097 PubMedGoogle Scholar
    22.
    PROSPERO. Systematic Review of Health Utilities Mapped to the Modified Rankin Scale in Stroke Patients. CRD42018099915. Accessed March 14, 2020. https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=99915
    23.
    Gupta  A, Kesavabhotla  K, Baradaran  H,  et al.  Plaque echolucency and stroke risk in asymptomatic carotid stenosis: a systematic review and meta-analysis.   Stroke. 2015;46(1):91-97. doi:10.1161/STROKEAHA.114.006091 PubMedGoogle Scholar
    24.
    Gupta  A, Giambrone  AE, Gialdini  G,  et al.  Silent brain infarction and risk of future stroke: a systematic review and meta-analysis.   Stroke. 2016;47(3):719-725. doi:10.1161/STROKEAHA.115.011889 PubMedGoogle Scholar
    25.
    Rivero-Arias  O, Ouellet  M, Gray  A, Wolstenholme  J, Rothwell  PM, Luengo-Fernandez  R.  Mapping the modified Rankin scale (mRS) measurement into the generic EuroQol (EQ-5D) health outcome.   Med Decis Making. 2010;30(3):341-354. doi:10.1177/0272989X09349961 PubMedGoogle Scholar
    26.
    Szende  A, Oppe  M, Devlin  N, eds.  EQ-5D Value Sets: Inventory, Comparative Review and User Guide. Springer; 2007. doi:10.1007/1-4020-5511-0
    27.
    Lai  SM, Perera  S, Duncan  PW, Bode  R.  Physical and social functioning after stroke: comparison of the Stroke Impact Scale and Short Form-36.   Stroke. 2003;34(2):488-493. doi:10.1161/01.STR.0000054162.94998.C0 PubMedGoogle Scholar
    28.
    Hong  KS, Saver  JL.  Quantifying the value of stroke disability outcomes: WHO Global Burden of Disease Project disability weights for each level of the modified Rankin Scale.   Stroke. 2009;40(12):3828-3833. doi:10.1161/STROKEAHA.109.561365 PubMedGoogle Scholar
    29.
    Naidech  AM, Beaumont  JL, Berman  M,  et al.  Web-based assessment of outcomes after subarachnoid and intracerebral hemorrhage: a new patient centered option for outcomes assessment.   Neurocrit Care. 2015;23(1):22-27. doi:10.1007/s12028-014-0098-1 PubMedGoogle Scholar
    30.
    Owolabi  MO.  Psychometric properties of the German version of the Health-Related Quality of Life in Stroke Patients (HRQOLISP) instrument.   NeuroRehabilitation. 2013;33(2):241-250. doi:10.3233/NRE-130951 PubMedGoogle Scholar
    31.
    Katzan  IL, Fan  Y, Uchino  K, Griffith  SD.  The PROMIS physical function scale: a promising scale for use in patients with ischemic stroke.   Neurology. 2016;86(19):1801-1807. doi:10.1212/WNL.0000000000002652 PubMedGoogle Scholar
    32.
    Casaubon  LK, Boulanger  JM, Blacquiere  D,  et al; Heart and Stroke Foundation of Canada Canadian Stroke Best Practices Advisory Committee.  Canadian Stroke Best Practice Recommendations: Hyperacute Stroke Care Guidelines, Update 2015.   Int J Stroke. 2015;10(6):924-940. doi:10.1111/ijs.12551 PubMedGoogle Scholar
    33.
    Powers  WJ, Rabinstein  AA, Ackerson  T,  et al; American Heart Association Stroke Council.  2018 Guidelines for the early management of patients with acute ischemic stroke: a guideline for healthcare professionals from the American Heart Association/American Stroke Association  [published corrections appear in Stroke. 2018;49(3):e138 and 2018;49(6):e233-e234].  Stroke. 2018;49(3):e46-e110. doi:10.1161/STR.0000000000000158 PubMedGoogle Scholar
    34.
    Powers  WJ, Rabinstein  AA, Ackerson  T,  et al.  Guidelines for the early management of patients with acute ischemic stroke: 2019 update to the 2018 guidelines for the early management of acute ischemic stroke: a guideline for healthcare professionals from the American Heart Association/American Stroke Association  [published correction appears in Stroke. 2019;50(12):e440-e441].  Stroke. 2019;50(12):e344-e418. doi:10.1161/STR.0000000000000211 PubMedGoogle Scholar
    35.
    Baeten  SA, van Exel  NJA, Dirks  M, Koopmanschap  MA, Dippel  DW, Niessen  LW.  Lifetime health effects and medical costs of integrated stroke services: a non-randomized controlled cluster-trial based life table approach.   Cost Eff Resour Alloc. 2010;8:21. doi:10.1186/1478-7547-8-21 PubMedGoogle Scholar
    36.
    Golicki  D, Niewada  M, Buczek  J,  et al.  Validity of EQ-5D-5L in stroke.   Qual Life Res. 2015;24(4):845-850. doi:10.1007/s11136-014-0834-1 PubMedGoogle Scholar
    37.
    Hattori  N, Hirayama  T, Katayama  Y.  Medical care for chronic-phase stroke in Japan.   Neurol Med Chir (Tokyo). 2012;52(4):175-180. doi:10.2176/nmc.52.175 PubMedGoogle Scholar
    38.
    Carod-Artal  FJ, Coral  LF, Trizotto  DS, Moreira  CM.  The Stroke Impact Scale 3.0: evaluation of acceptability, reliability, and validity of the Brazilian version.   Stroke. 2008;39(9):2477-2484. doi:10.1161/STROKEAHA.107.513671 PubMedGoogle Scholar
    39.
    Vellone  E, Savini  S, Fida  R,  et al.  Psychometric evaluation of the Stroke Impact Scale 3.0.   J Cardiovasc Nurs. 2015;30(3):229-241. doi:10.1097/JCN.0000000000000145 PubMedGoogle Scholar
    40.
    Carod-Artal  FJ, Ferreira Coral  L, Stieven Trizotto  D, Menezes Moreira  C.  Self- and proxy-report agreement on the Stroke Impact Scale.   Stroke. 2009;40(10):3308-3314. doi:10.1161/STROKEAHA.109.558031 PubMedGoogle Scholar
    41.
    Katzan  IL, Lapin  B.  PROMIS GH (Patient-Reported Outcomes Measurement Information System Global Health) scale in stroke: a validation study.   Stroke. 2018;49(1):147-154. doi:10.1161/STROKEAHA.117.018766 PubMedGoogle Scholar
    42.
    Duncan  PW, Lai  SM, Keighley  J.  Defining post-stroke recovery: implications for design and interpretation of drug trials.   Neuropharmacology. 2000;39(5):835-841. doi:10.1016/S0028-3908(00)00003-4 PubMedGoogle Scholar
    43.
    Rangaraju  S, Haussen  D, Nogueira  RG, Nahab  F, Frankel  M.  Comparison of 3-month stroke disability and quality of life across Modified Rankin Scale categories.   Interv Neurol. 2017;6(1-2):36-41. doi:10.1159/000452634 PubMedGoogle Scholar
    44.
    Wang  YL, Pan  YS, Zhao  XQ,  et al; CHANCE Investigators.  Recurrent stroke was associated with poor quality of life in patients with transient ischemic attack or minor stroke: finding from the CHANCE trial.   CNS Neurosci Ther. 2014;20(12):1029-1035. doi:10.1111/cns.12329 PubMedGoogle Scholar
    45.
    Dewilde  S, Annemans  L, Lloyd  A,  et al.  The combined impact of dependency on caregivers, disability, and coping strategy on quality of life after ischemic stroke.   Health Qual Life Outcomes. 2019;17(1):31. doi:10.1186/s12955-018-1069-6 PubMedGoogle Scholar
    46.
    King  JT  Jr, Tsevat  J, Roberts  MS.  Measuring preference-based quality of life using the EuroQol EQ-5D in patients with cerebral aneurysms.   Neurosurgery. 2009;65(3):565-572. doi:10.1227/01.NEU.0000350980.01519.D8 PubMedGoogle Scholar
    47.
    Sallinen  H, Sairanen  T, Strbian  D.  Quality of life and depression 3 months after intracerebral hemorrhage.   Brain Behav. 2019;9(5):e01270. doi:10.1002/brb3.1270 PubMedGoogle Scholar
    48.
    Duncan  PW, Wallace  D, Lai  SM, Johnson  D, Embretson  S, Laster  LJ.  The Stroke Impact Scale Version 2.0: evaluation of reliability, validity, and sensitivity to change.   Stroke. 1999;30(10):2131-2140. doi:10.1161/01.STR.30.10.2131 PubMedGoogle Scholar
    49.
    Bracard  S, Ducrocq  X, Mas  JL,  et al; THRACE Investigators.  Mechanical thrombectomy after intravenous alteplase versus alteplase alone after stroke (THRACE): a randomised controlled trial [published correction appears in Lancet Neurol. 2016;15(12):1203].   Lancet Neurol. 2016;15(11):1138-1147. doi:10.1016/S1474-4422(16)30177-6 PubMedGoogle Scholar
    50.
    Molina  CA, Chamorro  A, Rovira  À,  et al.  REVASCAT: a randomized trial of revascularization with SOLITAIRE FR device vs. best medical therapy in the treatment of acute stroke due to anterior circulation large vessel occlusion presenting within eight-hours of symptom onset.   Int J Stroke. 2015;10(4):619-626. doi:10.1111/ijs.12157 PubMedGoogle Scholar
    51.
    Delcourt  C, Huang  Y, Wang  J,  et al; INTERACT2 Investigators.  The second (main) phase of an open, randomised, multicentre study to investigate the effectiveness of an intensive blood pressure reduction in acute cerebral haemorrhage trial (INTERACT2).   Int J Stroke. 2010;5(2):110-116. doi:10.1111/j.1747-4949.2010.00415.x PubMedGoogle Scholar
    52.
    Qureshi  AI, Palesch  YY.  Antihypertensive Treatment of Acute Cerebral Hemorrhage (ATACH) II: design, methods, and rationale.   Neurocrit Care. 2011;15(3):559-576. doi:10.1007/s12028-011-9538-3 PubMedGoogle Scholar
    53.
    Albers  GW, Lansberg  MG, Kemp  S,  et al.  A multicenter randomized controlled trial of endovascular therapy following imaging evaluation for ischemic stroke (DEFUSE 3).   Int J Stroke. 2017;12(8):896-905. doi:10.1177/1747493017701147 PubMedGoogle Scholar
    54.
    Hacke  W, Kaste  M, Fieschi  C,  et al; Second European-Australasian Acute Stroke Study Investigators.  Randomised double-blind placebo-controlled trial of thrombolytic therapy with intravenous alteplase in acute ischaemic stroke (ECASS II).   Lancet. 1998;352(9136):1245-1251. doi:10.1016/S0140-6736(98)08020-9 PubMedGoogle Scholar
    55.
    de Los Ríos la Rosa  F, Khoury  J, Kissela  BM,  et al.  Eligibility for intravenous recombinant tissue-type plasminogen activator within a population: the effect of the European Cooperative Acute Stroke Study (ECASS) III Trial.   Stroke. 2012;43(6):1591-1595. doi:10.1161/STROKEAHA.111.645986 PubMedGoogle Scholar
    56.
    Anderson  CS, Huang  Y, Lindley  RI,  et al; ENCHANTED Investigators and Coordinators.  Intensive blood pressure reduction with intravenous thrombolysis therapy for acute ischaemic stroke (ENCHANTED): an international, randomised, open-label, blinded-endpoint, phase 3 trial.   Lancet. 2019;393(10174):877-888. doi:10.1016/S0140-6736(19)30038-8 PubMedGoogle Scholar
    57.
    Goyal  M, Demchuk  AM, Menon  BK,  et al; ESCAPE Trial Investigators.  Randomized assessment of rapid endovascular treatment of ischemic stroke.   N Engl J Med. 2015;372(11):1019-1030. doi:10.1056/NEJMoa1414905 PubMedGoogle Scholar
    58.
    Saver  JL, Starkman  S, Eckstein  M,  et al; FAST-MAG Investigators and Coordinators.  Prehospital use of magnesium sulfate as neuroprotection in acute stroke.   N Engl J Med. 2015;372(6):528-536. doi:10.1056/NEJMoa1408827 PubMedGoogle Scholar
    59.
    Broderick  JP, Palesch  YY, Demchuk  AM,  et al; Interventional Management of Stroke (IMS) III Investigators.  Endovascular therapy after intravenous t-PA versus t-PA alone for stroke.  [published correction appears in N Engl J Med. 2013;368(13):1265].  N Engl J Med. 2013;368(10):893-903. doi:10.1056/NEJMoa1214300 PubMedGoogle Scholar
    60.
    Molyneux  A, Kerr  R, Stratton  I,  et al; International Subarachnoid Aneurysm Trial (ISAT) Collaborative Group.  International Subarachnoid Aneurysm Trial (ISAT) of neurosurgical clipping versus endovascular coiling in 2143 patients with ruptured intracranial aneurysms: a randomized trial.   J Stroke Cerebrovasc Dis. 2002;11(6):304-314. doi:10.1053/jscd.2002.130390 PubMedGoogle Scholar
    61.
    Ogawa  A, Mori  E, Minematsu  K,  et al; MELT Japan Study Group.  Randomized trial of intraarterial infusion of urokinase within 6 hours of middle cerebral artery stroke: the middle cerebral artery embolism local fibrinolytic intervention trial (MELT) Japan.   Stroke. 2007;38(10):2633-2639. doi:10.1161/STROKEAHA.107.488551 PubMedGoogle Scholar
    62.
    Berkhemer  OA, Fransen  PS, Beumer  D,  et al; MR CLEAN Investigators.  A randomized trial of intraarterial treatment for acute ischemic stroke.  [published correction appears in N Engl J Med. 2015;372(4):394].  N Engl J Med. 2015;372(1):11-20. doi:10.1056/NEJMoa1411587 PubMedGoogle Scholar
    63.
    Logallo  N, Novotny  V, Assmus  J,  et al.  Tenecteplase versus alteplase for management of acute ischaemic stroke (NOR-TEST): a phase 3, randomised, open-label, blinded endpoint trial.   Lancet Neurol. 2017;16(10):781-788. doi:10.1016/S1474-4422(17)30253-3 PubMedGoogle Scholar
    64.
    Furlan  A, Higashida  R, Wechsler  L,  et al.  Intra-arterial prourokinase for acute ischemic stroke. The PROACT II study: a randomized controlled trial. Prolyse in Acute Cerebral Thromboembolism.   JAMA. 1999;282(21):2003-2011. doi:10.1001/jama.282.21.2003 PubMedGoogle Scholar
    65.
    Saver  JL, Goyal  M, Bonafe  A,  et al; SWIFT PRIME Investigators.  Stent-retriever thrombectomy after intravenous t-PA vs. t-PA alone in stroke.   N Engl J Med. 2015;372(24):2285-2295. doi:10.1056/NEJMoa1415061 PubMedGoogle Scholar
    66.
    Anderson  CS, Heeley  E, Huang  Y,  et al; INTERACT2 Investigators.  Rapid blood-pressure lowering in patients with acute intracerebral hemorrhage.   N Engl J Med. 2013;368(25):2355-2365. doi:10.1056/NEJMoa1214609 PubMedGoogle Scholar
    67.
    Jovin  TG, Chamorro  A, Cobo  E,  et al; REVASCAT Trial Investigators.  Thrombectomy within 8 hours after symptom onset in ischemic stroke.   N Engl J Med. 2015;372(24):2296-2306. doi:10.1056/NEJMoa1503780 PubMedGoogle Scholar
    68.
    Lubetkin  EI, Jia  H, Franks  P, Gold  MR.  Relationship among sociodemographic factors, clinical conditions, and health-related quality of life: examining the EQ-5D in the U.S. general population.   Qual Life Res. 2005;14(10):2187-2196. doi:10.1007/s11136-005-8028-5 PubMedGoogle Scholar
    69.
    Depaola  SJ, Griffin  M, Young  JR, Neimeyer  RA.  Death anxiety and attitudes toward the elderly among older adults: the role of gender and ethnicity.   Death Stud. 2003;27(4):335-354. doi:10.1080/07481180302904 PubMedGoogle Scholar
    70.
    Kim  SK, Kim  SH, Jo  MW, Lee  SI.  Estimation of minimally important differences in the EQ-5D and SF-6D indices and their utility in stroke.   Health Qual Life Outcomes. 2015;13(1):32. doi:10.1186/s12955-015-0227-3 PubMedGoogle Scholar
    71.
    Revicki  DA, Cella  D, Hays  RD, Sloan  JA, Lenderking  WR, Aaronson  NK.  Responsiveness and minimal important differences for patient reported outcomes.   Health Qual Life Outcomes. 2006;4:70. doi:10.1186/1477-7525-4-70 PubMedGoogle Scholar
    72.
    Post  PN, Stiggelbout  AM, Wakker  PP.  The utility of health states after stroke: a systematic review of the literature.   Stroke. 2001;32(6):1425-1429. doi:10.1161/01.STR.32.6.1425 PubMedGoogle Scholar
    73.
    Irony  TZ.  The “utility” in composite outcome measures: measuring what is important to patients.   JAMA. 2017;318(18):1820-1821. doi:10.1001/jama.2017.14001 PubMedGoogle Scholar
    74.
    Mulhern  B, Feng  Y, Shah  K,  et al.  Comparing the UK EQ-5D-3L and English EQ-5D-5L value sets.   Pharmacoeconomics. 2018;36(6):699-713. doi:10.1007/s40273-018-0628-3 PubMedGoogle Scholar
    75.
    Selivanova  A, Buskens  E, Krabbe  PFM.  Head-to-head comparison of EQ-5D-3L and EQ-5D-5L health values.   Pharmacoeconomics. 2018;36(6):715-725. doi:10.1007/s40273-018-0647-0 PubMedGoogle Scholar
    76.
    Pan  JH, Song  XY, Lee  SY, Kwok  T.  Longitudinal analysis of quality of life for stroke survivors using latent curve models.   Stroke. 2008;39(10):2795-2802. doi:10.1161/STROKEAHA.108.515460 PubMedGoogle Scholar
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