INFOMATAS multi-center systematic review and meta-analysis individual patient data of dynamic cerebral autoregulation in ischemic stroke

L Beishon, J S Minhas, R Nogueira, P Castro, C Budgeon, M Aries, S Payne, T G Robinson, R B Panerai, L Beishon, J S Minhas, R Nogueira, P Castro, C Budgeon, M Aries, S Payne, T G Robinson, R B Panerai

Abstract

Rationale: Disturbances in dynamic cerebral autoregulation after ischemic stroke may have important implications for prognosis. Recent meta-analyses have been hampered by heterogeneity and small samples.

Aim and/or hypothesis: The aim of study is to undertake an individual patient data meta-analysis (IPD-MA) of dynamic cerebral autoregulation changes post-ischemic stroke and to determine a predictive model for outcome in ischemic stroke using information combined from dynamic cerebral autoregulation, clinical history, and neuroimaging.

Sample size estimates: To detect a change of 2% between categories in modified Rankin scale requires a sample size of ∼1500 patients with moderate to severe stroke, and a change of 1 in autoregulation index requires a sample size of 45 healthy individuals (powered at 80%, α = 0.05). Pooled estimates of mean and standard deviation derived from this study will be used to inform sample size calculations for adequately powered future dynamic cerebral autoregulation studies in ischemic stroke.

Methods and design: This is an IPD-MA as part of an international, multi-center collaboration (INFOMATAS) with three phases. Firstly, univariate analyses will be constructed for primary (modified Rankin scale) and secondary outcomes, with key co-variates and dynamic cerebral autoregulation parameters. Participants clustering from within studies will be accounted for with random effects. Secondly, dynamic cerebral autoregulation variables will be validated for diagnostic and prognostic accuracy in ischemic stroke using summary receiver operating characteristic curve analysis. Finally, the prognostic accuracy will be determined for four different models combining clinical history, neuroimaging, and dynamic cerebral autoregulation parameters.

Study outcome(s): The outcomes for this study are to determine the relationship between clinical outcome, dynamic cerebral autoregulation changes, and baseline patient demographics, to determine the diagnostic and prognostic accuracy of dynamic cerebral autoregulation parameters, and to develop a prognostic model using dynamic cerebral autoregulation in ischemic stroke.

Discussion: This is the first international collaboration to use IPD-MA to determine prognostic models of dynamic cerebral autoregulation for patients with ischemic stroke.

Keywords: Cerebral autoregulation; autoregulation index; blood pressure; cerebral hemodynamics; ischemic stroke; meta-analysis.

Figures

Figure 1.
Figure 1.
TFA metrics of gain, phase, and coherence.20,2 MAP: mean arterial pressure; MCAv: cerebral blood flow-velocity in the middle cerebral artery; CBF: cerebral blood flow.

References

    1. Panerai RB. Transcranial Doppler for evaluation of cerebral autoregulation. Clin Auton Res 2009; 19: 197–211.
    1. Claassen JA, Meel-van den Abeelen AS, Simpson DM, Panerai RB. Transfer function analysis of dynamic cerebral autoregulation: a white paper from the International Cerebral Autoregulation Research Network. J Cereb Blood Flow Metab 2016; 36: 665–680.
    1. Aries MJH, Elting JW, Keyser JD, Kremer BPH, Vroomen PCAJ. Cerebral Autoregulation in Stroke. Stroke 2010; 41: 2697–2704.
    1. Salinet ASM, Minhas JS, Panerai RB, Bor-Seng-Shu E, Robinson TG. Do acute stroke patients develop hypocapnia? A systematic review and meta-analysis. J Neurol Sci 2019; 402: 30–39.
    1. Salinet AS, Silva NC, Caldas J, et al. Impaired cerebral autoregulation and neurovascular coupling in middle cerebral artery stroke: influence of severity? J Cereb Blood Flow Metab 2019; 39: 2277–2285.
    1. Patel N, Panerai RB, Haunton V, et al. The Leicester cerebral haemodynamics database: normative values and the influence of age and sex. Physiol Meas 2016; 37: 1485–1498.
    1. Sanders ML, Claassen J, Aries M, et al. Reproducibility of dynamic cerebral autoregulation parameters: a multi-centre, multi-method study. Physiol Meas 2018; 39: 125002.
    1. Salinet ASM, Panerai RB, Caldas J, et al. Pooling data from different populations: should there be regional differences in cerebral haemodynamics? BMC Neurol 2018; 18: 156.
    1. Sanders ML, Elting JWJ, Panerai RB, et al. Dynamic cerebral autoregulation reproducibility is affected by physiological variability. Front Physiol 2019; 10: 865.
    1. Riley RD, Lambert PC, Abo-Zaid G. Meta-analysis of individual participant data: rationale, conduct, and reporting. BMJ 2010; 340: c221.
    1. Tierney JF, Vale C, Riley R, et al. Individual participant data (IPD) meta-analyses of randomised controlled trials: guidance on their use. PLoS Med 2015; 12: e1001855.
    1. Thomas D, Radji S, Benedetti A. Systematic review of methods for individual patient data meta- analysis with binary outcomes. BMC Med Res Methodol 2014; 14: 79.
    1. Kontopantelis E. A comparison of one-stage vs two-stage individual patient data meta-analysis methods: a simulation study. Res Synth Meth 2018; 9: 417–430.
    1. Stewart LA, Clarke M, Rovers M, et al. Preferred reporting items for systematic review and meta-analyses of individual participant data: the PRISMA-IPD statement. JAMA 2015; 313: 1657–1665.
    1. Falci SG, Marques LS. CONSORT: when and how to use it. Dental Press J Orthod 2015; 20: 13–15.
    1. Cuschieri S. The STROBE guidelines. Saudi J Anaesth 2019; 13(Suppl 1): S31–S34.
    1. Whiting PF, Rutjes AW, Westwood ME, et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med 2011; 155: 529–536.
    1. Freeman SC, Kerby CR, Patel A, Cooper NJ, Quinn T, Sutton AJ. Development of an interactive web-based tool to conduct and interrogate meta-analysis of diagnostic test accuracy studies: MetaDTA. BMC Med Res Methodol 2019; 19: 81.
    1. Olavarría VV, Brunser A, Cabral N, et al. The distribution of the modified Rankin scale scores change according to eligibility criteria in acute ischemic stroke trials: a consideration for sample size calculations when using ordinal regression analysis. Contemp Clin Trials Commun 2017; 5: 133–136.
    1. JD Smirl ‘The relationship between arterial blood pressure and cerebral blood flow: insights into aging, altitude and exercise,’ PhD Thesis, The University of British Columbia (Okanagan), June 2015 and published previously.

Source: PubMed

Sponsorer og samarbejdspartnere

Medicinske tilstande

Narkotikainterventioner

3
Abonner