Reducing patient delay with symptoms of acute coronary syndrome: a research protocol for a systematic review of previous interventions to investigate which behaviour change techniques are associated with effective interventions

Barbara Farquharson, Stephan Dombrowski, Alex Pollock, Marie Johnston, Shaun Treweek, Brian Williams, Karen Smith, Nadine Dougall, Claire Jones, Stuart Pringle, Barbara Farquharson, Stephan Dombrowski, Alex Pollock, Marie Johnston, Shaun Treweek, Brian Williams, Karen Smith, Nadine Dougall, Claire Jones, Stuart Pringle

Abstract

Introduction: Delay to presentation with symptoms of acute coronary syndrome (ACS) is common meaning many fail to achieve optimal benefit from treatments. Interventions have had variable success in reducing delay. Evidence suggests inclusion of behaviour change techniques (BCTs) may improve effectiveness of interventions but this has not yet been systematically evaluated. Data from other time-critical conditions may be relevant.

Methods and analysis: A systematic review will be undertaken to identify which BCTs are associated with effective interventions to reduce patient delay (or prompt rapid help-seeking) among people with time-critical conditions (eg, chest pain, ACS, lumps, stroke, cancer and meningitis). A systematic search of a wide range of databases (including Cochrane Library, MEDLINE, EMBASE, CINAHL, PsycInfo) and grey literature will be undertaken to identify all relevant intervention studies (randomised controlled trials, controlled clinical trials and cohort studies). Two independent reviewers will screen abstracts to identify relevant studies, apply inclusion criteria to full papers, assess methodological quality and extract data.

Primary outcome measure: Change in patient decision time BCTs reported in each of the included studies will be categorised and presented according to the latest reliable taxonomy. Results of included studies will be synthesised, exploring relationships between inclusion of each BCT and effectiveness of the overall intervention. Where possible, means and SDs for differences in delay time will be calculated and combined within meta-analyses to derive a standardised mean difference and 95% CI. Analysis of (1) all time-critical and (2) ACS-only interventions will be undertaken.

Ethics and dissemination: No ethical issues are anticipated. Results will be submitted for publication in a relevant peer-reviewed journal.

Keywords: Quality of Care and Outcomes.

References

    1. Goldberg R, Yarzebski J, Lessard D, et al. Decade-long trends and factors associated with time to hospital presentation in patients with acute myocardial infarction: the Worcester Heart Attack Study. Arch Intern Med 2000;160:3217–23
    1. Gruppo Italiano per lo Studio della Sopravvivenza nell'Infarto (GISSI). Epidemiology of avoidable delay in the care of patients with acute myocardial infarction in Italy: a GISSI-Generated Study. Arch Intern Med 1995;155:1481–8
    1. Gibler BW, Armstrong PW, Ohman EM, et al. Persistance of delays in presentation and treatment for patients with acute myocardial infarction: the GUSTO-I and GUSTO-III experience. Ann Emerg Med 2002;39:123–30
    1. Saczynski JS, Yarzebski J, Lessard D, et al. Trends in prehospital delay in patients with acute myocardial infarction (from the Worcester Heart Attack Study). Am J Cardiol 2008;102:1589–94
    1. O'Carroll R, Smith K, Grubb N, et al. Psychological factors associated with delay in attending hospital following myocardial infarction. J Psychosom Res 2001;51:611–14
    1. Dracup K, Moser D, McKinley S, et al. An international perspective of the time to presentation with myocardial infarction. J Nurs Scholarsh 2003;35:317–23
    1. O'Donnell S, McKee G, Mooney M, et al. Slow-onset and fast-onset symptom presentations in Acute Coronary Syndrome (ACS): new perspectives on pre-hospital delay in patients with ACS. J Emerg Med 2014;46:507–15
    1. Eagle KA, Goodman SG, Avezum A, et al. Practice variation and missed opportunities for reperfusion in ST-segment-elevation myocardial infarction: findings from the Global Registry of Acute Coronary Events (GRACE). Lancet 2002;359:373–7
    1. Tubaro M, Danchin N, Goldstein P, et al. Pre-hospital treatment of STEMI patients. A scientific statement of the Working Group Acute Cardiac Care of the European Society of Cardiology. Acute Card Care 2011;13:56–67
    1. Anderson JL, Adams CD, Antman EM. ACC/AHA 2007 guidelines for the management of patients with unstable angina/non ST-elevation myocardial infarction: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines (Writing Committee to Revise the 2002 Guidelines for the Management of Patients With Unstable Angina/Non ST-Elevation Myocardial Infarction). J Am Coll Cardiol 2007;50:e1–e157
    1. Ting HH, Bradley EH. Patient education to reduce prehospital delay time in acute coronary syndrome: necessary but not sufficient. Circ Cardiovasc Qual Outcomes 2009;2:522–3
    1. Dracup K, McKinley S, Riegel B, et al. A randomized clinical trial to reduce patient prehospital delay to treatment in acute coronary syndrome. Circ Cardiovasc Qual Outcomes 2009;2:524–32
    1. Kainth A, Hewitt A, Sowden A, et al. Systematic review of interventions to reduce delay in patients with suspected heart attack. Emerg Med J 2004;21:506–8
    1. Luepker RV, Raczynski JM, Osganian S, et al. Effect of a community intervention on patient delay and emergency medical service use in acute coronary heart disease: the Rapid Early Action for Coronary Treatment (REACT) Trial. JAMA 2000;284:60.
    1. Michie S, Johnston M, Francis J, et al. From theory to intervention: mapping theoretically derived behavioural determinants to behaviour change techniques. Appl Psychol 2008;57:660–80
    1. Craig P, Dieppe P, Macintyre S, et al. Developing and evaluating complex interventions: the new medical research council guidance . BMJ 2008;337:979–83
    1. Campbell M, Fitzpatrick R, Haines A, et al. Framework for design and evaluation of complex interventions to improve health. BMJ 2000;321:694–6
    1. Campbell NC, Murray E, Darbyshire J, et al. Designing and evaluating complex interventions to improve health care. BMJ 2007;334:455–9
    1. Webb TL, Joseph J, Yardley L, et al. Using the internet to promote health behavior change: a systematic review and meta-analysis of the impact of theoretical basis, use of behavior change techniques, and mode of delivery on efficacy. J Med Internet Res 2010;12:e4.
    1. Smith S, Fielding S, Murchie P, et al. Reducing time before consulting with symptoms of lung cancer: randomised controlled trial. Br J Gen Pract 2013;63:e47–54
    1. Smith S, Murchie P, Devereux G, et al. Developing a complex intervention to reduce time to presentation with symptoms of lung cancer. Br J Gen Pract 2012;62:e605–15
    1. Khraim FM, Carey MG. Review: Predictors of pre-hospital delay among patients with acute myocardial infarction. Patient Educ Couns 2009;75:155–61
    1. Abraham C, Michie S. A taxonomy of behaviour change techniques used in interventions. Health Psychol 2008;27:379–87
    1. Higgins JPT, Altman DG, eds. Chapter 8: assessing risk of bias in included studies. In: Higgins JPT, Green S, eds. Cochrane Handbook for Systematic Reviews of Interventions Version 5.0.1 [updated September 2008]. The Cochrane Collaboration, 2008.
    1. Critical Appraisal Skills Programme. (accessed 28 Feb 2014).
    1. Schulz KF, Altman DG, Moher D; for the CONSORT Group. CONSORT 2010 Statement: updated guidelines for reporting parallel group randomised trials. BMJ 2010;340:c332.
    1. Hoffmann, Glasziou PP, Boutron I, et al. Better reporting of interventions: the Template for Intervention Description and Replication (TIDieR) checklist and guide’. BMJ 2014;348:g1687.
    1. Michie S, Richardson M, Johnston M, et al. The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: building an international consensus for the reporting of behavior change interventions. Ann Behav Med 2013;46:81–95
    1. Hozo S, Djbegovic B, Hozo I. Estimating the mean and variance from the median, range, and the size of the sample. BMC Med Res Methodol 2005;5:13.
    1. Altman DB, Bland JB. Detecting skewness from summary information. BMJ 1996;313:1200.
    1. Higgins JPT, Thompson SG, Deeks JJ, et al. Measuring inconsistency in meta-analyses. BMJ 2003;327:557–60

Source: PubMed

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