Early warning scores for detecting deterioration in adult hospital patients: a systematic review protocol

Stephen Gerry, Jacqueline Birks, Timothy Bonnici, Peter J Watkinson, Shona Kirtley, Gary S Collins, Stephen Gerry, Jacqueline Birks, Timothy Bonnici, Peter J Watkinson, Shona Kirtley, Gary S Collins

Abstract

Introduction: Early warning scores (EWSs) are used extensively to identify patients at risk of deterioration in hospital. Previous systematic reviews suggest that studies which develop EWSs suffer methodological shortcomings and consequently may fail to perform well. The reviews have also identified that few validation studies exist to test whether the scores work in other settings. We will aim to systematically review papers describing the development or validation of EWSs, focusing on methodology, generalisability and reporting.

Methods: We will identify studies that describe the development or validation of EWSs for adult hospital inpatients. Each study will be assessed for risk of bias using the Prediction model Risk of Bias ASsessment Tool (PROBAST). Two reviewers will independently extract information. A narrative synthesis and descriptive statistics will be used to answer the main aims of the study which are to assess and critically appraise the methodological quality of the EWS, to describe the predictors included in the EWSs and to describe the reported performance of EWSs in external validation.

Ethics and dissemination: This systematic review will only investigate published studies and therefore will not directly involve patient data. The review will help to establish whether EWSs are fit for purpose and make recommendations to improve the quality of future research in this area.

Prospero registration number: CRD42017053324.

Keywords: development; early warning scores; risk of bias; validation.

Conflict of interest statement

Competing interests: None declared.

© Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

References

    1. Brennan TA, Leape LL, Laird NM, et al. . Incidence of adverse events and negligence in hospitalized patients. N Engl J Med 1991;324:370–6. 10.1056/NEJM199102073240604
    1. Institute of Medicine Committee on Quality of Health Care in A. : Kohn LT, Corrigan JM, Donaldson MS, To Err is human: building a safer health system. Washington DC: National Academies Press (US), 2000.
    1. Vincent C, Neale G, Woloshynowych M. Adverse events in British hospitals: preliminary retrospective record review. BMJ 2001;322:517–9. 10.1136/bmj.322.7285.517
    1. Hillman KM, Bristow PJ, Chey T, et al. . Duration of life-threatening antecedents prior to intensive care admission. Intensive Care Med 2002;28:1629–34. 10.1007/s00134-002-1496-y
    1. Kause J, Smith G, Prytherch D, et al. . A comparison of antecedents to cardiac arrests, deaths and emergency intensive care admissions in Australia and New Zealand, and the United Kingdom--the ACADEMIA study. Resuscitation 2004;62:275–82. 10.1016/j.resuscitation.2004.05.016
    1. Hogan H, Healey F, Neale G, et al. . Preventable deaths due to problems in care in English acute hospitals: a retrospective case record review study. BMJ Qual Saf 2012;21:737–45. 10.1136/bmjqs-2011-001159
    1. McQuillan P, Pilkington S, Allan A, et al. . Confidential inquiry into quality of care before admission to intensive care. BMJ 1998;316:1853–8. 10.1136/bmj.316.7148.1853
    1. Morgan RJM, Williams F, Wright MM. An early warning scoring system for detecting developing critical illness. Clin Intensive Care 1997;8:100.
    1. Gao H, McDonnell A, Harrison DA, et al. . Systematic review and evaluation of physiological track and trigger warning systems for identifying at-risk patients on the ward. Intensive Care Med 2007;33:667–79. 10.1007/s00134-007-0532-3
    1. Smith GB, Prytherch DR, Schmidt PE, et al. . Review and performance evaluation of aggregate weighted ‘track and trigger’ systems. Resuscitation 2008;77:170–9. 10.1016/j.resuscitation.2007.12.004
    1. Smith ME, Chiovaro JC, O’Neil M, et al. . Early warning system scores for clinical deterioration in hospitalized patients: a systematic review. Ann Am Thorac Soc 2014;11:1454–65. 10.1513/AnnalsATS.201403-102OC
    1. NICE. Acutely ill adults in hospital: recognising and responding to deterioration (NICE guideline CG50), 2007.
    1. Centre HSCI. Hospital Episode Statistics. Admitted Patient Care, England - 2014-15, 2015.
    1. Damen JA, Hooft L, Schuit E, et al. . Prediction models for cardiovascular disease risk in the general population: systematic review. BMJ 2016;353:i2416 10.1136/bmj.i2416
    1. Kleinrouweler CE, Cheong-See FM, Collins GS, et al. . Prognostic models in obstetrics: available, but far from applicable. Am J Obstet Gynecol 2016;214:79–90. 10.1016/j.ajog.2015.06.013
    1. Bouwmeester W, Zuithoff NP, Mallett S, et al. . Reporting and methods in clinical prediction research: a systematic review. PLoS Med 2012;9:1–12. 10.1371/journal.pmed.1001221
    1. Collins GS, Mallett S, Omar O, et al. . Developing risk prediction models for type 2 diabetes: a systematic review of methodology and reporting. BMC Med 2011;9:103 10.1186/1741-7015-9-103
    1. Collins GS, de Groot JA, Dutton S, et al. . External validation of multivariable prediction models: a systematic review of methodological conduct and reporting. BMC Med Res Methodol 2014;14:40 10.1186/1471-2288-14-40
    1. Mallett S, Royston P, Waters R, et al. . Reporting performance of prognostic models in cancer: a review. BMC Med 2010;8:21 10.1186/1741-7015-8-21
    1. NCEPOD. Time to Intervene? A review of patients who underwent cardiopulmonary rescuscitation as a result of an in-hospital cardiorespiratory arrest, 2012.
    1. Kyriacos U, Jelsma J, Jordan S. Monitoring vital signs using early warning scoring systems: a review of the literature. J Nurs Manag 2011;19:311–30. 10.1111/j.1365-2834.2011.01246.x
    1. Alam N, Hobbelink EL, van Tienhoven AJ, et al. . The impact of the use of the Early Warning Score (EWS) on patient outcomes: a systematic review. Resuscitation 2014;85:587–94. 10.1016/j.resuscitation.2014.01.013
    1. McGaughey J, Alderdice F, Fowler R, et al. . Outreach and Early Warning Systems (EWS) for the prevention of intensive care admission and death of critically ill adult patients on general hospital wards. Cochrane Database Syst Rev 2007;3:CD005529 10.1002/14651858.CD005529.pub2
    1. Collins GS, Reitsma JB, Altman DG, et al. . Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement. Ann Intern Med 2015;162:55–63. 10.7326/M14-0697
    1. Moons KG, Altman DG, Reitsma JB, et al. . Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med 2015;162:W1–73. 10.7326/M14-0698
    1. Moons KG, Kengne AP, Woodward M, et al. . Risk prediction models: I. Development, internal validation, and assessing the incremental value of a new (bio)marker. Heart 2012;98:683–90. 10.1136/heartjnl-2011-301246
    1. Steyerberg EW, Moons KG, van der Windt DA, et al. . PROGRESS Group. Prognosis Research Strategy (PROGRESS) 3: prognostic model research. PLoS Med 2013;10:e1001381 10.1371/journal.pmed.1001381
    1. Steyerberg EW, Vergouwe Y. Towards better clinical prediction models: seven steps for development and an ABCD for validation. Eur Heart J 2014;35:1925–31. 10.1093/eurheartj/ehu207
    1. Justice AC, Covinsky KE, Berlin JA. Assessing the generalizability of prognostic information. Ann Intern Med 1999;130:515–24. 10.7326/0003-4819-130-6-199903160-00016
    1. Moons KG, de Groot JA, Bouwmeester W, et al. . Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist. PLoS Med 2014;11:e1001744 10.1371/journal.pmed.1001744
    1. Moher D, Liberati A, Tetzlaff J, et al. . Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med 2009;6:e1000097 10.1371/journal.pmed.1000097
    1. Harris PA, Taylor R, Thielke R, et al. . Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform 2009;42:377–81. 10.1016/j.jbi.2008.08.010
    1. Debray TP, Damen JA, Snell KI, et al. . A guide to systematic review and meta-analysis of prediction model performance. BMJ 2017;356:i6460 10.1136/bmj.i6460
    1. Wong D, Bonnici T, Knight J, et al. . SEND: a system for electronic notification and documentation of vital sign observations. BMC Med Inform Decis Mak 2015;15:68 10.1186/s12911-015-0186-y

Source: PubMed

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