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. 2021 Apr 8;11(4):e045849.
doi: 10.1136/bmjopen-2020-045849.

Performance of universal early warning scores in different patient subgroups and clinical settings: a systematic review

Baneen Alhmoud  1 , Timothy Bonnici  1   2 , Riyaz Patel  2   3   4 , Daniel Melley  4 , Bryan Williams  2   3 , Amitava Banerjee  5   2   4
Affiliations

Performance of universal early warning scores in different patient subgroups and clinical settings: a systematic review

Baneen Alhmoud et al. BMJ Open. .

Abstract

Objective: To assess predictive performance of universal early warning scores (EWS) in disease subgroups and clinical settings.

Design: Systematic review.

Data sources: Medline, CINAHL, Embase and Cochrane database of systematic reviews from 1997 to 2019.

Inclusion criteria: Randomised trials and observational studies of internal or external validation of EWS to predict deterioration (mortality, intensive care unit (ICU) transfer and cardiac arrest) in disease subgroups or clinical settings.

Results: We identified 770 studies, of which 103 were included. Study designs and methods were inconsistent, with significant risk of bias (high: n=16 and unclear: n=64 and low risk: n=28). There were only two randomised trials. There was a high degree of heterogeneity in all subgroups and in national early warning score (I2=72%-99%). Predictive accuracy (mean area under the curve; 95% CI) was highest in medical (0.74; 0.74 to 0.75) and surgical (0.77; 0.75 to 0.80) settings and respiratory diseases (0.77; 0.75 to 0.80). Few studies evaluated EWS in specific diseases, for example, cardiology (n=1) and respiratory (n=7). Mortality and ICU transfer were most frequently studied outcomes, and cardiac arrest was least examined (n=8). Integration with electronic health records was uncommon (n=9).

Conclusion: Methodology and quality of validation studies of EWS are insufficient to recommend their use in all diseases and all clinical settings despite good performance of EWS in some subgroups. There is urgent need for consistency in methods and study design, following consensus guidelines for predictive risk scores. Further research should consider specific diseases and settings, using electronic health record data, prior to large-scale implementation.

Prospero registration number: PROSPERO CRD42019143141.

Keywords: adult intensive & critical care; clinical governance; epidemiology; risk management.

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Conflict of interest statement

Competing interests: AB has received research grants from AstraZeneca.

Figures

Figure 1
Figure 1
Search strategy and included studies regarding universal early warning scores (EWS) in different disease subgroups and clinical settings.
Figure 2
Figure 2
Number of studies regarding performance of early warning scores in different disease subgroups and clinical settings. Each bubble represents the disease subgroup and/or setting where different early warning scores were examined. The size of the bubble represents the number of studies (n), and overlapping bubbles show studies where disease subgroup and settings overlap. CVD, cardiovascular diseases; ED, emergency department; GiI, gastrointestinal diseases; ICU, intensive care unit.
Figure 3
Figure 3
Early warning score performance in different disease subgroups. Each bubble represents critical events predicted by early warning scores for each disease subgroup with average AUC of studies beside each event type. The size of the bubble represents the number of studies in each subgroup. CA, cardiac arrest; CVD, cardiovascular diseases; GI, gastrointestinal diseases; ICU, intensive care unit; OF, organ failure; RA, respiratory arrest.
Figure 4
Figure 4
Early warning score performance in different clinical settings. Each bubble represents critical events predicted by early warning scores for each disease subgroup with average AUC of studies beside each event type. The size of the bubble represents the number of studies in each subgroup. CA, cardiac arrest; ED, emergency department; ICU, intensive care units; OF, organ failure; RA, respiratory arrest.
Figure 5
Figure 5
Forest plot of predictive accuracy of universal early warning scores (EWS) for mortality in different disease subgroups and clinical settings. CVD, cardiovascular diseases; ED, emergency department; GI, gastrointestinal diseases; Hem, haematological diseases; ICU, intensive care units; Infec, infectious diseases; Med, medical settings; Onco, oncology diseases; Renal, renal diseases; renal diseases; Resp, respiratory diseases; stroke, patients who had a stroke; Surg, surgical settings. Note: number following author(s) and year indicate more than one EWS evaluated in the study.
Figure 6
Figure 6
Forest plot of predictive accuracy of NEWS for mortality. AUC, area under the curve; NEWS, national early warning score.
See this image and copyright information in PMC

References

    1. Cetınkaya HB, Koksal O, Sigirli D, et al. The predictive value of the modified early warning score with rapid lactate level (ViEWS-L) for mortality in patients of age 65 or older visiting the emergency department. Intern Emerg Med 2017;12:1253–7. 10.1007/s11739-016-1559-7 - DOI - PubMed
    1. Cei M, Bartolomei C, Mumoli N. In-hospital mortality and morbidity of elderly medical patients can be predicted at admission by the modified early warning score: a prospective study. Int J Clin Pract 2009;63:591–5. 10.1111/j.1742-1241.2008.01986.x - DOI - PubMed
    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 - DOI - PubMed
    1. Hogan H, Hutchings A, Wulff J, et al. Interventions to reduce mortality from in-hospital cardiac arrest: a mixed-methods study. Health Serv Deliv Res 2019;7:1–110. 10.3310/hsdr07020 - DOI - PubMed
    1. Adhikari NKJ, Fowler RA, Bhagwanjee S, et al. Critical care and the global burden of critical illness in adults. Lancet 2010;376:1339–46. 10.1016/S0140-6736(10)60446-1 - DOI - PMC - PubMed

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