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Scorecards that save lives: But which score is the winner?

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Presentation on theme: "Scorecards that save lives: But which score is the winner?"— Presentation transcript:

1 Scorecards that save lives: But which score is the winner?
A critical appraisal of Early Warning System scores Joshua Adams, BS, RN, CNML The Pennsylvania State University In adult inpatients in an acute care hospital setting, which early warning score system (EWSS) is most effective in predicting adverse outcomes (e.g. cardiac/respiratory arrest or unplanned transfer to the intensive care unit)? Background/Problem Hospitalized patients who suffer adverse events may show subtle signs hours before they crash Subtle signs can be viewed in aggregate in an EWS, for which a variety of systems for calculating exist The IHI supports using an EWS system but does not recommend a particular one This review evaluates evidence for which EWSS is most effective in predicting adverse events Summary of Literature Synthesis/Summary of Findings Six JH Level III studies, one JH Level I systematic review, and one JH Level II quasi-experimental study Variability among EWSS in predicting adverse events It could not be definitively determined which EWSS is most effective due to measurement of different endpoints, though the RI showed most promise with AUC of 0.93 All systems incorporated use of EMR vs. manual scoring Source Design/Sample Variables/ Intervention Measurement Findings Quality and Strength Albert et al., 2011 RCR; N=150 codes & RRTs; 550 bed tertiary AMC with Magnet IV = MEWS DV=RRT or codes MEWS calculated from EMR at time of event, 4 & 8 hours prior -Pre-event score >= 3, P=.002 for respiratory events -Pre-event score >=3, P=.065 for cardiac -S: simple tool in EMR -L: small N, 1 hospital -JH Level: III -JH Qual: B Churpek et al., 2013 RCR; N=59,643 admissions, 109 cardiac arrests, 291 deaths, 2,655 upgrades IV=scores of various EWSS DV=cardiac arrest, ICU transfer, mortality, composite Vital signs from EMR -CART had highest AUC for cardiac arrest (0.83), ICU upgrade (0.77) and composite (0.78) -CART, ViEWS, SEWS had same AUC (0.88) for mortality -S: compared 8 tools -L: unclear whether EWSS improves outcomes Finlay et al., 2014 RCR; N=32,472 pt visits to a 665 bed AMC in PA IV=RI or MEWS DV=death RI (including nursing assessment parameters), and MEWS calculated from EMR -RI had AUC of 0.93, MEWS 0.82 -S: RI incorporating nursing assessment -L: retrospective design Hollis et al., 2016 RCR; N=552 gen surg procedures involving GI and surg onc at 1 facility IV=EWS DV=likelihood of postop complication EWS derived from algorithm against the rate of occurrence of serious complications -EWS had AUC of 0.9 for grade IV/V complications -S: understudied pop. of surgical pt -L: retrospective, limited high severity comp Kang et al., 2016 Prospective black box validation study; N=3,889 pts in a single center IV=eCART score DV=cardiac arrests and ICU transfers eCART score at defined risk threshold against ICU transfer and cardiac arrest -eCART had AUC of 0.8 for preducting ICU transfer, 0.88 for predicting cardiac arrest -S: use of EMR -L: unable to make generalizations d/t ICU transfer criteria being local -JH Qual: C Mapp et al., 2013 Systematic review; 9 studies from individual facilities IV=MEWS, CART, SI, EWS adapted for facility DV=RRT, codes, unplanned upgrades, death Vitals, LOC, age, BMI, UOP, age -Studies using AUC analysis ranged from 0.67 to 0.84, higher than competing systems -S: comparison study -L: single facilities with retrospective analysis -JH Level: II Nishijima et al., 2016 Quasi-experimental; single site acute care hospital in Japan IV=MEWS DV=IHCA MEWS was calculated from EMR and incidence of IHCAs per 1000 admissions analyzed -IHCA per 1000 admissions 5.21 before introducing, 2.39 after -S: alert threshold for team to be activated -L: single site study, unable to exclude other factors that could decrease IHCA Smith et al., 2014 Systematic review; one controlled trial and 20 observation studies IV=CART, MEWS, EWS, ViEWS, proprietary clinical marker DV=multiple HR, RR, SBP, Temp, UOP, sat, dyspnea, increased O2 need, LOC, subj. concern -8 studies finding strong predictive value for death (AUC ) Methods Keywords: scorecards, early warning score, EWS, modified early warning score, MEWS, Rothman Index, track and trigger Inclusion: Adults, scholarly, peer-reviewed, published after Exclusion: Duplicates, studies of pediatric, obstetric, or behavioral health patients Critical appraisal of 8 articles for quality & strength using Johns Hopkins (JH) Critical Appraisal Tool Limitations Low generalizability Measurement of different endpoints Implications for Practice Based on current evidence, hospitals wishing to create or implement an existing EWSS should pilot concepts before implementing housewide Once an EWSS is validated in a particular setting, it can then be used to trigger proactive interventions by the healthcare team preventing further decline Total articles identified (n=26) CINAHL (n=4) PubMed MEDLINE (n=21) Cochrane (n=1) Duplicate articles (n=2) Distinct articles after exclusion of 2 duplicates (n=24) Eligible articles (n=24) Articles excluded for content (16) Articles identified after content (n=8) More research examining how EWSS can be used to direct resources to a potentially deteriorating patient before an adverse event …..Next Steps Abbreviations: EMR – Electronic Medical Record, MEWS – Modified Early Warning Score, RI – Rothman Index (measure of X), EWS – Early Warning System, ICU – Intensive Care Unit, RRT – Rapid Response Team, RCR – retrospective chart review, RCC – retrospective case control, AMC – academic medical center, accred. – accreditation, AUC – area under curve, LOC – level of consciousness, UOP – urine output, BMI – body mass index, LOS – length of stay, ROC – receiver operating characteristic, eCART – electronic Cardiac Arrest Risk triage, IHCA – in-hospital cardiac arrest, tx Johns Hopkins Level of Evidence (JH Level) where I = RCT or meta-analysis of RCTs, II = Quasi-experimental, III = non-experimental, qualitative, meta-synthesis; Quality Ratings (JH Qual) where A= High, B= Good, C= low/major flaws


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