Predicting Cardiac Arrest on the Wards A Nested Case-Control Study

Section of Pulmonary and Critical Care, University of Chicago, Chicago, IL 60637, USA.
Chest (Impact Factor: 7.13). 11/2011; 141(5):1170-6. DOI: 10.1378/chest.11-1301
Source: PubMed

ABSTRACT Current rapid response team activation criteria were not statistically derived using ward vital signs, and the best vital sign predictors of cardiac arrest (CA) have not been determined. In addition, it is unknown when vital signs begin to accurately detect this event prior to CA.
We conducted a nested case-control study of 88 patients experiencing CA on the wards of a university hospital between November 2008 and January 2011, matched 1:4 to 352 control subjects residing on the same ward at the same time as the case CA. Vital signs and Modified Early Warning Scores (MEWS) were compared on admission and during the 48 h preceding CA.
Case patients were older (64 ± 16 years vs 58 ± 18 years; P = .002) and more likely to have had a prior ICU admission than control subjects (41% vs 24%; P = .001), but had similar admission MEWS (2.2 ± 1.3 vs 2.0 ± 1.3; P = .28). In the 48 h preceding CA, maximum MEWS was the best predictor (area under the receiver operating characteristic curve [AUC] 0.77; 95% CI, 0.71-0.82), followed by maximum respiratory rate (AUC 0.72; 95% CI, 0.65-0.78), maximum heart rate (AUC 0.68; 95% CI, 0.61-0.74), maximum pulse pressure index (AUC 0.61; 95% CI, 0.54-0.68), and minimum diastolic BP (AUC 0.60; 95% CI, 0.53-0.67). By 48 h prior to CA, the MEWS was higher in cases (P = .005), with increasing disparity leading up to the event.
The MEWS was significantly different between patients experiencing CA and control patients by 48 h prior to the event, but includes poor predictors of CA such as temperature and omits significant predictors such as diastolic BP and pulse pressure index.

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    ABSTRACT: Severe adverse events such as cardiac arrest and death are often heralded by abnormal vital signs hours before the event. This necessitates an organized track and trigger approach of early recognition and response to subtle changes in a patient's condition. The Modified Early Warning System (MEWS) is one of such systems that use temperature, blood pressure, pulse, respiratory rate, and level of consciousness with each progressive higher score triggering an action. Root cause analysis for mortalities in our institute has led to the implementation of MEWS in an effort to improve patient outcomes. Here we discuss our experience and the impact of MEWS implementation on patient care at our community academic hospital. MEWS was implemented in a protocolized manner in June 2013. The following data were collected from non-ICU wards on a monthly basis from January 2010 to June 2014: 1) number of rapid response teams (RRTs) per 100 patient-days (100PD); 2) number of cardiopulmonary arrests 'Code Blue' per 100PD; and 3) result of each RRT and Code Blue (RRT progressed to Code Blue, higher level of care, ICU transfer, etc.). Overall inpatient mortality data were also analyzed. Since the implementation of MEWS, the number of RRT has increased from 0.24 per 100PD in 2011 to 0.38 per 100PD in 2013, and 0.48 per 100PD in 2014. The percentage of RRTs that progressed to Code Blue, an indicator of poor outcome of RRT, has been decreasing. In contrast, the numbers of Code Blue in non-ICU floors has been progressively decreasing from 0.05 per 100PD in 2011 to 0.02 per 100PD in 2013 and 2014. These improved clinical outcomes are associated with a decline of overall inpatient mortality rate from 2.3% in 2011 to 1.5% in 2013 and 1.2% in 2014. Implementation of MEWS in our institute has led to higher rapid response system utilization but lower cardiopulmonary arrest events; this is associated with a lower mortality rate, and improved patient safety and clinical outcomes. We recommend the widespread use of MEWS to improve patient outcomes.
    04/2015; 5(2):26716. DOI:10.3402/jchimp.v5.26716
  • Circulation 03/2013; 127(14). DOI:10.1161/CIR.0b013e31828b2770 · 14.95 Impact Factor
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    ABSTRACT: Purpose Intensive care unit (ICU) readmission negatively impacts patients’ outcomes. We aimed to characterize and determine risk factors for ICU readmission within the initial hospital stay after liver transplant (LT). Materials and Methods The reference cohort included 369 LT recipients from a Canadian center between 2005 and 2012. One control was randomly selected per each case of ICU readmission within the initial hospital stay following LT. Survival analysis used the Kaplan-Meier method. Associations were studied by conditional logistic regression. Results Fifty-two (14%) LT recipients were readmitted to the ICU within the initial hospital stay after LT; they had longer first hospital stay (P < 0.001) and lower 1-month to 2-year cumulative survival (P < 0.001). Respiratory failure was the major cause of ICU readmission (61%). Respiratory rate at discharge from the first ICU stay following LT was an independent risk factor for ICU readmission (OR = 1.24). The cutoff point > 20 breaths/min prognosticated ICU readmission with a specificity of 90% and a positive predictive value of 80%. Conclusions ICU readmission within the initial hospital stay following LT negatively impacts LT recipients’ outcomes. Monitoring respiratory rate at discharge from the first ICU stay after LT is important to prevent readmission.
    Journal of Critical Care 10/2014; 29(5). DOI:10.1016/j.jcrc.2014.03.038 · 2.19 Impact Factor


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