Using a stratified Hamilton Early Warning Score (HEWS) at admission to predict critical events and workload
CCCF ePoster library. Tam B. Oct 26, 2015; 117344; P47 Disclosure(s): Hamilton Health Sciences Quality and Patient Safety Research Award - 2014 Hamilton Health Sciences Resident Research Grant in Patient Safety - 2014
Dr. Benjamin Tam
Dr. Benjamin Tam
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Abstract
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P47


Topic: Retrospective or Prospective Cohort Study


Using a stratified Hamilton Early Warning Score (HEWS) at admission to predict critical events and workload



Benjamin Tam, M. Xu, A. Fox-Robichaud

Department of Critical Care, McMaster University, Hamilton, Canada | Masters of Health Research Methodology Canidate, McMaster University, Hamilton, Canada | Department of Critical Care, McMaster University, Hamilton, Canada

Introduction: In 2014, we implemented the Hamilton Early Warning Score (HEWS), a locally developed score, to improve patient safety at our centre independent of whether a site had a critical care response team (CCRT). We decided to study the characteristics of HEWS at hospital admission which we considered a high yield hospitalization checkpoint. We discovered from our early warning score implementation process that there was a learning curve associated with the bedside use of HEWS. This may have occurred because assessment of deteriorating patients was more often driven by intuitive and qualitative observations rather than one or more vital sign abnormalities. (1-3)

Objectives: To demonstrate the value of the Hamilton Early Warning Score (HEWS) as a tool for bedside clinicians, we studied the predictive ability of a stratified HEWS at ward admission and the number of evaluations required to detect a critical event during hospitalization.

Methods: We prospectively identified a consecutive cohort of medical and surgical ward patients for retrospective review over 6 months in 2014 at two academic hospitals. Critical event was defined as a composite of inpatient death, cardio-pulmonary arrest or ICU transfer. Cases were stratified based on the admission HEWS into low risk, moderate risk, high risk and very high risk subgroups. Likelihood ratio of a critical event during hospitalization and the number needed to evaluate to detect a critical event was determined based on a stratified admission HEWS. The number needed to evaluate was calculated as the inverse of the positive predictive value. This metric was recently proposed by Romero-Brufau as a more realistic measure of early warning score value. (4)

Results: We found 506 critical events occurred in 7130 cases. The majority of critical events were either death (49.3% of all critical events), or unanticipated ICU transfer (44.0% of all critical events). The inpatient arrest rate was 4.95 arrests/1000 hospital admissions. We found that patients who suffered a critical event were more likely to be older, male, and have other co-morbidities. In addition, we found that admission to the centre with a CCRT was associated with event-free hospitalization. We found that 96% of patients (6869/7130) were admitted to the ward with low to moderate risk vital signs. (See table 1) However, likelihood ratios of 0.56 and 1.82 in the low and moderate risk group respectively were insufficient to rule out occurrence of critical events. Compared to the low and moderate risk population, the high risk group and very high risk group comprised only 4% of patients admitted to the ward (261/7130). The likelihood ratio of critical event was 7.99 and 16.7 in the high risk and very high risk respectively. In terms of workload, 25 and 8.3 patients in the low risk and moderate risk subgroup needed evaluation to detect a critical event whereas 2.6 and 1.8 patients needed evaluation in the high risk and very high risk subgroups respectively to detect a critical event.

Conclusion: A stratified admission HEWS identified patients at risk for critical events and outlined the expected workload required to potentially prevent a critical event. Relatively few patients required evaluation to detect a critical event in patients with high risk or very high risk HEWS at admission. However, further evaluation is required to determine the ideal number needed to evaluate.

References:

References

1. Donohue LA, Endacott R. Track, trigger and teamwork: Communication of deterioration in acute medical and surgical wards. Intensive and Critical Care Nursing. 2010;26(1):10–17. doi:10.1016/j.iccn.2009.10.006.

2. Brady PW, Goldenhar LM. A qualitative study examining the influences on situation awareness and the identification, mitigation and escalation of recognised patient risk. BMJ Quality & Safety. 2014;23(2):153–161. doi:10.1136/bmjqs-2012-001747.

3. Odell M, Victor C, Oliver D. Nurses' role in detecting deterioration in ward patients: systematic literature review. J Adv Nurs. 2009;65(10):1992–2006. doi:10.1111/j.1365-2648.2009.05109.

4. Romero-Brufau S, Huddleston JM, Escobar GJ, Liebow M. Why the C-statistic is not informative to evaluate early warning scores and what metrics to use. Crit Care. 2015;19(1):285. doi:10.1186/s13054-015-0999-1.

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