Erschienen in:
01.04.2007 | Systematic Review
Systematic review and evaluation of physiological track and trigger warning systems for identifying at-risk patients on the ward
verfasst von:
Haiyan Gao, Ann McDonnell, David A. Harrison, Tracey Moore, Sheila Adam, Kathleen Daly, Lisa Esmonde, David R. Goldhill, Gareth J. Parry, Arash Rashidian, Christian P Subbe, Sheila Harvey
Erschienen in:
Intensive Care Medicine
|
Ausgabe 4/2007
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Abstract
Objective
Physiological track and trigger warning systems (TTs) are used to identify patients outside critical care areas at risk of deterioration and to alert a senior clinician, Critical Care Outreach Service, or equivalent. The aims of this work were: to describe published TTs and the extent to which each has been developed according to established procedures; to review the published evidence and available data on the reliability, validity and utility of existing systems; and to identify the best TT for timely recognition of critically ill patients.
Design and setting
Systematic review of studies identified from electronic, citation and hand searching, and expert informants. Cohort study of data from 31 acute hospitals in England and Wales.
Measurements and results
Thirty-six papers were identified describing 25 distinct TTs. Thirty-one papers described the use of a TT, and five were studies examining the development or testing of TTs. None of the studies met all methodological quality standards. For the cohort study, outcome was measured by a composite of death, admission to critical care, ‘do not attempt resuscitation’ or cardiopulmonary resuscitation. Fifteen datasets met pre-defined quality criteria. Sensitivities and positive predictive values were low, with median (quartiles) of 43.3 (25.4–69.2) and 36.7 (29.3–43.8), respectively.
Conclusion
A wide variety of TTs were in use, with little evidence of reliability, validity and utility. Sensitivity was poor, which might be due in part to the nature of the physiology monitored or to the choice of trigger threshold. Available data were insufficient to identify the best TT.