Journal
JOURNAL OF HOSPITAL MEDICINE
Volume 10, Issue 1, Pages 26-31Publisher
JOHN WILEY & SONS INC
DOI: 10.1002/jhm.2259
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Funding
- National Center for Research Resources [UL1RR024134]
- National Center for Advancing Translational Sciences [UL1TR000003]
- NATIONAL CENTER FOR ADVANCING TRANSLATIONAL SCIENCES [UL1TR000003] Funding Source: NIH RePORTER
- NATIONAL CENTER FOR RESEARCH RESOURCES [UL1RR024134] Funding Source: NIH RePORTER
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BACKGROUNDEarly recognition and timely intervention significantly reduce sepsis-related mortality. OBJECTIVEDescribe the development, implementation, and impact of an early warning and response system (EWRS) for sepsis. DESIGNAfter tool derivation and validation, a preimplementation/postimplementation study with multivariable adjustment measured impact. SETTINGUrban academic healthcare system. PATIENTSAdult non-ICU patients admitted to acute inpatient units from October 1, 2011 to October 31, 2011 for tool derivation, June 6, 2012 to July 5, 2012 for tool validation, and June 6, 2012 to September 4, 2012 and June 6, 2013 to September 4, 2013 for the preimplementation/postimplementation analysis. INTERVENTIONAn EWRS in our electronic health record monitored laboratory values and vital signs in real time. If a patient had 4 predefined abnormalities at any single time, the provider, nurse, and rapid response coordinator were notified and performed an immediate bedside patient evaluation. MEASUREMENTSScreen positive rates, test characteristics, predictive values, and likelihood ratios; system utilization; and resulting changes in processes and outcomes. RESULTSThe tool's screen positive, sensitivity, specificity, and positive and negative predictive values and likelihood ratios for our composite of intensive care unit (ICU) transfer, rapid response team call, or death in the derivation cohort was 6%, 16%, 97%, 26%, 94%, 5.3, and 0.9, respectively. Validation values were similar. The EWRS resulted in a statistically significant increase in early sepsis care, ICU transfer, and sepsis documentation, and decreased sepsis mortality and increased discharge to home, although neither of these latter 2 findings reached statistical significance. CONCLUSIONSAn automated prediction tool identified at-risk patients and prompted a bedside evaluation resulting in more timely sepsis care, improved documentation, and a suggestion of reduced mortality. Journal of Hospital Medicine 2015;10:26-31. (c) 2014 Society of Hospital Medicine
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