Diagnostic accuracy of a screening electronic alert tool for severe sepsis and septic shock in the emergency department.

Abstract

BACKGROUND:

Early recognition of severe sepsis and septic shock is challenging. The aim of this study was to determine the diagnostic accuracy of an electronic alert system in detecting severe sepsis or septic shock among emergency department (ED) patients.

METHODS:

An electronic sepsis alert system was developed as a part of a quality-improvement project for severe sepsis and septic shock. The system screened all adult ED patients for a combination of systemic inflammatory response syndrome and organ dysfunction criteria (hypotension, hypoxemia or lactic acidosis). This study included all patients older than 14 years who presented to the ED of a tertiary care academic medical center from Oct. 1, 2012 to Jan. 31, 2013. As a comparator, emergency medicine physicians or the critical care physician identified the patients with severe sepsis or septic shock. In the ED, vital signs were manually entered into the hospital electronic heath record every hour in the critical care area and every two hours in other areas. We also calculated the time from the alert to the intensive care unit (ICU) referral.

RESULTS:

Of the 49,838 patients who presented to the ED, 222 (0.4%) were identified to have severe sepsis or septic shock. The electronic sepsis alert had a sensitivity of 93.18% (95% CI, 88.78% - 96.00%), specificity of 98.44 (95% CI, 98.33% - 98.55%), positive predictive value of 20.98% (95% CI, 18.50% - 23.70%) and negative predictive value of 99.97% (95% CI, 99.95% - 99.98%) for severe sepsis and septic shock. The alert preceded ICU referral by a median of 4.02 hours (Q1 - Q3: 1.25-8.55).

CONCLUSIONS:

Our study shows that electronic sepsis alert tool has high sensitivity and specificity in recognizing severe sepsis and septic shock, which may improve early recognition and management.

• Authors: Sami Alsolamy, Majid Al Salamah, Majed Al Thagafi, Hasan M Al-Dorzi, Abdellatif M Marini, Nawfal Aljerian, Farhan Al-Enezi, Fatimah Al-Hunaidi, Ahmed M Mahmoud, Ahmed Alamry, Yaseen M Arabi
• Journal: BMC medical informatics and decision making (BMC Med Inform Decis Mak) Vol. 14 Pg. 105 (Dec 05 2014) ISSN: 1472-6947 [Electronic] England
• PMID: 25476738 (Publication Type: Journal Article, Research Support, Non-U.S. Gov't)

Topics

• Adult
• Critical Pathways
• Decision Support Systems, Clinical
• Early Diagnosis
• Emergency Service, Hospital (standards, statistics & numerical data)
• Humans
• Mass Screening (methods, standards)
• Organ Dysfunction Scores
• Prospective Studies
• Quality Improvement (standards)
• Sensitivity and Specificity
• Sepsis (diagnosis)
• Shock, Septic (diagnosis)
• Systemic Inflammatory Response Syndrome (diagnosis)