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Building and Validating a Computerized Algorithm for Surveillance of Ventilator-Associated Events

Published online by Cambridge University Press:  15 June 2015

Tal Mann
Affiliation:
General ICU, Assaf-Harofeh Medical Center, Tsrifin, Israel Department of Medicine, Wayne State University/Detroit Medical Center, Detroit, Michigan, United States
Joseph Ellsworth
Affiliation:
Department of Corporate Quality and Safety/Epidemiology, Detroit Medical Center, Detroit, Michigan, United States
Najia Huda
Affiliation:
Department of Medicine, Henry Ford Hospital, Detroit, Michigan, United States
Anupama Neelakanta
Affiliation:
Carolina Health System, Charlotte, North Carolina, United States
Thomas Chevalier
Affiliation:
Department of Corporate Quality and Safety/Epidemiology, Detroit Medical Center, Detroit, Michigan, United States
Kristin L. Sims
Affiliation:
Detroit Medical Center, Detroit Michigan, United States
Sorabh Dhar
Affiliation:
Department of Medicine, Wayne State University/Detroit Medical Center, Detroit, Michigan, United States
Mary E. Robinson
Affiliation:
Department of Corporate Quality and Safety/Epidemiology, Detroit Medical Center, Detroit, Michigan, United States
Keith S. Kaye*
Affiliation:
Department of Medicine, Wayne State University/Detroit Medical Center, Detroit, Michigan, United States
*
Address correspondence to Keith S. Kaye, MD, MPH, Professor of Medicine, Corporate Vice President of Quality and Patient Safety, Corporate Medical Director, Infection Prevention, Epidemiology and Antimicrobial Stewardship, Wayne State University/Detroit Medical Center, University Health Center, 4201 Saint Antoine, Suite 2B, Box 331, Detroit, MI 48201 (KKaye@dmc.org).

Abstract

OBJECTIVE

To develop an automated method for ventilator-associated condition (VAC) surveillance and to compare its accuracy and efficiency with manual VAC surveillance

SETTING

The intensive care units (ICUs) of 4 hospitals

METHODS

This study was conducted at Detroit Medical Center, a tertiary care center in metropolitan Detroit. A total of 128 ICU beds in 4 acute care hospitals were included during the study period from August to October 2013. The automated VAC algorithm was implemented and utilized for 1 month by all study hospitals. Simultaneous manual VAC surveillance was conducted by 2 infection preventionists and 1 infection control fellow who were blinded to each another’s findings and to the automated VAC algorithm results. The VACs identified by the 2 surveillance processes were compared.

RESULTS

During the study period, 110 patients from all the included hospitals were mechanically ventilated and were evaluated for VAC for a total of 992 mechanical ventilation days. The automated VAC algorithm identified 39 VACs with sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 100%. In comparison, the combined efforts of the IPs and the infection control fellow detected 58.9% of VACs, with 59% sensitivity, 99% specificity, 91% PPV, and 92% NPV. Moreover, the automated VAC algorithm was extremely efficient, requiring only 1 minute to detect VACs over a 1-month period, compared to 60.7 minutes using manual surveillance.

CONCLUSIONS

The automated VAC algorithm is efficient and accurate and is ready to be used routinely for VAC surveillance. Furthermore, its implementation can optimize the sensitivity and specificity of VAC identification.

Infect. Control Hosp. Epidemiol. 2015;36(9):999–1003

Type
Original Articles
Copyright
© 2015 by The Society for Healthcare Epidemiology of America. All rights reserved 

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