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Real-Time, Automated Detection of Ventilator-Associated Events: Avoiding Missed Detections, Misclassifications, and False Detections Due to Human Error

Published online by Cambridge University Press:  17 May 2018

Erica S. Shenoy ,
Eric S. Rosenthal ,
Yu-Ping Shao ,
Siddharth Biswal ,
Manohar Ghanta ,
Erin E. Ryan ,
Dolores Suslak ,
Nancy Swanson ,
Valdery Moura Junior  and
David C. Hooper
...Show all authors
Erica S. Shenoy*
Affiliation:
Infection Control Unit, Massachusetts General Hospital, Boston, Massachusetts Division of Infectious Diseases, Massachusetts General Hospital, Boston, Massachusetts Harvard Medical School, Boston, Massachusetts Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts Health Sciences and Technology Program, Harvard Medical School, Boston, Massachusetts
Eric S. Rosenthal
Affiliation:
Harvard Medical School, Boston, Massachusetts Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts Health Sciences and Technology Program, Harvard Medical School, Boston, Massachusetts
Yu-Ping Shao
Affiliation:
Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts
Siddharth Biswal
Affiliation:
Department of Computer Science, Georgia Institute of Technology College of Computing, Atlanta, Georgia
Manohar Ghanta
Affiliation:
Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts
Erin E. Ryan
Affiliation:
Infection Control Unit, Massachusetts General Hospital, Boston, Massachusetts
Dolores Suslak
Affiliation:
Infection Control Unit, Massachusetts General Hospital, Boston, Massachusetts
Nancy Swanson
Affiliation:
Infection Control Unit, Massachusetts General Hospital, Boston, Massachusetts
Valdery Moura Junior
Affiliation:
Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts
David C. Hooper
Affiliation:
Infection Control Unit, Massachusetts General Hospital, Boston, Massachusetts Division of Infectious Diseases, Massachusetts General Hospital, Boston, Massachusetts Harvard Medical School, Boston, Massachusetts
M. Brandon Westover
Affiliation:
Harvard Medical School, Boston, Massachusetts Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts Clinical Data Animation Center, Massachusetts General Hospital, Boston, Massachusetts Health Sciences and Technology Program, Harvard Medical School, Boston, Massachusetts
*
Address correspondence to Erica S. Shenoy, MD, PhD, Massachusetts General Hospital, 55 Fruit Street, Bulfinch 334, Boston, MA 02114 (eshenoy@mgh.harvard.edu).
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Abstract

OBJECTIVE

To validate a system to detect ventilator associated events (VAEs) autonomously and in real time.

DESIGN

Retrospective review of ventilated patients using a secure informatics platform to identify VAEs (ie, automated surveillance) compared to surveillance by infection control (IC) staff (ie, manual surveillance), including development and validation cohorts.

SETTING

The Massachusetts General Hospital, a tertiary-care academic health center, during January–March 2015 (development cohort) and January–March 2016 (validation cohort).

PATIENTS

Ventilated patients in 4 intensive care units.

METHODS

The automated process included (1) analysis of physiologic data to detect increases in positive end-expiratory pressure (PEEP) and fraction of inspired oxygen (FiO2); (2) querying the electronic health record (EHR) for leukopenia or leukocytosis and antibiotic initiation data; and (3) retrieval and interpretation of microbiology reports. The cohorts were evaluated as follows: (1) manual surveillance by IC staff with independent chart review; (2) automated surveillance detection of ventilator-associated condition (VAC), infection-related ventilator-associated complication (IVAC), and possible VAP (PVAP); (3) senior IC staff adjudicated manual surveillance–automated surveillance discordance. Outcomes included sensitivity, specificity, positive predictive value (PPV), and manual surveillance detection errors. Errors detected during the development cohort resulted in algorithm updates applied to the validation cohort.

RESULTS

In the development cohort, there were 1,325 admissions, 479 ventilated patients, 2,539 ventilator days, and 47 VAEs. In the validation cohort, there were 1,234 admissions, 431 ventilated patients, 2,604 ventilator days, and 56 VAEs. With manual surveillance, in the development cohort, sensitivity was 40%, specificity was 98%, and PPV was 70%. In the validation cohort, sensitivity was 71%, specificity was 98%, and PPV was 87%. With automated surveillance, in the development cohort, sensitivity was 100%, specificity was 100%, and PPV was 100%. In the validation cohort, sensitivity was 85%, specificity was 99%, and PPV was 100%. Manual surveillance detection errors included missed detections, misclassifications, and false detections.

CONCLUSIONS

Manual surveillance is vulnerable to human error. Automated surveillance is more accurate and more efficient for VAE surveillance.

Infect Control Hosp Epidemiol 2018;826–833

Type
Original Article
Information
Infection Control & Hospital Epidemiology , Volume 39 , Issue 7 , July 2018 , pp. 826 - 833
Copyright
© 2018 by The Society for Healthcare Epidemiology of America. All rights reserved. 

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Footnotes

PREVIOUS PRESENTATION. This work was presented at ID Week 2017 (abstract no. 2151) on October 7, 2017, in San Diego, California.

a

Senior authors with equal contribution.

References

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