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Clinical Predictors for Laboratory-Confirmed Influenza Infections: Exploring Case Definitions for Influenza-Like Illness

Published online by Cambridge University Press:  13 January 2015

Shital C. Shah ,
Dino P. Rumoro ,
Marilyn M. Hallock ,
Gordon M. Trenholme ,
Gillian S. Gibbs ,
Julio C. Silva  and
Michael J. Waddell
Shital C. Shah
Affiliation:
Rush University Medical Center, Department of Emergency Medicine, Chicago, Illinois Rush University Medical Center, Department of Health Systems Management, Chicago, Illinois
Dino P. Rumoro
Affiliation:
Rush University Medical Center, Department of Emergency Medicine, Chicago, Illinois Rush University Medical Center, Department of Health Systems Management, Chicago, Illinois
Marilyn M. Hallock
Affiliation:
Rush University Medical Center, Department of Emergency Medicine, Chicago, Illinois
Gordon M. Trenholme
Affiliation:
Rush University Medical Center, Division of Infectious Diseases, Department of Medicine, Chicago, Illinois
Gillian S. Gibbs*
Affiliation:
Rush University Medical Center, Department of Emergency Medicine, Chicago, Illinois
Julio C. Silva
Affiliation:
Rush University Medical Center, Department of Emergency Medicine, Chicago, Illinois
Michael J. Waddell
Affiliation:
Pangaea Information Technologies, Chicago, Illinois
*
Address correspondence to Gillian S. Gibbs, MPH, Project Coordinator, Department of Emergency Medicine, Rush University, 1717 W Congress Pkwy, Suite 108 Kellogg, Chicago, IL, 60612 (Gillian_Gibbs@rush.edu).
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Abstract

OBJECTIVE

To identify clinical signs and symptoms (ie, “terms”) that accurately predict laboratory-confirmed influenza cases and thereafter generate and evaluate various influenza-like illness (ILI) case definitions for detecting influenza. A secondary objective explored whether surveillance of data beyond the chief complaint improves the accuracy of predicting influenza.

DESIGN

Retrospective, cross-sectional study.

SETTING

Large urban academic medical center hospital.

PARTICIPANTS

A total of 1,581 emergency department (ED) patients who received a nasopharyngeal swab followed by rRT-PCR testing between August 30, 2009, and January 2, 2010, and between November 28, 2010, and March 26, 2011.

METHODS

An electronic surveillance system (GUARDIAN) scanned the entire electronic medical record (EMR) and identified cases containing 29 clinical terms relevant to influenza. Analyses were conducted using logistic regressions, diagnostic odds ratio (DOR), sensitivity, and specificity.

RESULTS

The best predictive model for identifying influenza for all ages consisted of cough (DOR=5.87), fever (DOR=4.49), rhinorrhea (DOR=1.98), and myalgias (DOR=1.44). The 3 best case definitions that included combinations of some or all of these 4 symptoms had comparable performance (ie, sensitivity=89%–92% and specificity=38%–44%). For children <5 years of age, the addition of rhinorrhea to the fever and cough case definition achieved a better balance between sensitivity (85%) and specificity (47%). For the fever and cough ILI case definition, using the entire EMR, GUARDIAN identified 37.1% more influenza cases than it did using only the chief complaint data.

CONCLUSIONS

A simplified case definition of fever and cough may be suitable for implementation for all ages, while inclusion of rhinorrhea may further improve influenza detection for the 0–4-year-old age group. Finally, ILI surveillance based on the entire EMR is recommended.

Infect Control Hosp Epidemiol 2015;00(0): 1–8

Type
Original Articles
Information
Infection Control & Hospital Epidemiology , Volume 36 , Issue 3 , March 2015 , pp. 241 - 248
Copyright
© 2015 by The Society for Healthcare Epidemiology of America. All rights reserved 

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