Skip to main content

Main menu

  • HOME
  • ARTICLES
    • Current Issue
    • Abstracts In Press
    • Archives
    • Special Issue Archive
    • Subject Collections
  • INFO FOR
    • Authors
    • Reviewers
    • Call For Papers
    • Subscribers
    • Advertisers
  • SUBMIT
    • Manuscript
    • Peer Review
  • ABOUT
    • The JABFM
    • The Editing Fellowship
    • Editorial Board
    • Indexing
    • Editors' Blog
  • CLASSIFIEDS
  • Other Publications
    • abfm

User menu

Search

  • Advanced search
American Board of Family Medicine
  • Other Publications
    • abfm
American Board of Family Medicine

American Board of Family Medicine

Advanced Search

  • HOME
  • ARTICLES
    • Current Issue
    • Abstracts In Press
    • Archives
    • Special Issue Archive
    • Subject Collections
  • INFO FOR
    • Authors
    • Reviewers
    • Call For Papers
    • Subscribers
    • Advertisers
  • SUBMIT
    • Manuscript
    • Peer Review
  • ABOUT
    • The JABFM
    • The Editing Fellowship
    • Editorial Board
    • Indexing
    • Editors' Blog
  • CLASSIFIEDS
  • JABFM on Bluesky
  • JABFM On Facebook
  • JABFM On Twitter
  • JABFM On YouTube
Research ArticleOriginal Research

A Systematic Review of Clinical Prediction Rules for the Diagnosis of Influenza

Mark H. Ebell, Ivan Rahmatullah, Xinyan Cai, Michelle Bentivegna, Cassie Hulme, Matthew Thompson and Barry Lutz
The Journal of the American Board of Family Medicine November 2021, 34 (6) 1123-1140; DOI: https://doi.org/10.3122/jabfm.2021.06.210110
Mark H. Ebell
From Department of Epidemiology, College of Public Health, University of Georgia, Athens, GA (MHE, XC, MB, CH); Department of Family Medicine, University of Washington, Seattle, WA (IR, MT); Faculty of Medicine, Universitas Airlangga, Indonesia (IR); Department of Bioengineering, University of Washington, Seattle, WA (BL).
MD, MS
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Ivan Rahmatullah
From Department of Epidemiology, College of Public Health, University of Georgia, Athens, GA (MHE, XC, MB, CH); Department of Family Medicine, University of Washington, Seattle, WA (IR, MT); Faculty of Medicine, Universitas Airlangga, Indonesia (IR); Department of Bioengineering, University of Washington, Seattle, WA (BL).
MD, MPH
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Xinyan Cai
From Department of Epidemiology, College of Public Health, University of Georgia, Athens, GA (MHE, XC, MB, CH); Department of Family Medicine, University of Washington, Seattle, WA (IR, MT); Faculty of Medicine, Universitas Airlangga, Indonesia (IR); Department of Bioengineering, University of Washington, Seattle, WA (BL).
MPH
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Michelle Bentivegna
From Department of Epidemiology, College of Public Health, University of Georgia, Athens, GA (MHE, XC, MB, CH); Department of Family Medicine, University of Washington, Seattle, WA (IR, MT); Faculty of Medicine, Universitas Airlangga, Indonesia (IR); Department of Bioengineering, University of Washington, Seattle, WA (BL).
MPH
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Cassie Hulme
From Department of Epidemiology, College of Public Health, University of Georgia, Athens, GA (MHE, XC, MB, CH); Department of Family Medicine, University of Washington, Seattle, WA (IR, MT); Faculty of Medicine, Universitas Airlangga, Indonesia (IR); Department of Bioengineering, University of Washington, Seattle, WA (BL).
MPH
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Matthew Thompson
From Department of Epidemiology, College of Public Health, University of Georgia, Athens, GA (MHE, XC, MB, CH); Department of Family Medicine, University of Washington, Seattle, WA (IR, MT); Faculty of Medicine, Universitas Airlangga, Indonesia (IR); Department of Bioengineering, University of Washington, Seattle, WA (BL).
MBChB, MPH, DPhil
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Barry Lutz
From Department of Epidemiology, College of Public Health, University of Georgia, Athens, GA (MHE, XC, MB, CH); Department of Family Medicine, University of Washington, Seattle, WA (IR, MT); Faculty of Medicine, Universitas Airlangga, Indonesia (IR); Department of Bioengineering, University of Washington, Seattle, WA (BL).
PhD
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Article
  • Figures & Data
  • References
  • Info & Metrics
  • PDF
Loading

Article Figures & Data

Figures

  • Tables
  • Figure 1.
    • Download figure
    • Open in new tab
    Figure 1.

    PRISMA diagram describing the search.

Tables

  • Figures
    • View popup
    Table 1.

    Characteristics of Included Studies That Develop or Validate Clinical Prediction Rules for Influenza

    StudyNo. of PatientsMean Age (Years)Population StudiedFlu StrainReference StdCountry (Year)
    Afonso, 2012*456Swiss: 34.3
    US: 38.8
    Swiss: Adult outpatients with ILI
    US: adult outpatients with acute RTI symptoms
    A + BSwiss: Culture
    US: PCR
    Switzerland (1999 to 2000), US (2002)
    Ebell, 2012*,†456Swiss: 34.3
    US: 38.8
    Swiss: Adult outpatients with ILI
    US: adult outpatients with acute RTI symptoms
    A + BSwiss: Culture
    US: PCR
    Switzerland (1999 to 2000), US (2002)
    Anderson, 2018457210.7Patients > 6 months of age with fever and cough or sore throat, presenting within 3 days if outpatient and within 5 days if in-patientA + BPCRThailand (2009 to 2014)
    Dugas, 2019194148.6An ED patient who reported 1 or more of the following: measured fever >100.4°F, subjective fever, cough, nasal congestion, sinus congestion, rhinorrhea, sore throat, or shortness of breathNRPCRUS (2013 to 2014)
    Govaert, 19981791Range: 60–91Adults aged 60 or older presenting to GP with ILI
    Patients with heart, lung, kidney disease, or diabetes excluded
    NRSerologyNetherlands (1991 to 1992)
    Ranjan, 201263846.4Travelers quarantined in an airport during swine flu outbreak with ILIAPCRIndia (2009)
    van Vugt, 2015†180148Adult outpatients and older with clinical presentation of LRTI, symptom onset < 7 daysA + BPCR12 European countries (2007 to 2010)
    Vuichard-Gysin, 20192191NRChildren and adults of Canadian Hutterite communities with ARTI defined as at least 2 of: chills, cough, earache, fatigue, fever, headache, myalgias, coryza, or sore throatA + BPCRCanada (2008 to 2011)
    Woolpert, 201278931.2Outpatients with subjective fever and/or temperature >38.0°C, plus cough or sore throatA + BPCRUS (2007 to 2008)
    Zimmerman, 2016417334.1Outpatients > 6 months of age, seeking care for ARTI with cough or feverA + BPCRUS (2011 to 2012)
    • ARTI, acute respiratory tract infection; ED, emergency department; GP, general practice; ILI, influenza-like illness; LRTI, lower respiratory tract infection; NR, not reported; PCR, polymerase chain reaction; RTI, respiratory tract infection; Std, standard; US, United States.

    • ↵* These studies used the same dataset but different methods for generating a clinical prediction rule.

    • ↵† The FluScore was developed in the study by Ebell and colleagues and validated in the study by van Vugt and colleagues.

    • View popup
    Table 2.

    Evaluation of Study Quality Using the QUADAS-2 Framework for Studies of Influenza Clinical Prediction Rules

    Study, YearPatient SelectionIndex TestReference StdFlow and TimingOverall
    1234567891011121314151617
    ConsecutiveNot Case Control or Retrospective-CohortExclusion CriteriaRisk of BiasApplicabilityIndex BlindedThreshold Pre-SpecifiedRisk of BiasApplicabilityPCR Used for Some or All PatientsReference BlindedRisk of BiasApplicabilityAll Got Reference StdAll Got Same Ref StdAll AccountedRisk of Bias
    Anderson, 2018YYYLNYYLLYULLYYYLL
    Ranjan, 2012YYYLNYYLLYULLYYYLL
    van Vugt, 2015YYYLNYYLLYYLLYYYLL
    Vuichard-Gysin, 2019YYYLNYYLLYULLYYYLL
    Woolpert, 2012YYYLNYYLLYULLYYYLL
    Zimmerman, 2016YYYLNYYLLYULLYYYLL
    Afonso, 2012YYYLNYYLLYULLYNYHM
    Ebell, 2012YYYLNYYLLYULLYNYHM
    Dugas, 2019YYYLNYYLLYULLYYNHM
    Govaert, 2013YYYLNYYLLNUHLYYYLM
    • H, High; L, Low; M, Moderate; N, No; PCR, polymerase chain reaction; Std, standard; U, Uncertain; Y, Yes.

    • View popup
    Table 3.

    Predictor Variables Included in Each Influenza Risk Score or Classification and Regression Tree (CART) Model

    Study, YearCoughFeverChills or SweatsMyalgiaAcute OnsetCoryzaSore ThroatAgeHeadacheFatigue or malaiseSneezingFlu in communityClose ContactSexSinus Problems
    Afonso, 2012××××
    Afonso, 2012××
    Afonso, 2012××
    Anderson, 2018×××××
    Dugas, 2019×××
    Ebell, 2012 (FluScore)×××××
    Govaert, 1998××××××××
    Ranjan, 2012×××××××
    Vuichard-Gysin, 2019 (children)×××××
    Vuichard-Gysin, 2019 (children)××××
    Vuichard-Gysin, 2019 (adults)×××××
    Vuichard-Gysin, 2019 (adults)×××
    Woolpert, 2012××××
    Zimmerman, 2016×××
    Number of models:12119743322211111
    • View popup
    Table 4.

    Accuracy of Clinical Prediction Rules for Influenza

    Study or DatasetType of Validation and Number of PatientsAUROCCOverall Prevalence of Flu% Flu and LR by Risk Group (Derivation)% Flu and LR by Risk Group (Validation)% of Patients in Each Risk Group
    FluScore risk score
        Ebell, 2012Split sample internal: derivation n = 326, validation n = 1330.79 *34.2%Low: 9.2%, LR 0.20Low: 5.0%, LR 0.10Low: 32.5% *
    Mod: 27.8%, LR 0.76Mod: 35.9%, LR 1.02Mod: 28.1%
    High: 59.1%, LR 2.83High: 57.4%, LR 2.47High: 39.4%
        van Vugt, 2015 (flu season)Prospective external: n = 5050.71 †23.6%NALow: 13.6%, LR 0.51Low: 60% ‡
    Mod: 32.1%, LR 1.53Mod: 27%
    High: 50.0%, LR 3.24High: 14%
    Classification and Regression Tree (CART) models
        Afonso, 2012: Model 1Split sample internal: derivation n = 322, validation n = 1340.82/0.80 *34.2%Low: 5.6% flu, LR 0.12Low: 8%, LR 0.15Low: 21% *
    Mod: 29% flu, LR 0.83Mod: 37%, LR 0.98Mod: 62%
    High: 78% flu, LR 7.2High: 82%, LR 7.8High: 17%
        Afonso, 2012: Model 2Split sample internal: derivation n = 322, validation n = 1340.75/0.76 *34.2%Low: 5.6% flu, LR 0.12Low: 8%, LR 0.15Low: 21% *
    Mod: 18% flu, LR 0.44Mod: 18%, LR 0.37Mod: 30%
    High: 55% flu, LR 2.4High: 62%, LR 2.7High: 49%
        Afonso, 2012: Model 3Split sample internal: derivation n = 322, validation n = 1370.76/0.77 *34.2%Low: 7.5% flu, LR 0.16Low: 3%, LR 0.06Low: 24% *
    Mod: 26% flu, LR 0.72Mod: 34%, LR 0.89Mod: 46%
    High: 63% flu, LR 3.4High: 70%, LR 3.9High: 30%
        Anderson, 2018Prospective internal: derivation n = 3782 (2009 to 2013),validation n = 790 (2014)0.69 *32.7%Low: 21.9% flu, LR 0.52Low: 20.2% flu, LR NRLow: 71% †
    High: 59.7% flu, LR 2.73High: 63.8%, LR NRHigh: 29%
        Vuichard-Gysin, 2019 (children)Split sample internal: derivation n = 819, validation n = 4220.77/0.74 $12.3%Low: 8% flu, LR 4.5Low: 8%, LR 0.72Low: 88.6% †
    High: 36% flu, LR 4.5High: 34%, LR 4.3High: 11.4%
        Vuichard-Gysin, 2019 (adults)Split sample internal: derivation n = 627, validation n = 3230.80/0.75 $7.1%Low 4% flu, LR 0.59Low: 5%, LR 0.68Low: 89.3%†
    High 30% flu, LR 5.8High: 26%, LR 4.6High: 10.7%
        Zimmerman, 2016Split sample internal: derivation n = 2087, validation n = 20860.68/0.69 $15.4%Low: 6% flu, LR 0.33Low 5%43% *
    High: 23% flu, LR 1.63High: 23%57%
    Other risk scores
        Dugas, 2019Split sample internal: derivation n = 1553, validation n = 388NR9.4%Low (0 to 2): 1.5%, LR 0.16Low (0 to 2): 3.3%, LR 0.25Low: 33.4% *
    High (3+): 12.5%, LR 1.48High (3+): 16.2%, LR 1.40High: 66.6%
        Govaert, 1998 (post hoc)None, derivation only,n = 1791NR6.8%Low (0): 3.2%, LR 0.46;NALow: 64.5%†
    Mod (1 to 2): 6.2%, LR 0.91Mod: 8.1%
    High (≥ 3): 15%, LR 2.49High: 27.4%
        Ranjan, 2012None, derivation only,n = 638NR19.9%Low (0 to 6): 4%NANR
    High (7+): 64%
        Vuichard-Gysin, 2019 (children)Split sample internal: derivation n = 819, validation n = 4220.76/0.70 $12.3%Low (0 to 4): 6% flu, LR 0.53Low (0 to 4): 7%, LR 0.59Low: 80.1% *
    High (5+): 31% flu, LR 3.7High (5+): 28%, LR 3.1High: 19.9%
        Vuichard-Gysin, 2019 (adults)Split sample internal: derivation n = 627, validation n = 3230.78/0.79 $7.1%Low (0 to 3): 6% flu, LR 0.59;Low (0 to 3): 5%, LR 0.67Low: 94.2% *
    High (4+): 34% flu, LR 5.8High (4+): 26%, LR 4.8High: 5.8%
        Woolpert, 2012 (original)None, derivation only,n = 523NR30.0%0 pts: 0.0%, LR 0.0NA0 pts: 0.6% *
    1 pts: 10.1%, LR 0.261 pt: 15.1%
    2 pts: 20.9%, LR 0.622 pts: 53.0%
    3 pts: 50.0%, LR 2.333 pts: 26.0%
    4 pts: 82.1%, LR 10.74 pts: 5.4%
        Woolpert, 2012 (post hoc)None, derivation only,n = 523NR30.0%Low (0 to 1): 9.8%, LR 0.25NALow: 15.7% *
    Mod (2): 20.9%, LR 0.61Mod: 53.0%
    High (3+): 55.5%, LR 2.87High: 31.4%
    • AUROCC, area under the receiver operating characteristic curve; EAST-PC, Enhancing Antibiotic Stewardship in Primary Care dataset; Mod, moderate; LR, likelihood ratio; NA, not applicable; NR, not reported.

    • ↵* All subjects.

    • ↵† Derivation group only.

    • ↵‡ Validation group only.

    • ↵$ Derivation/Validation.

  • StudyRisk Score or CART model
    Afonso, 2012CART model 1
    Fever, chills or sweats, duration < 2 days, cough
    Afonso, 2012CART model 2
    Fever, chills, sweating
    Afonso, 2012CART model 3
    Fever, myalgia
    Anderson, 2018CART modelAge ≥ 5 years, cough, coryza, chills, myalgia
    Dugas, 2019Risk score
    2 points for new or increased cough, 1 point for headache, 1 point for subjective fever, and 1 point for measured temperature > 100.4 F.
    Low-risk group: 0 to 2 points
    High-risk group: ≥ 3 points
    Ebell, 2012Risk score (“FluScore”)
    2 points for fever and cough, 2 points for myalgias, 1 point for chills or sweats, and 1 point for duration < 2 days
    Low-risk group: 0 to 2 points
    Moderate risk group: 3 points
    High-risk group: 4 to 6 points
    Govaert, 1998Risk score (original)
    1 point for each of the following 8 symptoms:
    cough, fever, acute onset, malaise, rigor or chills, myalgia, headache, sore throat.
    Probability of influenza is then reported for each point score.
    Govaert, 1998Risk score (post hoc)
    1 point for each of the following 8 symptoms: cough, fever, acute onset, malaise, rigor or chills, myalgia, headache, sore throat
    Low-risk group: 0 points
    Moderate risk group: 1 to 2 points
    High-risk group: 3 + points
    Ranjan, 2012Risk score
    1 to 3 points for different levels of fever, 1 point each for sneezing, coryza, sore throat, cough or wheeze, 1 point for flu circulating in community, and 2 points for close contact with confirmed flu.
    Low-risk group: 0 to 6 points
    High-risk group: ≥ 7 points
    Vuichard-Gysin, 2019 (children)CART model
    Fever, chills, cough, coryza, male sex
    Vuichard-Gysin, 2019 (children)Risk score (children)
    1 point for age 6 to 17 years, 2 points for chills, 2 points for cough, and 3 points for fever.
    Low-risk group: 0 to 4 points
    High-risk group: ≥ 5 points
    Vuichard-Gysin, 2019 (adults)CART model
    Chills, cough, myalgia, sinus problem, sore throat
    Vuichard-Gysin, 2019 (adults)Risk score (adults)
    2 points for chills, 2 points for cough, and 1 point for myalgias
    Low-risk group: 0 to 3 points
    High-risk group: ≥ 4 points
    Woolpert, 2012Risk score (original)
    1 point for each of the following 4 symptoms: acute onset, fever, cough, myalgia. Probability of influenza is then reported for each point score.
    Woolpert, 2012Risk score (post hoc)
    1 point for each of the following 4 symptoms: acute onset, fever, cough, myalgia
    Low-risk group: 0 to 1 points
    Moderate risk group: 2 points
    High-risk group: 3 + points
    Zimmerman, 2016CART model
    Fever, cough, fatigue
    • CART, Classification and Regression Tree.

PreviousNext
Back to top

In this issue

The Journal of the American Board of Family   Medicine: 34 (6)
The Journal of the American Board of Family Medicine
Vol. 34, Issue 6
November/December 2021
  • Table of Contents
  • Table of Contents (PDF)
  • Cover (PDF)
  • Index by author
  • Back Matter (PDF)
  • Front Matter (PDF)
Print
Download PDF
Article Alerts
Sign In to Email Alerts with your Email Address
Email Article

Thank you for your interest in spreading the word on American Board of Family Medicine.

NOTE: We only request your email address so that the person you are recommending the page to knows that you wanted them to see it, and that it is not junk mail. We do not capture any email address.

Enter multiple addresses on separate lines or separate them with commas.
A Systematic Review of Clinical Prediction Rules for the Diagnosis of Influenza
(Your Name) has sent you a message from American Board of Family Medicine
(Your Name) thought you would like to see the American Board of Family Medicine web site.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
7 + 4 =
Solve this simple math problem and enter the result. E.g. for 1+3, enter 4.
Citation Tools
A Systematic Review of Clinical Prediction Rules for the Diagnosis of Influenza
Mark H. Ebell, Ivan Rahmatullah, Xinyan Cai, Michelle Bentivegna, Cassie Hulme, Matthew Thompson, Barry Lutz
The Journal of the American Board of Family Medicine Nov 2021, 34 (6) 1123-1140; DOI: 10.3122/jabfm.2021.06.210110

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Share
A Systematic Review of Clinical Prediction Rules for the Diagnosis of Influenza
Mark H. Ebell, Ivan Rahmatullah, Xinyan Cai, Michelle Bentivegna, Cassie Hulme, Matthew Thompson, Barry Lutz
The Journal of the American Board of Family Medicine Nov 2021, 34 (6) 1123-1140; DOI: 10.3122/jabfm.2021.06.210110
Twitter logo Facebook logo Mendeley logo
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Jump to section

  • Article
    • Abstract
    • Introduction
    • Methods
    • Results
    • Discussion
    • Appendix 1. Search for Diagnosis of Influenza Using the History and Physical Examination
    • Appendix 2. Search for clinical prediction rules using RCSI filter1
    • Appendix 3. Definitions used for QUADAS-2 framework for quality assessment
    • Appendix 4. Clinical Prediction Rules Included in the Systematic Review
    • Notes
    • References
  • Figures & Data
  • References
  • Info & Metrics
  • PDF

Related Articles

  • No related articles found.
  • PubMed
  • Google Scholar

Cited By...

  • Accuracy of individual signs and symptoms and case definitions for the diagnosis of influenza in different age groups: a systematic review with meta-analysis
  • Use of Patient-Reported Symptom Data in Clinical Decision Rules for Predicting Influenza in a Telemedicine Setting
  • Research on the Issues Family Physicians Face Today: Controlled Substances, COVID-19, Hypertension, and "Slow Medicine," Among Many More Topics
  • Google Scholar

More in this TOC Section

  • Identifying and Addressing Social Determinants of Health with an Electronic Health Record
  • Integrating Adverse Childhood Experiences and Social Risks Screening in Adult Primary Care
  • A Pilot Comparison of Clinical Data Collection Methods Using Paper, Electronic Health Record Prompt, and a Smartphone Application
Show more Original Research

Similar Articles

Keywords

  • Clinical Decision Rules
  • Clinical Medicine
  • Influenza
  • Physical Examination
  • Prospective Studies
  • Respiratory Diseases
  • Systematic Reviews

Navigate

  • Home
  • Current Issue
  • Past Issues

Authors & Reviewers

  • Info For Authors
  • Info For Reviewers
  • Submit A Manuscript/Review

Other Services

  • Get Email Alerts
  • Classifieds
  • Reprints and Permissions

Other Resources

  • Forms
  • Contact Us
  • ABFM News

© 2025 American Board of Family Medicine

Powered by HighWire