RT Journal Article
SR Electronic
T1 A Systematic Review of Clinical Prediction Rules for the Diagnosis of Influenza
JF The Journal of the American Board of Family
                Medicine
JO J Am Board Fam Med
FD American Board of Family Medicine
SP 1123
OP 1140
DO 10.3122/jabfm.2021.06.210110
VO 34
IS 6
A1 Mark H. Ebell
A1 Ivan Rahmatullah
A1 Xinyan Cai
A1 Michelle Bentivegna
A1 Cassie Hulme
A1 Matthew Thompson
A1 Barry Lutz
YR 2021
UL http://www.jabfm.org/content/34/6/1123.abstract
AB Background: Clinical prediction rules (CPRs) can assist clinicians by focusing their clinical evaluation on the most important signs and symptoms, and if used properly can reduce the need for diagnostic testing. This study aims to perform an updated systematic review of clinical prediction rules and classification and regression tree (CART) models for the diagnosis of influenza.Methods: We searched PubMed, CINAHL, and EMBASE databases. We identified prospective studies of patients presenting with suspected influenza or respiratory infection and that reported a CPR in the form of a risk score or CART-based algorithm. Studies had to report at a minimum the percentage of patients in each risk group with influenza. Studies were evaluated for inclusion and data were extracted by reviewers working in parallel. Accuracy was summarized descriptively; where not reported by the authors the area under the receiver operating characteristic curve (AUROCC), predictive values, and likelihood ratios were calculated.Results: We identified 10 studies that presented 14 CPRs. The most commonly included predictor variables were cough, fever, chills and/or sweats, myalgias, and acute onset, all which can be ascertained by phone or telehealth visit. Most CPRs had an AUROCC between 0.7 and 0.8, indicating good discrimination. However, only 1 rule has undergone prospective external validation, with limited success. Data reporting by the original studies was in some cases inadequate to determine measures of accuracy.Conclusions: Well-designed validation studies, studies of interrater reliability between telehealth an in-person assessment, and studies using novel data mining and artificial intelligence strategies are needed to improve diagnosis of this common and important infection.