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

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.

Evaluating an app-guided self-test for influenza: lessons learned for improving the feasibility of study designs to evaluate self-tests for respiratory viruses.

BACKGROUND: Seasonal influenza leads to significant morbidity and mortality. Rapid self-tests could improve access to influenza testing in community settings. We aimed to evaluate the diagnostic accuracy of a mobile app-guided influenza rapid self-test for adults with influenza like illness (ILI), and identify optimal methods for conducting accuracy studies for home-based assays for influenza and other respiratory viruses. METHODS: This cross-sectional study recruited adults who self-reported ILI online. Participants downloaded a mobile app, which guided them through two low nasal swab self-samples. Participants tested the index swab using a lateral flow assay. Test accuracy results were compared to the reference swab tested in a research laboratory for influenza A/B using a molecular assay. RESULTS: Analysis included 739 participants, 80% were 25-64 years of age, 79% female, and 73% white. Influenza positivity was 5.9% based on the laboratory reference test. Of those who started their test, 92% reported a self-test result. The sensitivity and specificity of participants' interpretation of the test result compared to the laboratory reference standard were 14% (95%CI 5-28%) and 90% (95%CI 87-92%), respectively. CONCLUSIONS: A mobile app facilitated study procedures to determine the accuracy of a home based test for influenza, however, test sensitivity was low. Recruiting individuals outside clinical settings who self-report ILI symptoms may lead to lower rates of influenza and/or less severe disease. Earlier identification of study subjects within 48 h of symptom onset through inclusion criteria and rapid shipping of tests or pre-positioning tests is needed to allow self-testing earlier in the course of illness, when viral load is higher.