Objective: The objective of this study is to perform a comprehensive systematic review and meta-analysis of the accuracy of signs, symptoms and case definitions for the diagnosis of influenza.

Design: Systematic review and meta-analysis of diagnostic accuracy.

Setting: Inpatient or outpatient setting.

Participants: Three databases (PubMed, CINAHL and EMBASE) were searched through February 2024 for studies of clinical diagnosis of influenza using prospective data collection and a high-quality reference standard. Data were abstracted by researchers working in parallel and resolving discrepancies by discussion.

Primary and secondary outcome measures: Quality was assessed using QUADAS-2. Summary estimates (or ranges) of sensitivity and specificity, likelihood ratio (LR), the Youden Index and the area under the receiver operating characteristic curve were calculated.

Results: The final meta-analysis included 67 studies, each with between 119 and 155 866 participants. Most were judged to be low risk of bias. The signs and symptoms with the highest overall accuracy for all studies based on the Youden Index were any fever (0.32), overall clinical impression (0.28), coryza (0.25), cough and fever (0.25), and measured fever (0.25). Accuracy varied widely by age group. Only the overall clinical impression had a positive LR greater than 2.0. Cough was the most sensitive finding (0.92) with a negative LR of 0.28 in adults. The absence of any fever also had a low negative LR (0.30). The Centers for Disease Control and Prevention (CDC) definition of influenza-like illness (ILI) had good specificity but poor sensitivity in adults, while in infants, it had good sensitivity but widely varying specificity. The European CDC and WHO case definitions for ILI had modest sensitivity and specificity.

Conclusions: Individual signs and symptoms, their combinations, and ILI case definitions have very limited accuracy for identifying persons with influenza. More accurate surveillance and diagnosis will require the development and validation of accurate risk scores or greater use of point-of-care testing.