Deployability-First Benchmarking of LFIA Innovation: A Framework and COVID-19 Literature Audit
Year: 2026
Type: preprint
Abstract: Lateral flow immunoassays (LFIAs) are operationally simple, enabling low-barrier, decentralized testing. Their reported performance should therefore be interpreted in relation to the deployability of the required workflow. LFIA innovations often seek improved detection performance, commonly expressed as lower limits of detection (LODs), but workflows can differ substantially in burden and testing conditions. To guide experimental planning, interpretation of results, and formulation of performance claims, we developed a deployability-first benchmarking framework. At the planning stage, the intended deployment setting guides selection of a compatible workflow, while the performance question guides the type of evidence needed to address it. The framework distinguishes low-burden, enhanced lowburden, and higher-burden workflows. Once results are available, the implemented workflow, sample context, measured endpoint, LOD definition, and any comparator help determine what conclusions and comparisons are supported and how the claim should be framed. We applied these principles retrospectively to 96 COVID-19 LFIA innovation articles. Nearly two-thirds of workflows supporting the selected primary performance claims were higher-burden, requiring instrumentation, trained users, or laboratory conditions. Direct like-with-like benchmarking was often limited by differences in workflow burden, sample context, endpoint, LOD definition, readout mode, and comparator conditions. We operationalized result interpretation and claim framing as a claim-level checklist. Applied retrospectively to the same 96 selected primary claims, about 40% were supported as written, whereas about 60% required narrowing or reframing. COVID-19 served as a literature test case; the framework and checklist contain no COVID-19-specific criteria and were designed for medical and non-medical LFIA settings.
Source: ChemRxiv
Language: English
Subfield: Infectious Diseases
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7212056016
