Rapid HIV tests: from meta-analysis to field application

Translating diagnostic assays from the laboratory to the clinic: analytical and clinical metrics for device development and evaluation

As lab-on-a-chip health diagnostic technologies mature, there is a push to translate them from the laboratory to the clinic. For these diagnostics to achieve maximum impact on patient care, scientists and engineers developing the tests should understand the analytical and clinical statistical metrics that determine the efficacy of the test. Appreciating and using these metrics will benefit test developers by providing consistent measures to evaluate analytical and clinical test performance, as well as guide the design of tests that will most benefit clinicians and patients. This paper is broken into four sections that discuss metrics related to general stages of development including: (1) laboratory assay development (analytical sensitivity, limit of detection, analytical selectivity, and trueness/precision), (2) pre-clinical development (diagnostic sensitivity, diagnostic specificity, clinical cutoffs, and receiver-operator curves), (3) clinical use (prevalence, predictive values, and likelihood ratios), and (4) case studies from existing clinical data for tests relevant to the lab-on-a-chip community (HIV, group A strep, and chlamydia). Each section contains definitions of recommended statistical measures, as well as examples demonstrating the importance of these metrics at various stages of the development process. Increasing the use of these metrics in lab-on-a-chip research will improve the rigor of diagnostic performance reporting and provide a better understanding of how to design tests that will ultimately meet clinical needs.

Diagnostic point-of-care tests in resource-limited settings

The aim of diagnostic point-of-care testing is to minimise the time to obtain a test result, thereby allowing clinicians and patients to make a quick clinical decision. Because point-of-care tests are used in resource-limited settings, the benefits need to outweigh the costs. To optimise point-of-care testing in resource-limited settings, diagnostic tests need rigorous assessments focused on relevant clinical outcomes and operational costs, which differ from assessments of conventional diagnostic tests. We reviewed published studies on point-of-care testing in resource-limited settings, and found no clearly defined metric for the clinical usefulness of point-of-care testing. Therefore, we propose a framework for the assessment of point-of-care tests, and suggest and define the term test efficacy to describe the ability of a diagnostic test to support a clinical decision within its operational context. We also propose revised criteria for an ideal diagnostic point-of-care test in resource-limited settings. Through systematic assessments, comparisons between centralised testing and novel point-of-care technologies can be more formalised, and health officials can better establish which point-of-care technologies represent valuable additions to their clinical programmes.