Informative array testing with multiplex assays

Abstract

High‐volume testing of clinical specimens for sexually transmitted diseases is performed frequently by a process known as group testing. This algorithmic process involves testing portions of specimens from separate individuals together as one unit (or “group”) to detect diseases. Retesting is performed on groups that test positively in order to differentiate between positive and negative individual specimens. The overall goal is to use the least number of tests possible across all individuals without sacrificing diagnostic accuracy. One of the most efficient group testing algorithms is array testing. In its simplest form, specimens are arranged into a grid‐like structure so that row and column groups can be formed. Positive‐testing rows/columns indicate which specimens to retest. With the growing use of multiplex assays, the increasing number of diseases tested by these assays, and the availability of subject‐specific risk information, opportunities exist to make this testing process even more efficient. We propose specific specimen arrangements within an array that can reduce the number of retests needed when compared with other array testing algorithms. We examine how to calculate operating characteristics, including the expected number of tests and the SD for the number of tests, and then subsequently find a best arrangement. Our methods are illustrated for chlamydia and gonorrhea detection with the Aptima Combo 2 Assay. We also provide R functions to make our research accessible to laboratories.

Optimizing Pooled Testing for Estimating the Prevalence of Multiple Diseases

“…For example, when an optimal pooling design is to be identified for estimating the prevalence of any co-occurring pathogens, one only needs to update the value of p and assay sensitivity/specificity. The computation algorithm introduced in this article can be extended to accommodate data arising from more complex pooling protocols, such as hierarchical testing with three or more stages (Hou et al 2017) and array testing (Hou et al 2020;Bilder et al 2021).…”