{"doi":"10.1093/cid/ciaa1382","title":"The Limit of Detection Matters: The Case for Benchmarking Severe Acute Respiratory Syndrome Coronavirus 2 Testing","abstract":"BACKGROUND: Resolving the coronavirus disease 2019 (COVID-19) pandemic requires diagnostic testing to determine which individuals are infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The current gold standard is to perform reverse-transcription polymerase chain reaction (PCR) on nasopharyngeal samples. Best-in-class assays demonstrate a limit of detection (LoD) of approximately 100 copies of viral RNA per milliliter of transport media. However, LoDs of currently approved assays vary over 10,000-fold. Assays with higher LoDs will miss infected patients. However, the relative clinical sensitivity of these assays remains unknown. METHODS: Here we model the clinical sensitivities of assays based on their LoD. Cycle threshold (Ct) values were obtained from 4700 first-time positive patients using the Abbott RealTime SARS-CoV-2 Emergency Use Authorization test. We derived viral loads from Ct based on PCR principles and empiric analysis. A sliding scale relationship for predicting clinical sensitivity was developed from analysis of viral load distribution relative to assay LoD. RESULTS: Ct values were reliably repeatable over short time testing windows, providing support for use as a tool to estimate viral load. Viral load was found to be relatively evenly distributed across log10 bins of incremental viral load. Based on these data, each 10-fold increase in LoD is expected to lower assay sensitivity by approximately 13%. CONCLUSIONS: The assay LoD meaningfully impacts clinical performance of SARS-CoV-2 tests. The highest LoDs on the market will miss a majority of infected patients. Assays should therefore be benchmarked against a universal standard to allow cross-comparison of SARS-CoV-2 detection methods.","journal":"Clinical Infectious Diseases","year":2020,"id":52111,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":141,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9602,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":262686,"name":"Rose Lee","orcid":"0000-0001-5112-8766","position":1,"is_corresponding":false},{"id":262687,"name":"Ghee Rye Lee","orcid":"0000-0001-6614-0223","position":2,"is_corresponding":false},{"id":262688,"name":"Cody Callahan","orcid":"0000-0002-0267-7096","position":3,"is_corresponding":false},{"id":262689,"name":"Annie Cheng","orcid":"0000-0002-0507-3458","position":4,"is_corresponding":false},{"id":262690,"name":"Christina Yen","orcid":"0000-0002-6145-4693","position":5,"is_corresponding":false},{"id":265132,"name":"Kenneth P Smith","orcid":null,"position":6,"is_corresponding":false},{"id":262691,"name":"Rohit Arora","orcid":"0000-0001-7106-9089","position":7,"is_corresponding":false},{"id":262692,"name":"James E. Kirby","orcid":"0000-0003-0392-4500","position":8,"is_corresponding":false},{"id":87122,"name":"Ramy Arnaout","orcid":"0000-0001-6955-9310","position":0,"is_corresponding":true}],"reference_count":13,"raw_metadata":null,"created_at":"2026-07-18T20:42:26.843314Z","pmid":"33532847","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}