{"doi":"10.1101/2020.04.03.20052621","title":"A computational approach to identify interfering medications on urine drug screening assays without data from confirmatory testing","abstract":"Abstract Background Urine drug screening (UDS) assays can rapidly and sensitively detect drugs of abuse, but can also produce spurious results due to interfering substances. We previously developed an approach to identify interfering medications using electronic health record (EHR) data, but the approach was limited to UDS assays for which presumptive positives were confirmed using more specific methods. Here we adapted the approach to search for medications that cause false positives on UDS assays lacking confirmation data. Methods From our institution’s EHR data, we used our previous dataset of 698,651 UDS and confirmation results. We also collected 211,108 UDS results for acetaminophen, ethanol, and salicylates. Both datasets included individuals’ prior medication exposures. We hypothesized that the odds of a presumptive positive would increase following exposure to an interfering ingredient independently of exposure to the assay’s target drug(s). For a given assay-ingredient pair, we quantified potential interference as an odds ratio from logistic regression. We evaluated interference of selected compounds in spiking experiments. Results Compared to the approach requiring confirmation data, our adapted approach showed only modestly diminished ability to detect interfering ingredients. Applying our approach to the new data, three ingredients had a higher odds ratio on the acetaminophen assay than acetaminophen itself did: levodopa, carbidopa, and entacapone. The first two, as well as related compounds methyldopa and alpha-methyldopamine, produced presumptive positives at < 40 μg/mL. Conclusions Our approach can reveal interfering medications using EHR data from institutions at which UDS results are not routinely confirmed.","journal":"medRxiv","year":2020,"id":129304,"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":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9575,"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":580536,"name":"Layla Aref","orcid":"0000-0002-4785-8042","position":1,"is_corresponding":false},{"id":440074,"name":"Jennifer M. Colby","orcid":"0000-0002-8409-9014","position":2,"is_corresponding":false},{"id":19205,"name":"Jacob J. Hughey","orcid":"0000-0002-1558-6089","position":3,"is_corresponding":false},{"id":304604,"name":"Nadia Ayala‐Lopez","orcid":"0000-0001-8000-3997","position":0,"is_corresponding":true}],"reference_count":7,"raw_metadata":null,"created_at":"2026-07-18T23:15:45.920615Z","pmid":null,"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":[]}