scholarly journals Analysis of Electronic Health Records Reveals Medication-Related Interference on Point-of-Care Urine Drug Screening Assays

Author(s):  
Nadia Ayala-Lopez ◽  
Jennifer M Colby ◽  
Jacob J Hughey

Abstract Point-of-care (POC) urine drug screening (UDS) assays provide immediate information for patient management. However, POC UDS assays can produce false-positive results, which may not be recognized until confirmatory testing is completed several days later. To minimize the potential for patient harm, it is critical to identify sources of interference. Here, we applied an approach based on statistical analysis of electronic health record (EHR) data to identify medications that may cause false positives on POC UDS assays. From our institution’s EHR data, we extracted 120,670 POC UDS and confirmation results, covering 12 classes of target drugs, along with each individual’s prior medication exposures. Our approach is based on the idea that exposure to an interfering medication will increase the odds of a false-positive UDS result. For a given assay–medication pair, we quantified the association between medication exposures and UDS results as an odds ratio from logistic regression. We evaluated interference experimentally by spiking compounds into drug-free urine and testing the spiked samples on the POC device. Our dataset included 446 false-positive UDS results (presumptive positive screen followed by negative confirmation). We quantified the odds ratio of false positives for 528 assay–medication pairs. Of the six assay–medication pairs we evaluated experimentally, two showed interference capable of producing a presumptive positive: labetalol on the 3,4-methylenedioxymethamphetamine (MDMA) assay (at 200 μg/mL) and ranitidine on the methamphetamine assay (at 50 μg/mL). Ranitidine also produced a presumptive positive for opiates at 1,600 μg/mL and for propoxyphene at 800 μg/mL. These findings highlight the generalizability and the limits of our approach to use EHR data to identify medications that interfere with clinical immunoassays.

2020 ◽  
Author(s):  
Nadia Ayala-Lopez ◽  
Jennifer M. Colby ◽  
Jacob J. Hughey

AbstractBackgroundPoint-of-care (POC) urine drug screening (UDS) assays provide immediate information for patient management. However, POC UDS assays can produce false positive results, which may not be recognized until confirmatory testing is completed several days later. To minimize the potential for patient harm, it is critical to identify sources of interference. Here we applied an approach based on statistical analysis of electronic health record (EHR) data to identify medications that may cause false positives on POC UDS assays.MethodsFrom our institution’s EHR data, we extracted 120,670 POC UDS and confirmation results, covering 12 classes of target drugs, along with each individual’s prior medication exposures. For a given assay and medication ingredient, we quantified potential interference as an odds ratio from logistic regression. We evaluated interference experimentally by spiking compounds into drug-free urine and testing the spiked samples on the POC device (Integrated E-Z Split Key Cup II, Alere).ResultsOur dataset included 446 false positive UDS results (presumptive positive screen followed by negative confirmation). We quantified potential interference for 528 assay-ingredient pairs. Of the six assay-ingredient pairs we evaluated experimentally, two showed interference capable of producing a presumptive positive: labetalol on the MDMA assay (at 200 μg/mL) and ranitidine on the methamphetamine assay (at 50 μg/mL). Ranitidine also produced a presumptive positive for opiates at 1600 μg/mL and for propoxyphene at 800 μg/mL.ConclusionsThese findings support the generalizability of our approach to use EHR data to identify medications that interfere with clinical immunoassays.


2019 ◽  
Vol 65 (12) ◽  
pp. 1522-1531 ◽  
Author(s):  
Jacob J Hughey ◽  
Jennifer M Colby

Abstract BACKGROUND Exposure to drugs of abuse is frequently assessed using urine drug screening (UDS) immunoassays. Although fast and relatively inexpensive, UDS assays often cross-react with unrelated compounds, which can lead to false-positive results and impair patient care. The current process of identifying cross-reactivity relies largely on case reports, making it sporadic and inefficient, and rendering knowledge of cross-reactivity incomplete. Here, we present a systematic approach to discover cross-reactive substances using data from electronic health records (EHRs). METHODS Using our institution's EHR data, we assembled a data set of 698651 UDS results across 10 assays and linked each UDS result to the corresponding individual's previous medication exposures. We hypothesized that exposure to a cross-reactive ingredient would increase the odds of a false-positive screen. For 2201 assay–ingredient pairs, we quantified potential cross-reactivity as an odds ratio from logistic regression. We then evaluated cross-reactivity experimentally by spiking the ingredient or its metabolite into drug-free urine and testing the spiked samples on each assay. RESULTS Our approach recovered multiple known cross-reactivities. After accounting for concurrent exposures to multiple ingredients, we selected 18 compounds (13 parent drugs and 5 metabolites) to evaluate experimentally. We validated 12 of 13 tested assay–ingredient pairs expected to show cross-reactivity by our analysis, discovering previously unknown cross-reactivities affecting assays for amphetamines, buprenorphine, cannabinoids, and methadone. CONCLUSIONS Our findings can help laboratorians and providers interpret presumptive positive UDS results. Our data-driven approach can serve as a model for high-throughput discovery of substances that interfere with laboratory tests.


2020 ◽  
Author(s):  
Nadia Ayala-Lopez ◽  
Layla Aref ◽  
Jennifer M. Colby ◽  
Jacob J. Hughey

AbstractBackgroundUrine 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.MethodsFrom 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.ResultsCompared 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.ConclusionsOur approach can reveal interfering medications using EHR data from institutions at which UDS results are not routinely confirmed.


Author(s):  
Nadia Ayala-Lopez ◽  
Layla Aref ◽  
Jennifer M Colby ◽  
Jacob J Hughey

Abstract 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. 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 medication independently of exposure to the assay’s target drug(s). For a given assay–medication pair, we quantified potential interference as an odds ratio from logistic regression. We evaluated interference of selected compounds in spiking experiments. Compared to the approach requiring confirmation data, our adapted approach showed only modestly diminished ability to detect interfering medications. Applying our approach to the new data, we discovered and validated multiple compounds that can cause presumptive positives on the UDS assay for acetaminophen. Our approach can reveal interfering medications using EHR data from institutions at which UDS results are not routinely confirmed.


2010 ◽  
Vol 41 (8) ◽  
pp. 457-460 ◽  
Author(s):  
Michael F. Neerman ◽  
Chinemerem L. Uzoegwu

2001 ◽  
Vol 11 (3) ◽  
pp. 18???22
Author(s):  
Albert Jekelis

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