scholarly journals Evaluation of NexScreen Cup Point-of-Care Immunoassays for Urine Drug Screening in Chronic Pain Management Patients

2018 ◽  
Vol 150 (suppl_1) ◽  
pp. S163-S163
Author(s):  
Dan Wang ◽  
Nikolina Babic ◽  
Brittaney Dieppa ◽  
Shea Woodward
Keyword(s):  
Chronic Pain ◽  
Pain Management ◽  
Drug Screening ◽  
Point Of Care ◽  
Chronic Pain Management ◽  
Urine Drug ◽  
Urine Drug Screening

scholarly journals Impact of Urine Drug Screening on No Shows and Dropouts among Chronic Pain Patients: A Propensity-Matched Cohort Study

2016 ◽  
Vol 19 (2;2) ◽  
pp. 89-100
Author(s):  
Parthasarathy Krishnamurthy
Keyword(s):  
Chronic Pain ◽  
Cohort Study ◽  
Propensity Score ◽  
Drug Screening ◽  
Proportional Hazards ◽  
Pain Clinic ◽  
Urine Drug ◽  
The Impact ◽  
Pain Patients ◽  
Urine Drug Screening

Background: The last 2 decades have seen a substantial increase in both the prescription of opioids for managing chronic pain, and an increase in opioid-related deaths in the US. Urine drug screening (UDS) is the de facto monitoring tool aimed at detecting and deterring opioid misuse. Objective: We study whether administering UDS on pain patients influences post-screening behavior of no-shows and dropouts. Study Design: Observational cohort study of electronic medical records. Setting: Single urban academic pain-clinic. Methods: A retrospective cohort comparison of patients receiving UDS versus those not receiving UDS was conducted on the entire sample as well as in the propensity score-matched samples in which matching was based on age, gender, pain-score, procedure-scheduled, systolic and diastolic blood pressure (BP), pulse, temperature, physician ID, year of visit, psychology referral, and opioid prescription in the first visit. In addition, we conducted within-subjects logistic-regression to study no-shows and non-proportional hazards survival modeling to study dropout. Results: Analyses of 4,448 clinic visits by 723 pain patients indicated that UDS exposure in the first visit is associated with increased risk of no-show in the second visit (OR = 2.73, P < .0001); no-show rate was 10.24% for those without UDS compared to 23.75% for those with a UDS. Among those tested, the no-show rate was higher for those testing positive for illicit substances (34.57%) than for those testing negative (21.74%). These findings were replicated in 8 different propensity-score matched subsamples aimed at addressing potential nonrandom selection, as well as in within-subject analysis accounting for individual-level no-show propensity. Non-proportional hazards survival analysis shows that risk of dropout increased by 100.3% with every additional UDS (HR 95% CI: 1.54 to 2.61). Limitations: Retrospective design, non-randomized sample, single-setting. Conclusions: The results indicate that UDS is associated with increased no-shows and dropout from clinic subject to limitations of observational studies such as selection bias and confound by unobserved variables. These results serve as a call for additional prospective randomized studies to understand the impact of UDS, and where the patients might go when they dropout from the clinic. Key words: Chronic pain, opioid monitoring, UDS, urine-drug screening, no-show, dropout, adherence, propensity-score matching

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

2020 ◽  
Author(s):  
Nadia Ayala-Lopez ◽  
Jennifer M Colby ◽  
Jacob J Hughey
Keyword(s):  
Drug Screening ◽  
False Positive ◽  
Odds Ratio ◽  
Point Of Care ◽  
False Positives ◽  
Positive Screen ◽  
Urine Drug ◽  
Electronic Health ◽  
Drug Free ◽  
Urine Drug Screening

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.

scholarly journals Analysis of electronic health records reveals medication-related interference on point-of-care urine drug screening assays

2020 ◽  
Author(s):  
Nadia Ayala-Lopez ◽  
Jennifer M. Colby ◽  
Jacob J. Hughey
Keyword(s):  
Drug Screening ◽  
False Positive ◽  
Point Of Care ◽  
Positive Screen ◽  
Urine Drug ◽  
Screening Assays ◽  
Electronic Health ◽  
Drug Free ◽  
Positive Results ◽  
Urine Drug Screening

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.

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