Literature DB >> 27590035

Urine Creatinine Concentrations in Drug Monitoring Participants and Hospitalized Patients.

Sara A Love1, Jesse C Seegmiller2, Julie Kloss3, Fred S Apple4.   

Abstract

Urine drug testing is commonly performed in both clinical and forensic arenas for screening, monitoring and compliance purposes. We sought to determine if urine creatinine concentrations in monitoring program participants were significantly different from hospital in-patients and out-patients undergoing urine drug testing. We retrospectively reviewed urine creatinine submitted in June through December 2015 for all specimens undergoing urine drug testing. The 20,479 creatinine results were categorized as hospitalized patients (H) and monitoring/compliance groups for pain management (P), legal (L) or recovery (R). Median creatinine concentrations (interquartile range, mg/dL) were significantly different (P < 0.001) between groups: H 126 (122-136); P 138 (137-143); L 147 (144-154); R 95 (92-97). In the two groups subject to on-demand sampling time pressures, median creatinine concentrations were significantly lower in the R vs. L group (P<0.001). In conclusion, recovery (R) participants have more dilute specimens, reflected by significantly lower creatinine concentration and may indicate participants' attempts to tamper with their drug test results through dilution means.
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Year:  2016        PMID: 27590035     DOI: 10.1093/jat/bkw092

Source DB:  PubMed          Journal:  J Anal Toxicol        ISSN: 0146-4760            Impact factor:   3.367


  3 in total

1.  Evaluating the reliability of hair analysis in monitoring the compliance of ADHD patients under treatment with Lisdexamphetamine.

Authors:  Marianne Haedener; Wolfgang Weinmann; Dominique Eich; Michael Liebrenz; Thomas Wuethrich; Anna Buadze
Journal:  PLoS One       Date:  2021-03-30       Impact factor: 3.240

Review 2.  Mass spectrometry-based metabolomics in health and medical science: a systematic review.

Authors:  Xi-Wu Zhang; Qiu-Han Li; Zuo-di Xu; Jin-Jin Dou
Journal:  RSC Adv       Date:  2020-01-17       Impact factor: 4.036

3.  Medical recommender systems based on continuous-valued logic and multi-criteria decision operators, using interpretable neural networks.

Authors:  Juan G Diaz Ochoa; Orsolya Csiszár; Thomas Schimper
Journal:  BMC Med Inform Decis Mak       Date:  2021-06-11       Impact factor: 2.796

  3 in total

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