Analytical evaluation of a rapid on-site oral fluid drug test

There is a need for a reliable rapid on-site oral fluid test that can be used in police controls to detect impaired drivers. We evaluated the Varian OralabA (R) 6 and collected two oral fluid samples from 250 subjects, one with the Varian OralabA (R) 6 and one with the StatSure (TM) SalivaaEuro cent Sampler (TM). The OralabA (R) 6 can detect six drug types: amphetamines, methamphetamine, cocaine, opiates, delta9-tetrahydrocannabinol (THC), and phencyclidine (PCP). On-site results were obtained within 10 to 15 min. The sample collected with StatSure (TM) was analyzed using liquid chromatography-tandem mass spectrometry after liquid-liquid extraction and these results were used as a reference to determine prevalence, sensitivity, and specificity. Two cut-off values were used in the evaluation. The Varian cut-off values were: amphetamine 50 ng/mL, cocaine 20 ng/mL, opiates 40 ng/mL, and THC 50 ng/mL. The DRUID cut-offs were: amphetamine 25 ng/mL, cocaine 20 ng/mL, opiates 20 ng/mL, and THC 1 ng/mL. Applying the first cut-offs, prevalence, sensitivity, and specificity were: amphetamine 10%, 76%, 100%; cocaine 23%, 34%, 100%; opiates 38%, 83%, 94%; and THC 18%, 41%, 99%. The DRUID cut-off values gave the following results: amphetamine 14%, 56%, 100%; cocaine 28%, 34%, 100%; opiates 49%, 68%, 98%, and THC 45%, 16%, 99%. The specificity of the OralabA (R) 6 is generally good. For both cut-offs, sensitivity was low for cocaine and THC. Therefore, the Varian OralabA (R) 6 test is not sensitive enough to be applied during roadside police controls

Comparison of a checklist for clinical signs of impairment and detection of drugs in saliva

Objectives: In Belgium and some other countries, the police performs on-site drug screening when a driver is suspected of being drug impaired, since the test procedure is time consuming and the screening devices are relatively expensive. The objective of the study is to compare the results of a checklist with drug concentrations in saliva. Method: The checklist used in the DRUID study in the Netherlands was used. Two fifth-year medical students performed the tests on 250 subjects, 50 drivers and 200 subjects attending a methadone clinic. Saliva analysis was performed by UPLC-MS/MS. As several signs were rarely observed, the parameters were reduced to those that were positive in at least 3 out of 250 test subjects. This selection led to a reduction to 13 (out of 28) parameters. A statistical test (Fisher’s exact test) was used to test for correlations between the checklist parameters and the presence of substances in oral fluid. Results: Most parameters did not correlate significantly with drug intake. The pupil tests seemed to be the best predicting parameters, especially for amphetamine and THC. Remarkably, some correlations were found between parameters and drugs where no correlation was expected, e.g. sleepiness and amphetamines. This can possibly be caused by the presence of combination use of drugs in a lot of subjects. The signs were often observed when high drug concentrations were seen in saliva, but in many cases with high saliva drug concentrations, no signs were observed. Conclusions: In general, the checklist correlated badly with drug presence in this population of chronic drug users, but our results also confirm other studies that found that checklists are not very sensitive

Qualitative detection of desmopressin in plasma by liquid chromatography-tandem mass spectrometry

This work describes a liquid chromatography-electrospray tandem mass spectrometry method for detection of desmopressin in human plasma in the low femtomolar range. Desmopressin is a synthetic analogue of the antidiuretic hormone arginine vasopressin and it might be used by athletes as a masking agent in the framework of blood passport controls. Therefore, it was recently added by the World Anti-Doping Agency to the list of prohibited substances in sport as a masking agent. Mass spectrometry characterization of desmopressin was performed with a high-resolution Orbitrap-based mass spectrometer. Detection of the peptide in the biological matrix was achieved using a triple-quadrupole instrument with an electrospray ionization interface after protein precipitation, weak cation solid-phase extraction and high performance liquid chromatography separation with an octadecyl reverse-phase column. Identification of desmopressin was performed using three product ions, m/z 328.0, m/z 120.0, and m/z 214.0, from the parent ion, m/z 535.5. The extraction efficiency of the method at the limit of detection was estimated as 40% (n = 10), the ion suppression as 5% (n = 10), and the limit of detection was 50 pg/ml (signal-to-noise ratio greater than 3). The selectivity of the method was verified against several endogenous and synthetic desmopressin-related peptides. The performance and the applicability of the method were tested by analysis of clinical samples after administration of desmopressin via intravenous, oral, and intranasal routes. Only after intravenous administration could desmopressin be successfully detected

Development of a multivariate prediction model for nocturia, based on urinary tract etiologies

Purpose: The main objective of our study was to determine which combination of modifiable and non-modifiable parameters that could discriminate patients with nocturia from those without nocturia. This was a post-hoc analysis of 3 prospective, observational studies conducted in Ghent University. Participants completed frequency volume chart (FVC) to compare characteristics between patients with and without nocturia. Method: This was a post hoc analysis of three prospective, observational studies conducted in Ghent University. Participants completed frequency volume chart (FVC) to compare characteristics between adults with and without nocturia. Study 1: adults with and without nocturia (n = 148); Study 2: patients >= 65 years with and without nocturnal LUTS (n = 54); Study 3: menopausal women before and after hormone replacement therapy (n = 43). All eligible patients (n = 183) completed a FVC during 24 hours (n = 13), 48 hours (n = 30) or 72 hours (n = 140). The combination of algorithms and number of determinants obtaining the best average area under the receiver operating curve (AUC-ROC) led to the final model. Differences between groups were assessed using the AUC-ROC and Mann- Whitney-Wilcoxon tests. Holm corrections were applied for multiple statistical testing. Also, the stability of the feature selection was evaluated. Results: The best discrimination was obtained when 13 determinants were included. However, a logistic regression model based on seven determinants selected with random forest had comparable discrimination including an optimal signature stability. It was able to discriminate almost perfectly between nights with and without nocturia. Conclusion: Relevant information to accomplish the excellent predictability of the model is; functional bladder capacity, 24 hours urine output, nocturnal output, age, BMI. The multivariate model used in this analysis provides new insights into combination therapy as it allows simulating the effect of different available treatment modalities and its combinations