Validation of a liquid chromatography tandem mass spectrometry (LC-MS/MS) method to detect cannabinoids in whole blood and breath.

Background The widespread availability of cannabis raises concerns regarding its effect on driving performance and operation of complex equipment. Currently, there are no established safe driving limits regarding ∆9-tetrahydrocannabinol (THC) concentrations in blood or breath. Daily cannabis users build up a large body burden of THC with residual excretion for days or weeks after the start of abstinence. Therefore, it is critical to have a sensitive and specific analytical assay that quantifies THC, the main psychoactive component of cannabis, and multiple metabolites to improve interpretation of cannabinoids in blood; some analytes may indicate recent use. Methods A liquid chromatography tandem mass spectrometry (LC-MS/MS) method was developed to quantify THC, cannabinol (CBN), cannabidiol (CBD), 11-hydroxy-THC (11-OH-THC), (±)-11-nor-9-carboxy-Δ9-THC (THCCOOH), (+)-11-nor-Δ9-THC-9-carboxylic acid glucuronide (THCCOOH-gluc), cannabigerol (CBG), and tetrahydrocannabivarin (THCV) in whole blood (WB). WB samples were prepared by solid-phase extraction (SPE) and quantified by LC-MS/MS. A rapid and simple method involving methanol elution of THC in breath collected in SensAbues® devices was optimized. Results Lower limits of quantification ranged from 0.5 to 2 μg/L in WB. An LLOQ of 80 pg/pad was achieved for THC concentrations in breath. Calibration curves were linear (R2>0.995) with calibrator concentrations within ±15% of their target and quality control (QC) bias and imprecision ≤15%. No major matrix effects or drug interferences were observed. Conclusions The methods were robust and adequately quantified cannabinoids in biological blood and breath samples. These methods will be used to identify cannabinoid concentrations in an upcoming study of the effects of cannabis on driving

Serum prolactin and macroprolactin levels among outpatients with major depressive disorder following the administration of selective serotonin-reuptake inhibitors: a cross-sectional pilot study.

Clinical trials evaluating the rate of short-term selective serotonin-reuptake inhibitor (SSRI)-induced hyperprolactinemia have produced conflicting results. Thus, the aim of this study was to clarify whether SSRI therapy can induce hyperprolactinemia and macroprolactinemia. Fifty-five patients with major depressive disorder (MDD) were enrolled in this study. Serum prolactin and macroprolactin levels were measured at a single time point (i.e., in a cross-sectional design). All patients had received SSRI monotherapy (escitalopram, paroxetine, or sertraline) for a mean of 14.75 months. Their mean prolactin level was 15.26 ng/ml. The prevalence of patients with hyperprolactinemia was 10.9% for 6/55, while that of patients with macroprolactinemia was 3.6% for 2/55. The mean prolactin levels were 51.36 and 10.84 ng/ml among those with hyperprolactinemia and a normal prolactin level, respectively. The prolactin level and prevalence of hyperprolactinemia did not differ significantly within each SSRI group. Correlation analysis revealed that there was no correlation between the dosage of each SSRI and prolactin level. These findings suggest that SSRI therapy can induce hyperprolactinemia in patients with MDD. Clinicians should measure and monitor serum prolactin levels, even when both SSRIs and antipsychotics are administered

Development and Application of Computerized Risk Registry and Management Tool Based on FMEA and FRACAS for Total Testing Process

Background and Objectives: Risk management is considered an integral part of laboratory medicine to assure laboratory quality and patient safety. However, the concept of risk management is philosophical, so actually performing risk management in a clinical laboratory can be challenging. Therefore, we would like to develop a sustainable, practical system for continuous total laboratory risk management. Materials and Methods: This study was composed of two phases: the development phase in 2019 and the application phase in 2020. A concept flow diagram for the computerized risk registry and management tool (RRMT) was designed using the failure mode and effects analysis (FMEA) and the failure reporting, analysis, and corrective action system (FRACAS) methods. The failure stage was divided into six according to the testing sequence. We applied laboratory errors to this system over one year in 2020. The risk priority number (RPN) score was calculated by multiplying the severity of the failure mode, frequency (or probability) of occurrence, and detection difficulty. Results: 103 cases were reported to RRMT during one year. Among them, 32 cases (31.1%) were summarized using the FMEA method, and the remaining 71 cases (68.9%) were evaluated using the FRACAS method. There was no failure in the patient registration phase. Chemistry units accounted for the highest proportion of failure with 18 cases (17.5%), while urine test units accounted for the lowest portion of failure with two cases (1.9%). Conclusion: We developed and applied a practical computerized risk-management tool based on FMEA and FRACAS methods for the entire testing process. RRMT was useful to detect, evaluate, and report failures. This system might be a great example of a risk management system optimized for clinical laboratories

Mean prolactin levels before PEG among patients taking escitalopram, paroxetine, and sertraline (ESC; escitalopram, PRX; paroxetine, SERT; sertraline).

<p>Mean prolactin levels before PEG among patients taking escitalopram, paroxetine, and sertraline (ESC; escitalopram, PRX; paroxetine, SERT; sertraline).</p

Correlation between the treatment duration of each SSRI and prolactin levels.

<p>Correlation between the treatment duration of each SSRI and prolactin levels.</p

Mean prolactin levels after PEG among patients taking escitalopram, paroxetine, and sertraline (ESC; escitalopram, PRX; paroxetine, SERT; sertraline).

<p>Mean prolactin levels after PEG among patients taking escitalopram, paroxetine, and sertraline (ESC; escitalopram, PRX; paroxetine, SERT; sertraline).</p

Correlation between the dosage of each SSRI and prolactin levels.

<p>Correlation between the dosage of each SSRI and prolactin levels.</p

Comparison of Commercial Genetic-Testing Services in Korea with 23andMe Service

Introduction. Genetic testing services for disease prediction, drug responses, and traits are commercially available by several companies in Korea. However, there has been no evaluation study for the accuracy and usefulness of these services. We aimed to compare two genetic testing services popular in Korea with 23andMe service in the United States. Materials and Methods. We compared the results of two persons (one man and one woman) serviced by Hellogene Platinum (Theragen Bio Institute), DNAGPS Optimus (DNAlink), and 23andMe service. Results. Among 3 services, there were differences in the estimation of relative risks for the same disease. For lung cancer, the range of relative risk was from 0.9 to 2.09. These differences were thought to be due to the differences of applied single nucleotide polymorphisms (SNPs) in each service for the calculation of risk. Also, the algorithm and population database would have influence on the estimation of relative disease risks. The concordance rate of SNP calls between DNAGPS Optimus and 23andMe services was 100% (30/30). Conclusions. Our study showed differences in disease risk estimations among three services, although they gave good concordance rate for SNP calls. We realized that the genetic services need further evaluation and standardization, especially in disease risk estimation algorithm

An Algorithmic Approach Is Superior to the 99th Percentile Upper Reference Limits of High Sensitivity Troponin as a Threshold for Safe Discharge from the Emergency Department

Background and Objectives: High-sensitivity cardiac troponin I (hs-TnI) is an important indicator of acute myocardial infarction (AMI) among patients presenting with chest discomfort at the emergency department (ED). We aimed to determine a reliable hs-TnI cut-off by comparing various values for a baseline single measurement and an algorithmic approach. Materials and Methods: We retrospectively reviewed the hs-TnI values of patients who presented to our ED with chest discomfort between June 2019 and June 2020. We evaluated the diagnostic accuracy of AMI with the Beckman Coulter Access hs-TnI assay by comparing the 99th percentile upper reference limits (URLs) based on the manufacturer’s claims, the newly designated URLs in the Korean population, and an algorithmic approach. Results: A total of 1296 patients who underwent hs-TnI testing in the ED were reviewed and 155 (12.0%) were diagnosed with AMI. With a single measurement, a baseline hs-TnI cut-off of 18.4 ng/L showed the best performance for the whole population with a sensitivity of 78.7%, specificity of 95.7%, negative predictive value (NPV) of 97.1%, and positive predictive value (PPV) of 71.3%. An algorithm using baseline and 2–3 h hs-TnI values showed an 100% sensitivity, 97.7% specificity, an NPV of 100%, and a PPV of 90.1%. This algorithm used a cut-off of &lt;4 ng/L for a single measurement 3 h after symptom onset or an initial level of &lt;5 ng/L and a change of &lt;5 ng/L to rule a patient out, and a cut-off of ≥50 ng/L for a single measurement or a change of ≥20 ng/L to rule a patient in. Conclusions: The algorithmic approach using serial measurements could help differentiate AMI patients from patients who could be safely discharged from the ED, ensuring that patients were triaged accurately and did not undergo unnecessary testing. The cut-off values from previous studies in different countries were effective in the Korean population

Age-Standardized Breast Cancer Detection Rates of Breast Cancer Screening Program by Age Group in Korea; Comparison with Age-Standardized Incidence Rates from the Korea Central Cancer Registry

To compare the epidemiological characteristics of a breast cancer screening program of patients between 40&ndash;69 years of age and &ge;70 years of age, we calculated the age-standardized detection rate of the breast cancer screening program and compared it with the age-standardized incidence rate from the Korea Central Cancer Registry. The data of the breast cancer screening program from January 2009 to December 2016 and the data of the health insurance claims from January 2006 to August 2017 were used. In the 40&ndash;69 year age group, the age-standardized detection rate of breast cancer increased annually from 106.1 in 2009 to 158.6 in 2015 and did not differ from the age-standardized incidence rate. In the &ge;70 year age group, the age-standardized detection rate of breast cancer increased annually from 65.7 in 2009 to 120.3 in 2015 and was 1.9 to 2.7 fold of the age-standardized incidence rate. It shows that the early detection effect of breast cancer screening was greater for patients over 70 years old. Further studies are needed to evaluate the effect of breast cancer detection in the &ge;70 year age group on all-cause mortality or breast cancer mortality