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BackgroundA composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data.MethodsWe assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low-glucose and low-glucose hypoglycemia; very high-glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation.ResultsThe analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals.ConclusionThe GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments.

Background: A new Rapid Drug Test Device (RDTD) is available for analysis of urine fentanyl. With the rise in fentanyl abuse in the United States, we evaluated the analytical performance of the RDTD test strip compared to mass spectrometry and 2 urine fentanyl immunoassays. Methods: Leftover, deidentified urine samples collected from inpatients and outpatients from our psychiatric hospital and other clinics were frozen at <-70°C, thawed at room temperature, and centrifuged. Aliquots were tested with the RDTD (CLIA Waived, Inc.) test strips and 2 urine fentanyl immunoassays: the ARK Fentanyl II assay (ARK Diagnostics Inc.) and the Immunalysis SEFRIA Fentanyl assay (Immunalysis Corporation). Both assays were conducted on the Abbott Alinity c chemistry analyzer (Abbott Laboratories). Mass spectrometry analysis was performed at ARUP Laboratories. All assays had a 1 ng/mL positive cutoff. Results: A total of 142 urine samples included 70 positive and 72 negative samples. The RDTD strips had lower sensitivity (84.3%) and efficiency (85.9%) and showed a specificity of 87.5% compared to the other assays. The ARK Fentanyl II assay showed identical sensitivity (95.7%) to the Immunalysis SEFRIA Fentanyl assay but had higher specificity (94.4% vs 81.9%) and overall efficiency (95.1% vs 88.7%). Conclusions: Differences were noted in the number of false negatives and positives among the assays. The RDTD demonstrated acceptable performance in detecting urine fentanyl in our patient population and would provide faster test results at point-of-care testing sites in our healthcare enterprise. [ABSTRACT FROM AUTHOR]

Advances in technology have transformed healthcare and laboratory medicine. Biosensors have emerged as a promising technology in healthcare, providing a way to monitor human physiological parameters in a continuous, real-time, and non-intrusive manner and offering value and benefits in a wide range of applications. This position statement aims to present the current situation around biosensors, their perspectives and importantly the need to set the framework for their validation and safe use. The development of a qualification framework for biosensors should be conceptually adopted and extended to cover digitally measured biomarkers from biosensors for advancing healthcare and achieving more individualized patient management and better patient outcome. [ABSTRACT FROM AUTHOR]

Background: In this study, we evaluated the impact of hemoglobin (Hb) variants on the performance of the Abbott Alinity c and Bio-Rad Variant II Turbo 2.0 HPLC Hb A1c assays. Methods: The analytical performance of the Abbott Alinity c Hb A1c (enzymatic) assay was compared to the Bio-Rad Variant II Turbo 2.0 HPLC method using leftover whole blood EDTA samples with and without the presence of a hemoglobin variant. Assay precision was determined from an analysis of controls. Bias was estimated from analysis of a set of 40 samples analyzed by a Tosoh G8 HPLC instrument at the University of Missouri Diabetes Diagnostic Laboratory, an NGSP Secondary Reference Laboratory. Results: Between-day precision was excellent for both methods (<3%). Bias met NGSP criteria of ±5% to target value. Correlation between the Alinity and Bio-Rad methods was good for patient samples without a hemoglobinopathy (y = 1.028x – 0.38, standard error of the estimate (SEE) = 0.16, n = 36, mean bias = −0.22). A total of 700 hemoglobin variant samples were evaluated on the 2 methods. Of the hemoglobin variants, 640/700 gave results on both methods: hemoglobin (Hb) S trait (n = 452), C trait (n = 131), D trait (n = 23), E trait (n = 26), and a mixture of other hemoglobinopathies (n = 8) including beta thalassemia, high hemoglobin F, transfused Hb SC, transfused Hb SD, and transfused Hb SS, or unknown variant. There was good agreement for the 640 Hb variants between the methods with a range of mean differences of −0.10 to +0.06 depending on the variant, but more variability (SEE 0.25 to 0.39). Sixty samples did not have paired results. Conclusions: To our knowledge, this study was the largest investigation of the effect of hemoglobinopathies on the Abbott Alinity c Hb A1c assay. Analytical performance varied depending on the specific hemoglobin variant trait when compared to the Bio-Rad Variant II Turbo 2.0 HPLC method. [ABSTRACT FROM AUTHOR]

As testing for infectious diseases moves from manual, biological testing such as complement fixation to high throughput automated autoanalyzer, the methods for controlling these assays have also changed to reflect those used in clinical chemistry. However, there are many differences between infectious disease serology and clinical chemistry testing, and these differences have not been considered when applying traditional quality control methods to serology. Infectious disease serology, which is highly regulated, detects antibodies of varying classes and to multiple and different antigens that change according to the organisms' genotype/serotype and stage of disease. Although the tests report a numerical value (usually signal to cut-off), they are not measuring an amount of antibodies, but the intensity of binding within the test system. All serology assays experience lot-to-lot variation, making the use of quality control methods used in clinical chemistry inappropriate. In many jurisdictions, the use of the manufacturer-provided kit controls is mandatory to validate the test run. Use of third-party controls, which are highly recommended by ISO 15189 and the World Health Organization, must be manufactured in a manner whereby they have minimal lot-to-lot variation and at a level where they detect exceptional variation. This paper outlines the differences between clinical chemistry and infectious disease serology and offers a range of recommendations when addressing the quality control of infectious disease serology. [ABSTRACT FROM AUTHOR]

Comparison of four methods of establishing control limits for monitoring quality controls in infectious disease serology testing. It is representative of QC data obtained from any laboratory testing any infectious disease serology assay irrespective of assay or analyte. Time to address quality control processes applied to antibody testing for infectious diseases. [Extracted from the article]

Background: The AACC Academy revised the reproductive testing section of the Laboratory Medicine Practice Guidelines: Evidence-Based Practice for Point-of-Care Testing (POCT) published in 2007. Methods: A panel of Academy members with expertise in POCT and laboratory medicine was formed to develop guidance for the use of POCT in reproductive health, specifically ovulation, pregnancy, premature rupture of membranes (PROM), and high-risk deliveries. The committee was supplemented with clinicians having Emergency Medicine and Obstetrics/Gynecology training. Results: Key recommendations include the following. First, urine luteinizing hormone (LH) tests are accurate and reliable predictors of ovulation. Studies have shown that the use of ovulation predicting kits may improve the likelihood of conception among healthy fertile women seeking pregnancy. Urinary LH point-of-care testing demonstrates a comparable performance among other ovulation monitoring methods for timing intrauterine insemination and confirming sufficient ovulation induction before oocyte retrieval during in vitro fertilization. Second, pregnancy POCT should be considered in clinical situations where rapid diagnosis of pregnancy is needed for treatment decisions, and laboratory analysis cannot meet the required turnaround time. Third, PROM testing using commercial kits alone is not recommended without clinical signs of rupture of membranes, such as leakage of amniotic fluid from the cervical opening. Finally, fetal scalp lactate is used more than fetal scalp pH for fetal acidosis due to higher success rate and low volume of sample required. Conclusions: This revision of the AACC Academy POCT guidelines provides recommendations for best practice use of POCT in fertility and reproduction. [ABSTRACT FROM AUTHOR]