Nichols, James H.

Abstract Background The first international clinical standards of care for the gender-diverse population were formulated in the late 1970s. In the last 15 years, multiple subspecialty societies within the United States have developed clinical care guidelines for those with gender dysphoria, including gender-affirming hormone therapy (GAHT) and surgical treatments. To date, there are no pathology- and laboratory medicine–specific recommendations in the United States for the gender-diverse population. Content This document outlines pathology- and laboratory medicine–specific recommendations for providing optimal care to the gender-diverse population, with a predominant focus on the adult population. The scope of this document focuses on the following 5 topics: (a) reference intervals and interpretation of laboratory tests impacted by use of GAHT (testosterone for transgender men; estradiol with or without antiandrogens for transgender women), focusing on those stably on GAHT for 6 months or longer; (b) transfusion medicine considerations for the gender-diverse patient population, (c) consideration of gender diversity in autopsy and death investigation, (d) interpretation of histology from tissues that are impacted by GAHT and surgical procedures; and (e) pathology and laboratory medicine informatics challenges and opportunities. Summary A relatively small group of laboratory tests is significantly impacted by GAHT. The histology of some tissues shows changes attributable to GAHT or to nonhormonal medical procedures. Autopsy and transfusion medicine practices for the gender-diverse population currently lack standardization, although this is evolving. The landscape of pathology informatics related to gender diversity is complicated by varying electronic health record and laboratory informatics systems functionality.

Background: Evaluation of the performance of Cobas ® Pulse (Roche Diagnostics GmbH, Mannheim, Germany), a new blood glucose (BG) monitoring system (BGMS; referred to here as BGMSA) intended for point-of-care testing using samples from patients in diverse clinical settings by intended point-of-care test operators. Methods: Arterial, capillary, venous, or heel stick whole blood (WB) samples from patients in non-critical and critical care settings were collected and analyzed using BGMSA and the Nova StatStrip ® BGMS (Nova Biomedical, Waltham, MA; referred to here as BGMSB), and a hexokinase comparator (Cobas 6000 Analyzer Series; Roche Diagnostics GmbH). The blood glucose measurement accuracy was assessed by the Food and Drug Administration guidance criteria. Results: Two studies are presented. In the first, 2678 samples (622 arterial, 706 capillary, 1203 venous, 147 heel stick) were collected from 1577 patients in 14 US and three European sites. All accuracy criteria were met for arterial and venous samples considering all data combined. BGMSA showed better accuracy than BGMSB for arterial, venous, and heel stick, and similar results to BGMSB for capillary WB vs venous comparator. No endogenous interference from pO 2 , hematocrit, and sodium was identified. BGMSA was also accurate when analyzing contrived samples used to show accuracy over a wide range of glucose concentrations, alone and when combined with extreme hematocrit. In the second study, 117 capillary fingerstick samples collected at one US site were measured on both BGMSA and the comparator, and all accuracy criteria were met. No clinically significant medical risks were observed via Diabetes Technology Society Error Grids. Conclusions: BGMSA was effective for determining blood glucose in venous, arterial, neonatal arterial, neonatal heel stick, and capillary WB samples.

Abstract 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.

Background: A 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. Methods: We 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. Results: The 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. Conclusion: The 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.

This article is the work product of the Continuous Glucose Monitor and Automated Insulin Dosing Systems in the Hospital Consensus Guideline Panel, which was organized by Diabetes Technology Society and met virtually on April 23, 2020. The guideline panel consisted of 24 international experts in the use of continuous glucose monitors (CGMs) and automated insulin dosing (AID) systems representing adult endocrinology, pediatric endocrinology, obstetrics and gynecology, advanced practice nursing, diabetes care and education, clinical chemistry, bioengineering, and product liability law. The panelists reviewed the medical literature pertaining to five topics: (1) continuation of home CGMs after hospitalization, (2) initiation of CGMs in the hospital, (3) continuation of AID systems in the hospital, (4) logistics and hands-on care of hospitalized patients using CGMs and AID systems, and (5) data management of CGMs and AID systems in the hospital. The panelists then developed three types of recommendations for each topic, including clinical practice (to use the technology optimally), research (to improve the safety and effectiveness of the technology), and hospital policies (to build an environment for facilitating use of these devices) for each of the five topics. The panelists voted on 78 proposed recommendations. Based on the panel vote, 77 recommendations were classified as either strong or mild. One recommendation failed to reach consensus. Additional research is needed on CGMs and AID systems in the hospital setting regarding device accuracy, practices for deployment, data management, and achievable outcomes. This guideline is intended to support these technologies for the management of hospitalized patients with diabetes.

Abstract This automated assay determines the concentration of the antidepressant fluoxetine (Prozac) and its active metabolite norfluoxetine in serum by reversed-phase HPLC with spectrophotometric detection. Extraction, injection, and quantification are performed by the Gilson Aspec automated sample handler. The patient's specimen, with added protriptyline as internal standard, is extracted with solid-phase and liquid-liquid methods. Separation is conducted isocratically on a 5-microns (particle size) Supelcosil LC-8-DB reversed-phase column with a triethylamine acetate:acetonitrile mobile phase. The detection limit is 10 micrograms/L and absorbance varies linearly with concentration between 20 and 1,000 micrograms/L for both compounds. Mean recovery was 62% for fluoxetine and 70% for norfluoxetine over linear limits. Within-run and day-to-day imprecision (CV), evaluated at three concentrations (50, 200, and 500 micrograms/L), ranged from 2% to 7%. An extensive interference study of 108 drugs was conducted. Results (n = 58) by the automated method (y) correlated well with those by a manual HPLC method (x): y = 0.96x + 10.20 (r = 0.951, Sylx = 42.9) for fluoxetine, and y = 0.95x - 1.37 (r = 0.917, Sylx = 47.2) for norfluoxetine.