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The SOC-8 guidelines are intended to be flexible to meet the diverse health care needs of TGD people globally. While adaptable, they offer standards for promoting optimal health care and guidance for the treatment of people experiencing gender incongruence. As in all previous versions of the SOC, the criteria set forth in this document for gender-affirming medical interventions are clinical guidelines; individual health care professionals and programs may modify these in consultation with the TGD person.

BACKGROUND: To determine the protective effects of memantine on cognitive function in patients receiving whole-brain radiotherapy (WBRT). METHODS: Adult patients with brain metastases received WBRT and were randomized to receive placebo or memantine (20 mg/d), within 3 days of initiating radiotherapy for 24 weeks. Serial standardized tests of cognitive function were performed. RESULTS: Of 554 patients who were accrued, 508 were eligible. Grade 3 or 4 toxicities and study compliance were similar in the 2 arms. There was less decline in delayed recall in the memantine arm at 24 weeks (P = .059), but the difference was not statistically significant, possibly because there were only 149 analyzable patients at 24 weeks, resulting in only 35% statistical power. The memantine arm had significantly longer time to cognitive decline (hazard ratio 0.78, 95% confidence interval 0.62-0.99, P = .01); the probability of cognitive function failure at 24 weeks was 53.8% in the memantine arm and 64.9% in the placebo arm. Superior results were seen in the memantine arm for executive function at 8 (P = .008) and 16 weeks (P = .0041) and for processing speed (P = .0137) and delayed recognition (P = .0149) at 24 weeks. CONCLUSIONS: Memantine was well tolerated and had a toxicity profile very similar to placebo. Although there was less decline in the primary endpoint of delayed recall at 24 weeks, this lacked statistical significance possibly due to significant patient loss. Overall, patients treated with memantine had better cognitive function over time; specifically, memantine delayed time to cognitive decline and reduced the rate of decline in memory, executive function, and processing speed in patients receiving WBRT. RTOG 0614, ClinicalTrials.gov number CT00566852.

CONTEXT: National quality improvement initiatives implemented in the late 1990s were followed by substantial increases in opioid prescribing in the United States, but it is unknown whether opioid prescribing for treatment of pain in the emergency department has increased and whether differences in opioid prescribing by race/ethnicity have decreased. OBJECTIVES: To determine whether opioid prescribing in emergency departments has increased, whether non-Hispanic white patients are more likely to receive an opioid than other racial/ethnic groups, and whether differential prescribing by race/ethnicity has diminished since 2000. DESIGN AND SETTING: Pain-related visits to US emergency departments were identified using reason-for-visit and physician diagnosis codes from 13 years (1993-2005) of the National Hospital Ambulatory Medical Care Survey. MAIN OUTCOME MEASURE: Prescription of an opioid analgesic. RESULTS: Pain-related visits accounted for 156 729 of 374 891 (42%) emergency department visits. Opioid prescribing for pain-related visits increased from 23% (95% confidence interval [CI], 21%-24%) in 1993 to 37% (95% CI, 34%-39%) in 2005 (P < .001 for trend), and this trend was more pronounced in 2001-2005 (P = .02). Over all years, white patients with pain were more likely to receive an opioid (31%) than black (23%), Hispanic (24%), or Asian/other patients (28%) (P < .001 for trend), and differences did not diminish over time (P = .44), with opioid prescribing rates of 40% for white patients and 32% for all other patients in 2005. Differential prescribing by race/ethnicity was evident for all types of pain visits, was more pronounced with increasing pain severity, and was detectable for long-bone fracture and nephrolithiasis as well as among children. Statistical adjustment for pain severity and other factors did not substantially attenuate these differences, with white patients remaining significantly more likely to receive an opioid prescription than black patients (adjusted odds ratio, 0.66; 95% CI, 0.62-0.70), Hispanic patients (0.67; 95% CI, 0.63-0.72), and Asian/other patients (0.79; 95% CI, 0.67-0.93). CONCLUSION: Opioid prescribing for patients making a pain-related visit to the emergency department increased after national quality improvement initiatives in the late 1990s, but differences in opioid prescribing by race/ethnicity have not diminished.

Introduction: Diabetes has emerged as an important risk factor for severe illness and death from COVID-19. There is a paucity of information on glycemic control among hospitalized COVID-19 patients with diabetes and acute hyperglycemia. Methods: This retrospective observational study of laboratory-confirmed COVID-19 adults evaluated glycemic and clinical outcomes in patients with and without diabetes and/or acutely uncontrolled hyperglycemia hospitalized March 1 to April 6, 2020. Diabetes was defined as A1C ≥6.5%. Uncontrolled hyperglycemia was defined as ≥2 blood glucoses (BGs) &gt; 180 mg/dL within any 24-hour period. Data were abstracted from Glytec’s data warehouse. Results: Among 1122 patients in 88 U.S. hospitals, 451 patients with diabetes and/or uncontrolled hyperglycemia spent 37.8% of patient days having a mean BG &gt; 180 mg/dL. Among 570 patients who died or were discharged, the mortality rate was 28.8% in 184 diabetes and/or uncontrolled hyperglycemia patients, compared with 6.2% of 386 patients without diabetes or hyperglycemia ( P &lt; .001). Among the 184 patients with diabetes and/or hyperglycemia who died or were discharged, 40 of 96 uncontrolled hyperglycemia patients (41.7%) died compared with 13 of 88 patients with diabetes (14.8%, P &lt; .001). Among 493 discharged survivors, median length of stay (LOS) was longer in 184 patients with diabetes and/or uncontrolled hyperglycemia compared with 386 patients without diabetes or hyperglycemia (5.7 vs 4.3 days, P &lt; .001). Conclusion: Among hospitalized patients with COVID-19, diabetes and/or uncontrolled hyperglycemia occurred frequently. These COVID-19 patients with diabetes and/or uncontrolled hyperglycemia had a longer LOS and markedly higher mortality than patients without diabetes or uncontrolled hyperglycemia. Patients with uncontrolled hyperglycemia had a particularly high mortality rate. We recommend health systems which ensure that inpatient hyperglycemia is safely and effectively treated.

Continuous glucose monitoring provides maximal information about shifting blood glucose levels throughout the day and facilitates the making of optimal treatment decisions for the diabetic patient. This report discusses continuous glucose monitoring in terms of its purposes, technologies, target populations, accuracy, clinical indications, outcomes, and problems. In this context, the medical literature on continuous glucose monitoring available through the end of 2004 is reviewed. Continuous glucose monitoring provides information about the direction, magnitude, duration, frequency, and causes of fluctuations in blood glucose levels. Compared with conventional intensified glucose monitoring, defined as three to four blood glucose measurements per day, continuous monitoring provides much greater insight into glucose levels throughout the day. Continuous glucose readings that supply trend information can help identify and prevent unwanted periods of hypo- and hyperglycemia. The difference between an intermittent and a continuous monitor for monitoring blood glucose is similar to that between a regular camera and a continuous security camera for monitoring an important situation. A regular camera takes discrete, accurate snapshots; its pictures do not predict the future; it produces a small set of pictures that can all be carefully studied; and effort is required to take each picture. A continuous security camera, on the other hand, takes multiple, poorly focused frames; displays a sequential array of frames whose trend predicts the future; produces too much information for each frame to be studied carefully; and operates automatically after it is turned on. The two types of blood glucose monitors differ in much the same way: 1 ) an intermittent blood glucose monitor measures discrete glucose levels extremely accurately, whereas a continuous monitor provides multiple glucose levels of fair accuracy; 2 ) with an intermittent monitor, current blood glucose levels do not predict future glucose levels, but with a continuous monitor, trends in glucose levels do have …

BACKGROUND: Exenatide, an incretin mimetic for adjunctive treatment of type 2 diabetes (T2DM), reduced hemoglobin A(1c) (A1C) and weight in clinical trials. The objective of this study was to evaluate the effects of > or = 3 years exenatide therapy on glycemic control, body weight, cardiometabolic markers, and safety. METHODS: Patients from three placebo-controlled trials and their open-label extensions were enrolled into one open-ended, open-label clinical trial. Patients were randomized to twice daily (BID) placebo, 5 mug exenatide, or 10 mug exenatide for 30 weeks, followed by 5 mug exenatide BID for 4 weeks, then 10 mug exenatide BID for > or = 3 years of exenatide exposure. Patients continued metformin and/or sulfonylureas. RESULTS: 217 patients (64% male, age 58 +/- 10 years, weight 99 +/- 18 kg, BMI 34 +/- 5 kg/m(2), A1C 8.2 +/- 1.0% [mean +/- SD]) completed 3 years of exenatide exposure. Reductions in A1C from baseline to week 12 (-1.1 +/- 0.1% [mean +/- SEM]) were sustained to 3 years (-1.0 +/- 0.1%; p < 0.0001), with 46% achieving A1C < or = 7%. Exenatide progressively reduced body weight from baseline (-5.3 +/- 0.4 kg at 3 years; p < 0.0001). Patients with elevated serum alanine aminotransferase (ALT) at baseline (n = 116) had reduced ALT (-10.4 +/- 1.5 IU/L; p < 0.0001) and 41% achieved normal ALT. Patients with elevated ALT at baseline tended to lose more weight than patients with normal ALT at baseline (-6.1 +/- 0.6 kg vs. -4.4 +/- 0.5 kg; p = 0.03), however weight change was minimally correlated with baseline ALT (r = -0.01) or ALT change (r = 0.31). Homeostasis Model Assessment B (HOMA-B), blood pressure, and aspartate aminotransferase (AST) all improved. A subset achieved 3.5 years of exenatide exposure and had serum lipids available for analysis (n = 151). Triglycerides decreased 12% (p = 0.0003), total cholesterol decreased 5% (p = 0.0007), LDL-C decreased 6% (p < 0.0001), and HDL-C increased 24% (p < 0.0001). Exenatide was generally well tolerated. The most frequent adverse event was mild-to-moderate nausea. The main limitation of this study is the open-label, uncontrolled nature of the study design which does not provide a placebo group for comparison. CONCLUSION: Adjunctive exenatide treatment for > or = 3 years in T2DM patients resulted in sustained improvements in glycemic control, cardiovascular risk factors, and hepatic biomarkers, coupled with progressive weight reduction.

BACKGROUND: The threshold-suspend feature of sensor-augmented insulin pumps is designed to minimize the risk of hypoglycemia by interrupting insulin delivery at a preset sensor glucose value. We evaluated sensor-augmented insulin-pump therapy with and without the threshold-suspend feature in patients with nocturnal hypoglycemia. METHODS: We randomly assigned patients with type 1 diabetes and documented nocturnal hypoglycemia to receive sensor-augmented insulin-pump therapy with or without the threshold-suspend feature for 3 months. The primary safety outcome was the change in the glycated hemoglobin level. The primary efficacy outcome was the area under the curve (AUC) for nocturnal hypoglycemic events. Two-hour threshold-suspend events were analyzed with respect to subsequent sensor glucose values. RESULTS: A total of 247 patients were randomly assigned to receive sensor-augmented insulin-pump therapy with the threshold-suspend feature (threshold-suspend group, 121 patients) or standard sensor-augmented insulin-pump therapy (control group, 126 patients). The changes in glycated hemoglobin values were similar in the two groups. The mean AUC for nocturnal hypoglycemic events was 37.5% lower in the threshold-suspend group than in the control group (980 ± 1200 mg per deciliter [54.4 ± 66.6 mmol per liter] × minutes vs. 1568 ± 1995 mg per deciliter [87.0 ± 110.7 mmol per liter] × minutes, P<0.001). Nocturnal hypoglycemic events occurred 31.8% less frequently in the threshold-suspend group than in the control group (1.5 ± 1.0 vs. 2.2 ± 1.3 per patient-week, P<0.001). The percentages of nocturnal sensor glucose values of less than 50 mg per deciliter (2.8 mmol per liter), 50 to less than 60 mg per deciliter (3.3 mmol per liter), and 60 to less than 70 mg per deciliter (3.9 mmol per liter) were significantly reduced in the threshold-suspend group (P<0.001 for each range). After 1438 instances at night in which the pump was stopped for 2 hours, the mean sensor glucose value was 92.6 ± 40.7 mg per deciliter (5.1 ± 2.3 mmol per liter). Four patients (all in the control group) had a severe hypoglycemic event; no patients had diabetic ketoacidosis. CONCLUSIONS: This study showed that over a 3-month period the use of sensor-augmented insulin-pump therapy with the threshold-suspend feature reduced nocturnal hypoglycemia, without increasing glycated hemoglobin values. (Funded by Medtronic MiniMed; ASPIRE ClinicalTrials.gov number, NCT01497938.).

The significant and growing global prevalence of diabetes continues to challenge people with diabetes (PwD), healthcare providers, and payers. While maintaining near-normal glucose levels has been shown to prevent or delay the progression of the long-term complications of diabetes, a significant proportion of PwD are not attaining their glycemic goals. During the past 6 years, we have seen tremendous advances in automated insulin delivery (AID) technologies. Numerous randomized controlled trials and real-world studies have shown that the use of AID systems is safe and effective in helping PwD achieve their long-term glycemic goals while reducing hypoglycemia risk. Thus, AID systems have recently become an integral part of diabetes management. However, recommendations for using AID systems in clinical settings have been lacking. Such guided recommendations are critical for AID success and acceptance. All clinicians working with PwD need to become familiar with the available systems in order to eliminate disparities in diabetes quality of care. This report provides much-needed guidance for clinicians who are interested in utilizing AIDs and presents a comprehensive listing of the evidence payers should consider when determining eligibility criteria for AID insurance coverage.

Background and Objectives: The novel coronavirus COVID-19 pandemic causes great public health and socioeconomic harms. Worldwide many countries implemented quarantine policies to minimize the spread of this highly contagious disease. The present study aim was to investigate the impact of quarantine on the medical students’ mental wellbeing and learning behaviors. Methods: In this descriptive study, we used a questionnaire with a Five-Point Likert Scale to collect the information. The questionnaire was distributed among 625 medical students through their emails with a response rate of 530 (84.8%), majority 294 (55.47%) being female. The survey questionnaire consisted of total 20 items; 12 items were related to psychological wellbeing and stress-allied queries and 08 items were about learning behaviors. Results: The findings encompass two important characteristics related to quarantine, psychological wellbeing, and learning behaviors. A combined cohort of 234 medical students, either female or male, (which was 44.1% of the total responders) showed a sense of being emotionally detached from family, friends and fellow students, 125/ 530 (23.5%) medical students felt disheartened. Both female and male medical students showed a marked decrease in their overall work performance. Moreover, 56.2% of the total students (61.5% of the females and 49.5% of the males) felt a decrease in the time they spent studying. Conclusions: Both female and male medical students have identified that quarantine has caused them to feel emotionally detached from family, fellows, and friends and decrease their overall work performance and study period. The findings also show that one-fourth of the medical students who participated in this study felt disheartened during the quarantine period. The long-term quarantine due to COVID-19 pandemic may causes further worsening in the psychological and learning behaviors of these medical students. doi: https://doi.org/10.12669/pjms.36.COVID19-S4.2809 How to cite this:Meo SA, Abukhalaf AA, Alomar AA, Sattar K, Klonoff DC. COVID-19 Pandemic: Impact of Quarantine on Medical Students’ Mental Wellbeing and Learning Behaviors. Pak J Med Sci. 2020;36(COVID19-S4):COVID19-S43-S48. doi: https://doi.org/10.12669/pjms.36.COVID19-S4.2809 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

IMPORTANCE: There is wide variation in the management of thyroid nodules identified on ultrasound imaging. OBJECTIVE: To quantify the risk of thyroid cancer associated with thyroid nodules based on ultrasound imaging characteristics. METHODS: Retrospective case-control study of patients who underwent thyroid ultrasound imaging from January 1, 2000, through March 30, 2005. Thyroid cancers were identified through linkage with the California Cancer Registry. RESULTS: A total of 8806 patients underwent 11,618 thyroid ultrasound examinations during the study period, including 105 subsequently diagnosed as having thyroid cancer. Thyroid nodules were common in patients diagnosed as having cancer (96.9%) and patients not diagnosed as having thyroid cancer (56.4%). Three ultrasound nodule characteristics--microcalcifications (odds ratio [OR], 8.1; 95% CI, 3.8-17.3), size greater than 2 cm (OR, 3.6; 95% CI, 1.7-7.6), and an entirely solid composition (OR, 4.0; 95% CI, 1.7-9.2)--were the only findings associated with the risk of thyroid cancer. If 1 characteristic is used as an indication for biopsy, most cases of thyroid cancer would be detected (sensitivity, 0.88; 95% CI, 0.80-0.94), with a high false-positive rate (0.44; 95% CI, 0.43-0.45) and a low positive likelihood ratio (2.0; 95% CI, 1.8-2.2), and 56 biopsies will be performed per cancer diagnosed. If 2 characteristics were required for biopsy, the sensitivity and false-positive rates would be lower (sensitivity, 0.52; 95% CI, 0.42-0.62; false-positive rate, 0.07; 95% CI, 0.07-0.08), the positive likelihood ratio would be higher (7.1; 95% CI, 6.2-8.2), and only 16 biopsies will be performed per cancer diagnosed. Compared with performing biopsy of all thyroid nodules larger than 5 mm, adoption of this more stringent rule requiring 2 abnormal nodule characteristics to prompt biopsy would reduce unnecessary biopsies by 90% while maintaining a low risk of cancer (5 per 1000 patients for whom biopsy is deferred). CONCLUSIONS AND RELEVANCE: Thyroid ultrasound imaging could be used to identify patients who have a low risk of cancer for whom biopsy could be deferred. On the basis of these results, these findings should be validated in a large prospective cohort.

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.

BACKGROUND: Mobile stroke units (MSUs) are ambulances with staff and a computed tomographic scanner that may enable faster treatment with tissue plasminogen activator (t-PA) than standard management by emergency medical services (EMS). Whether and how much MSUs alter outcomes has not been extensively studied. METHODS: In an observational, prospective, multicenter, alternating-week trial, we assessed outcomes from MSU or EMS management within 4.5 hours after onset of acute stroke symptoms. The primary outcome was the score on the utility-weighted modified Rankin scale (range, 0 to 1, with higher scores indicating better outcomes according to a patient value system, derived from scores on the modified Rankin scale of 0 to 6, with higher scores indicating more disability). The main analysis involved dichotomized scores on the utility-weighted modified Rankin scale (≥0.91 or <0.91, approximating scores on the modified Rankin scale of ≤1 or >1) at 90 days in patients eligible for t-PA. Analyses were also performed in all enrolled patients. RESULTS: We enrolled 1515 patients, of whom 1047 were eligible to receive t-PA; 617 received care by MSU and 430 by EMS. The median time from onset of stroke to administration of t-PA was 72 minutes in the MSU group and 108 minutes in the EMS group. Of patients eligible for t-PA, 97.1% in the MSU group received t-PA, as compared with 79.5% in the EMS group. The mean score on the utility-weighted modified Rankin scale at 90 days in patients eligible for t-PA was 0.72 in the MSU group and 0.66 in the EMS group (adjusted odds ratio for a score of ≥0.91, 2.43; 95% confidence interval [CI], 1.75 to 3.36; P<0.001). Among the patients eligible for t-PA, 55.0% in the MSU group and 44.4% in the EMS group had a score of 0 or 1 on the modified Rankin scale at 90 days. Among all enrolled patients, the mean score on the utility-weighted modified Rankin scale at discharge was 0.57 in the MSU group and 0.51 in the EMS group (adjusted odds ratio for a score of ≥0.91, 1.82; 95% CI, 1.39 to 2.37; P<0.001). Secondary clinical outcomes generally favored MSUs. Mortality at 90 days was 8.9% in the MSU group and 11.9% in the EMS group. CONCLUSIONS: In patients with acute stroke who were eligible for t-PA, utility-weighted disability outcomes at 90 days were better with MSUs than with EMS. (Funded by the Patient-Centered Outcomes Research Institute; BEST-MSU ClinicalTrials.gov number, NCT02190500.).

Ordering and interpreting diagnostic tests is a critical part of emergency medicine (EM). In evaluating a study of diagnostic test accuracy, emergency physicians (EPs) need to recognize whether the study uses case–control or cross-sectional sampling and account for common biases. The authors group biases in studies of test accuracy into five categories: incorporation bias, partial verification bias, differential verification bias, imperfect gold standard bias, and spectrum bias. Other named biases are either equivalent to these biases or subtypes within these broader categories. The authors go beyond identifying a bias and predict the direction of its effect on sensitivity and specificity, providing numerical examples from published test accuracy studies. Understanding the direction of a bias may permit useful inferences from even a flawed study of test accuracy. La solicitud y la interpretación de las pruebas diagnósticas es una parte crucial de la medicina de urgencias y emergencias. En la evaluación de un estudio sobre la certeza de una prueba diagnóstica, los urgenciólogos necesitan reconocer si el estudio utiliza un muestreo de caso-control o transversal y dar explicaciones de los sesgos comunes. Los autores agrupan los sesgos de los estudios de certeza diagnóstica en cinco categorías: sesgo de selección, sesgo de verificación parcial, sesgo de verificación diferencial, sesgo criterio estándar imperfecto y sesgo de espectro. Otros sesgos citados son cualquier equivalente a estos sesgos, o subtipos de estas categorías más amplias. Los autores van más allá de la identificación de un sesgo y predicen la dirección de su efecto en la sensibilidad y la especificidad, y proporcionan ejemplos numéricos de estudios de certeza diagnóstica publicados. La compresión de la dirección de un sesgo puede permitir inferencias útiles incluso desde un estudio con fallos de certeza diagnóstica. Performing, ordering, and interpreting diagnostic tests plays a critical role in the practice of emergency medicine (EM).1, 2 Emergency physicians (EPs) are confronted with an increasing number of published studies evaluating the accuracy of new and old diagnostic tests. Approximately 63% of emergency department (ED) visits include laboratory tests, electrocardiograms (ECGs), or imaging studies,3 but we also include in the term “diagnostic test” elements of the history or physical examination findings used by EPs to help determine patients’ underlying conditions and guide clinical decisions about treatment or additional testing. Diagnostic tests are performed on patients with symptoms, as opposed to screening tests, which are performed on patients with no known symptoms.4 Because symptoms are what bring patients into the ED, screening tests are generally of less interest in EM. Prognostic tests help predict incident outcomes rather than diagnose prevalent disease. These outcomes require a time interval to elapse and depend on additional random events that occur to the patient after the test is performed. We focus here on assessing the accuracy of a single diagnostic test for prevalent disease in symptomatic patients. While we limit this discussion to studies of test accuracy, we should point out that accuracy is not enough. An accurate test is primarily useful if correctly diagnosing the cause of a patient's illness improves outcomes by prompting effective treatment that would not have occurred otherwise.5 The prototypical study of diagnostic test accuracy compares a single index test to a gold standard determination of disease status. Disease status is assumed to be dichotomous: disease present (D+) or disease absent (D–). The index test can be dichotomous, multilevel, or continuous (Table 1). We will not be discussing test results that fall into more than two unordered categories. For the most part, this discussion will assume that the index test is dichotomous (either positive or negative). For dichotomous tests, the most widely used indices of test accuracy are sensitivity and specificity. Sensitivity is the probability that a patient with the disease (a D+ patient) will have a positive test. Specificity is the probability that a patient without the disease (a D– patient) will have a negative test. Studies of test accuracy should also report likelihood ratios (LRs).6, 7 The general definition of the LR for a test result is the probability of obtaining that result in a patient with the disease divided by the probability of obtaining that result in a patient without the disease. The result need not be from a dichotomous test; it can be one of the several possible results from a multilevel test or a result in a particular interval from a continuous test.8 For dichotomous tests, the LR of a positive result, LR(+), is sensitivity/(1 – specificity), and the LR of a negative result, LR(–), is (1 – sensitivity)/specificity. Other quantities commonly reported in studies of test accuracy are prevalence (the proportion of the population that has the disease) or, better yet, pretest probability (the prevalence in a clinical population of patients like the one currently being evaluated), positive predictive value (PPV, the probability that a person with a positive test has the disease), and negative predictive value (NPV, the probability that a person with a negative result does not have the disease). We elaborate on these quantities below in our discussion of sampling schemes and in Figure 1. We will not discuss the use of the term “accuracy” as the prevalence-weighted average of sensitivity and specificity, because we do not find this to be a useful quantity. If a disease is very rare with a prevalence of, say, 0.1%, then a test that is always negative has an accuracy of 99.9% Bias is any systematic deviation of an estimate from the true value. As with all clinical research studies, test accuracy studies are susceptible to bias. A biased study of a dichotomous test will overestimate or underestimate the test's sensitivity, its specificity, or both. While several prior reviews discuss the identification of bias in test accuracy studies,9-11 we describe the direction of a bias's effect on sensitivity and specificity and provide detailed numerical examples. Understanding not only the likelihood of a bias, but its probable direction, may permit useful inferences even from a flawed study of diagnostic test accuracy. We group biases in studies of test accuracy into five categories (Table 2): incorporation bias, partial verification bias, differential verification bias, imperfect gold standard bias, and spectrum bias. After discussing the importance of a study's sampling scheme, we will separately discuss each of these biases and the direction of its effect on sensitivity and specificity. Incorporation bias (includes review bias) Raised For disease that can resolve spontaneously. Lowered For disease that only becomes detectable during the follow-up period.* Raised If errors on the index test and copper standard are correlated (conditional on true disease status). Lowered If errors on the index test and the gold standard are independent (conditional on true disease status). Sampling for test accuracy studies is most often either case–control or cross-sectional (Figure 1). In a case–control design, D+ patients are sampled separately from D– patients. This allows measurement of sensitivity, specificity, LR(+), and LR(–), but it does not allow measurement of disease prevalence, PPV, or NPV because the “prevalence” is determined by the ratio of cases to controls set by the investigator. With cross-sectional sampling, the sample is defined by a characteristic such as clinical presentation that is separate from disease status or test result. This allows measurement of a meaningful prevalence (or pretest probability), PPVs, and NPVs in addition to sensitivity, specificity, LR(+), and LR(–). It is also possible to sample separately on index test result. For a dichotomous index test, patients with positive test results could be sampled separately from patients with negative test results. Such a study would allow calculation of PPVs and NPVs but not sensitivity, specificity, LR(+), LR(–), or prevalence. In practice, separating patients based on test result is usually done in the context of a cross-sectional study that provides the overall proportion of patients with each test result. Some test accuracy studies fail to make their sampling schemes explicit. Equal numbers of D+ and D– patients suggest that they were sampled separately in a case–control design. However, some studies that use case–control sampling have different numbers of D+ and D– patients, conveying the impression of cross-sectional sampling. This is particularly misleading if the authors calculate and report PPVs and NPVs. For example, Davis et al.12 did a case–control study to assess the accuracy of the “classic triad” of fever, spine pain, and neurologic deficit to predict spinal epidural abscess in ED patients. They identified 63 cases of spinal epidural abscess and matched two controls per case resulting in 2 × 63 = 126 controls. Controls were ED patients with spine pain but without spinal epidural abscess. Since five of the 63 cases and one of the 126 controls had the classic triad, the authors reported a PPV of 5/6 = 83.3%. Readers could erroneously estimate the probability of spinal epidural abscess in a patient with the classic triad at 83.3%, but this is reasonable only if the prevalence of epidural abscess in similar patients is 1/3, since that was the “pretest probability” set by the authors when they chose two controls per case. The NPV of the classic triad was reported to be 68%, meaning that 100% – 68% = 32% of all ED patients with spine pain but without the classic triad have spinal epidural abscess. In addition to precluding calculation of pretest probability and predictive values, case–control sampling often leads to unrepresentative samples of D+ and D– patients. This is discussed below under “Spectrum Bias.” For a study of a diagnostic test to be valid, a positive index test should have no role in determining whether the gold standard classifies the patient as D+. If the index test is incorporated into the gold standard, both sensitivity and specificity will be biased up relative to a study that uses an independent gold standard. The circularity should be apparent; how can you determine whether a test identifies D+ patients if you define a D+ patient as someone with a positive test? Nevertheless, a study reporting the accuracy of troponin T in predicting major cardiac events defined one major cardiac event (myocardial infarction) by an elevated troponin T.13 In the authors’ defense, the primary goal of the study was to evaluate the accuracy of myeloperoxidase, not troponin T. As another example, Mower10 reviewed a study of the accuracy of radiolabeled biliary tract imaging that included the scan results in the criteria used to define cholecystitis.14 Sometimes the gold standard that determines disease status is review of clinical information by an expert or panel of experts. We include with incorporation bias the failure to blind the reviewers to the results of the index test, which is also referred to as review bias. 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The use of continuous subcutaneous insulin infusion (CSII) and continuous glucose monitoring (CGM) systems has gained wide acceptance in diabetes care. These devices have been demonstrated to be clinically valuable, improving glycemic control and reducing risks of hypoglycemia in ambulatory patients with type 1 diabetes and type 2 diabetes. Approximately 30-40% of patients with type 1 diabetes and an increasing number of insulin-requiring patients with type 2 diabetes are using pump and sensor technology. As the popularity of these devices increases, it becomes very likely that hospital health care providers will face the need to manage the inpatient care of patients under insulin pump therapy and CGM. The American Diabetes Association advocates allowing patients who are physically and mentally able to continue to use their pumps when hospitalized. Health care institutions must have clear policies and procedures to allow the patient to continue to receive CSII treatment to maximize safety and to comply with existing regulations related to self-management of medication. Randomized controlled trials are needed to determine whether CSII therapy and CGM systems in the hospital are associated with improved clinical outcomes compared with intermittent monitoring and conventional insulin treatment or with a favorable cost-benefit ratio.

Gamma-glutamyl transferase (GGT) is a second-generation enzymatic liver function test available for several decades, initially used as a sensitive indicator of alcohol ingestion, hepatic inflammation, fatty liver disease, and hepatitis. Longitudinal and cross-sectional investigational studies since 1990 have associated GGT with an increase in all-cause mortality, as well as chronic heart disease events such as congestive heart failure and components of the metabolic syndrome (abnormal body mass index and levels of high-density lipoprotein cholesterol, glucose, triglycerides, and systolic and diastolic blood pressure). In the upper reference range, GGT was found to be an independent biomarker of the metabolic syndrome, with a 20% per GGT quartile trend rise. Additionally, GGT was positively correlated with an 18% per quartile risk of cardiovascular events and a 26% per quartile increased risk of all-cause mortality. Furthermore, it may be considered a biomarker for "oxidative stress" associated with glutathione metabolism and possibly a "proatherogenic" marker because of its indirect relationship in the biochemical steps to low-density lipoprotein cholesterol oxidation. GGT is becoming an important addition to the multimarker approach to cardiovascular risk evaluation. It should be considered a valuable adjunct in stratifying patient risk and in assessing the aggressiveness of appropriate treatment, with hopes of preventing unnecessary cardiac events and deaths in future years.

OBJECTIVE: The aim was to formulate practice guidelines for determining settings where patients are most likely to benefit from the use of continuous glucose monitoring (CGM). PARTICIPANTS: The Endocrine Society appointed a Task Force of experts, a methodologist, and a medical writer. EVIDENCE: This evidence-based guideline was developed using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) system to describe both the strength of recommendations and the quality of evidence. CONSENSUS PROCESS: One group meeting, several conference calls, and e-mail communications enabled consensus. Committees and members of The Endocrine Society, the Diabetes Technology Society, and the European Society of Endocrinology reviewed and commented on preliminary drafts of these guidelines. CONCLUSIONS: The Task Force evaluated three potential uses of CGM: 1) real-time CGM in adult hospital settings; 2) real-time CGM in children and adolescent outpatients; and 3) real-time CGM in adult outpatients. The Task Force used the best available data to develop evidence-based recommendations about where CGM can be beneficial in maintaining target levels of glycemia and limiting the risk of hypoglycemia. Both strength of recommendations and quality of evidence were accounted for in the guidelines.

The American Diabetes Association (ADA), European Association for the Study of Diabetes (EASD), Joint British Diabetes Societies for Inpatient Care (JBDS), American Association of Clinical Endocrinology (AACE), and Diabetes Technology Society (DTS) convened a panel of internists and diabetologists to update the ADA consensus statement on hyperglycemic crises in adults with diabetes, published in 2001 and last updated in 2009. The objective of this consensus report is to provide up-to-date knowledge about the epidemiology, pathophysiology, clinical presentation, and recommendations for the diagnosis, treatment, and prevention of diabetic ketoacidosis (DKA) and hyperglycemic hyperosmolar state (HHS) in adults. A systematic examination of publications since 2009 informed new recommendations. The target audience is the full spectrum of diabetes health care professionals and individuals with diabetes.

We describe eight patients in whom cocaine use was related to stroke and review 39 cases from the literature. Among these 47 patients the mean (+/- SD) age was 32.5 +/- 12.1 years; 76% (34/45) were men. Stroke followed cocaine use by inhalation, intranasal, intravenous, and intramuscular routes. Intracranial aneurysms or arteriovenous malformations were present in 17 of 32 patients studied angiographically or at autopsy; cerebral vasculitis was present in two patients. Cerebral infarction occurred in 10 patients (22%), intracerebral hemorrhage in 22 (49%), and subarachnoid hemorrhage in 13 (29%). These data indicate that (1) the apparent incidence of stroke related to cocaine use is increasing; (2) cocaine-associated stroke occurs primarily in young adults; (3) stroke may follow any route of cocaine administration; (4) stroke after cocaine use is frequently associated with intracranial aneurysms and arteriovenous malformations; and (5) in cocaine-associated stroke, the frequency of intracranial hemorrhage exceeds that of cerebral infarction.

Currently used error grids for assessing clinical accuracy of blood glucose monitors are based on out-of-date medical practices. Error grids have not been widely embraced by regulatory agencies for clearance of monitors, but this type of tool could be useful for surveillance of the performance of cleared products. Diabetes Technology Society together with representatives from the Food and Drug Administration, the American Diabetes Association, the Endocrine Society, and the Association for the Advancement of Medical Instrumentation, and representatives of academia, industry, and government, have developed a new error grid, called the surveillance error grid (SEG) as a tool to assess the degree of clinical risk from inaccurate blood glucose (BG) monitors. A total of 206 diabetes clinicians were surveyed about the clinical risk of errors of measured BG levels by a monitor. The impact of such errors on 4 patient scenarios was surveyed. Each monitor/reference data pair was scored and color-coded on a graph per its average risk rating. Using modeled data representative of the accuracy of contemporary meters, the relationships between clinical risk and monitor error were calculated for the Clarke error grid (CEG), Parkes error grid (PEG), and SEG. SEG action boundaries were consistent across scenarios, regardless of whether the patient was type 1 or type 2 or using insulin or not. No significant differences were noted between responses of adult/pediatric or 4 types of clinicians. Although small specific differences in risk boundaries between US and non-US clinicians were noted, the panel felt they did not justify separate grids for these 2 types of clinicians. The data points of the SEG were classified in 15 zones according to their assigned level of risk, which allowed for comparisons with the classic CEG and PEG. Modeled glucose monitor data with realistic self-monitoring of blood glucose errors derived from meter testing experiments plotted on the SEG when compared to the data plotted on the CEG and PEG produced risk estimates that were more granular and reflective of a continuously increasing risk scale. The SEG is a modern metric for clinical risk assessments of BG monitor errors that assigns a unique risk score to each monitor data point when compared to a reference value. The SEG allows the clinical accuracy of a BG monitor to be portrayed in many ways, including as the percentages of data points falling into custom-defined risk zones. For modeled data the SEG, compared with the CEG and PEG, allows greater precision for quantifying risk, especially when the risks are low. This tool will be useful to allow regulators and manufacturers to monitor and evaluate glucose monitor performance in their surveillance programs.

Underutilization of glucose data and lack of easy and standardized glucose data collection, analysis, visualization, and guided clinical decision making are key contributors to poor glycemic control among individuals with type 1 diabetes mellitus. An expert panel of diabetes specialists, facilitated by the International Diabetes Center and sponsored by the Helmsley Charitable Trust, met in 2012 to discuss recommendations for standardizing the analysis and presentation of glucose monitoring data, with the initial focus on data derived from continuous glucose monitoring systems. The panel members were introduced to a universal software report, the Ambulatory Glucose Profile, and asked to provide feedback on its content and functionality, both as a research tool and in clinical settings. This article provides a summary of the topics and issues discussed during the meeting and presents recommendations from the expert panel regarding the need to standardize glucose profile summary metrics and the value of a uniform glucose report to aid clinicians, researchers, and patients.