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Most prior studies characterizing post-transplantation diabetes mellitus (PTDM) have been limited to single-cohort, single-organ studies. This retrospective study determined PTDM across organs by comparing incidence and risk factors among 346 liver and 407 kidney transplant recipients from a single center. Univariate and multivariate regression-based analyses were conducted to determine association of various risk factors and PTDM in the two cohorts, as well as differences in glucometrics and insulin use across time points. There was a higher incidence of PTDM among liver versus kidney transplant recipients (30% vs. 19%) at 1-year post-transplant. Liver transplant recipients demonstrated a 337% higher odds association to PTDM (OR 3.37, 95% CI (1.38-8.25), p<0.01). 1-month FBG was higher in kidney patients (135 mg/dL vs 104 mg/dL; p < .01), while 1-month insulin use was higher in liver patients (61% vs 27%, p < .01). Age, BMI, insulin use, and inpatient FBG were also significantly associated with differential PTDM risk. Kidney and liver transplant patients have different PTDM risk profiles, both in terms of absolute PTDM risk as well as time course of risk. Management of this population should better reflect risk heterogeneity to short-term need for insulin therapy and potentially long-term outcomes.

Hyperglycemia following solid organ transplant is common among patients without pre-existing diabetes mellitus (DM). Post-transplant hyperglycemia can occur once or multiple times, which if continued, causes new-onset diabetes after transplantation (NODAT). To study if the first and recurrent incidence of hyperglycemia are affected differently by immunosuppressive regimens, demographic and medical-related risk factors, and inpatient hyperglycemic conditions (i.e., an emphasis on the time course of post-transplant complications). We conducted a retrospective analysis of 407 patients who underwent kidney transplantation at Mayo Clinic Arizona. Among these, there were 292 patients with no signs of DM prior to transplant. For this category of patients, we evaluated the impact of (1) immunosuppressive drugs (e.g., tacrolimus, sirolimus, and steroid), (2) demographic and medical-related risk factors, and (3) inpatient hyperglycemic conditions on the first and recurrent incidence of hyperglycemia in one year post-transplant. We employed two versions of Cox regression analyses: (1) a time-dependent model to analyze the recurrent cases of hyperglycemia and (2) a time-independent model to analyze the first incidence of hyperglycemia. Age (P = 0.018), HDL cholesterol (P = 0.010), and the average trough level of tacrolimus (P<0.0001) are significant risk factors associated with the first incidence of hyperglycemia, while age (P<0.0001), non-White race (P = 0.002), BMI (P = 0.002), HDL cholesterol (P = 0.003), uric acid (P = 0.012), and using steroid (P = 0.007) are the significant risk factors for the recurrent cases of hyperglycemia. This study draws attention to the importance of analyzing the risk factors associated with a disease (specially a chronic one) with respect to both its first and recurrent incidence, as well as carefully differentiating these two perspectives: a fact that is currently overlooked in the literature.

Autoimmune polyglandular syndromes (APS) are rare disorders characterized by auto‐destruction of endocrine and non‐endocrine organs by organ‐specific antibody‐directed T‐lymphocytic infiltration. This case highlights a 29‐year‐old Caucasian man with vitiligo found to have significant neurological abnormalities in the setting of newly diagnosed pernicious anemia and thyroid autoimmune disease. Autoimmune polyglandular syndromes can have widely variable presentations. If a patient is found to have newly diagnosed thyroid autoimmune disease, it would be valuable to screen for other autoimmune diseases such as pernicious anemia; as these associated endocrinopathies can lead to significant neurological consequences.

To assess the impact of an intervention designed to increase basal-bolus insulin therapy administration in postoperative patients with diabetes mellitus. Educational sessions and direct support for surgical services were provided by a nurse practitioner (NP). Outcome data from the intervention were compared to data from a historical (control) period. Changes in basal-bolus insulin use were assessed according to hyperglycemia severity as defined by the percentage of glucose measurements >180 mg/dL. Patient characteristics were comparable for the control and intervention periods (all P≥.15). Overall, administration of basal-bolus insulin occurred in 9% (8/93) of control and in 32% (94/293) of intervention cases (P<.01). During the control period, administration of basal-bolus insulin did not increase with more frequent hyperglycemia (P = .22). During the intervention period, administration increased from 8% (8/96) in patients with the fewest number of hyperglycemic measurements to 60% (57/95) in those with the highest frequency of hyperglycemia (P<.01). The mean glucose level was lower during the intervention period compared to the control period (149 mg/dL vs. 163 mg/dL, P<.01). The proportion of glucose values >180 mg/dL was lower during the intervention period than in the control period (21% vs. 31% of measurements, respectively, P<.01), whereas the hypoglycemia (glucose >70 mg/dL) frequencies were comparable (P = .21). An intervention to overcome clinical inertia in the management of postoperative patients with diabetes led to greater utilization of basal-bolus insulin therapy and improved glucose control without increasing hypoglycemia. These efforts are ongoing to ensure the delivery of effective inpatient diabetes care by all surgical services.

Purpose of ReviewInpatient glucose data analysis, or glucometrics, has developed alongside the growing emphasis on glycemic control in the hospital. Shortcomings in the initial capabilities for glucometrics have pushed advancements in defining meaningful units of measurement and methods for capturing glucose data. This review addresses the growth in glucometrics and ends with its promising new state.Recent FindingsStandardization, allowing for benchmarking and purposeful comparison, has been a goal of the field. The National Quality Foundation glycemic measures and recently enacted Center for Medicare and Medicaid Services (CMS) electronic quality measures for hypo- and hyperglycemia have allowed for improved integration and consistency.SummaryPrior systems have culminated in an upcoming measure from the Center for Disease Control and Prevention’s National Healthcare Safety Network. It is poised to create a new gold standard for glucometrics by expanding and refining the CMS metrics, which should empower both local improvement and benchmarking as the program matures.

Rapid A1c Availability Improves Clinical Decision-Making in an Urban Primary Care Clinic Christopher D. Miller , MD , Catherine S. Barnes , PHD , Lawrence S. Phillips , MD , David C. Ziemer , MD , Daniel L. Gallina , MD , Curtiss B. Cook , MD , Sandra D. Maryman , MD and Imad M. El-Kebbi , MD From the Emory University School of Medicine, Atlanta, Georgia Abstract OBJECTIVE —Failure to meet goals for glycemic control in primary care settings may be due in part to lack of information critical to guide intensification of therapy. Our objective is to determine whether rapid-turnaround A1c availability would improve intensification of diabetes therapy and reduce A1c levels in patients with type 2 diabetes. RESEARCH DESIGN AND METHODS —In this prospective controlled trial, A1c was determined on capillary glucose samples and made available to providers, either during (“rapid”) or after (“routine”) the patient visit. Frequency of intensification of pharmacological diabetes therapy in inadequately controlled patients and A1c levels were assessed at baseline and after follow-up. RESULTS —We recruited 597 subjects. Patients were 79% female and 96% African American, with average age of 61 years, duration of diabetes 10 years, BMI 33 kg/m 2 , and A1c 8.5%. The rapid and routine groups had similar clinical demographics. Rapid A1c availability resulted in more frequent intensification of therapy when A1c was ≥7.0% at the baseline visit (51 vs. 32% of patients, P = 0.01), particularly when A1c was >8.0% and/or random glucose was in the 8.4–14.4 mmol/l range (151–250 mg/dl). In 275 patients with two follow-up visits, A1c fell significantly in the rapid group (from 8.4 to 8.1%, P = 0.04) but not in the routine group (from 8.1 to 8.0%, P = 0.31). CONCLUSIONS —Availability of rapid A1c measurements increased the frequency of intensification of therapy and lowered A1c levels in patients with type 2 diabetes in an urban neighborhood health center. Footnotes Address correspondence and reprint requests to Imad M. El-Kebbi, MD, Emory University School of Medicine, Diabetes Unit, 69 Jesse Hill Jr. Dr., SE, Atlanta, GA 30303. E-mail: ielkebb{at}emory.edu . Received for publication 13 August 2002 and accepted in revised form 10 January 2003. A table elsewhere in this issue shows conventional and Système International (SI) units and conversion factors for many substances. DIABETES CARE

This retrospective study analyzed glycemic trends, incidence of post-transplant diabetes mellitus (PTDM) incidence and associated risk factors in a cohort of patients who underwent first-time heart transplantation (HT). Univariate analyses compared patient with and without pretransplant diabetes mellitus (DM). Multivariate regression analyses were conducted to determine association between PTDM and different risk factors. Finally, trends in glucometrics and other outcomes are described across follow-up time points. There were 152 patients who underwent HT between 2010 and 2015, 109 of whom had no pretransplant history of DM. PTDM incidence was 38% by the 1-year follow-up. Pretransplant body mass index (odds ratio [OR] 1.12, 95% confidence interval [CI] 1.01 to 1.23, p = 0.03), insulin use during the final 24 hours of inpatient stay (OR 4.26, 95% CI 1.72 to 10.56, p <0.01), mean inpatient glucose (OR 2.21, 95% CI 1.33 to 3.69, p <0.01), and mean glucose in the final 24 hours before discharge (OR 1.29, 95% CI 1.03 to 1.60, p = 0.03) were associated with increased odds of PTDM at 1 year. In patients on insulin before discharge, blood glucose values were significantly higher compared with those who were not (136 mg/dl vs 114 mg/dl at 1 to 3 months, 112 vs 100 at 4 to 6 months, 109 vs 98 at 8 to 12 months, all p <0.01). This analysis improves understanding of PTDM incidence, glucometric trends, and risk differences by DM status in the HT population. Similar to liver and kidney patients, inpatient glucometrics may be informative of PTDM risk in HT patients. Guidelines for this population should be developed to account for risk heterogeneity and need for differential management.

Purpose of ReviewSummarize safety issues related to patients using insulin pump therapy and continuous glucose monitoring systems (CGMS) in the outpatient setting when they are hospitalized and to review steps that can be taken to mitigate risk associated with use or discontinuation of these devices.Recent FindingsTwo recent consensus conferences were held on the topics of inpatient use of insulin pumps and CGMS devices. In addition to commonly known safety issues (e.g., device malfunction, infection), cybersecurity and the vulnerability of contemporary technology to hacking have emerged. CGMS capabilities offer the promise of advancing the goal for development of glucometry (centralized monitoring of real-time glucose data). Strategies to assuring safe use of insulin pumps and CGMS in the hospital include collaboration between the patient and staff, proper patient selection, and clear policies and procedures outlining safe use. Available data indicates few adverse events associated with these devices in the hospital.SummaryCurrent data suggests, with proper patient selection and a clear process in place for glycemic management, that adverse events are rare, and consensus favors allowing use of the technology in the hospital. The topic of insulin pump and CGMS in the hospital would greatly benefit from more institutions reporting on their experiences and prospective clinical trials.

To provide data on glucose control in hospitals in the United States, analyzing measurements from the largest number of facilities to date. Point-of-care bedside glucose (POC-BG) test results were extracted from 575 hospitals from January 2009 to December 2009 by using a laboratory information management system. Glycemic control for patients in the intensive care unit (ICU) and non-ICU areas was assessed by calculating patient-day-weighted mean POC-BG values and rates of hypoglycemia and hyperglycemia. The relationship between POC-BG levels and hospital characteristics was determined. A total of 49,191,313 POC-BG measurements (12,176,299 ICU and 37,015,014 non-ICU values) were obtained from 3,484,795 inpatients (653,359 in the ICU and 2,831,436 in non-ICU areas). The mean POC-BG was 167 mg/dL for ICU patients and 166 mg/dL for non-ICU patients. The prevalence of hyperglycemia (>180 mg/dL) was 32.2% of patient-days for ICU patients and 32.0% of patient-days for non-ICU patients. The prevalence of hypoglycemia (<70 mg/dL) was 6.3% of patient-days for ICU patients and 5.7% of patient-days for non-ICU patients. Patient-day-weighted mean POC-BG levels varied on the basis of hospital size (P<.01), type (P<.01), and geographic location (P<.01) for ICU and non-ICU patients, with larger hospitals (≥400 beds), academic hospitals, and US hospitals in the West having the lowest mean POC-BG values. The percentage of patient-days in the ICU characterized by hypoglycemia was highest among larger and academic hospitals (P<.05) and least among hospitals in the Northeast (P<.001). Hyperglycemia is common in hospitals in the United States, and glycemic control may vary on the basis of hospital characteristics. Increased hospital participation in data collection may support a national benchmarking process for the development of optimal practices to manage inpatient hyperglycemia.

Purpose of Review Glucometrics is the systematic analysis of inpatient glucose data and is of key interest as hospitals strive to improve inpatient glycemic control. Insulinometrics is the systematic analysis and reporting of inpatient insulin therapy. This paper reviews some of the questions to be resolved before a national benchmarking process can be developed that will allow institutions to track and compare inpatient glucose control performance against established guidelines. Recent Findings There remains a lack of standardization on how glucometrics should be measured and reported. Before hospitals can commit resources to compiling and extracting data, consensus must be reached on such questions as which measures to report, definitions of glycemic targets, and how data should be obtained. Examples are provided on how insulin administration can be measured and reported. Summary Hospitals should begin assessment of glucometrics and insulinometrics. However, consensus and standardization must first occur to allow for a national benchmarking process.