Treatment of Diabetes in Older Adults: An Endocrine Society* Clinical Practice Guideline.

OBJECTIVE: The objective is to formulate clinical practice guidelines for the treatment of diabetes in older adults. CONCLUSIONS: Diabetes, particularly type 2, is becoming more prevalent in the general population, especially in individuals over the age of 65 years. The underlying pathophysiology of the disease in these patients is exacerbated by the direct effects of aging on metabolic regulation. Similarly, aging effects interact with diabetes to accelerate the progression of many common diabetes complications. Each section in this guideline covers all aspects of the etiology and available evidence, primarily from controlled trials, on therapeutic options and outcomes in this population. The goal is to give guidance to practicing health care providers that will benefit patients with diabetes (both type 1 and type 2), paying particular attention to avoiding unnecessary and/or harmful adverse effects.

A quadruply-asymmetric sigmoid to describe the insulin-glucose relationship during intravenous insulin infusion.

For hospitalized patients requiring intravenous insulin therapy, an objective is to quantify the intravenous insulin infusion rate (IR) across the domain of blood glucose (BG) values at a single timepoint. The algorithm parameters include low BG (70 mg/dL), critical high BG, target range BG limits, and maintenance rate (MR) of insulin infusion, which, after initialization, depends on rate of change of blood glucose, previous IR, and other inputs. The restraining rate (RR) is a function of fractional completeness of ascent of BG (FCABG) from BG 70 mg/dL to target. The correction rate (CR) is a function of fractional elevation of BG (FEBG), in comparison to elevation of a critical high BG, above target. IR = RR + CR. The proposed mathematical model describing a sigmoidal relationship between IR and BG may offer a safety advantage over the linear relationship currently employed in some intravenous glucose management systems.

Target attainment through algorithm design during intravenous insulin infusion.

BACKGROUND: Algorithms were designed under a single model, to attain differing designated glycemic targets during intravenous insulin infusion, and evaluated in order to justify computerization of the model. The approximate maintenance rate (MR) of insulin infusion is discovered according to rate of change of blood glucose (BG) and previous insulin infusion rate (IR). During treatment, re-assignment of IR depends on MR and BG. For each MR, a roughly sigmoidal relationship between BG and IR is specified, such that the inflection point falls approximately at a true target BG. MATERIALS AND METHODS: Performance at St. Francis Hospital, Evanston, IL, was examined during use of tabular algorithms targeting three distinct BG ranges, appropriate for the treatment of hyperglycemic hyperosmolar state, diabetic ketoacidosis, or hyperglycemia accompanying other critical illness. Group membership was defined according to algorithm used for patient treatment during the first 6 months of 2012. The group geometric mean (GGM) and multiplicative surrogate standard deviation (MSSD) are reported as group measures, respectively typifying the central tendency and variability of individual patient BG distributions. RESULTS: Between first attainment of target range BG control and a data collection end point, BG data were evaluable during treatment courses for 58 patients. During this time frame, in the group treated with target 100-149 mg/dL, there were five episodes of BG <70 mg/dL for each of five patients, with the lowest being 57 mg/dL. The GGM (with multiplicative standard deviation) was 269.4 (÷/× 1.06) mg/dL for the algorithm having target 200-299 mg/dL (n = 3 treatment courses), 172.6 (÷/× 1.15) mg/dL for target 150-199 mg/dL (n = 7), and 131.3 (÷/× 1.19) mg/dL for target 100-149 mg/dL (n = 48). The values of MSSD for the three groups were (÷/× 1.14), (÷/× 1.20), and (÷/× 1.20), respectively. CONCLUSIONS: The pilot series suggests that once target range BG is attained, maintenance of control within each of three distinct BG target ranges is achievable, according to choice of algorithm.

Multiplicative surrogate standard deviation: a group metric for the glycemic variability of individual hospitalized patients.

OBJECTIVE: Group metrics are described to quantify blood glucose (BG) variability of hospitalized patients. METHODS: The "multiplicative surrogate standard deviation" (MSSD) is the reverse-transformed group mean of the standard deviations (SDs) of the logarithmically transformed BG data set of each patient. The "geometric group mean" (GGM) is the reverse-transformed group mean of the means of the logarithmically transformed BG data set of each patient. Before reverse transformation is performed, the mean of means and mean of SDs each has its own SD, which becomes a multiplicative standard deviation (MSD) after reverse transformation. Statistical predictions and comparisons of parametric or nonparametric tests remain valid after reverse transformation. A subset of a previously published BG data set of 20 critically ill patients from the first 72 h of treatment under the SPRINT protocol was transformed logarithmically. After rank ordering according to the SD of the logarithmically transformed BG data of each patient, the cohort was divided into two equal groups, those having lower or higher variability. RESULTS: For the entire cohort, the GGM was 106 (÷/× 1.07) mg/dl, and MSSD was 1.24 (÷/× 1.07). For the subgroups having lower and higher variability, respectively, the GGM did not differ, 104 (÷/× 1.07) versus 109 (÷/× 1.07) mg/dl, but the MSSD differed, 1.17 (÷/× 1.03) versus 1.31 (÷/× 1.05), p = .00004. CONCLUSIONS: By using the MSSD with its MSD, groups can be characterized and compared according to glycemic variability of individual patient members.

Pathways to quality inpatient management of hyperglycemia and diabetes: a call to action.

Currently patients with diabetes comprise up to 25-30% of the census of adult wards and critical care units in our hospitals. Although evidence suggests that avoidance of hyperglycemia (>180 mg/dL) and hypoglycemia (<70 mg/dL) is beneficial for positive outcomes in the hospitalized patient, much of this evidence remains controversial and at times somewhat contradictory. We have recently formed a consortium for Planning Research in Inpatient Diabetes (PRIDE) with the goal of promoting clinical research in the area of management of hyperglycemia and diabetes in the hospital. In this article, we outline eight aspects of inpatient glucose management in which randomized clinical trials are needed. We refer to four as system-based issues and four as patient-based issues. We urge further progress in the science of inpatient diabetes management. We hope this call to action is supported by the American Diabetes Association, The Endocrine Society, the American Association of Clinical Endocrinologists, the American Heart Association, the European Association for the Study of Diabetes, the International Diabetes Federation, and the Society of Hospital Medicine. Appropriate federal research funding in this area will help ensure high-quality investigations, the results of which will advance the field. Future clinical trials will allow practitioners to develop optimal approaches for the management of hyperglycemia in the hospitalized patient and lessen the economic and human burden of poor glycemic control and its associated complications and comorbidities in the inpatient setting.

Glycemic variability in hospitalized patients: choosing metrics while awaiting the evidence.

Hyperglycemia, hypoglycemia, preexisting diabetes, and glycemic variability each may affect hospital outcomes. Observational findings derived from randomized trials or retrospective studies suggest that independent of hypoglycemia and hyperglycemia, a relationship exists between variability and hospital outcomes. A review of studies conducted in diverse hospital populations is reported here, showing a relationship between measures of variability and nonglycemic outcomes, including ICU and hospital mortality and length of stay. "Glycemic variability" has an intuitive meaning, understood as a propensity of a single patient to develop repeated episodes of excursions of BG over a relatively short period of time that exceed the amplitude expected in normal physiology. It is proposed that each of 3 dimensions of variability should be separately studied: (1) magnitude of glycemic excursions during intervals of relative stability of the moving average of BG, (2) frequency with which a critical magnitude of excursion is exceeded, and (3) presence or absence of fine tuning. Multiple hospital studies have found that the standard deviation (SD) of the data set of blood glucose values (BG) of individual patients predicts outcomes. An appropriate refinement would be to report the "Reverse-transformed group mean of the SD of the logarithmically transformed BG data set of each patient," with confidence intervals. In logarithmic space, group means of the SD of BGs of each patient may be compared, using an appropriate parametric test. Upon reverse transformation, the upper and lower bounds of the confidence intervals become asymmetric about the reverse-transformed group mean of the SD. There is a need to understand what patterns of dispersion of BG over time are captured by SD as a predictor of outcomes. Among the causes of high SD, a subgroup may consist of patients having frequent oscillations of BG. Another subgroup may consist of patients experiencing a major change of overall glycemia during the timeframe of data collection. Appropriate metrics should be developed to recognize both variability in the sense of recurrent large oscillations of BG, and separately to recognize any time-dependent change of overall glycemia during hospitalization. Especially in relation to uncontrolled diabetes, there is a need to know whether rapid correction of chronic hyperglycemia adversely affects hospital outcomes. We have some understanding of how to control or prevent change of overall glycemia, and less understanding of how to control variability. Each may be associated with outcomes, and each may be detected by a high SD, but it remains uncertain whether intervention to prevent either pattern of changing glycemia would affect outcomes.

Individualization through standardization: electronic orders for subcutaneous insulin in the hospital.

OBJECTIVE: The objective was to design electronic order sets that would promote safe, effective, and individualized order entry for subcutaneous insulin in the hospital, based on a review of best practices. METHODS: Saint Francis Hospital in Evanston, Illinois, a community teaching hospital, was selected as the pilot site for 6 hospitals in the Health Care System to introduce an electronic medical record. Articles dealing with management of hospital hyperglycemia, medical order entry systems, and patient safety were reviewed selectively. RESULTS: In the published literature on institutional glycemic management programs and insulin order sets, features were identified that improve safety and effectiveness of subcutaneous insulin therapy. Subcutaneous electronic insulin order sets were created, designated in short: "patients eating", "patients not eating", and "patients receiving overnight enteral feedings." Together with an option for free text entry, menus of administration instructions were designed within each order set that were applicable to specific insulin orders and expressed in standardized language, such as "hold if tube feeds stop" or "do not withhold." CONCLUSION: Two design features are advocated for electronic order sets for subcutaneous insulin that will both standardize care and protect individualization. First, within the order sets, the glycemic management plan should be matched to the carbohydrate exposure of the patients, with juxtaposition of appropriate orders for both glucose monitoring and insulin. Second, in order to convey precautions of insulin use to pharmacy and nursing staff, the prescriber must be able to attach administration instructions to specific insulin orders.

Guidelines for the use of an insulin infusion for the management of hyperglycemia in critically ill patients.

OBJECTIVE: To evaluate the literature and identify important aspects of insulin therapy that facilitate safe and effective infusion therapy for a defined glycemic end point. METHODS: Where available, the literature was evaluated using Grades of Recommendation, Assessment, Development, and Evaluation (GRADE) methodology to assess the impact of insulin infusions on outcome for general intensive care unit patients and those in specific subsets of neurologic injury, traumatic injury, and cardiovascular surgery. Elements that contribute to safe and effective insulin infusion therapy were determined through literature review and expert opinion. The majority of the literature supporting the use of insulin infusion therapy for critically ill patients lacks adequate strength to support more than weak recommendations, termed suggestions, such that the difference between desirable and undesirable effect of a given intervention is not always clear. RECOMMENDATIONS: The article is focused on a suggested glycemic control end point such that a blood glucose ≥ 150 mg/dL triggers interventions to maintain blood glucose below that level and absolutely <180 mg/dL. There is a slight reduction in mortality with this treatment end point for general intensive care unit patients and reductions in morbidity for perioperative patients, postoperative cardiac surgery patients, post-traumatic injury patients, and neurologic injury patients. We suggest that the insulin regimen and monitoring system be designed to avoid and detect hypoglycemia (blood glucose ≤ 70 mg/dL) and to minimize glycemic variability.Important processes of care for insulin therapy include use of a reliable insulin infusion protocol, frequent blood glucose monitoring, and avoidance of finger-stick glucose testing through the use of arterial or venous glucose samples. The essential components of an insulin infusion system include use of a validated insulin titration program, availability of appropriate staffing resources, accurate monitoring technology, and standardized approaches to infusion preparation, provision of consistent carbohydrate calories and nutritional support, and dextrose replacement for hypoglycemia prevention and treatment. Quality improvement of glycemic management programs should include analysis of hypoglycemia rates, run charts of glucose values <150 and 180 mg/dL. The literature is inadequate to support recommendations regarding glycemic control in pediatric patients. CONCLUSIONS: While the benefits of tight glycemic control have not been definitive, there are patients who will receive insulin infusion therapy, and the suggestions in this article provide the structure for safe and effective use of this therapy.

Stress hyperglycaemia.

Results of randomised controlled trials of tight glycaemic control in hospital inpatients might vary with population and disease state. Individualised therapy for different hospital inpatient populations and identification of patients at risk of hyperglycaemia might be needed. One risk factor that has received much attention is the presence of pre-existing diabetes. So-called stress hyperglycaemia is usually defined as hyperglycaemia resolving spontaneously after dissipation of acute illness. The term generally refers to patients without known diabetes, although patients with diabetes might also develop stress hyperglycaemia-a fact overlooked in many studies comparing hospital inpatients with or without diabetes. Investigators of several studies have suggested that patients with stress hyperglycaemia are at higher risk of adverse consequences than are those with pre-existing diabetes. We describe classification of stress hyperglycaemia, mechanisms of harm, and management strategies.

Evaluation of hospital glycemic control at US academic medical centers.

OBJECTIVE: To evaluate contemporary hospital glycemic management in US academic medical centers. DESIGN: This retrospective cohort study was conducted on patients discharged from 37 academic medical centers between July 1 and September 30, 2004; 1,718 eligible adult patients met at least 1 of the inclusion criteria: 2 consecutive blood glucose readings >180 mg/dL within 24 hours, or insulin treatment at any time during hospitalization. We assessed 3 consecutive measurement days of glucose values, glycemic therapy, and additional clinical and laboratory characteristics. RESULTS: In this diverse cohort, 79% of patients had a prior diagnosis of diabetes, and 84.6% received insulin on the second measurement day. There was wide variation in hospital performance of recommended hospital diabetes care measures such as glycosylated hemoglobin (A1C) assessment (range, 3%-63%) and timely admission laboratory glucose measurement (range, 39%-97%). Median glucose was significantly lower for patients in the intensive care unit (ICU) compared to ward/intermediate care. ICU patients treated with intravenous insulin had significantly lower median glucose when compared to subcutaneous insulin. Only 25% of ICU patients on day 3 had estimated 6 AM glucose or=1 glucose measurement >or=180 mg/dL on measurement days 2 and 3. Severe hypoglycemia (<50 mg/dL) occurred in 2.8% of all patient days. CONCLUSIONS: Despite frequent insulin use, glucose control was suboptimal. Academic medical centers have opportunities to improve care to meet current American Diabetes Association hospital diabetes care standards.

Intermediary variables and algorithm parameters for an electronic algorithm for intravenous insulin infusion.

BACKGROUND: Algorithms for intravenous insulin infusion may assign the infusion rate (IR) by a two-step process. First, the previous insulin infusion rate (IR(previous)) and the rate of change of blood glucose (BG) from the previous iteration of the algorithm are used to estimate the maintenance rate (MR) of insulin infusion. Second, the insulin IR for the next iteration (IR(next)) is assigned to be commensurate with the MR and the distance of the current blood glucose (BG(current)) from target. With use of a specific set of algorithm parameter values, a family of iso-MR curves is created, each giving IR as a function of MR and BG. METHOD: To test the feasibility of estimating MR from the IR(previous) and the previous rate of change of BG, historical hyperglycemic data points were used to compute the "maintenance rate cross step next estimate" (MR(csne)). Historical cases had been treated with intravenous insulin infusion using a tabular protocol that estimated MR according to column-change rules. The mean IR on historical stable intervals (MR(true)), an estimate of the biologic value of MR, was compared to MR(csne) during the hyperglycemic iteration immediately preceding the stable interval. Hypothetically calculated MR(csne)-dependent IR(next) was compared to IR(next) assigned historically. An expanded theory of an algorithm is developed mathematically. Practical recommendations for computerization are proposed. RESULTS: The MR(true) determined on each of 30 stable intervals and the MR(csne) during the immediately preceding hyperglycemic iteration differed, having medians with interquartile ranges 2.7 (1.2-3.7) and 3.2 (1.5-4.6) units/h, respectively. However, these estimates of MR were strongly correlated (R(2) = 0.88). During hyperglycemia at 941 time points the IR(next) assigned historically and the hypothetically calculated MR(csne)-dependent IR(next) differed, having medians with interquartile ranges 4.0 (3.0-6.0) and 4.6 (3.0-6.8) units/h, respectively, but these paired values again were correlated (R(2) = 0.87). This article describes a programmable algorithm for intravenous insulin infusion. The fundamental equation of the algorithm gives the relationship among IR; the biologic parameter MR; and two variables expressing an instantaneous rate of change of BG, one of which must be zero at any given point in time and the other positive, negative, or zero, namely the rate of change of BG from below target (rate of ascent) and the rate of change of BG from above target (rate of descent). In addition to user-definable parameters, three special algorithm parameters discoverable in nature are described: the maximum rate of the spontaneous ascent of blood glucose during nonhypoglycemia, the glucose per daily dose of insulin exogenously mediated, and the MR at given patient time points. User-assignable parameters will facilitate adaptation to different patient populations. CONCLUSIONS: An algorithm is described that estimates MR prior to the attainment of euglycemia and computes MR-dependent values for IR(next). Design features address glycemic variability, promote safety with respect to hypoglycemia, and define a method for specifying glycemic targets that are allowed to differ according to patient condition.

Algorithms for intravenous insulin delivery.

This review aims to classify algorithms for intravenous insulin infusion according to design. Essential input data include the current blood glucose (BG(current)), the previous blood glucose (BG(previous)), the test time of BG(current) (test time(current)), the test time of BG(previous) (test time(previous)), and the previous insulin infusion rate (IR(previous)). Output data consist of the next insulin infusion rate (IR(next)) and next test time. The classification differentiates between "IR" and "MR" algorithm types, both defined as a rule for assigning an insulin infusion rate (IR), having a glycemic target. Both types are capable of assigning the IR for the next iteration of the algorithm (IR(next)) as an increasing function of BG(current), IR(previous), and rate-of-change of BG with respect to time, each treated as an independent variable. Algorithms of the IR type directly seek to define IR(next) as an incremental adjustment to IR(previous). At test time(current), under an IR algorithm the differences in values of IR(next) that might be assigned depending upon the value of BG(current) are not necessarily continuously dependent upon, proportionate to, or commensurate with either the IR(previous) or the rate-of-change of BG. Algorithms of the MR type create a family of IR functions of BG differing according to maintenance rate (MR), each being an iso-MR curve. The change of IR(next) with respect to BG(current) is a strictly increasing function of MR. At test time(current), algorithms of the MR type use IR(previous) and the rate-of-change of BG to define the MR, multiplier, or column assignment, which will be used for patient assignment to the right iso-MR curve and as precedent for IR(next). Bolus insulin therapy is especially effective when used in proportion to carbohydrate load to cover anticipated incremental transitory enteral or parenteral carbohydrate exposure. Specific distinguishing algorithm design features and choice of parameters may be important to establish freedom from hypoglycemia, eliminate the need for administration of concentrated dextrose during euglycemia, control variability within the treatment course of individual patients, achieve adaptability to differing blood glucose targets, and minimize variability of glycemic control between treatment courses of different patients or patient populations. Areas for future work include the reduction of nursing burden, the development of a theory that will account for lag time of interstitial monitoring and pharmacodynamic delay of insulin action, and management strategies for the narrow euglycemic range. It is hoped that hypoglycemia and variability of control will become negligible problems, and that fear of hypoglycemia no longer will deflect investigators and caregivers from providing optimal glycemic management.

Patient-level glucose reporting: averages, episodes, or something in between?

The article by Van Herpe and colleagues in the previous issue of Critical Care describes the glycemic penalty index (GPI), which weights both hyperglycemic and hypoglycemic blood glucose measurements commensurate to their clinically significant difference from target. Although certain adverse consequences result from isolated severe hyperglycemic episodes, several specific outcomes depend upon overall hyperglycemia. In contrast, although mortality has been related epidemiologically to overall low blood glucose, specific negative outcomes may depend upon isolated episodes. Capturing both hypoglycemia and hyperglycemia in a single index will be shown to be useful if the GPI enables us to better define insulin strategies, outcomes, and targets.