Nonlinear Metabolic Effect of Insulin across the Blood Glucose Range in Patients with Type 1 Diabetes Mellitus

Background

For insulin therapy to successfully maintain blood glucose (BG) levels of patients with type 1 diabetes mellitus (T1DM) in normoglycemia, it is necessary to understand if the metabolic effect of insulin across the BG range is linear or not.

Methods

We assess the ability of insulin to lower BG in patients with T1DM in hypoglycemia and hyperglycemia. The net metabolic effect of insulin, defined as the total effect resulting from both reduced endogenous glucose production and increased glucose uptake, was used to define the insulin effectiveness (IE), a measure that indicates the amplitude of glucose lowering that a unit of active insulin can achieve at a given BG level. The IE was assessed in hypoglycemia and hyperglycemia through two separate studies. In the first study, patients were subjected to a hyperinsulinemic euglycemic and hypoglycemic glucose clamp. In the second study, another group of patients were clamped at a hyperglycemic level.

Results

The IE increased by 75% when BG dropped from 90 to 50 mg/dl at a steady rate of 1 mg/dl/min and decreased by 10% when BG was increased from 100 to 200 mg/dl.

Conclusions

The net metabolic effect of insulin is nonlinear across the BG range and is amplified in hypoglycemia and dampened in hyperglycemia. Most importantly, the BG lowering per unit of insulin is accelerated when falling into hypoglycemia. The understanding of the accelerated risk for hypoglycemia with falling glucose levels will help the design of more robust hypoglycemia prevention and detection systems.     

Computerized Algorithms: Safety and Effectiveness of a Computerized Subcutaneous Insulin Program to Treat Inpatient Hyperglycemia

Background

This proof of concept study was designed to evaluate the safety and effectiveness of a computerized insulin program, the Clarian GlucoStabilizer™ Subcutaneous Insulin Program (CGS-SQ). This paper discusses the CGS-SQ''s impact on the glycemic control of hospitalized patients with hyperglycemia.

Methods

Patients at Methodist and Indiana University Hospitals requiring subcutaneous insulin were treated using the CGS-SQ. This program calculates subcutaneous bolus insulin doses based on the current blood glucose (BG), using an insulin sensitivity factor, the number of grams of carbohydrates eaten, and an insulin-to-carbohydrate ratio, with a goal of maintaining the patient''s BG in a prespecified target range. The target range, insulin sensitivity factor, and insulin-to-carbohydrate ratio are established by the physician.

Results

From April 2006 to September 2007, the CGS-SQ treated 1772 patients at Methodist and Indiana University Hospitals, with 46,575 BGs in its database. For these patients, the average BG was 158.3 mg/dl, 40.5% percent of BGs were in the default target range of 100–150 mg/dl, and 69.8% were in the wider range of 70–180 mg/dl. The hypoglycemia (BG <40 mg/dl) rate was 0.18%.

Conclusions

The CGS-SQ provided a means to deliver insulin in a standardized manner, resulting in satisfactory BG control with a low hypoglycemia rate, thus serving as a tool for safe and effective insulin therapy for hospitalized patients.     

Effect of diabetes mellitus on outcomes of hyperglycemia in a mixed medical surgical intensive care unit

Background:

Intensive insulin therapy and degree of glycemic control in critically ill patients remains controversial, particularly in patients with diabetes mellitus. We hypothesized that diabetic patients who achieved tight glucose control with continuous insulin therapy would have less morbidity and lower mortality than diabetic patients with uncontrolled blood glucose.

Method:

A retrospective chart review was performed on 395 intensive care unit (ICU) patients that included 235 diabetic patients. All patients received an intravenous insulin protocol targeted to a blood glucose (BG) level of 80–140mg/dl. Outcomes were compared between (a) nondiabetic and diabetic patients, (b) diabetic patients with controlled BG levels (80–140mg/dl) versus uncontrolled levels (>140 mg/dl), and (c) diabetic survivors and nonsurvivors.

Results:

Diabetic patients had a shorter ICU stay compared to nondiabetic patients (10 ± 0.7 vs 13 ± 1.1, p = .01). The mean BG of the diabetic patients was 25% higher on average in the uncontrolled group than in the controlled (166 ± 26 vs 130 ± 9.4 mg/dl, p < .01). There was no difference in ICU and hospital length of stay (LOS) between diabetic patients who were well controlled compared to those who were uncontrolled. Diabetic nonsurvivors had a significantly higher incidence of hypoglycemia (BG <60 mg/dl) compared to diabetic survivors.

Conclusion:

The results showed that a diagnosis of diabetes was not an independent predictor of mortality, and that diabetic patients who were uncontrolled did not have worse outcomes. Diabetic nonsurvivors were associated with a greater amount of hypoglycemic episodes, suggesting these patients may benefit from a more lenient blood glucose protocol.     

Computerized Algorithms: Use of a Computerized Intravenous Insulin Algorithm within a Nurse-Directed Protocol for Patients Undergoing Cardiovascular Surgery

Background

Several studies have shown the benefits of tight glycemic control in the intensive care unit. A large hospital became concerned about certain deficiencies in the management of glucose control in conjunction with cardiovascular surgery. A multidisciplinary steering committee was formed, which implemented a glycemic protocol, the subject of this study.

Methods

The glycemic protocol is a perioperative, nurse-directed program that incorporates the computerized intravenous (IV) insulin algorithm, Glucommander. Upon admission, hemoglobin A1c and blood glucose (BG) were tested, and patients were screened for previously diagnosed diabetes. This information was used to determine if preoperative insulin will be used, if the patient will be transitioned post-IV to subcutaneous (SC) basal-bolus insulin, and if insulin will be prescribed on discharge. IV insulin was initiated perioperatively in known diabetes cases or if one BG value >140 mg/dl or two BG values >110 mg/dl within 24 hours before or during surgery. The target range was 90 to 120 mg/dl.

Results

In the 9 months after protocol implementation, 93% of the patients had no BG value >200 mg/dl during the first 48 hours postoperatively. In the 6 months of study data, there were 457 patients. The mean time to target range was 3.0 hours. The mean IV insulin run time was 37 hours. The mean BG value was 107 mg/dl. Only 2% of patients had transient BG <50 mg/dl, and no BG values were <40 mg/dl. Of the patients, 52% were transitioned to SC basal-bolus, and 26% were discharged on insulin.

Conclusions

The Glucommander earned high respect from the nurses for the way it scheduled BG tests and eliminated the calculation time and calculation errors associated with manual methods. The protocol was highly effective in normalizing glucose without hypoglycemia. The multidisciplinary steering committee proved to be a good approach to implementing a glycemic protocol.     

Diabetes Technologies and Hospital Care: Transitioning Insulin Pump Therapy from the Outpatient to the Inpatient Setting: A Review of 6 Years' Experience with 253 Cases

Background

We reviewed the care of a large cohort of patients with diabetes mellitus on insulin pump therapy who required an inpatient stay.

Methods

Records were reviewed of patients hospitalized between January 1, 2006, and December 31, 2011.

Results

A total of 136 patients using insulin pumps had 253 hospitalizations. Mean (standard deviation) patient age was 55 (16) years, diabetes duration was 29 (15) years, and pump duration was 6 (5) years. Insulin pump therapy was continued in 164 (65%) hospitalizations. Adherence to core process measures improved over time: by 2011, 100% of cases had an endocrinology consultation, 100% had the required insulin pump order set completed, and 94% had documentation of the signed agreement specifying patient responsibilities for continued use of the technology while hospitalized. Documentation of the insulin pump flow sheet also increased but could still be located in only 64% of cases by the end of 2011. Mean glucose was not significantly different among patients who remained on insulin pump therapy compared to those for whom it was discontinued (p > .1), but episodes of severe hyperglycemia (>300 mg/dl) and hypoglycemia (<40 mg/dl) were significantly less common among pump users. No pump site infections, mechanical pump failures, or episodes of diabetic ketoacidosis were observed among patients remaining on therapy.

Conclusions

With appropriate patient selection and usage guidelines, most patients using insulin pumps can safely have their therapy transitioned to the inpatient setting. Further study is needed to determine whether this approach can be translated to other hospital settings.     

In Silico Evaluation of an Artificial Pancreas Combining Exogenous Ultrafast-Acting Technosphere Insulin with Zone Model Predictive Control

Background

Because of the slow pharmacokinetics of subcutaneous (SC) insulin, avoiding postprandial hyperglycemia has been a major challenge for an artificial pancreas (AP) using SC insulin without a meal announcement.

Methods

A semiautomated AP with Technosphere® Insulin (TI; MannKind Corporation, Valencia, CA) was designed to combine pulmonary and SC insulin. Manual inhalation of 10 U ultrafast-absorbing TI at mealtime delivers the first, or cephalic, phase of insulin, and an SC insulin pump controlled by zone model predictive controller delivers second-phase and basal insulin. This AP design was evaluated on 100 in silico subjects from the University of Virginia/Padova metabolic simulator using a protocol of two 50 g carbohydrate (CHO) meals and two 15 g CHO snacks.

Results

Simulation analysis shows that the semiautomated AP with TI provides 32% and 16% more time in the controller target zone (80–140 mg/dl) during the 4 h postprandial period, with 39 and 20 mg/dl lower postprandial blood glucose peak on average than the pure feedback AP and the AP with manual feed-forward SC bolus, respectively. No severe hypoglycemia (<50 mg/dl) was observed in any cases.

Conclusions

The semiautomated AP with TI provides maximum time in the clinically accepted region when compared with pure feedback AP and AP with manual feed-forward SC bolus. Furthermore, the semiautomated AP with TI provides a flexible operation (optional TI inhalation) with minimal user interaction, where the controller design can be tailored to specific user needs and abilities to interact with the device.     

Usage and effectiveness of the low glucose suspend feature of the Medtronic Paradigm Veo insulin pump

Background

Sensor-augmented insulin pumps may be programmed to suspend insulin delivery in response to hypoglycemia. The Medtronic Paradigm® Veo™ pump with automatic low glucose suspend (LGS) was released in 2009. Data from 7 months of real-world use of the system were analyzed to assess usage patterns and effectiveness of LGS.

Method

Data from 935 patients totaling 49,867 patient days were collected; the LGS feature was on for 82% of these days. A subset of 278 subjects who used the pump for ≥3 months was analyzed separately; these subjects provided 28,401 patient days of data, with LGS used for 92% of the time.

Results

The LGS threshold was most commonly set between 50 and 60 mg/dl. A total of 27,216 LGS events occurred, and 60% began in the afternoon or evening. The median duration of LGS events was 9.87 min, 45% lasted for <5 min, and 11% lasted for >115 min (equivalent to the full extent of the LGS event between 115 and 120 min). Among the episodes lasting for >115 min, the mean sensor glucose (SG) was 58.8 ± 12.4 mg/dl at LGS activation (time 0), rose to 102.2 ± 52.8 mg/dl by the end of the LGS episode (when insulin delivery was automatically resumed), and was 150.1 ± 68.6 mg/dl at 240 min. In the 278-subject subgroup, LGS usage significantly reduced the number of SG readings <50 mg/dl (p = 0.001) and >300 mg/dl (p = 0.001).

Conclusions

The LGS feature was on for most of the patient days in the study. Most LGS episodes lasted for <10 min. Use of the LGS feature significantly reduced exposure to hypoglycemia. Profound hyperglycemia resulting from LGS episodes lasting >115 min was not observed.     

Determinants of the accuracy of continuous glucose monitoring in non-critically ill patients with heart failure or severe hyperglycemia

Background

The accuracy of continuous glucose monitoring (CGM) in non-critically ill hospitalized patients with heart failure or severe hyperglycemia (SH) is unknown.

Methods

Hospitalized patients with congestive heart failure (CHF) exacerbation (receiving IV or subcutaneous insulin) or SH requiring insulin infusion were compared to outpatients referred for retrospective CGM.

Results

Forty-three patients with CHF, 15 patients with SH, and 88 outpatients yielded 470, 164, and 2150 meter–sensor pairs, respectively. Admission glucose differed (188 versus 509 mg/dl in CHF and SH, p < .001) but not the first sensor glucose (p = .35). In continuous glucose error grid analysis, 67–78% of pairs during hypoglycemia were in zones A+B (p = .63), compared with 98–100% in euglycemia (p < .001) and 98%, 92%, and 99% (p = .001) during hyperglycemia for the CHF, SH, and outpatient groups, respectively. Mean absolute relative difference (MARD) was lower in the CHF versus the SH group in glucose strata above 100 mg/dl, but there was no difference between the CHF and outpatient groups. Linear regression models showed that CHF versus outpatient, SH versus CHF, and coefficient of variation were significant predictors of higher MARD. Among subjects with CHF, MARD was not associated with brain natriuretic peptide or change in plasma volume, but it was significantly higher in subjects randomized to IV insulin (p = .04).

Conclusions

The results suggest that SH and glycemic variability are more important determinants of CGM accuracy than known CHF status alone in hospitalized patients.     

Perioperative Management of Patients with Diabetes Undergoing Ambulatory Elective Surgery

Objective

The objective was to assess processes of care for patients with diabetes undergoing elective surgery.

Methods

A retrospective review of medical records was conducted to determine frequency of perioperative glucose monitoring, changes in glucose control, and treatment of intraoperative hyperglycemia.

Results

A total of 268 patients underwent 287 elective procedures. Mean age was 67 years, 63% were men, 97% had type 2 diabetes, and most (57%) were treated with oral hypoglycemic agents. Average perioperative time was approximately 8 h. Mean preoperative hemoglobin A1c was 7.0%; however, this value was checked in only 52% of cases. A glucose measurement was obtained in 89% of cases in the preoperative area and in 87% in the postanesthesia care unit, but in only 33% of cases did a value get checked intraoperatively. Average glucose was 139 mg/dl preoperatively, increasing to 166 mg/dl postoperatively (p <.001). Glucose levels increased regardless of type of outpatient medical therapy used to treat hyperglycemia, except for those on combination oral agents plus insulin (p =.06).

Conclusions

These data indicate suboptimal documentation of outpatient hemoglobin A1c. Intraoperative glucose monitoring seldom occurred, despite prolonged periods under anesthesia and perioperative deterioration of glycemic control. Standards need to be developed and interventions are needed to enhance management of diabetes patients undergoing elective procedures.     

A Closed-Loop Artificial Pancreas Using a Proportional Integral Derivative with Double Phase Lead Controller Based on a New Nonlinear Model of Glucose Metabolism

Background

Most closed-loop insulin delivery systems rely on model-based controllers to control the blood glucose (BG) level. Simple models of glucose metabolism, which allow easy design of the control law, are limited in their parametric identification from raw data. New control models and controllers issued from them are needed.

Methods

A proportional integral derivative with double phase lead controller was proposed. Its design was based on a linearization of a new nonlinear control model of the glucose–insulin system in type 1 diabetes mellitus (T1DM) patients validated with the University of Virginia/Padova T1DM metabolic simulator. A 36 h scenario, including six unannounced meals, was tested in nine virtual adults. A previous trial database has been used to compare the performance of our controller with their previous results. The scenario was repeated 25 times for each adult in order to take continuous glucose monitoring noise into account. The primary outcome was the time BG levels were in target (70–180 mg/dl).

Results

Blood glucose values were in the target range for 77% of the time and below 50 mg/dl and above 250 mg/dl for 0.8% and 0.3% of the time, respectively. The low blood glucose index and high blood glucose index were 1.65 and 3.33, respectively.

Conclusion

The linear controller presented, based on the linearization of a new easily identifiable nonlinear model, achieves good glucose control with low exposure to hypoglycemia and hyperglycemia.     

Prediction and prevention of treatment-related inpatient hypoglycemia

Background

Prolonged severe hypoglycemia (SH) in hospitalized patients is associated with increased morbidity and mortality. This study was undertaken to identify risk factors for SH, to apply that knowledge to the development of a prediction algorithm, and to institute a prevention program at a tertiary medical center.

Methods

We analyzed SH events for 172 patients and developed computer algorithms to predict SH that were tested on a population of 3028 inpatients who were found to have blood glucose (BG) <90 mg/dl during their hospital stay. Variables with significant bivariate associations were entered into partition analyses to identify interactions. Logistic regression was performed by calculating parameters related to the odds of hypoglycemia below each cut point. Sensitivity and specificity were determined at various cut points. The cut points resulting in 50% sensitivity for each hypoglycemia level were determined. These algorithms were tested against the initial 172 adjudicated patients.

Results

Variables related to the BG <40 mg/dl cut off point were basal and adjustment scale insulin doses, weight, and creatinine clearance, while variables related to the 60 mg/dl and 70 mg/dl cut points were basal, prandial, and adjustment scale insulin doses, weight, creatinine clearance, and sulfonylurea use. The 50% sensitivity cut point developed using the <70 mg/dl algorithm correctly identified 71% of the adjudicated cases, while the <60 mg/dl and <40 mg/dl algorithms identified 70% and 55% respectively.

Conclusions

A validated prediction algorithm for SH can aid in the identification of patients at risk for SH and may be useful in the development of prevention strategies.     

Artificial Pancreas Systems: Overnight Closed-Loop Insulin Delivery with Model Predictive Control: Assessment of Hypoglycemia and Hyperglycemia Risk Using Simulation Studies

Background

Hypoglycemia and hyperglycemia during closed-loop insulin delivery based on subcutaneous (SC) glucose sensing may arise due to (1) overdosing and underdosing of insulin by control algorithm and (2) difference between plasma glucose (PG) and sensor glucose, which may be transient (kinetics origin and sensor artifacts) or persistent (calibration error [CE]). Using in silico testing, we assessed hypoglycemia and hyperglycemia incidence during over-night closed loop. Additionally, a comparison was made against incidence observed experimentally during open-loop single-night in-clinic studies in young people with type 1 diabetes mellitus (T1DM) treated by continuous SC insulin infusion.

Methods

Simulation environment comprising 18 virtual subjects with T1DM was used to simulate overnight closed-loop study with a model predictive control (MPC) algorithm. A 15 h experiment started at 17:00 and ended at 08:00 the next day. Closed loop commenced at 21:00 and continued for 11 h. At 18:00, protocol included meal (50 g carbo-hydrates) accompanied by prandial insulin. The MPC algorithm advised on insulin infusion every 15 min. Sensor glucose was obtained by combining model-calculated noise-free interstitial glucose with experimentally derived tran-sient and persistent sensor artifacts associated with FreeStyle Navigator® (FSN). Transient artifacts were obtained from FSN sensor pairs worn by 58 subjects with T1DM over 194 nighttime periods. Persistent difference due to FSN CE was quantified from 585 FSN sensor insertions, yielding 1421 calibration sessions from 248 subjects with diabetes.

Results

Episodes of severe (PG ≤ 36 mg/dl) and significant (PG ≤ 45 mg/dl) hypoglycemia and significant hy-perglycemia (PG ≥ 300 mg/dl) were extracted from 18,000 simulated closed-loop nights. Severe hypoglycemia was not observed when FSN CE was less than 45%. Hypoglycemia and hyperglycemia incidence during open loop was assessed from 21 overnight studies in 17 young subjects with T1DM (8 males; 13.5 ± 3.6 years of age; body mass index 21.0 ± 4.0 kg/m2; duration diabetes 6.4 ± 4.1 years; hemoglobin A1c 8.5% ± 1.8%; mean ± standard deviation) participating in the Artificial Pancreas Project at Cambridge. Severe and significant hypoglycemia during simulated closed loop occurred 0.75 and 17.11 times per 100 person years compared to 1739 and 3479 times per 100 person years during experimental open loop, respectively. Signifi-cant hyperglycemia during closed loop and open loop occurred 75 and 15,654 times per 100 person years, respec-tively.

Conclusions

The incidence of severe and significant hypoglycemia reduced 2300- and 200-fold, respectively, during simu-lated overnight closed loop with MPC compared to that observed during open-loop overnight clinical studies in young subjects with T1DM. Hyperglycemia was 200 times less likely. Overnight closed loop with the FSN and the MPC algorithm is expected to reduce substantially the risk of hypoglycemia and hyperglycemia.     

Model-Based Closed-Loop Glucose Control in Type 1 Diabetes: The DiaCon Experience

Background

To improve type 1 diabetes mellitus (T1DM) management, we developed a model predictive control (MPC) algorithm for closed-loop (CL) glucose control based on a linear second-order deterministic-stochastic model. The deterministic part of the model is specified by three patient-specific parameters: insulin sensitivity factor, insulin action time, and basal insulin infusion rate. The stochastic part is identical for all patients but identified from data from a single patient. Results of the first clinical feasibility test of the algorithm are presented.

Methods

We conducted two randomized crossover studies. Study 1 compared CL with open-loop (OL) control. Study 2 compared glucose control after CL initiation in the euglycemic (CL-Eu) and hyperglycemic (CL-Hyper) ranges, respectively. Patients were studied from 22:00–07:00 on two separate nights.

Results

Each study included six T1DM patients (hemoglobin A1c 7.2% ± 0.4%). In study 1, hypoglycemic events (plasma glucose < 54 mg/dl) occurred on two OL and one CL nights. Average glucose from 22:00–07:00 was 90 mg/dl [74–146 mg/dl; median (interquartile range)] during OL and 108 mg/dl (101–128 mg/dl) during CL (determined by continuous glucose monitoring). However, median time spent in the range 70–144 mg/dl was 67.9% (3.0–73.3%) during OL and 80.8% (70.5–89.7%) during CL. In study 2, there was one episode of hypoglycemia with plasma glucose <54 mg/dl in a CL-Eu night. Mean glucose from 22:00–07:00 and time spent in the range 70–144 mg/dl were 121 mg/dl (117–133 mg/dl) and 69.0% (30.7–77.9%) in CL-Eu and 149 mg/dl (140–193 mg/dl) and 48.2% (34.9–72.5%) in CL-Hyper, respectively.

Conclusions

This study suggests that our novel MPC algorithm can safely and effectively control glucose overnight, also when CL control is initiated during hyperglycemia.     

Perioperative hyperglycemia and postoperative infection after lower limb arthroplasty

Background

One of the most serious complications after major orthopedic surgery is deep wound or periprosthetic joint infection. Various risk factors for infection after hip and knee replacement surgery have been reported, including patients'' comorbidities and surgical technique factors. We investigated whether hyperglycemia and diabetes mellitus (DM) are associated with infection that requires surgical intervention after total hip and knee arthroplasty.

Methods

We reviewed our computerized database for elective primary total hip and knee arthroplasty from 2000 to 2008. Demographic information, past medical history of patients, perioperative biochemistry, and postoperative complications were reviewed.Patients were divided into two groups: infected group (101 patients who had surgical intervention for infection at our institution within 2 years after primary surgery) and noninfected group (1847 patients with no intervention with a minimum of one year follow-up. The data were analyzed using t, chi-squared, and Fisher''s exact tests.

Results

There were significantly more diabetes patients in the infected group compared with the noninfected group (22% versus 9%, p < .001). Infected patients had significantly higher perioperative blood glucose (BG) values: preoperative BG (112 ± 36 versus 105 ± 31 mg/dl, p = .043) and postoperative day (POD) 1 BG (154 ± 37 versus 138 ± 31 mg/dl, p < .001). Postoperative morning hyperglycemia (BG >200 mg/dl) increased the risk for the infection more than two-fold. Non-DM patients were three times more likely to develop the infection if their morning BG was >140 mg/dl on POD 1, p = .001. Male gender, higher body mass index, knee arthroplasty, longer operative time and hospital stay, higher comorbidity index, history of myocardial infarction, congestive heart failure, and renal insufficiency were also associated with the infection.

Conclusions

Diabetes mellitus and morning postoperative hyperglycemia were predictors for postoperative infection following total joint arthroplasty. Even patients without a diagnosis of DM who developed postoperative hyperglycemia had a significantly increased risk for the infection.     

Retrospective Outcomes of Glucose Control in Critically Ill Children

Background

Hyperglycemia is a significant problem for critically ill children. Treatment for hyperglycemia remains controversial. This study explores the effect of controlling blood glucose (BG) in hyperglycemic critically ill children.

Methods

A retrospective cohort of nondiabetic critically ill children (defined as requiring mechanical ventilation and/or vasopressors) with BG persistently ≥150 mg/dl and treated with insulin (treatment group) were compared with a historical cohort of similar children who did not receive interventions to control hyperglycemia (baseline group).

Results

There were 130 children in the treatment group and 137 children in the baseline group. Mean BG in the treatment group was 140 ± 24 mg/dl compared with 179 ± 47 mg/dl in the baseline group (p < .001). After adjusting for patient characteristics, cointerventions, and glucose metrics, patients in the treatment group had 2.5 fewer intensive care unit (ICU)-free days (i.e., number of days alive and discharged from ICU within 28 days after inclusion) than the baseline group (p = .023). Glucose control was not independently associated with duration of ICU stay, ventilator-free days, vasopressor-free days, or mortality.

Conclusions

Blood glucose control appears associated with worse outcomes in critically ill children. Our data combined with conflicting results in adults leads us to strongly advocate for the conduct of randomized trials on glucose control in critically ill children.     

Failure to Control Hyperglycemia in NonCritically Ill Diabetes Patients Despite Standard Glycemic Management in a Hospital Setting

Background

Successful control of hyperglycemia has been shown to improve outcomes for diabetes patients in a clinical setting. We assessed the quality of physician-based glycemic management in two general wards, considering the most recent recommendations for glycemic control for noncritically ill patients (<140 mg/dl for premeal glucose).

Methods

Quality of glycemic management of 50 patients in two wards (endocrinology, cardiology) was assessed retrospectively by analyzing blood glucose (BG) levels, the glycemic management effort, and the online questionnaire.

Results

Glycemic control was clearly above the recommended target (mean BG levels: endocrinology: 175 ± 62 mg/dl; cardiology: 186 ± 68 mg/dl). When comparing the first half with the second half of the hospital stay, we found no difference in glycemic control (endocrinology: 168 ± 32 vs 164 ± 42 mg/dl, P = .67; cardiology: 174 ± 36 mg/dl vs 170 ± 42 mg/dl, P =.51) and in insulin dose (endocrinology: 15 ± 14 IU vs 15 ± 13 IU per day, P = .87; cardiology: 27 ± 17 IU vs 27 ± 18 IU per day, P = .92), despite frequent BG measurements (endocrinology: 2.7 per day; cardiology: 3.2 per day). A lack of clearly defined BG targets was indicated in the questionnaire.

Conclusions

The recommended BG target range was not achieved in both wards. Analysis of routine glycemic management demonstrated considerable glycemic management effort, but also a lack of translation into adequate insulin therapy. Implementation of corrective measures, such as structured treatment protocols, is essential.     

Evaluation of Point-of-Care Blood Glucose Measurements in Patients with Diabetic Ketoacidosis or Hyperglycemic Hyperosmolar Syndrome Admitted to a Critical Care Unit

Background

Point-of-care (POC) blood glucose (BG) measurement is currently not recommended in the treatment of patients presenting with diabetic ketoacidosis (DKA) or hyperglycemic hyperosmolar syndrome (HHS).

Methods

We prospectively evaluated and compared capillary and venous POC BG values with laboratory venous glucose in patients with DKA or HHS admitted to one critical care unit over 8 months.

Results

Venous laboratory glucose was strongly correlated with venous (r = 0.98) and capillary (r = 0.96) POC glucose values, though POC glucose values were higher than venous laboratory values (venous POC 21 ± 3 mg/dl, capillary POC 30 ± 4 mg/dl; both p < .001). Increased plasma osmolality had no effect on glucose meter error, while acidemia (pH < 7.3) was associated with greater glucose meter error (p = .04) independent of glucose levels. Comparing hypothetical insulin infusion rates based on laboratory venous glucose to actual infusion rates based on POC glucose values showed that 33/61 insulin infusion rates would have been unchanged, while 28 out of 61 rates were on average 7% ± 2% higher. There were no instances of hypoglycemia in any of the patients.

Conclusions

Overall, both venous and capillary POC BG values were safe for the purpose of titrating insulin infusions in patients with severe hyperglycemia. Acidemia, but not hyperosmolality, increased POC BG value errors.     

Bolus Guide: A Novel Insulin Bolus Dosing Decision Support Tool Based on Selection of Carbohydrate Ranges

Background

Optimal continuous subcutaneous insulin infusion (CSII) therapy emphasizes the relationship between insulin dose and carbohydrate consumption. One widely used tool (bolus calculator) requires the user to enter discrete carbohydrate values; however, many patients might not estimate carbohydrates accurately. This study assessed carbohydrate estimation accuracy in type 1 diabetes CSII users and compared simulated blood glucose (BG) outcomes using the bolus calculator and the “bolus guide,” an alternative system based on ranges of carbohydrate load.

Methods

Patients (n = 60) estimated the carbohydrate load of a representative sample of meals of known carbohydrate value. The estimated error distribution [coefficient of variation (CV)] was the basis for a computer simulation (n = 1.6 million observations) of insulin recommendations for the bolus guide and bolus calculator, translated into outcome blood glucose (OBG) ranges (≤60, 61–200, >201 mg/dl). Patients (n = 30) completed questionnaires assessing satisfaction with the bolus guide.

Results

The CV of typical meals ranged from 27.9% to 44.5%. The percentage of simulated OBG for the calculator and the bolus guide in the <60 mg/dl range were 20.8% and 17.2%, respectively, and 13.8% and 15.8%, respectively, in the >200 mg/dl range. The mean and median scores of all bolus guide satisfaction items and ease of learning and use were 4.17 and 4.2, respectively (of 5.0).

Conclusion

The bolus guide recommendation based on carbohydrate range selection is substantially similar to the calculator based on carbohydrate point estimation and appears to be highly accepted by type 1 diabetes insulin pump users.     

Increasing the doses of both diuretics and angiotensin receptor blockers is beneficial in subjects with uncontrolled systolic hypertension

BACKGROUND:

Blood pressure (BP) control is frequently difficult to achieve in patients with predominantly elevated systolic BP. Consequently, these patients frequently require combination therapy including a thiazide diuretic such as hydrochlorothiazide (HCTZ) and an agent blocking the renin-angiotensin-aldosterone system. Current clinical practice usually limits the daily dose of HCTZ to 25 mg. This often leads to the necessity of using additional antihypertensive agents to control BP in a high proportion of patients.

OBJECTIVES:

To compare the efficacy of two doses of losartan (LOS)/HCTZ combinations in patients with uncontrolled ambulatory systolic hypertension after six weeks of treatment with LOS 100 mg/HCTZ 25 mg (LOS100/HCTZ25).

METHODS:

Following a two- to four-week washout period, subjects with a mean clinic sitting systolic BP of 160 mmHg or higher and a mean ambulatory daytime systolic BP (MDSBP) of 135 mmHg or higher on LOS100/HCTZ25 (n=105; 33 women and 72 men) were randomly assigned to receive LOS 150 mg/HCTZ 25 mg (group 1; n=53) or LOS 150 mg/HCTZ 37.5 mg (LOS150/HCTZ37.5, group 2; n=52). The primary end point was the difference in MDSBP reductions.

RESULTS:

At the end of the six-week treatment period, the respective additional decreases in MDSBP were 1.2 mmHg (P=0.335) on LOS 150 mg/HCTZ 25 mg and 5.6 mmHg (P<0.0001) on LOS150/HCTZ37.5 (difference of 4.4 mmHg; P=0.011). Daytime systolic ambulatory BP goal (lower than 130 mmHg) achievement tended to be higher (25% versus 17%; P=0.313) with LOS150/HCTZ37.5, while it was significantly higher (65% versus 43%; P=0.024) for mean daytime diastolic BP (lower than 80 mmHg). No deleterious metabolic changes were observed.

CONCLUSIONS:

In patients with uncontrolled systolic ambulatory hypertension receiving LOS100/HCTZ25, increasing both HCTZ and LOS dosages simultaneously to LOS150/HCTZ37.5 may be an effective strategy that does not affect metabolic parameters.     

Glycemic Control and Survival in Peritoneal Dialysis Patients with Diabetes Mellitus

Summary

Background and objectives

The optimal target for glycemic control has not been established for diabetic peritoneal dialysis (PD) patients.

Design, setting, participants, & measurements

We examined mortality-predictability of hemoglobin A1c random serum glucose in a contemporary cohort of diabetic PD patients treated in DaVita dialysis clinics July 2001 through June 2006 with follow-up through June 2007.

Results

We identified 2798 diabetic PD patients with A1c data. Serum glucose correlated with A1C (r = 0.51). Adjusted all-cause death hazard ratio and 95% confidence interval for baseline A1c increments of 7.0 to 7.9%, 8.0 to 8.9%, 9.0 to 9.9%, and ≥10%, compared with 6.0 to 6.9% (reference), were 1.13 (0.97 to 1.32), 1.05 (0.88 to 1.27), 1.06 (0.84 to 1.34), and 1.48 (1.18 to 1.86); and for time-averaged A1c values were 1.10 (0.96 to 1.27), 1.28 (1.07 to 1.53), 1.34 (1.05 to 1.70), and 1.81 (1.33 to 2.46), respectively. The A1c-mortality association was modified by hemoglobin level such that higher all-cause mortality was evident only in nonanemic patients. Similar but non-significant trends in cardiovascular death risk was found across A1c increments. Adjusted all-cause death HR for time-averaged blood glucose 150 to 199, 200 to 249, 250 to 299, and ≥300 mg/dl, compared with 60 to 99 mg/dl (reference), were 1.02 (0.70 to 1.47), 1.12 (0.77 to 1.63), 1.45 (0.97 to 2.18), and 2.10 (1.37 to 3.20), respectively.

Conclusions

Poor glycemic control appears associated incrementally with higher mortality in PD patients. Moderate to severe hyperglycemia is associated with higher death risk especially in certain subgroups.