Paul C. Davidson

Reduction in Severe Hypoglycemia With Long-Term Continuous Subcutaneous Insulin Infusion in Type I Diabetes

OBJECTIVE To compare the incidence of severe hypoglycemia in patients crossed over from multiple daily injections (MDIs) of insulin to continuous subcutaneous insulin infusion (CSII). RESEARCH DESIGN AND METHODS From a population of 255 patients using CSII, all patients who met the following selection criteria were included in the present study: 1) a minimum of 12 months on intensive therapy with MDIs before switching to CSII, and 2) a minimum of 12 months on CSII after crossover. Glycemic control and adverse event rates for the 1-year MDI control period were compared with those for the CSII therapy period. RESULTS The incidence of severe hypoglycemia during MDI therapy declined from 138 to 22 events per 100 patient-years during the 1st year of CSII (P < 0.0001) and remained significantly lower in years 2, 3, and 4 on CSII (26, 39, and 36, respectively). HbA1c levels did not change significantly between the MDI phase and any year on CSII. However, in the subgroup of patients who had pre-CSII HbA1c levels of ≥8.0%, the change to CSII was associated with a significant reduction in HbA1c from baseline to year 1 (8.9 ± 0.8 vs. 8.1 ± 1.0%, P = 0.0004). The difference in diabetic ketoacidosis rates between the MDI year (14.6 events per 100 patientyears) and the CSII period (7.2 events per 100 patient-years) was not statistically significant. CONCLUSIONS CSII therapy was associated with a marked and sustained reduction in the rate of severe hypoglycemia without adversely affecting the level of glycemic control attained during MDI therapy. The more reproducible and flexible insulin delivery afforded by CSII was considered to be the major factor contributing to the improvement in severe hypoglycemia rates.

Intravenous insulin infusion therapy: indications, methods, and transition to subcutaneous insulin therapy.

OBJECTIVE To describe indications for intravenous (IV) insulin infusion therapy and glycemic thresholds, discuss methods and protocols, and promote use of and access to IV insulin infusion therapy for all appropriate patients in the hospital setting. RESULTS Randomized, prospectively designed trials support the use of IV insulin infusion therapy for patients in the surgical intensive-care unit, including postoperative cardiac patients and patients having myocardial infarction. Among patients in the surgical intensive-care unit, reanalysis of the data suggested no threshold at which benefit occurred above the blood glucose level of 110 mg/dL. In another study, retrospective analysis of data among critically ill medical and surgical patients suggested a target blood glucose level of 145 mg/dL or less. In other populations, the threshold or ideal target blood glucose range has not been determined. Three protocols for IV insulin infusion are described that maintain blood glucose levels safely below the upper limit of their respective target ranges without substantial risk of hypoglycemia. CONCLUSION The threshold for initiation of IV insulin infusion is 110 mg/dL for critically ill surgical patients, 140 mg/dL for other medical or surgical patients, 180 mg/dL for patients in whom subcutaneous insulin regimens fail, and 100 mg/dL for pregnant women. The blood glucose target range is 80 to 110 mg/dL for selected critically ill surgical patients, 70 to 100 mg/dL for pregnant women, and 90 to 140 mg/dL for all other patients. Hospitals should develop procedures to make IV insulin infusion therapy available to all appropriate patients.

Insulin pump therapy in the 21st century. Strategies for successful use in adults, adolescents, and children with diabetes.

PREVIEW Use of insulin pump therapy in the United States has dramatically increased during the last decade. Pump therapy is now regarded as a safe and viable alternative in adults, adolescents, and children with diabetes. In a series of follow-up studies involving more than 800 patients, Drs Bode, Tamborlane, and Davidson have documented the advantages of pump therapy and the keys to its successful use. Here, they present these and other findings, along with their predictions for the future of pump therapy.

ANALYSIS OF GUIDELINES FOR BASAL-BOLUS INSULIN DOSING: BASAL INSULIN, CORRECTION FACTOR, AND CARBOHYDRATE-TO-INSULIN RATIO

OBJECTIVE To analyze and compare the underlying mathematical models for basal-bolus insulin-dosing guidelines in patients with type 1 diabetes in a retrospective controlled study. METHODS Algebraic model-development yielded several systems of models with unknown constants, including 3 systems currently in use. These systems were compared for logic and consistency. One of these systems was the accurate insulin management (AIM) system, which we developed in the setting of our large endocrine practice. Our database consisted of retrospective clinical records for a 7-month period. During this time, correction factor (CF), carbohydrate-to-insulin ratio (CIR), and basal insulin were being adjusted incrementally by titration. The variables studied were height, body weight in pounds (BWlb), CF, CIR, hemoglobin A1c (A1C), basal insulin, and 6-day mean total daily dose of insulin (TDD). The values of the variables used in the study were those determined on arrival of the patients at the office. The last 6 TDDs were entered into the database, and the mean was calculated by formulas within the database. We sorted our database into 2 groups, a well-controlled test group (n = 167; A1C <or=7%, time on pump >180 days, no severe hypoglycemic events since the last office visit, and C-peptide level <or=0.5 ng/mL) and a control group with poor control (n = 209; A1C >7% or time on pump <180 days). We obtained one office visit per patient, as follows: from the test group, we chose the visit with the lowest A1C value; from the control group, we chose one visit by use of a computers random number generator. A significant difference was demonstrated between the correlation constants of the test group versus the control group by performing T tests between the means and F tests between the standard deviations. The least squares estimates of the correlation constants from the test group were recommended in the guidelines, in place of the means, to gain accuracy. By these methods, the guidelines used by the patients with good glycemic control are made available for all patients. RESULTS With use of the AIM system, the TDD for continuous subcutaneous insulin infusion = 0.24 * BWlb; basal insulin = 0.47 * TDD; CF = 1,700/TDD; and CIR = 2.8 * BWlb/TDD. CONCLUSION Three mathematical models for CIR are presented, with a rationale for supporting one of them (the AIM model). This model, together with 3 related AIM models, when provided with statistically correlated constants, constitutes the AIM system of guidelines, a consistent and convenient means of estimating insulin-dosing variables for patients with type 1 diabetes.

Improving hyperglycemia management in the intensive care unit: preliminary report of a nurse-driven quality improvement project using a redesigned insulin infusion algorithm.

Purpose The purpose of this study was to assess the feasibility of a nurse-driven effort to improve hyperglycemia management in the intensive care unit (ICU) setting. Methods The setting was the ICU of a large urban hospital. The program was composed of 3 components: nurses as leaders, a clinical pathway to identify patients in need of hyperglycemia therapy, and implementation of a redesigned insulin infusion algorithm (the Columnar Insulin Dosing Chart). Time to reach a target glucose range of 80 to 110 mg/dL (4.4-6.1 mmol/L) was evaluated. Results One hundred sixteen ICU nurses were trained in the project. The Columnar Insulin Dosing Chart was applied to 20 patients. The average time required to reach the target blood glucose range was 12.8 hours. Below-target blood glucose levels were 6.9% of all blood glucose levels recorded, but only 0.9% were below 60 mg/dL (3.3 mmol/L). There was no sustained hypoglycemia, and no persistent clinical findings attributable to hypoglycemia were noted. Barriers to implementing the project included an increased nursing workload, the need for more finger-stick blood glucose monitors, and the need to acquire new finger-lancing devices that allowed for shallower skin puncture and increased patient comfort. Conclusions Tighter glycemic control goals can be attained in a busy ICU by a nurse-led team using a pathway for identifying and treating hyperglycemia, clear decision support tools, and adequate nurse education. The novel chart based insulin infusion algorithm chosen as the standard for this pilot was an effective tool for reducing the blood glucose to target range with no clinically significant hypoglycemia.

A Comparison Study of Continuous Insulin Infusion Protocols in the Medical Intensive Care Unit: Computer-Guided Vs. Standard Column-Based Algorithms

PURPOSE To compare the safety and efficacy of continuous insulin infusion (CII) via a computer-guided and a standard paper form protocol in a medical intensive care unit (ICU). METHODS Multicenter randomized trial of 153 ICU patients randomized to CII using the Glucommander (n = 77) or a standard paper protocol (n = 76). Both protocols used glulisine insulin and targeted blood glucose (BG) between 80 mg/dL and 120 mg/dL. RESULTS The Glucommander resulted in a lower mean BG value (103 ± 8.8 mg/dL vs. 117 ± 16.5 mg/dL, P < 0.001) and in a shorter time to reach BG target (4.8 ± 2.8 vs.7.8 hours ± 9.1 hours, P < 0.01), and once at target resulted in a higher percentage of BG readings within target (71.0 ± 17.0% vs. 51.3 ± 19.7%, P < 0.001) than the standard protocol. Mean insulin infusion rate in the Glucommander was similar to the standard protocol (P = 0.12). The percentages of patients with ≥1 episode of BG <40 mg/dL and <60 mg/dL were 3.9% and 42.9% in the Glucommander and 5.6% and 31.9% in the standard, respectively [P = not significant (NS)]. Repeated measures analyses show that the probabilities of BG reading <40 mg/dL or <60 mg/dL were not significantly different between groups (P = 0.969, P = 0.084) after accounting for within-patient correlations with or without adjusting for time effect. There were no differences between groups in the length of hospital stay (P = 0.704), ICU stay (P = 0.145), or inhospital mortality (P = 0.561). CONCLUSION Both treatment algorithms resulted in significant improvement in glycemic control in critically ill patients in the medical ICU. The computer-based algorithm resulted in tighter glycemic control without an increased risk of hypoglycemic events compared to the standard paper protocol.

What's ahead in glucose monitoring? New techniques hold promise for improved ease and accuracy.

PREVIEW The discovery of insulin in 1922 and the development of self-monitoring of blood glucose levels in 1978 mark the two greatest advances in the management of diabetes mellitus in the 20th century. As the new millennium dawns, important strides are being made in glucose monitoring that allow collection of better data to guide diabetes management. In this article, Drs Bode, Sabbah, and Davidson discuss the latest technological advances, including streamlined, easy-to-use meters and development of continuous glucose monitoring systems.

Computer-Assisted Instruction in Intense Insulin Therapy Using a Mathematical Model for Clinical Simulation With a Clinical Algorithm and Flow Sheet

patient’s ability to make appropriate day-to-day therapeutic decisions. Hence, training must be followed up with weekly telephone instruction and monthly office visits before the patient develops a full understanding of the algorithm and acquires adequate experience. Because of these concerns, we developed an interactive microcomputer program that utilizes clinical simulation to enable patients to practice insulin selfdose adjustment. The program, called GLUCOFLOW, uses a mathematical model to predict the blood glucose response to insulin administration. The data are displayed on a flow sheet. The program user (patient or professional)

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.

A Cause-and-Effect-Based Mathematical Curvilinear Model That Predicts the Effects of Self-Monitoring of Blood Glucose Frequency on Hemoglobin A1c and Is Suitable for Statistical Correlations

Background: Previous studies have shown an association between the frequency of self-monitored blood glucose (SMBG) and hemoglobin A1c. Randomized controlled trials (RCTs) have shown this to be a causal correlation for insulin-using patients. Several studies have used linear regression, but a straight line will descend into negative hemoglobin A1c values (an impossibility). This study developed a cause-and-effect-based nonlinear model to predict the outcome of RCTs on this subject, tested this model with clinical data, and offered this model in place of linear regression, especially for the still-debated case of noninsulin-using patients. Method: The model was developed from cause-and-effect principles. The clinical study utilized retrospective data from patient histories of a large endocrine practice. Data sets were obtained for five treatment regimens: continuous subcutaneous insulin infusion (CSII), subcutaneous insulin (SC), no insulin (NI), oral medication (OM), and no medication (NM). OM and NM are subgroups of NI. The model was fitted to each group using nonlinear least-squares methods. Each group was ordered by SMBG tests per day (BGpd) and was divided in half; t tests were run between the AlCs of the two halves. Results: Self-monitored blood glucose readings from 1255 subjects were analyzed (CSII, N = 417; SC, N = 286; NI, N = 552; OM, N = 505; NM, N = 47). The CSII, SC, NI, and OM groups showed the expected declining statistically fitted curve and a significant association of BGpd with hemoglobin A1c (P < 0.004). The NM group showed insignificant results. Conclusions: The nonlinear model is based on cause-and-effect principles and mathematics. It yields a prediction that RCTs will be able to reveal that higher SMBG frequency causes lower hemoglobin A1c.