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 180 days, no severe hypoglycemic events since the last office visit, and C-peptide level 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 computer's 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.

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. METHODS: 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 leastsquares methods. Each group was ordered by SMBG tests per day (BGpd) and was divided in half; t tests were run between the A1C's 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.