Self-monitored Blood Glucose Values Research Articles

Real-world use of self-monitoring of blood glucose in people with type 2 diabetes: an urgent need for improvement.

To assess use of self-monitoring of blood glucose (SMBG) in type 2 diabetes (T2DM) in the context of a continuous quality improvement initiative (AMD Annals). 14 quality-of-care indicators were developed, including frequency of SMBG, fasting blood glucose (FBG), and post-prandial glucose (PPG) levels, and hypoglycemia and hyperglycemia episodes. Clinical data and SMBG values downloaded from any glucose meter were obtained from electronic medical records. The most frequently used glucose-lowering treatment regimens were identified and the indicators were assessed separately by regimen. Overall, 21 Italian centers and 13,331 patients (accounting for 35,657 HbA1c tests and 8.44million SMBG values collected during 2014 and 2015) were included in the analysis; 11 therapeutic regimens were selected. Patients in regimens not including insulin performed 15-23 measurements per patient-month, those treated with basal insulin 32.1 tests/patient-month, and those treated with basal and short-acting insulin 53-58 tests/patient-month. In all treatment regimens, PPG measurements represented a minority of all tests; pre-breakfast measurements accounted for about 50% of all FBG values. Mean FBG levels exceeded 130mg/dl in 49.3-88.3% of the cases in the different treatment regimens, while PPG levels were over 140mg/dl in 46.7-81.0%. From 5.7 to 32.7%, patients in the different regimens had at least one episode of hypoglycemia (< 70mg/dl), while from 3.7 to 47.7% had at least one episode of hyperglycemia (> 300mg/dl). SMBG is underutilized in patients with T2DM treated or not with insulin. In all treatment groups, PPG is seldom investigated. Poor metabolic control and rates of hyper- and hypoglycemia deserve consideration in all treatment groups.

Examination of Accuracy of Self-Reported Blood Glucose Using mHealth Data Collection

Introduction: Daily self-monitoring of blood glucose (SMBG) is essential for type 1 diabetes (T1D) management. Adherence to SMBG is often suboptimal during adolescence. SMBG paper diaries are often inaccurate compared to glucometer readings. The current study aimed to compare self-reported SMBG values gathered using ecological momentary assessment via smartphones to glucometer readings. Methods: Youth (n=54, 11-21 years, 59.3% male) with T1D received 3 text messages/day for 10 consecutive days containing REDCap surveys inquiring to SMBG values. SMBG values from glucometers were obtained during the same period. Based on a previously developed system, paired values were compared and categorized as accurate or discrepant. A discrepancy was categorized as: omission (i.e., glucometer reading with no self-reported SMBG value), addition (i.e., self-reported SMBG value with no glucometer reading), alteration (i.e., self-reported SMBG value different by &amp;gt;10 mg/dL from glucometer value), or digit transformation (i.e., 164 instead of 146). Self-reported values were categorized further to reflect whether patients reported a more clinically favorable or unfavorable profile. Results: A significant positive correlation was found between glucometer and self-reported SMBG values for all matched pairs (r=.628, p&amp;lt;.001). Out of 817 pairings, 39.3% of the self-reported values were congruent to glucometer readings and 60.7% were discrepant. Of the discrepant, 45% were alterations, 40.9% were additions, 13% were omissions, and .01% were digit transformations. Also, 54.5% of self-reported SMBG appeared clinically favorable, while 45.5% appeared clinically unfavorable. Conclusions: These findings are consistent with previous studies comparing paper SMBG diaries to glucometers in a pediatric population. Self-reported mHealth data is likely to produce discrepant SMBG values compared to glucometers. Providers are urged to use objective blood glucose devices for clinical decisions. Disclosure J. Warnick: None. S.C. Westen: None. A. Albanese-O'Neill: None. D. Schatz: None. M.J. Haller: None. D. Janicke: None.

Fourth-Generation Glucose Sensor with an Advanced Algorithm Featuring No Calibrations and Optional Calibration Modes of Operation

An advanced algorithm was developed for the Medtronic fourth-generation glucose sensor to eliminate the need for blood glucose meter calibrations. The algorithm uses electrochemical impedance spectroscopy and other measurements to self-calibrate the sensor and adjust CGM system sensitivity over time. Self-calibration adjusts for potential sensor-to-sensor variability, as well as subject-dependent sensitivity changes over time. The new algorithm was designed to not require self-monitored blood glucose (SMBG) input during use, but accepts calibrations at any time to improve CGM system performance. It contains multiple independent sensor glucose (SG) calculation units that implement a distinct dynamic model of interstitial glucose that accounts for differences in response caused by sensor-dependent and time-dependent nonlinearities. This approach is particularly useful during the post-sensor insertion period and during periods of hypoglycemia. Each dynamic model was selected for its unique pattern of strengths, which complement each other to augment overall performance. The output of each model is combined to provide a final SG value and a measure of confidence in the calculated value. Performance was evaluated on 31 subjects with type 1 and type 2 diabetes against SMBG values (2441 points over 80 sensors). Sensors were inserted in the arm. The overall (7-day) mean absolute relative difference was 10.8% with no calibrations, 9.5% with 1 calibration/day. There were 86.7% of points that met the 20% agreement rate with no calibrations, 90.0% with 1 calibration/day. A new advanced algorithm for the fourth-generation glucose sensor enables the CGM system to function with good accuracy without SMBG calibrations. Disclosure T. Engel: Employee; Self; Medtronic MiniMed, Inc. P. Ajemba: Employee; Self; Medtronic. J. Nishida: Employee; Self; Medtronic MiniMed, Inc. K. Nogueira: Employee; Self; Medtronic. B. Grosman: Employee; Self; Medtronic. A. Tsai: None. Y. Lu: None. A. Sullivan: None. A. Varsavsky: Employee; Self; Medtronic MiniMed, Inc..

MiniMed™ Veo™ Insulin Pump Analysis for Real-World Nonadjunctive CGM Insights

To assess glycemic outcomes during non-adjunctive use of a Medtronic continuous glucose monitoring system, CareLink™ personal software data were analyzed after insulin bolus delivery to correct hyperglycemia. Voluntarily uploaded data captured between January 1, 2015-August 12, 2016, from European users of the MiniMed™ Veo™ insulin pump with SmartGuard™ technology, were de-identified and analyzed. Non-adjunctive sensor glucose (SG) values were defined as manually-entered self-monitored blood glucose (SMBG) values that were within ±5mg/dL of the preceding SG value. Non-adjunct vs. adjunct performance of the system in frequent non-adjunct users (at least, 10% non-adjunct use) was evaluated based on the frequency of post-corrective bolus hypoglycemia (nadir) and hyperglycemia (peak) over the 4 hours following insulin bolus delivery. All statistics were conducted per patient and analyzed using Wilcoxon signed rank test. Among 23,784 total users, 5,851 were identified as frequent non-adjunct users. Of the over 1.7 million corrective boluses evaluated, 32% met the non-adjunctive criteria. Outcomes results are presented in the Table below. The frequency and severity of hypoglycemia and hyperglycemia, although lower statistically with non-adjunctive vs. adjunctive corrective bolus delivery, showed likely clinical equivalency. Disclosure B. Jiang: None. S. Soleymani: Employee; Self; Medtronic MiniMed, Inc. R. Vigersky: Employee; Self; Medtronic MiniMed, Inc.. A. Sullivan: None. Y. Zhong: None. P. Agrawal: None. F.R. Kaufman: Employee; Self; Medtronic.

Accuracy of a Factory-Calibrated, Real-Time Continuous Glucose Monitoring System During 10 Days of Use in Youth and Adults with Diabetes.

Frequent use of continuous glucose monitoring (CGM) systems is associated with improved glycemic outcomes in persons with diabetes, but the need for calibrations and sensor insertions are often barriers to adoption. In this study, we evaluated the performance of G6, a sixth-generation, factory-calibrated CGM system specified for 10-day wear. The study enrolled participants of ages 6 years and up with type 1 diabetes or insulin-treated type 2 diabetes at 11 sites in the United States. Participation involved one sensor wear period of up to 10 days. Adults wore the system on the abdomen; youth of ages 6-17 years could choose to wear it on the abdomen or upper buttocks. Clinic sessions for frequent comparison with reference blood glucose measurements took place on days 1, 4-5, 7, and/or 10. Participants of ages 13 years and up underwent purposeful supervised glucose manipulation during in-clinic sessions. During the study, participants calibrated the systems once daily. However, analysis was performed on glucose values that were derived from reprocessed raw sensor data, independently of self-monitored blood glucose values used for calibration. Reprocessing used assigned sensor codes and a factory-calibration algorithm. Performance evaluation included the proportion of CGM values that were within ±20% of reference glucose values >100 mg/dL or within ±20 mg/dL of reference glucose values ≤100 mg/dL (%20/20), the analogous %15/15, and the mean absolute relative difference (MARD, expressed as a percentage) between temporally matched CGM and reference values. Data from 262 study participants (21,569 matched CGM reference pairs) were analyzed. The overall %15/15, %20/20, and MARD were 82.4%, 92.3%, and 10.0%, respectively. Matched pairs from 134 adults and 128 youth of ages 6-17 years were similar with respect to %20/20 (92.4% and 91.9%) and MARD (9.9% and 10.1%). Overall %20/20 values on days 1 and 10 of sensor wear were 88.6% and 90.6%, respectively. The system's "Urgent Low Soon" (predictive of hypoglycemia within 20 min) hypoglycemia alert was correctly provided 84% of the time within 30 min before impending biochemical hypoglycemia (<70 mg/dL). The 10-day sensor survival rate was 87%. The new factory-calibrated G6 real-time CGM system provides accurate readings for 10 days and removes several clinical barriers to broader CGM adoption.

Proportion of Abnormal Glucose Values and Perinatal Outcome [13J

INTRODUCTION: Practical targets for glycemic control are unclear. This study examines associations between different proportions of abnormal self-monitored blood glucose (SMBG) values and adverse perinatal outcomes. METHODS: We conducted a retrospective cohort study of women undergoing SMBG between 1/1/2011-12/31/2016. Four weeks of SMBG values initiated before 32 weeks with at least 50% of expected values present were required. Women with type 1 diabetes or major congenital anomalies were excluded. The primary outcome was a composite of stillbirth, neonatal death, cesarean for arrest, shoulder dystocia, birth injury, new hypertensive disorder, birth weight &gt;90% (LGA), or treatment for neonatal hypoglycemia. The primary exposure was the total percent abnormal SMBG values, examined by degree of abnormality: &lt;25% (tight), &lt;33% (moderate), or &lt;50% (loose) abnormal. RESULTS: 120 women met inclusion criteria of whom 47.9% had gestational diabetes (GDM) and 52.1% had type 2 diabetes. Overall, 29.4% had tight control, 37.0% had moderate control, and 33.6% had loose control. Women with tight control were less likely to experience the primary outcome (34.3% vs 69.4%, RR 0.49, 95%CI 0.31-0.80), LGA infants (14.3% vs 34.5%, RR 0.41, 95% CI 0.17-0.98), or neonatal hypoglycemia (11.4% vs 34.1%, RR 0.34, 95%CI 0.13-0.88) than women without tight control. After adjusting for age, parity, and diabetes type, both moderate and tight control were associated with a reduced odds for the primary outcome, however loose control was not. CONCLUSION: Keeping SMBG to less than 33% abnormal is associated with a reduced risk of adverse perinatal outcomes. These data may help clinicians set targets for glycemic control.

Estimating HbA1c from timed Self-Monitored Blood Glucose values

AimsTo analyze mathematical relationships between timed Self-Monitored Blood Glucose (SMBG) and HbA1c, and to identify the SMBG values that correlate most strongly with HbA1c. MethodsWe utilized the average premeal (Pre) and 90-min postmeal (Post) SMBG results from 547 A1c-Derived Average Glucose (ADAG) study participants (285 type 1, 178 type 2 and 84 non-diabetic) to analyze the mathematical relationships with HbA1c levels. Specific times of daily SMBG that best correlate with HbA1c were identified. ResultsLinear regression analyses showed the following correlations for Pre and Post, Pre only and Post only, respectively: HbA1c = 2.488 + 0.018 × Premg/dl + 0.012 × Postmg/dl, R2 = 0.741, P < 0.0001; HbA1c = 2.887 + 0.029 × Premg/dl, R2 = 0.695, P < 0.0001; and HbA1c = 2.815 + 0.025 × Postmg/dl, R2 = 0.657, P < 0.0001.Among patients with type 2 diabetes mellitus (DM), of the 6 individual timepoints, pre-dinner SMBG had the strongest correlation with HbA1c (R2 = 0.577). This was followed by pre-breakfast (R2 = 0.562). Examining combinations of timepoints revealed that pre-breakfast + pre-dinner (R2 = 0.666) performed similarly to the full 6-timepoints (pre-meals + post-meals, R2 = 0.712). ConclusionsWe have established mathematical relationships between HbA1c and timed SMBG values and identified pre-dinner and pre-breakfast as the two SMBG timepoints that best correlate with HbA1c in patients with type 2 DM.

Positive Impact of a Pilot Pharmacist-Run Diabetes Pharmacotherapy Clinic in Solid-Organ Transplant Recipients.

Purpose.Post-transplant diabetes mellitus (PTDM) can lead to significant morbidity and cardiovascular death with a functioning graft. A paucity of literature exists regarding glycemic control in solid-organ transplant (SOT) recipients, including pharmacist management of PTDM. This study aimed to assess the impact of pharmacist interventions on diabetes management in a pharmacist-run PTDM clinic.Methods.This was a single-center, prospective, observational study of 24 adult SOT recipients enrolled in a pilot pharmacist-managed PTDM clinic from 1 January to 30 June 2015.Results.Improvements were realized in markers of glycemic control, including changes in A1C, average daily self-monitoring of blood glucose (SMBG) results, fasting SMBG results, and pre-lunch SMBG results from enrollment through at least 3 months of follow-up. Median A1C decreased significantly from 8.05% (interquartile range [IQR] 6.33–11.75) at baseline to 6.45% (IQR 6.05–7.3) at the last follow-up encounter (P = 0.0010). Average daily SMBG results decreased significantly from a median of 191 mg/dL (IQR 138–232 mg/dL) at baseline to 125 mg/dL (IQR 111–167 mg/dL) at the final encounter (P = 0.0023). Median fasting and pre-lunch SMBG results decreased significantly from 153 mg/dL (IQR 117–208 mg/dL) at baseline to 120 mg/dL (IQR 102–134 mg/dL) (P = 0.0064) and from 212 mg/dL (IQR 159–258 mg/dL) to 122 mg/dL (IQR 110–169 mg/dL) (P = 0.0161), respectively. Changes from baseline in other SMBG values, lipid levels, and BMI were not statistically significant.Conclusion.The results of our study demonstrate that a pharmacist-managed PTDM clinic can significantly affect glycemic control in SOT recipients.

A Fixed Ratio Combination of Insulin Degludec and Liraglutide (IDegLira) Reduces Glycemic Fluctuation and Brings More Patients with Type 2 Diabetes Within Blood Glucose Target Ranges.

Background: Reducing glycemic fluctuation is important for optimal diabetes management. This post hoc analysis examined glycemic fluctuations and the proportion of subjects achieving recommended blood glucose targets with the fixed ratio combination of insulin degludec and liraglutide (IDegLira) compared to insulin degludec (IDeg) and liraglutide alone.Methods: We analyzed nine-point self-monitored blood glucose (SMBG) profiles from two randomized trials involving IDegLira in patients with type 2 diabetes (T2D), and continuous glucose monitoring (CGM) data from a subset of patients in one of these trials to assess glycemic fluctuation and day-to-day variability.Results: Compared with IDeg, IDegLira resulted in a greater proportion of subjects with SMBG values within target ranges (3.9–9.0 mmol/L) than IDeg for all pre- and postprandial values, and for the full nine-point profile (P < 0.05 for all). IDegLira also resulted in a greater reduction in the range of SMBG values over 24 h than IDeg (P ≤ 0.0001). CGM data showed that IDegLira provided greater reductions in interstitial glucose (IG) fluctuation (P = 0.0018) and postprandial IG increment (P = 0.0288) compared with IDeg. Compared with liraglutide, IDegLira brought a higher proportion of subjects within SMBG target ranges (all pre- and all postprandial values, and the full nine-point profile, P < 0.01 for all) and resulted in a greater reduction of time outside the IG target range (P = 0.0072). IDegLira also reduced mean IG more than liraglutide (P < 0.0001).Conclusions: Treatment with IDegLira allows more patients with T2D to maintain blood glucose within target ranges throughout the day than either IDeg or liraglutide alone.

Continuous glucose monitoring and HbA1c in the evaluation of glucose metabolism in children at high risk for type 1 diabetes mellitus

AimsContinuous glucose monitoring (CGM) parameters, self-monitored blood glucose (SMBG), HbA1c and oral glucose tolerance test (OGTT) were studied during preclinical type 1 diabetes mellitus. MethodsTen asymptomatic children with multiple (⩾2) islet autoantibodies (cases) and 10 age and sex-matched autoantibody-negative controls from the Type 1 Diabetes Prediction and Prevention (DIPP) Study were invited to 7-day CGM with Dexcom G4 Platinum Sensor. HbA1c and two daily SMBG values (morning and evening) were analyzed. Five-point OGTTs were performed and carbohydrate intake was assessed by food records. The matched pairs were compared with the paired sample t-test. ResultsThe cases showed higher mean values and higher variation in glucose levels during CGM compared to the controls. The time spent ⩾7.8mmol/l was 5.8% in the cases compared to 0.4% in the controls (p=0.040). Postprandial CGM values were similar except after the dinner (6.6mmol/l in cases vs. 6.1mmol/l in controls; p=0.023). When analyzing the SMBG values higher mean level, higher evening levels, as well as higher variation were observed in the cases when compared to the controls. HbA1c was significantly higher in the cases [5.7% (39mmol/mol) vs. 5.3% (34mmol/mol); p=0.045]. No differences were observed in glucose or C-peptide levels during OGTT. Daily carbohydrate intake was slightly higher in the cases (254.2g vs. 217.7g; p=0.034). ConclusionsGlucose levels measured by CGM and SMBG are useful indicators of dysglycemia during preclinical type 1 diabetes mellitus. Increased evening glucose values seem to be common in children with preclinical type 1 diabetes mellitus.

Prior knowledge of blood glucose meter download improves the accuracy of verbal self-reported blood glucose in teenagers with type I diabetes at ski camp.

Despite advances in diabetes management, self-monitoring of blood glucose (SMBG) remains fundamental. A number of studies, principally in adults, have confirmed that logbook entries and verbal SMBG reports are prone to common errors. In the context of an adolescent diabetes camp, the accuracy of verbally reported SMBG is crucial for guiding safe therapeutic management, and negating the risk of exercise-induced hypoglycemia. We aimed to assess whether awareness of a planned meter download at the completion of a diabetes camp would improve the overall accuracy of verbally reported SMBG. Adolescents with type one diabetes (n=26) attended a 3-day ski camp in 2014. Verbally reported SMBG values were recorded by camp supervisors at multiple time points throughout the camp. The intervention involved ensuring that all participants (at camp commencement) were aware of a planned meter download and SMBG review at camp conclusion. These data were then compared with historical camp data from 2012, collected using identical methodology, in which participants (n=20) were unaware of the planned meter download. For analysis, blood glucose (BGL) data were classified as: matching, phantom (verbal SMBG value with no corresponding meter download value), and over- or underestimate (verbally reported value>/<meter downloaded value). Dual data regarding verbal SMBG and meter downloads were obtained on 550instances of BGL testing during the 2014 camp (the intervention group). This was compared to dual data for 396 historical tests from the 2012 control group. For the intervention group, the overall error rate was 4.7%, over 34% of participants. There was a statistically significant improvement in accuracy compared to historical nonintervention data, in which there was an error rate of 14.1% over 70% of participants (p<0.001). There was also a decrease in phantom readings to 2%, from 8.6% in 2012 (p<0.001). This study demonstrates an improvement in accuracy and reliability of verbally reported SMBG, following a simple intervention of ensuring participants were aware of a meter download at the completion of camp. This intervention could be easily incorporated into adolescent diabetes camp safety protocols and may provide an easy, low-cost way of improving verbally reported SMBG accuracy and therefore safety on camp.

Case Study: Remission of Type 2 Diabetes After Outpatient Basal Insulin Therapy.

Diabetes is a chronic, progressive disease with potentially serious sequelae. Treatment for type 2 diabetes often begins with oral agents and eventually requires insulin therapy. As the disease progresses, drug therapies are often intensified and rarely reduced to control glycemia. Conversely, in type 1 diabetes, some patients experience a “honeymoon period” shortly after diagnosis, wherein insulin needs decrease significantly before intensification is needed (1). No comparable honeymoon period has been widely described for type 2 diabetes. However, a few studies have demonstrated that drug-free glycemic control can be achieved in type 2 diabetes for 12 months on average after a 2-week continuous insulin infusion (2–4). Here, we describe an unusual case of a 26-month drug holiday induced with outpatient basal insulin in a patient newly diagnosed with type 2 diabetes. A 69-year-old white woman (weight 72.7 kg, height 59 inches, BMI 32.3 kg/m2) was diagnosed with type 2 diabetes in June 2011. She presented with an A1C of 17.6% (target <7%) and a fasting blood glucose (FBG) of 452 mg/dL (target 70–130 mg/dL). Before diagnosis, the patient had not used any oral or parenteral steroids nor had she experienced any traumatic physical or emotional event or illness that could have abruptly increased her blood glucose. Metformin 500 mg twice daily was initiated at diagnosis, but was discontinued 9 days later to avoid risk of lactic acidosis, as her serum creatinine was 1.5 mg/dL. At that time, her fasting self-monitoring of blood glucose (SMBG) values ranged from 185 to 337 mg/dL. Treatment with 25 units of insulin detemir daily (0.34 units/kg/day) was initiated in place of metformin. The patient was counseled on diet modifications and encouraged to exercise. One month later (July 2011), the patient’s fasting SMBG values had improved to a range of 71–212 mg/dL with a …

Glycated Albumin at 4 Weeks Correlates with A1C Levels at 12 Weeks and Reflects Short-Term Glucose Fluctuations

Evaluate the performance of glycated albumin (GA) monitoring by comparing it to other measures of glycemic control during intensification of antidiabetic therapy. This 12-week, prospective, multicenter study compared the diagnostic clinical performance of GA to glycated hemoglobin A1C (A1C), fructosamine corrected for albumin (FRA), fasting plasma glucose (FPG), and mean blood glucose (MBG) estimated from self-monitoring of blood glucose (SMBG) and continuous glucose monitoring (CGM) in 30 patients with suboptimally controlled type 1 or 2 diabetes. Mean A1C decreased from 9.5% to 8.1%. Mean SMBG correlated closely with CGM (Pearson r = 0.783 for daily estimates and r = 0.746 for weekly estimates, P<.0001). Both GA and FRA levels significantly correlated with changes from baseline in A1C and mean weekly SMBG (P<.001). The lowest observed median GA occurred at 4 weeks, followed by a small increase and then a slight reduction, mirroring changes in overall mean SMBG values. The median A1C fell throughout the treatment period, failing to reflect short-term changes in SMBG. A ≥1% reduction in GA at 4 weeks was significantly associated with a ≥0.5% change in A1C at 12 weeks (odds ratio [OR] = 19.0, 95% confidence interval [CI]: 1.4, 944, P = .018). In patients receiving glucose-lowering therapy, changes in GA at 4 weeks were concordant with changes in A1C at 12 weeks, and both GA and FRA more accurately reflected short-term blood glucose fluctuations than A1C.

Measures of Risk and Glucose Variability in Adults Versus Youths.

The article by Patton et al.1 published in this issue of Diabetes Technology & Therapeutics reports an array of correlations between various metrics of glucose variability (GV), derived from 1 month of self-monitoring of blood glucose (SMBG) data, with the frequency of hypo- and hyperglycemic events in a subsequent month of SMBG records. The data were derived from the clinical patient records of 3,453 individuals, 436 of whom met the study inclusion criteria (e.g., ages between 0 and 18 years old and a minimum of 14 days of SMBG in each month with three or more SMBG values per day). The purpose of the study was to assess whether the Average Daily Risk Range (ADRR), introduced in 2006 as a risk-based metric of GV, is a valid predictor of hypo- and hyperglycemia in youths with type 1 diabetes. The other GV metrics used for this prediction included SD and coefficient of variation of blood glucose (BG), Low Blood Glucose Index (LBGI), High Blood Glucose Index (HBGI), the percentage of glucose values >70 and 180 mg/dL and >400 mg/dL, and the percentage of low glucose values <70 mg/dL and <40 mg/dL in Month 1. Patton et al.1 concluded that “In a large pediatric sample, the ADRR was not the strongest predictor of future glycemic excursion. The percentages of current hyper- and hypoglycemic episodes appear to be stronger predictors.”

Hypoglycemia Prediction Using Machine Learning Models for Patients With Type 2 Diabetes

Minimizing the occurrence of hypoglycemia in patients with type 2 diabetes is a challenging task since these patients typically check only 1 to 2 self-monitored blood glucose (SMBG) readings per day. We trained a probabilistic model using machine learning algorithms and SMBG values from real patients. Hypoglycemia was defined as a SMBG value < 70 mg/dL. We validated our model using multiple data sets. In addition, we trained a second model, which used patient SMBG values and information about patient medication administration. The optimal number of SMBG values needed by the model was approximately 10 per week. The sensitivity of the model for predicting a hypoglycemia event in the next 24 hours was 92% and the specificity was 70%. In the model that incorporated medication information, the prediction window was for the hour of hypoglycemia, and the specificity improved to 90%. Our machine learning models can predict hypoglycemia events with a high degree of sensitivity and specificity. These models-which have been validated retrospectively and if implemented in real time-could be useful tools for reducing hypoglycemia in vulnerable patients.

Glycemic control after 6 days of insulin pump reservoir use in type 1 diabetes: results of double-blind and open-label cross-over trials of insulin lispro and insulin aspart.

The objective of the current study was to assess mean self-monitored blood glucose (SMBG), on day 6 of 6 days of continuous reservoir wear (6D), with insulin lispro (Lis) or insulin aspart (Asp). Two 24-week, randomized trials were conducted in subjects with type 1 diabetes treated by continuous subcutaneous insulin infusion (CSII) for ≥6 months, with a mean total daily insulin dose capable of supporting 6 days of in-reservoir use. Study 1 had an open-label, six-sequence, three-treatment, three-period, cross-over design. Study 2 had a double-blind, two-sequence, two-treatment, two-period, cross-over design. The primary efficacy measure was the mean of Day 6, seven-point SMBG profiles for insulin lispro 6D (Lis6D) and insulin aspart 6D (Asp6D) treatment periods. Safety measures were also assessed. Lis did not achieve noninferiority (SMBG; margin = 0.6 mmol/L [10.8 mg/dL]) to Asp on Day 6 of reservoir wear in either Study (least-squares mean difference: Study 1 = 0.48 mmol/L [8.64 mg/dL]; 95% confidence interval [CI] [0.20, 0.76], Study 2 = 0.36 mmol/L [6.49 mg/dL]; 95% CI [0.06, 0.66]). Noninferiority was demonstrated for overall daily mean of SMBG values over days 1 to 6 of reservoir use during each treatment period. In the Lis treatment period, subjects reported a lower documented and total hypoglycemia rate per 30 days and a higher rate of non-explained hyperglycemia than in the Asp treatment period. While the mean blood glucose on Day 6 of Lis6D did not meet non-inferiority, the overall daily mean blood glucose was not different, with a decreased rate of hypoglycemia with Lis.

Neuropathic truncal pain in patients with type 2 diabetes mellitus relieved by topical cream and nerve block

Neuropathic truncal pain in patients with type 2 diabetes mellitus relieved by topical cream and nerve block

Implementation of a pharmacist-supervised outpatient diabetes treatment clinic

The implementation of a pharmacist-managed Veterans Affairs (VA) clinic for high-risk patients with type 2 diabetes is described, including a review of first-year performance data. As part of VA's ongoing initiative to improve chronic disease management by creating multidisciplinary Patient-Aligned Care Teams (PACTs) that include clinical pharmacy specialists, an outpatient diabetes treatment clinic was implemented at a VA site in Massachusetts. Operated two days weekly, the clinic provides continuity of care between physician visits by applying standard protocols and procedures for insulin therapy; oral medication and insulin doses are adjusted according to self-monitored blood glucose values and other health data transmitted by patients via telephone or secure messaging system. The clinic pharmacist monitors patients' progress toward attainment of glycosylated hemoglobin (HbA1c) goals and provides frequent followup and education to prevent hypoglycemia. During its first year of operation, the clinic enrolled 160 veterans. Among 85 patients included in a three-month review of outcomes data, the mean ± S.D. HbA1c concentration declined by more than one percentage point (from 9.12% ± 1.41% to 7.94% ± 1.18%). Further HbA1c improvements were documented in a subset of 46 patients followed for six months after clinic enrollment, with no episodes of severe hypoglycemia. An outpatient clinic successfully integrated a pharmacist into a multidisciplinary PACT focused on veterans with poorly controlled type 2 diabetes. The clinic's initial experience suggests that frequent pharmacist interventions have a positive impact in promoting appropriate insulin use in this high-risk patient population.

Children’s and adolescent’s self - assessment of metabolic control versus professional judgment: a cross-sectional retrospective and prospective cohort study

BackgroundMorbidity and mortality in T1DM depend on metabolic control, which is assessed by HbA1c measurements every 3–4 months. Patients’ self-perception of glycemic control depends on daily blood glucose monitoring. Little is known about the congruence of patients’ and professionals’ perception of metabolic control in T1DM.ObjectiveTo assess the actual patients’ self-perception and objective assessment (HbA1c) of metabolic control in T1DM children and adolescents and to investigate the possible factors involved in any difference.MethodsPatients with T1DM aged 8 – 18 years were recruited in a cross-sectional, retrospective and prospective cohort study. Data collection consisted of clinical details, measured HbA1c, self-monitored blood glucose values and questionnaires assessing self and professionals’ judgment of metabolic control.Results91 patients participated. Mean HbA1c was 8.03%. HbA1c was higher in patients with a diabetes duration > 2 years (p = 0.025) and in patients of lower socioeconomic level (p = 0.032). No significant correlation was found for self-perception of metabolic control in well and poorly controlled patients. We found a trend towards false-positive memory of the last HbA1c in patients with a HbA1c > 8.5% (p = 0.069) but no difference in patients’ knowledge on target HbA1c between well and poorly controlled patients.ConclusionsT1DM patients are aware of a target HbA1c representing good metabolic control. Ill controlled patients appear to have a poorer recollection of their HbA1c. Self-perception of actual metabolic control is similar in well and poorly controlled T1DM children and adolescents. Therefore, professionals should pay special attention that ill controlled T1DM patients perceive their HbA1c correctly.

Blood Glucose Pattern Management in Diabetes: Creating Order from Disorder

Self-monitoring of blood glucose (SMBG) is the most accessible way to assess glycemic patterns, and interpretation of these patterns can provide reasons for poor glycemic control and suggest management strategies. Furthermore, diabetes management based on blood glucose (BG) patterns is associated with improved patient outcomes. The aim of this review is therefore to evaluate the impact of pattern management in clinical practice. We included a review of available literature, a discussion of obstacles to implementation of SMBG and pattern management, and suggestions on how clinicians and patients might work together to optimize this management feature. The literature review revealed eight publications specifically describing structured approaches to SMBG and pattern management. Specific information on how SMBG might be structured to detect BG patterns, however, remains limited. Barriers to pattern management include not just practical reasons, but emotional and psychological reasons as well. Patterns are not always easy to detect or interpret, but on-meter and web-based tools can support both patients and clinicians. Ultimately, successful pattern management requires education and mutual commitment from the clinician and patient--ongoing collaboration is needed to obtain, review, and interpret SMBG values and to make changes based on the patterns.