Diabetes mellitus and blood glucose variability increases the 30-day readmission rate after kidney transplantation

INTRODUCTION

Inpatient hyperglycemia is an established independent risk factor among several patient cohorts for hospital readmission. This has not been studied after kidney transplantation. Nearly one-third of patients who have undergone a kidney transplant reportedly experience 30-day readmission.

METHODS

Data on first-time solitary kidney transplantations were retrieved between September 2015 and December 2018. Information was linked to the electronic health records to determine diagnosis of diabetes mellitus and extract glucometric and insulin therapy data. Univariate logistic regression analysis and the XGBoost algorithm were used to predict 30-day readmission. We report the average performance of the models on the testing set on bootstrapped partitions of the data to ensure statistical significance.

RESULTS

The cohort included 1036 patients who received kidney transplantation; 224 (22%) experienced 30-day readmission. The machine learning algorithm was able to predict 30-day readmission with an average area under the receiver operator curve (AUC) of 78% with (76.1%, 79.9%) 95% confidence interval (CI). We observed statistically significant differences in the presence of pretransplant diabetes, inpatient-hyperglycemia, inpatient-hypoglycemia, minimum and maximum glucose values among those with higher 30-day readmission rates. The XGBoost model identified the index admission length of stay, presence of hyper- and hypoglycemia, the recipient and donor body mass index (BMI) values, presence of delayed graft function, and African American race as the most predictive risk factors of 30-day readmission. Additionally, significant variations in the therapeutic management of blood glucose by providers were observed.

CONCLUSIONS

Suboptimal glucose metrics during hospitalization after kidney transplantation are associated with an increased risk for 30-day hospital readmission. Optimizing hospital blood glucose management, a modifiable factor, after kidney transplantation may reduce the risk of 30-day readmission.

A Consensus Statement for Continuous Glucose Monitoring Metrics for Inpatient Clinical Trials

Diabetes Technology Society organized an expert consensus panel to develop metrics for research in the use of continuous glucose monitors (CGMs) in a hospital setting. The experts met virtually in small groups both before and after an April 13, 2023 virtual meeting of the entire panel. The goal of the panel was to develop consensus definitions in anticipation of greater use of CGMs in hospital settings in the future. Establishment of consensus definitions of inpatient analytical metrics will be easier to compare outcomes between studies. Panelists defined terms related to 10 dimensions of measurements related to the use of CGMs including (1) hospital hypoglycemia, (2) hospital hyperglycemia, (3) hospital time in range, (4) hospital glycemic variability, (5) hospital glycemia risk index, (6) accuracy of CGM devices and reference methods for CGMs in the hospital, (7) meaningful time blocks for hospital glycemic goals, (8) hospital CGM data sufficiency, (9) using CGM data for insulin dosing, and (10) miscellaneous factors. The panelists voted on 51 proposed recommendations. Based on the panel vote, 51 recommendations were classified as either strong (43) or mild (8). Additional research is needed on CGM performance in the hospital. This consensus report is intended to support that type of research intended to improve outcomes for hospitalized people with diabetes.

A Glycemia Risk Index (GRI) of Hypoglycemia and Hyperglycemia for Continuous Glucose Monitoring Validated by Clinician Ratings

BACKGROUND

A composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data.

METHODS

We assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low-glucose and low-glucose hypoglycemia; very high-glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation.

RESULTS

The analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals.

CONCLUSION

The GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments.

Glucometrics: Where Are We Now?

PURPOSE OF REVIEW

Inpatient glucose data analysis, or glucometrics, has developed alongside the growing emphasis on glycemic control in the hospital. Shortcomings in the initial capabilities for glucometrics have pushed advancements in defining meaningful units of measurement and methods for capturing glucose data. This review addresses the growth in glucometrics and ends with its promising new state.

RECENT FINDINGS

Standardization, allowing for benchmarking and purposeful comparison, has been a goal of the field. The National Quality Foundation glycemic measures and recently enacted Center for Medicare and Medicaid Services (CMS) electronic quality measures for hypo- and hyperglycemia have allowed for improved integration and consistency. Prior systems have culminated in an upcoming measure from the Center for Disease Control and Prevention's National Healthcare Safety Network. It is poised to create a new gold standard for glucometrics by expanding and refining the CMS metrics, which should empower both local improvement and benchmarking as the program matures.

Unsafe Sharps Disposal Among Insulin-Using Patients With Diabetes Mellitus: An Emerging Global Crisis

Approximately eight billion therapeutic injections are administered outside of medical treatment facilities annually. The management of diabetes mellitus (DM) includes self-monitoring of blood glucose levels and administration of insulin and injectable non-insulin-related medications. The lancets, needles, and syringes used for DM management are categorized as medical sharps. Improperly discarded medical sharps can cause needlestick injuries in unsuspecting individuals and thereby pose a considerable public health risk. Release of these items into the environment will likely increase with the rising worldwide prevalence of DM, and a public safety crisis will emerge if proper disposal measures are not emphasized. This article reviews the literature from various geopolitical regions and describes how a substantial number of patients with DM improperly discard their sharps. Data support the need to develop multifaceted and innovative approaches to reduce risk associated with improper disposal of DM-related medical sharps into local communities.

Use of Cross-sectional and Perspective Mapping to Spatially and Statistically Represent Inpatient Glucose Control

BACKGROUND

The use of inpatient location for the depiction of glycemic control is an alternative approach to the traditional analysis of hospital-derived glucometric data. Our aim was to develop a method of spatial representation and to test for corresponding statistical variation in inpatient glucose control data.

METHODS

Point-of-care blood glucose data from inpatients with diabetes mellitus were extracted. Calculations included patient-day weighted means (PDWMs) and percentage of patient hospital days with hypoglycemia. Results were overlaid onto hospital floor plans, and room numbers were used as geolocators to generate cross-sectional (2-dimensional) and perspective (3-dimensional) views of the data. Linear mixed and mixed-effects logistic regression models were used to compare the location effect and to assess statistical variation in the data after adjusting for age, sex, and severity of illness.

RESULTS

Visual inspection of cross-sectional and perspective maps demonstrated variation in glucometric outcomes across areas within the hospital. Statistical analysis confirmed significant variation between some hospital wings and floors.

CONCLUSIONS

Spatial depiction of glucometric data within the hospital could yield insights into hot spots of poor glycemic control. Future studies on how to operationalize this approach, and whether this method of analysis can drive changes in glycemic management practices, need to be conducted.

Cancer, diabetes, survival and glycemic control: a large multisite analysis

Aim

To determine overall survival (OS) and glycemic control in patients with cancer and diabetes.

Materials & methods

Patients of our institution with breast, colon, lung, pancreas and prostate cancer were retrospectively reviewed. OS was compared between matched patients with and without diabetes, and changes in glucose value over time were assessed.

Results

For 3934 patients each with and without diabetes, adjusted analysis showed no difference in OS according to diabetes status (hazard ratio: 1.07; 95% CI: 0.96-1.20). Mean glucose values decreased over time in patients with and without diabetes (p = 0.01).

Conclusion

In this large study of patients with five common cancers, the co-occurrence of diabetes did not affect OS. Cancer did not adversely affect glucose levels.

A Plant-Based Dietary Intervention for Preoperative Glucose Optimization in Diabetic Patients Undergoing Total Joint Arthroplasty

Purpose. The purpose of this study was to assess the feasibility and effectiveness of a whole food plant-based diet (WFPBD) to improve day of surgery fasting blood glucose (FBG) among patients with type 2 diabetes (T2D). Patients and Methods. Ten patients with T2D scheduled for a total hip or total knee replacement were recruited. For 3 weeks preceding their surgeries, subjects were asked to consume an entirely WFPBD. Frozen WFPBD meals were professionally prepared and delivered to each participant for the 3 weeks prior to surgery. FBG was reassessed on the morning of surgery and compared with preintervention values. Compliance with the diet was assessed. Results. Mean age of subjects and reported duration of diabetes was 65 and 8 years, respectively, average hemoglobin A1c (HbA1c) was 6.6%, and 6 were women. Mean FBG decreased from 127 to 116 mg/dL (P = .2). Five of the subjects experienced improvement in glycemic control, with an average decline of 11 mg/dL. Conclusion. A WFPBD is a potentially effective intervention to improve glycemic control among patients with T2D during the period leading up to surgery. Future controlled trials on a larger sample of patients to assess the impact of a WFPBD on glycemic control and surgical outcomes are warranted.

Use of Imputation and Decision Modeling to Improve Diagnosis and Management of Patients at Risk for New-Onset Diabetes After Transplantation

BACKGROUND New-onset diabetes after transplantation (NODAT) is a complication of solid organ transplantation. We sought to determine the extent to which NODAT goes undiagnosed over the course of 1 year following transplantation, analyze missed or later-diagnosed cases of NODAT due to poor hemoglobin A1c (HbA1c) and fasting blood glucose (FBG) collection, and to estimate the impact that improved NODAT screening metrics may have on long-term outcomes. MATERIAL AND METHODS This was a retrospective study utilizing 3 datasets from a single center on kidney, liver, and heart transplantation patients. Retrospective analysis was supplemented with an imputation procedure to account for missing data and project outcomes under perfect information. In addition, the data were used to inform a simulation model used to estimate life expectancy and cost-effectiveness of a hypothetical intervention. RESULTS Estimates of NODAT incidence increased from 27% to 31% in kidney transplantation patients, from 31% to 40% in liver transplantation patients, and from 45% to 67% in heart transplantation patients, when HbA1c and FBG were assumed to be collected perfectly at all points. Perfect screening for kidney transplantation patients was cost-saving, while perfect screening for liver and heart transplantation patients was cost-effective at a willingness-to-pay threshold of $100 000 per life-year. CONCLUSIONS Improved collection of HbA1c and FBG is a cost-effective method for detecting many additional cases of NODAT within the first year alone. Additional research into both improved glucometric monitoring as well as effective strategies for mitigating NODAT risk will become increasingly important to improve health in this population.

Implications of neuroendocrine tumor and diabetes mellitus on patient outcomes and care: a matched case-control study

Aim:

We aimed to determine the impact of diabetes mellitus (DM) on survival of patients with neuroendocrine tumors (NETs) and of NETs on glycemic control.

Patients & methods:

Patients with newly diagnosed NETs with/without DM were matched 1:1 by age, sex and diagnosis year (2005–2017), and survival compared (Kaplan–Meier and Cox proportional hazards). Mixed models compared hemoglobin A_1c (HbA_1c) and glucose during the year after cancer diagnosis.

Results:

Three-year overall survival was 72% (95% CI: 60–86%) for DM patients versus 80% (95% CI: 70–92%) for non-DM patients (p = 0.82). Hazard ratio was 1.33 (95% CI: 0.56–3.16; p = 0.51); mean DM HbA_1c, 7.3%.

Conclusion:

DM did not adversely affect survival of patients with NET. NET and its treatment did not affect glycemic control.

The aim of this study was to evaluate the effect of diabetes mellitus (DM) on survival of patients with neuroendocrine tumor (NET) and to determine whether NET affected glycemic control. From an institutional cancer registry, 118 patients with NET were identified and grouped by DM (n = 59) or no DM (n = 59). The two groups were matched by age, sex and year of NET diagnosis. DM did not decrease survival, and NET did not significantly affect glycemic control in patients with DM.

Survival and glycemic control in patients with co-existing squamous cell carcinoma and diabetes mellitus

Aim:

This study examined the impact of diabetes mellitus (DM) on survival in squamous cell carcinoma (SCC) patients, and the impact of SCC on glycemic control.

Materials & methods:

Patients with newly diagnosed SCC with and without DM were matched 1:1 (2007–2017). Overall survival and recurrence-free survival were estimated using the Kaplan–Meier method. Hemoglobin A_1c (HbA_1c) and glucose level during the year following cancer diagnosis were compared using mixed models.

Results:

HbA_1c decreased over time in DM patients (p = 0.04). The 5-year overall survival was 61% in DM patients, compared with 78% in patients without DM (p = 0.004).

Conclusion:

The presence of co-existing DM adversely impacted survival in patients with SCC. SCC did not affect glycemic control.

The objective of this study was to identify the effect of diabetes mellitus (DM) on survival of patients with squamous cell carcinoma (SCC) and to determine whether SCC and its treatment affected glycemic control. We used an institutional cancer registry to identify 190 patients with SCC and grouped them by the presence (n = 95) or absence (n = 95) of DM. Patients were matched by age and year of SCC diagnosis. For individuals with SCC, DM did decrease survival rates, and the diagnosis of SCC did not affect glycemic control.

Patterns of Sharps Handling and Disposal Among Insulin-Using Patients With Diabetes Mellitus

BACKGROUND

The objective of this study was to assess disposal patterns for "sharps" among a cohort of patients with diabetes mellitus (DM) receiving insulin therapy.

METHOD

A convenience sample of inpatients and outpatients was surveyed about how they disposed of sharps, how often they reused lancets and needles, and what education they had received about proper disposal. Safe disposal was defined as discarding sharps into a formal sharps or sealable container; otherwise, disposal was categorized as unsafe.

RESULTS

Of 150 respondents, 56% were men and 75% were white. The mean (SD) age was 56 (15) years; duration of DM, 20 (13) years; and hemoglobin A_1c, 8.1% (2.0%). Half the respondents reused a lancet two or more times, and 21% reused an insulin needle two or more times. Thirty-eight percent of respondents discarded lancets unsafely, and 33% discarded insulin needles unsafely, typically by throwing these items into household trash. Most respondents (75%) discarded insulin pens, vials, cartridges, insulin pump supplies, and continuous glucose monitor sensors into household trash. Most (64%) indicated that they had not received education on safe sharps-disposal practices, and 84% had never visited their municipal website for information on medical waste disposal.

CONCLUSION

Approximately one-third of patients unsafely disposed of sharps. Unsafe disposal could cause millions of sharps to appear in the municipal solid waste stream, thereby posing a substantial public health hazard. Point-of-care patient education is important, but a broader public health campaign may be required.

Continuous Glucose Monitors and Automated Insulin Dosing Systems in the Hospital Consensus Guideline

This article is the work product of the Continuous Glucose Monitor and Automated Insulin Dosing Systems in the Hospital Consensus Guideline Panel, which was organized by Diabetes Technology Society and met virtually on April 23, 2020. The guideline panel consisted of 24 international experts in the use of continuous glucose monitors (CGMs) and automated insulin dosing (AID) systems representing adult endocrinology, pediatric endocrinology, obstetrics and gynecology, advanced practice nursing, diabetes care and education, clinical chemistry, bioengineering, and product liability law. The panelists reviewed the medical literature pertaining to five topics: (1) continuation of home CGMs after hospitalization, (2) initiation of CGMs in the hospital, (3) continuation of AID systems in the hospital, (4) logistics and hands-on care of hospitalized patients using CGMs and AID systems, and (5) data management of CGMs and AID systems in the hospital. The panelists then developed three types of recommendations for each topic, including clinical practice (to use the technology optimally), research (to improve the safety and effectiveness of the technology), and hospital policies (to build an environment for facilitating use of these devices) for each of the five topics. The panelists voted on 78 proposed recommendations. Based on the panel vote, 77 recommendations were classified as either strong or mild. One recommendation failed to reach consensus. Additional research is needed on CGMs and AID systems in the hospital setting regarding device accuracy, practices for deployment, data management, and achievable outcomes. This guideline is intended to support these technologies for the management of hospitalized patients with diabetes.

Mortality and glycemic control among patients with acute and chronic myeloid leukemia and diabetes: a case-control study

Aim

We examined the association between diabetes and survival in patients with acute and chronic myeloid leukemia and the association of leukemia with glycemic control.

Patients & methods

Patients with leukemia with and without diabetes (2007-2015) were retrospectively identified and matched 1:1 (n = 70 per group). Overall survival was estimated by the Kaplan-Meier method. Hemoglobin A_1c and glucose levels the year after leukemia diagnosis were compared by mixed models.

Results

Among 25 of 70 patients with diabetes, mean hemoglobin A_1c during the year after leukemia diagnosis was 6.8%. Kaplan-Meier-estimated 3-year survival was 46% for diabetes patients versus 45% for controls (p = 0.79).

Conclusion

No associations were found between leukemia, diabetes, survival and glycemic control.

Incidence, Risk Factors, and Trends for Postheart Transplantation Diabetes Mellitus

This retrospective study analyzed glycemic trends, incidence of post-transplant diabetes mellitus (PTDM) incidence and associated risk factors in a cohort of patients who underwent first-time heart transplantation (HT). Univariate analyses compared patient with and without pretransplant diabetes mellitus (DM). Multivariate regression analyses were conducted to determine association between PTDM and different risk factors. Finally, trends in glucometrics and other outcomes are described across follow-up time points. There were 152 patients who underwent HT between 2010 and 2015, 109 of whom had no pretransplant history of DM. PTDM incidence was 38% by the 1-year follow-up. Pretransplant body mass index (odds ratio [OR] 1.12, 95% confidence interval [CI] 1.01 to 1.23, p = 0.03), insulin use during the final 24 hours of inpatient stay (OR 4.26, 95% CI 1.72 to 10.56, p <0.01), mean inpatient glucose (OR 2.21, 95% CI 1.33 to 3.69, p <0.01), and mean glucose in the final 24 hours before discharge (OR 1.29, 95% CI 1.03 to 1.60, p = 0.03) were associated with increased odds of PTDM at 1 year. In patients on insulin before discharge, blood glucose values were significantly higher compared with those who were not (136 mg/dl vs 114 mg/dl at 1 to 3 months, 112 vs 100 at 4 to 6 months, 109 vs 98 at 8 to 12 months, all p <0.01). This analysis improves understanding of PTDM incidence, glucometric trends, and risk differences by DM status in the HT population. Similar to liver and kidney patients, inpatient glucometrics may be informative of PTDM risk in HT patients. Guidelines for this population should be developed to account for risk heterogeneity and need for differential management.

Implications of neuroendocrine tumor and diabetes mellitus on patient outcomes and care: A matched case control study

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Background: The aim of this study was to examine the impact of diabetes mellitus (DM) on survival in neuroendocrine tumor and the impact of neuroendocrine tumor on glycemic control in DM. Methods: Patients with newly diagnosed neuroendocrine tumor with and without DM were matched 1:1 according to age, gender, and year of cancer diagnosis (2005-2017). The file was linked to the electronic medical record to obtain information on DM and neuroendocrine tumor therapies and laboratory results. There were 59 matched pairs (total 118 patients) included in the analysis. We compared characteristics between cases and controls and assessed survival with the Kaplan-Meier method and Cox proportional hazards model. Mixed models compared hemoglobin A1c and glucose levels over time. Results: Median age of patients at diagnosis was 67 (40-86); 41% had stage IV disease. Women constituted 49% of the study population; 22% had pancreatic neuroendocrine tumor and 45% had another GI primary neuroendocrine tumor. No differences in race/ethnicity, marital status, alcohol or tobacco use were detected between cancer patients with and without DM. Mean BMI was significantly different between DM and non-DM patients (31.0 [7.90] versus 26.4 [5.27]); p = 0.011. Among those with DM, mean HbA1c during the year following cancer diagnosis was 7.3%. Mean glucose was significantly different between DM (159.1 [43.5] versus non-DM pts 117 [31.5]); p < 0.001. Median follow-up time was 32.8 (2.4-165.4) months in alive patients. Three year survival was estimated at 72% (95% CI: 60-86%) for DM patients versus 80% (95% CI: 70-92%) in non-DM patients by Kaplan Meier method (p = 0.82 log rank test). Hazard ratio (stratification for matched pairs) = 1.33 (95% CI: 0.56 – 3.16; p = 0.51). Conclusions: DM did not adversely impact survival in patients with neuroendocrine tumor. Neuroendocrine tumor and its treatment did not affect glycemic control. This should be reassuring to oncologists and endocrinologists who treat patients with neuroendocrine tumors and diabetes.

Comparison of post-transplantation diabetes mellitus incidence and risk factors between kidney and liver transplantation patients

Background

Most prior studies characterizing post-transplantation diabetes mellitus (PTDM) have been limited to single-cohort, single-organ studies. This retrospective study determined PTDM across organs by comparing incidence and risk factors among 346 liver and 407 kidney transplant recipients from a single center.

Methods

Univariate and multivariate regression-based analyses were conducted to determine association of various risk factors and PTDM in the two cohorts, as well as differences in glucometrics and insulin use across time points.

Results

There was a higher incidence of PTDM among liver versus kidney transplant recipients (30% vs. 19%) at 1-year post-transplant. Liver transplant recipients demonstrated a 337% higher odds association to PTDM (OR 3.37, 95% CI (1.38–8.25), p<0.01). 1-month FBG was higher in kidney patients (135 mg/dL vs 104 mg/dL; p < .01), while 1-month insulin use was higher in liver patients (61% vs 27%, p < .01). Age, BMI, insulin use, and inpatient FBG were also significantly associated with differential PTDM risk.

Conclusions

Kidney and liver transplant patients have different PTDM risk profiles, both in terms of absolute PTDM risk as well as time course of risk. Management of this population should better reflect risk heterogeneity to short-term need for insulin therapy and potentially long-term outcomes.

Patient Perception and Satisfaction With Insulin Pump System: Pilot User Experience Survey

BACKGROUND

The goal of this study was to assess patient perspectives and satisfaction with the MiniMed 670G insulin pump. Those participants who used the pump as part of a hybrid closed loop were also asked to provide their views on the automatic feature (auto mode).

METHODS

Adults with type 1 diabetes mellitus using the Medtronic™ 670G pump were asked about their experience with the device using a semi-structured survey developed by the research team. Responses were quantified to identify emergent themes.

RESULTS

Seventeen participants used the pump as part of a hybrid closed loop system, while four participants used the pump in combination with a nonintegrated continuous glucose monitoring system. Overall, participants indicated a high level of satisfaction with the pump (14/21) mostly because of improvements in blood glucose (BG) control (15/21). Least liked features were physical design and structure (6/21), frequency of user input (5/21), alert frequency (4/21), and difficulty of use (3/21). Those using the hybrid closed loop were satisfied with the auto mode feature (11/17), mostly because of improvements in BG control (9/17). The least liked features of the auto mode technology were that blood glucose levels remained elevated (5/17) and the frequency of alerts (4/17).

CONCLUSION

Participants indicated a high level of satisfaction with the pump and its auto mode featured mostly because of improvements in BG control. They also pointed out some key aspects of the device that are of potential clinical or commercial relevance. Additional research is needed to further evaluate users' perspectives on this new device.

Impact of measurement error on predicting population-based inpatient glucose control

Aim:

Instrument measurement error (ME) may affect ability of damped trend analysis to forecast inpatient glycemic control.

Materials & methods:

A statistical approach was developed to introduce ME into damped trend analysis algorithm. Point-of-care blood glucose device data were extracted from the laboratory system. Forecasts were generated from various inpatient subgroups and time intervals.

Results:

ME produced differences in damped trend model during the forecast learning cycle. However, forecast trajectory stayed identical regardless of ME in 85% (119/140) of studied scenarios. Forecasts did not change with greater ME.

Conclusion:

ME inherent in the point-of-care blood glucose device had little effect on trajectory of damped trend exponential forecasts and apparently would not influence decision making in inpatient glycemic control algorithms.

High blood glucose (sugar) levels can lead to complications for hospitalized patients, including more surgical infections or longer hospital stays. The ability to forecast glucose control through trend analysis could identify problems sooner and allow earlier care to keep levels in the recommended range. However, measurement error (ME) is inherent in the glucometer used to check point-of-care glucose values and could limit the usefulness of forecasting methods. This study examined how ME affects forecasting. It showed little effect on glucose forecasts and showed potential robustness of trend analysis in assessment of inpatient glucose control.

Glycemic control and survival of patients with coexisting diabetes mellitus and gastric or esophageal cancer

AIM

To examine effects of diabetes mellitus (DM) on survival in gastric or esophageal (GE) cancer and the cancers' effects on glycemic control.

MATERIALS & METHODS

Patients with GE cancer with and without DM were matched 1 to 1 (2006-2016). Characteristics were compared and survival assessed with Kaplan-Meier method and Cox regression. Mixed models compared hemoglobin A_1c and glucose over time.

RESULTS

Among DM cases, mean hemoglobin A_1c was 6.8% in the year after cancer diagnosis. Three-year overall survival was 46% with DM versus 52% without DM (hazard ratio [95% CI]: 1.95 [1.14-3.34]; p = 0.02).

CONCLUSION

GE cancer and its treatment did not affect glycemic control. Risks of death and progression were greater for patients with DM than patients without DM.

Mortality and glycemic control among patients with gastric and esophageal cancer and diabetes mellitus

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Background: This study evaluated the impact of diabetes mellitus (DM) on survival in gastric and esophageal cancer patients, and examined the impact of these cancers on glycemic control in DM. Methods: Patients with newly diagnosed gastric and esophageal cancers with DM (n = 92) were identified from the Institutional Cancer Registry and matched to 92 gastric and esophageal cancer patients without DM according to age, gender and year of cancer diagnosis (2006 to 2016). The electronic medical record provided information on DM and cancer therapies and laboratory results. Overall survival (OS) was estimated with the Kaplan-Meier method and compared by Cox regression analysis using stratification for matched pairs. Mixed models were used to compare hemoglobin A1c (HbA1c) and glucose during the year following cancer diagnosis. Results: Mean age of the entire cohort was 68 years, 91% were white, 78% were men, and 53% had stage III/IV disease. Adenocarcinoma (79%) was the most common histologic type. BMI was significantly different between DM and non-DM patients (p = 0.006). Alcohol use at time of cancer diagnosis was more prevalent in non-DM patients vs. DM (p = 0.018). Among those with DM, mean HbA1c during the year following cancer diagnosis was 6.8%. Mean glucose was significantly different between DM and non-DM patients (149 mg/dL vs. 116 mg/dL, p < 0.001). For glucose, there was a significant interaction effect (p = 0.005) as DM patients demonstrated a decrease in glucose values over time compared to non-DM patients. Median follow-up time was 35 months. Three year OS was estimated at 46% (95% CI: 36-58%) for DM patients versus 52.0% (95% CI: 41-64%) in non-DM (p = 0.25). Hazard ratio (stratification for matched pairs) was 1.95 (95% CI: 1.14 – 3.43; p = 0.02). Three year PFS was estimated at 40% (95% CI: 31-53%) for DM patients versus 50% (95% CI: 40-63%) for non-DM patients (p = 0.12). Hazard ratio (stratification for matched pairs) was 1.74 (95% CI: 1.04-2.90; p = 0.03). Conclusions: Gastric and esophageal cancer and its treatment did not affect glycemic control. Risks of death and progression are greater in DM patients as compared to non-DM.

Insulin Pumping Patches: Emerging Insulin Delivery Systems

In this special section of JDST, patch-pump delivery systems ("patch pumps") are discussed. Patch pumps are novel insulin delivery systems that have emerged on the market; offering flexible insulin delivery options. These systems offer several advantages over conventional insulin pump delivery systems and are gaining popularity. Patch pumps are free of tubing, small, and lightweight. In this special section, the authors discuss both the simple and complex patch pumps currently available on the US market as well as those that are currently under development. Current technologies used to operate these pumps are discussed in detail, and potential promising technologies are presented. Available data on patient preferences, clinical trial data, and the future of patch pumps are discussed.

Comparing Real-Time Self-Tracking and Device-Recorded Exercise Data in Subjects with Type 1 Diabetes

BACKGROUND

Insulin therapy, medical nutrition therapy, and physical activity are required for the treatment of type 1 diabetes (T1D). There is a lack of studies in real-life environments that characterize patient-reported data from logs, activity trackers, and medical devices (e.g., glucose sensors) in the context of exercise.

OBJECTIVE

The objective of this study was to compare data from continuous glucose monitor (CGM), wristband heart rate monitor (WHRM), and self-tracking with a smartphone application (app), iDECIDE, with regards to exercise behaviors and rate of change in glucose levels.

METHODS

Participants with T1D on insulin pump therapy tracked exercise for 1 month with the smartphone app while WHRM and CGM recorded data in real time. Exercise behaviors tracked with the app were compared against WHRM. The rate of change in glucose levels, as recorded by CGM, resulting from exercise was compared between exercise events documented with the app and recorded by the WHRM.

RESULTS

Twelve participants generated 277 exercise events. Tracking with the app aligned well with WHRM with respect to frequency, 3.0 (2.1) and 2.5 (1.8) days per week, respectively (p = 0.60). Duration had very high agreement, the mean duration from the app was 65.6 (55.2) and 64.8 (54.9) minutes from WHRM (p = 0.45). Intensity had a low concordance between the data sources (Cohen's kappa = 0.2). The mean rate of change of glucose during exercise was -0.27 mg/(dL*min) and was not significantly different between data sources or intensity (p = 0.21).

CONCLUSION

We collated and analyzed data from three heterogeneous sources from free-living participants. Patients' perceived intensity of exercise can serve as a surrogate for exercise tracked by a WHRM when considering the glycemic impact of exercise on self-care regimens.

An Overview of Safety Issues on Use of Insulin Pumps and Continuous Glucose Monitoring Systems in the Hospital

PURPOSE OF REVIEW

Summarize safety issues related to patients using insulin pump therapy and continuous glucose monitoring systems (CGMS) in the outpatient setting when they are hospitalized and to review steps that can be taken to mitigate risk associated with use or discontinuation of these devices.

RECENT FINDINGS

Two recent consensus conferences were held on the topics of inpatient use of insulin pumps and CGMS devices. In addition to commonly known safety issues (e.g., device malfunction, infection), cybersecurity and the vulnerability of contemporary technology to hacking have emerged. CGMS capabilities offer the promise of advancing the goal for development of glucometry (centralized monitoring of real-time glucose data). Strategies to assuring safe use of insulin pumps and CGMS in the hospital include collaboration between the patient and staff, proper patient selection, and clear policies and procedures outlining safe use. Available data indicates few adverse events associated with these devices in the hospital. Current data suggests, with proper patient selection and a clear process in place for glycemic management, that adverse events are rare, and consensus favors allowing use of the technology in the hospital. The topic of insulin pump and CGMS in the hospital would greatly benefit from more institutions reporting on their experiences and prospective clinical trials.

Consensus Statement on Use of Continuous Subcutaneous Insulin Infusion Therapy in the Hospital

In May 2017, the Diabetes Technology Society convened a panel of US experts on inpatient diabetes management to discuss the current and potential role of continuous subcutaneous insulin infusion (CSII) therapy in the hospital. The panel (1) discussed evidence for current use of CSII in the hospital, (2) recommended contraindications for use in the hospital, and (3) recommended guidelines to maximize chances for safe use of CSII in the hospital. Panel members agreed that larger, prospective, randomized studies are needed to evaluate safety and efficacy of CSII use in the hospital. As CSII technology becomes more complex and its use increases, it is imperative that institutional protocols be in place to ensure safe use of this technology and safe transitions across care areas. Providers need to be cognizant that not all patients currently using CSII as an outpatient are appropriate candidates for continued use in the hospital. This consensus statement provides guidelines for practitioners who may encounter patients using this technology in the inpatient setting.

A Disposable Tear Glucose Biosensor-Part 5: Improvements in Reagents and Tear Sampling Component

A tear glucose (TG) sensor with an integrated tear sampler can provide a noninvasive method for calibrating the continuous TG contact lens and monitoring glucose. Expanding from previous work, an improved TG sensor that implements dried reagents, genetically modified glucose dehydrogenase (GDH), and a tear sampler was developed and compared against the TG sensor prepared with commercial GDH. It was found that neither sensor was affected by the tear interferents: ascorbic acid, acetaminophen, and uric acid. The sensor prepared with commercial GDH generated higher current. This suggests that using enzymes with lower K_m may be advantageous when operating in low glucose environments like tears. The improved TG sensor also demonstrated the potential of integrating Schirmer's test strip as a tear sampler for self-monitoring of TG.

Should patients with diabetes be encouraged to integrate social media into their care plan?

AIM

To evaluate the use of social media of individuals with diabetes mellitus (DM).

MATERIALS & METHODS

Both web-based and in-clinic surveys were collected from individuals with DM. Descriptive and correlation analyses were employed to evaluate respondents' diabetes-specific social networking site behaviors.

RESULTS

Forty-five patients with DM completed the web-based survey and 167, the clinic-based survey, of whom only 40 visited diabetes-specific social networking sites. Analysis of online survey data indicated that self-reported adherence to lifestyle recommendations was significantly correlated (p < 0.01) with visiting the sites. Clinic-based survey data found that patients who reported using DM-specific web sites monitored home glucose values more often and had better compliance with insulin administration (both p < 0.05) compared with nonusers.

CONCLUSION

This study provides insight into why individuals visit DM-specific social networking sites. Certain self-management behaviors may improve as a result of visiting these sites. Further work is needed to explore how to leverage social media technology to assist patients with the management of DM.

Design and Testing of a Smartphone Application for Real-Time Self-Tracking Diabetes Self-Management Behaviors

BACKGROUND

Type 1 diabetes (T1D) care requires multiple daily self-management behaviors (SMBs). Preliminary studies on SMBs rely mainly on self-reported survey and interview data. There is little information on adult T1D SMBs, along with corresponding compensation techniques (CTs), gathered in real-time.

OBJECTIVE

The article aims to use a patient-centered approach to design iDECIDE, a smartphone application that gathers daily diabetes SMBs and CTs related to meal and alcohol intake and exercise in real-time, and contrast patients' actual behaviors against those self-reported with the app.

METHODS

Two usability studies were used to improve iDECIDE's functionality. These were followed by a 30-day pilot test of the redesigned app. A survey designed to capture diabetes SMBs and CTs was administered prior to the 30-day pilot test. Survey results were compared against iDECIDE logs.

RESULTS

Usability studies revealed that participants desired advanced features for self-tracking meals and alcohol intake. Thirteen participants recorded over 1,200 CTs for carbohydrates during the 30-day study. Participants also recorded 76 alcohol and 166 exercise CTs. Comparisons of survey responses and iDECIDE logs showed mean% (standard deviation) concordance of 77% (25) for SMBs related to meals, where concordance of 100% indicates a perfect match. There was low concordance of 35% (35) and 46% (41) for alcohol and exercise events, respectively.

CONCLUSION

The high variability found in SMBs and CTs highlights the need for real-time diabetes self-tracking mechanisms to better understand SMBs and CTs. Future work will use the developed app to collect SMBs and CTs and identify patient-specific diabetes adherence barriers that could be addressed with individualized education interventions.

Self-Reported Compensation Techniques for Carbohydrate, Exercise, and Alcohol Behaviors in Patients With Type 1 Diabetes on Insulin Pump Therapy

Studies have found variability in self-care behaviors in patients with type 1 diabetes, particularly when incorporating exercise and alcohol consumption. The objective of this study was to provide results from a survey to understand (1) insulin pump behaviors, (2) reported self-management behaviors for exercise and alcohol, and (3) perceptions of the effects of exercise and alcohol on blood glucose (BG) control. Fourteen participants from an outpatient endocrinology practice were recruited and administered an electronic survey. Compensation techniques for exercise and alcohol, along with reasons for employing the techniques were identified. Also identified were factors that participants said affected BG control with regard to exercise and alcohol. These results confirm the considerable inconsistency patients have about incorporating exercise and alcohol into decisions about self-management behaviors.

Implications of pancreatic cancer with diabetes mellitus on patient outcomes and care: A matched case-control study

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Background: The aim of this case-control study was to determine the impact of DM on survival in pancreatic cancer patients, and to examine the impact of pancreatic cancer on glycemic control in DM. Methods: Ninety-two patients with newly diagnosed pancreatic cancer from 2007 to 2015 with DM were identified from the institutional Cancer Registry and matched to ninety-two pancreatic cancer patients without DM according to age, gender, and year of pancreatic cancer diagnosis. The file was linked to the electronic medical record to obtain information on DM and pancreatic cancer therapies, and laboratory results. Overall survival (OS) was estimated with the Kaplan-Meier method and compared by Cox regression analysis. Mixed models were used to compare hemoglobin A1c (HbA1c) and glucose over time. Results: Mean age of the entire pancreatic cancer cohort was 70 years, most (92%) were white, most common (88%) histology was adenocarcinoma, and majority (41%) were stage IV. No differences in age, race/ethnicity, histology, or tumor stage were detected between patients with and without DM, although DM patients had higher body mass index (P = 0.014). Mean ca 19-9 (U/ml) was 804 for diabetics, and 395 for non-diabetics. Among those with DM the mean HbA1c during the year following cancer diagnosis was 7.3%. Time (days since diagnosis) was significant in DM patients (p = 0.014) as HbA1c decreased over time. Mean glucose during the year following diagnosis among DM patients was significantly higher compared to non-DM patients [160.6 (SD = 38.0) versus 117.2 (SD = 19.0); p < 0.001]. Both groups had a decline in glucose over time (p = 0.008). In Kaplan-Meier survival analysis (median follow-up time of 11.9 months), 2 year overall survival was estimated at 15% [95% CI: 8-24%] for DM patients versus 26% [95% CI: 17-36%] in non-DM patients. Hazard ratio (for matched pairs) was 1.15 (95% CI: 0.75-1.77; p = 0.51). Conclusions: DM did not adversely impact survival in patients with pancreatic cancer. Pancreatic cancer did not affect glycemic control. Elevated ca 19-9 in diabetic patients may be an unreliable marker for gauging disease progression.

A Methodology to Compare Insulin Dosing Recommendations in Real-Life Settings

BACKGROUND

We propose a methodology to analyze complex real-life glucose data in insulin pump users.

METHODS

Patients with type 1 diabetes (T1D) on insulin pumps were recruited from an academic endocrinology practice. Glucose data, insulin bolus (IB) amounts, and self-reported alcohol consumption and exercise events were collected for 30 days. Rules were developed to retrospectively compare IB recommendations from the insulin pump bolus calculator (IPBC) against recommendations from a proposed decision aid (PDA) and for assessing the PDA's recommendation for exercise and alcohol.

RESULTS

Data from 15 participants were analyzed. When considering instances where glucose was below target, the PDA recommended a smaller dose in 14%, but a larger dose in 13% and an equivalent IB in 73%. For glucose levels at target, the PDA suggested an equivalent IB in 58% compared to the subject's IPBC, but higher doses in 20% and lower in 22%. In events where postprandial glucose was higher than target, the PDA suggested higher doses in 25%, lower doses in 13%, and equivalent doses in 62%. In 64% of all alcohol events the PDA would have provided appropriate advice. In 75% of exercise events, the PDA appropriately advised an IB, a carbohydrate snack, or neither.

CONCLUSIONS

This study provides a methodology to systematically analyze real-life data generated by insulin pumps and allowed a preliminary analysis of the performance of the PDA for insulin dosing. Further testing of the methodological approach in a broader diabetes population and prospective testing of the PDA are needed.

Glucometrics and Insulinometrics

PURPOSE OF REVIEW

Glucometrics is the systematic analysis of inpatient glucose data and is of key interest as hospitals strive to improve inpatient glycemic control. Insulinometrics is the systematic analysis and reporting of inpatient insulin therapy. This paper reviews some of the questions to be resolved before a national benchmarking process can be developed that will allow institutions to track and compare inpatient glucose control performance against established guidelines.

RECENT FINDINGS

There remains a lack of standardization on how glucometircs should be measured and reported. Before hospitals can commit resources to compiling and extracting data, consensus must be reached on such questions as which measures to report, definitions of glycemic targets, and how data should be obtained. Examples are provided on how insulin administration can be measured and reported. Hospitals should begin assessment of glucometrics and insulinometrics. However, consensus and standardization must first occur to allow for a national benchmarking process.

Enhancing Glycemic Control via Detection of Insulin Using Electrochemical Impedance Spectroscopy

BACKGROUND

Currently, glycemic management for individuals with diabetes mellitus involves monitoring glucose only, which is insufficient as glucose metabolism involves other biomarkers such as insulin. Monitoring additional biomarkers alongside glucose has been proposed to improve glycemic control. In this work, the development of a rapid and label-free insulin biosensor with high sensitivity and accuracy is presented. The insulin sensor prototype also serves as a prior study for a multimarker sensing platform technology that can further improve glycemic control in the future.

METHODS

Electrochemical impedance spectroscopy was used to identify an optimal frequency specific to insulin detection on a gold disk electrode with insulin antibody immobilized, which was accomplished by conjugating the primary amines of insulin antibody to the carboxylic bond of the self-assembling monolayer on the gold surface. After blocking with ethanolamine, the insulin physiological concentration gradient was tested. The imaginary impedance was correlated to insulin concentration and the results were compared with standard equivalent circuit analysis and correlation of charge transfer resistance to target concentration.

RESULTS

The optimal frequency of insulin is 810.5 Hz, which is characterized by having the highest sensitivity and sufficient specificity. The lower limit of detection was 2.26 [Formula: see text] which is comparable to a standard and better than traditional approaches.

CONCLUSION

An insulin biosensor prototype capable of detecting insulin in physiological range without complex data normalization was developed. This prototype will be the ground works of a multimarker platform sensor technology for future all-in-one glycemic management sensors.

Consensus Statement on Inpatient Use of Continuous Glucose Monitoring

In June 2016, Diabetes Technology Society convened a panel of US experts in inpatient diabetes management to discuss the current and potential role of continuous glucose monitoring (CGM) in the hospital. This discussion combined with a literature review was a follow-up to a meeting, which took place in May 2015. The panel reviewed evidence on use of CGM in 3 potential inpatient scenarios: (1) the intensive care unit (ICU), (2) non-ICU, and (3) transitioning outpatient CGM use into the hospital setting. Panel members agreed that data from limited studies and theoretical considerations suggested that use of CGM in the hospital had the potential to improve patient clinical outcomes, and in particular reduction of hypoglycemia. Panel members discussed barriers to widespread adoption of CGM, which patients would benefit most from use of this technology, and what type of outcome studies are needed to guide use of CGM in the inpatient setting.

CO-EXISTING PROSTATE CANCER AND DIABETES MELLITUS: IMPLICATIONS FOR PATIENT OUTCOMES AND CARE

OBJECTIVE

To investigate how diabetes mellitus (DM) impacts short-term overall survival (OS) for patients with prostate cancer and to examine how prostate cancer impacts glycemic control in DM.

METHODS

Patients with DM and prostate cancer newly diagnosed from 2007 to 2014 were identified from the institutional cancer registry and matched to patients with prostate cancer but no DM according to age and year of prostate cancer diagnosis.

RESULTS

The study included 276 cases and 276 controls; the mean age was 72 years, most (93%) were white, the most common Gleason score (52%) was 7, and the majority (56%) were tumor stage II. Patients with DM had a higher mean body mass index (P = .03). Alcohol use and performance status differed by group (P<.001), but the 2 groups otherwise were not significantly different. Among those with DM, the mean hemoglobin A1c (HbA1c) was 6.7%. In Kaplan-Meier survival analysis (median follow-up time, 43.7 months), the 5-year OS rates were estimated at 88% and 93% for patients with and without DM, respectively (hazard ratio, 1.64; 95% confidence interval, 0.77-3.46; P = .20). Mean glucose among patients with DM was significantly higher (P<.001) compared with non-DM patients, but mean HbA1c and glucose values did not change significantly over 1 year (P≥.13).

CONCLUSION

DM did not adversely impact survival in patients with prostate cancer. In addition, prostate cancer and its treatment did not affect glycemic control. Patients and their providers can be reassured that the concurrent diagnoses do not adversely interact to worsen short-term outcomes.

ABBREVIATIONS

DM = diabetes mellitus; HbA1c = hemoglobin A1c; OS = overall survival.

Self-Management Behaviors in Adults on Insulin Pump Therapy

BACKGROUND

Successful diabetes management requires behavioral changes. Little is known about self-management behaviors (SMB) in adults on insulin pump (IP) therapy.

OBJECTIVE

Analyze and characterize observed common diabetes SMB in adult participants with type 1 diabetes (T1D) using IPs and to correlate behaviors with glycemic outcomes based on participant's individual glucose targets.

MATERIALS AND METHODS

One month of IP data from adults with T1D were downloaded. Computer programs were written to automatically quantify the observed frequency of expected behaviors such as: insulin bolusing, checking blood glucose (BG), and recording carbohydrate intake, and other interactions with the IP.

RESULTS

Nineteen participants were recruited and 4,249 IP interactions were analyzed to ascertain behaviors. Intersubject variability of adherence to minimally expected behaviors was observed: daily documentation of carbohydrates and BG checks in 76.6 (31.7)% and 60.0 (32.5)%, respectively, and bolusing without consulting the IPBC in 13.0 (16.9)% of delivered boluses, while daily insulin bolus delivery was consistent 96.8 (5.7)%. Higher frequency of adherence to daily behaviors correlated with a higher number of glucose readings at target.

CONCLUSION

Results indicate variability in SMB and do not always match recommendations. Case-scenarios based on observed real-life SMB could be incorporated into interviews/surveys to elucidate ways to improve SMB.

Characterization of Exercise and Alcohol Self-Management Behaviors of Type 1 Diabetes Patients on Insulin Pump Therapy

BACKGROUND

There is a lack of systematic ways to analyze how diabetes patients use their insulin pumps to self-manage blood glucose to compensate for alcohol ingestion and exercise. The objective was to analyze "real-life" insulin dosing decisions occurring in conjunction with alcohol intake and exercise among patients using insulin pumps.

METHODS

We recruited adult type 1 diabetes (T1D) patients on insulin pump therapy. Participants were asked to maintain their daily routines, including those related to exercising and consuming alcohol, and keep a 30-day journal on exercise performed and alcohol consumed. Thirty days of insulin pump data were downloaded. Participants' actual insulin dosing behaviors were compared against their self-reported behaviors in the setting of exercise and alcohol.

RESULTS

Nineteen T1D patients were recruited and over 4000 interactions with the insulin pump were analyzed. The analysis exposed variability in how subjects perceived the effects of exercise/alcohol on their blood glucose, inconsistencies between self-reported and observed behaviors, and higher rates of blood glucose control behaviors for exercise versus alcohol.

CONCLUSION

Compensation techniques and perceptions on how exercise and alcohol affect their blood glucose levels vary between patients. Improved individualized educational techniques that take into consideration a patient's unique life style are needed to help patients effectively apply alcohol and exercise compensation techniques.

Implications of prostate cancer with diabetes mellitus on patient outcomes and care: A matched case-control study

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Background: Diabetes mellitus (DM) has been associated with an increased risk of mortality among patients with many types of cancers. The aim of this case-control study was to determine the impact of DM on short-term overall survival in prostate cancer patients, and to examine the impact of prostate cancer on glycemic control in DM. Methods: 276 patients with newly diagnosed prostate cancer from 2007-2014 with DM were identified from the Institutional Cancer Registry and matched to 276 prostate cancer patients without DM according to age and year of prostate cancer diagnosis. The file was linked to the electronic medical record to obtain information on DM and cancer therapies, and laboratory results. Results: The mean age of the entire prostate cancer cohort was 72 years, most (93%) were white, the most common Gleason score (in 52%) was 7, and the majority (56%) were tumor stage 2. No differences in age, race/ethnicity, Gleason score, or tumor stage were detected between patients with and without DM, although DM patients had higher body mass index (P = .031). Among those with DM the mean hemoglobin A1c (HbA1c) was 6.7%. In Kaplan-Meier survival analysis (median follow-up time of 43.7 months), 5-year overall survival was estimated at 88% for DM patients versus 93% in non-DM patients. Hazard ratio (for matched pairs) was 1.64 (95% CI: 0.77-3.46, P = .20). Moreover, mean HbA1c and glucose values among DM cases did not significantly change over 1 year (P ≥ .13). Mean glucose among DM patients was significantly (p < 0.01) higher compared to non-DM patients. Conclusions: DM did not adversely impact survival in prostate cancer patients. In addition, prostate cancer or its treatment does not appear to affect glycemic control. Both patients and their providers can be reassured that the two concurrent diagnoses do not adversely interact to worsen short term outcomes.

Improving Hyperglycemia Management in the Intensive Care Unit

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.

Use of a Glucose Algorithm to Direct Diabetes Therapy Improves A1C Outcomes and Defines an Approach to Assess Provider Behavior

Purpose

The purpose of this study was to determine whether an algorithm that recommended individualized changes in therapy would help providers to change therapy appropriately and improve glycemic control in their patients.

Methods

The algorithm recommended specific doses of oral agents and insulin based on a patient's medications and glucose or A1C levels at the time of the visit. The prospective observational study analyzed the effect of the algorithm on treatment decisions and A1C levels in patients with type 2 diabetes.

Results

The study included 1250 patients seen in pairs of initial and follow-up visits during a 7-month baseline and/or a subsequent 7-month algorithm period. The patients had a mean age of 62 years, body mass index of 33 kg/m2, duration of diabetes of 10 years, were 94% African American and 71% female, and had average initial A1C level of 7.7%. When the algorithm was available, providers were 45% more likely to intensify therapy when indicated (P = .005) and increased therapy by a 20% greater amount (P < .001). A1C level at follow-up was 90% more likely to be <7% in the algorithm group, even after adjusting for differences in age, sex, body mass index, race, duration of diabetes and therapy, glucose, and A1C level at the initial visit (P < .001).

Conclusions Use of an algorithm that recommends patient-specific changes in diabetes medications improves both provider behavior and patient A1C levels and should allow quantitative evaluation of provider actions for that provider's patients.

Clinical Inertia Contributes to Poor Diabetes Control in a Primary Care Setting

Purpose

The purpose of this study was to determine whether “clinical inertia”—inadequate intensification of therapy by the provider—could contribute to high A1C levels in patients with type 2 diabetes managed in a primary care site.

Methods

In a prospective observational study, management was compared in the Medical Clinic, a primary care site supervised by general internal medicine faculty, and the Diabetes Clinic, a specialty site supervised by endocrinologists. These municipal hospital clinics serve a common population that is largely African American, poor, and uninsured.

Results

Four hundred thirty-eight African American patients in the Medical Clinic and 2157 in the Diabetes Clinic were similar in average age, diabetes duration, body mass index, and gender, but A1C averaged 8.6% in the Medical Clinic versus 7.7% in the Diabetes Clinic (P < .0001). Use of pharmacotherapy was less intensive in the Medical Clinic (less use of insulin), and when patients had elevated glucose levels during clinic visits, therapy was less than half as likely to be advanced in the Medical Clinic compared to the Diabetes Clinic (P < .0001). Intensification rates were lower in the Medical Clinic regardless of type of therapy (P < .0001), and intensification of therapy was independently associated with improvement in A1C (P < .001).

Conclusions

Medical Clinic patients had worse glycemic control, were less likely to be treated with insulin, and were less likely to have their therapy intensified if glucose levels were elevated. To improve diabetes management and glycemic control nationwide, physicians in training and generalists must learn to overcome clinical inertia, to intensify therapy when appropriate, and to use insulin when clinically indicated.

Exercise Preferences and Barriers in Urban African Americans With Type 2 Diabetes

PURPOSE

The purpose of this study was to determine physical activity preferences and barriers to exercise in an urban diabetes clinic population.

METHODS

A survey was conducted of all patients attending the clinic for the first time. Evaluation measures were type and frequency of favorite leisure-time physical activity, prevalence and types of reported barriers to exercise, and analysis of patient characteristics associated with reporting an obstacle to exercise.

RESULTS

For 605 patients (44% male, 89% African American, mean age = 50 years, mean duration of diabetes = 5.6 years), the average frequency of leisure activity was 3.5 days per week (mean time = 45 minutes per session). Walking outdoors was preferred, but 52% reported an exercise barrier (predominantly pain). Patients who cited an impediment to physical activity exercised fewer days per week and less time each session compared with persons without a barrier. Increasing age, body mass index, college education, and being a smoker increased the odds of reporting a barrier; being male decreased the chances. Men reported more leisure-time physical activity than women. Exercise preferences and types of barriers changed with age.

CONCLUSIONS

Recognition of patient exercise preferences and barriers should help in developing exercise strategies for improving glycemic control.

Little Time for Diabetes Management in the Primary Care Setting

PURPOSE

This study was conducted to determine how time is allocated to diabetes care.

METHODS

Patients with type 2 diabetes who were receiving care from the internal medicine residents were shadowed by research nurses to observe the process of management. The amount of time spent with patients and the care provided were observed and documented.

RESULTS

The total time patients spent in the clinic averaged 2 hours and 26 minutes: 1 to 9 minutes waiting, 25 minutes with the resident, and 12 minutes with medical assistants and nurses. The residents spent an average of only 5 minutes on diabetes. Glucose monitoring was addressed in 70% of visits; a history of hypoglycemia was sought in only 30%. Blood pressure values were mentioned in 75% of visits; hemoglobin A1c (A1C) values were addressed in only 40%. The need for proper foot care was discussed in 55% of visits; feet were examined in only 40%. Although 65% of patients had capillary glucose levels greater than 150 mg/dL during the visit and their A1C averaged 8.9%, therapy was intensified for only 15% of patients.

CONCLUSIONS

During a routine office visit in a resident-staffed general medicine clinic, little time is devoted to diabetes management. Given the time pressures on the primary care practitioner and the need for better diabetes care, it is essential to teach an efficient but systematic approach to diabetes care.