Cook, Curtiss B.

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.

Abstract Background: Unlike with solid organ cancers, little data is available on how diabetes mellitus (DM) and hematologic malignancies interact to affect survival and glycemic control. We examined the impact of DM on survival in patients with leukemia and the effect of leukemia on glycemic control. Materials and Methods: Patients with leukemia with and without DM were matched 1:1 (2007–2017), 70 matched pairs (total N=140 pts) were 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 (HbA1c) and glucose levels over time. Results: The median age of patients at diagnosis was 56 (18–94); 60% were male and 89% had acute leukemia. Among those with DM, HbA1c was only recorded in 25 of 70 patients during the year following cancer diagnosis and was 6.8%. There was no change in HbA1c values over time in these DM patients. Mean glucose was significantly different between DM and non-DM patients (p<0.001). Time (days since leukemia diagnosis) was significant (p<0.001) and there was a significant interaction effect (p=0.01). Glucose values increased in the DM patients during the year following diagnosis, while remaining stable in those without DM. Median follow-up time was 23.2 months. Three-year survival was estimated at 46% for DM patients versus 45% in non-DM pts by Kaplan Meier method (p=0.79). Hazard ratio (stratification for matched pairs) was 1.05 (95% CI: 0.57 - 1.94; p=0.88). Three-year relapse-free survival was estimated at 34% for DM patients versus 43% for non-DM patients (p=0.58). Hazard ratio (stratification for matched pairs) = 1.10 (95% CI: 0.61–1.98; p=0.76). Conclusions: DM did not adversely impact survival in patients with leukemia. Leukemia and its treatment did not affect glycemic control. This should be reassuring to hematologists and endocrinologists who treat patients with leukemia and diabetes.

Abstract Background: We have previously examined the link between diabetes mellitus (DM) and malignancy with respect to mortality, but little is known about how the cancer affects glycemic control. Therefore, the aim of this study was to evaluate metabolic parameters in patients with cancer with and without DM. Methods: We selected 1404 patients with newly diagnosed prostate, breast, lung, colorectal, and pancreatic cancer from the institutional cancer registry from 2010–2015. Records were selected and matched 2:1 cancer without DM (n=936) and cancer and concurrent DM patients (n=468). Patients were matched by year of diagnosis, age, gender, and cancer type. Metabolic parameters during the year after cancer diagnosis were examined using linear mixed models with fixed effects for time, cancer type, DM status and individual-specific random effect allowing each patient to have a different intercept. Results: Prostate cancer accounted for the majority of cases (n=199), followed by lung cancer (n=91), breast cancer (n=71), colorectal cancer (n=54), and pancreas cancer (n=53). Mean overall hemoglobin A1c (HbA1c) was 6.9 (1.2)%. Pancreas cancer patients had higher HbA1c overall (p=0.02). There was no change in HbA1c one year post cancer diagnosis (p=0.28). Mean glucose in non-DM was 108(18) mg/dL and 146 (45) mg/dL in DM pts. Lung and pancreas cancer patients had higher glucose values overall (p<0.001). Time (p<0.001), DM (p<0.001) and time * DM (p=0.02) were significant in a mixed model. A decrease in glucose over the year was observed in DM patients across all cancer types. Conclusions: A diagnosis of malignancy does not worsen glycemic control within the first year of cancer diagnosis. This should be reassuring to endocrinologists and oncologists who treat patients with DM and cancer. Longer term studies, and analysis in larger and more diverse patients groups are needed.

612 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.

Problem definition: Organ-transplanted patients typically receive high amounts of immunosuppressive drugs (e.g., tacrolimus) as a mechanism to reduce their risk of organ rejection. However, because of the diabetogenic effect of these drugs, this practice exposes them to a greater risk of new-onset diabetes after transplantation (NODAT), and hence, becoming insulin dependent. We study and develop effective medication management strategies to address the common conundrum of balancing the risk of organ rejection versus that of NODAT. Academic/practical relevance: Our research contributes to the healthcare operations management literature by developing a robust stochastic decision-making framework that allows for incorporating (1) false-positive and false-negative errors of medical tests, (2) inevitable estimation errors when data sets are used, (3) variability among physician’ attitudes toward ambiguous outcomes, and (4) dynamic and patient risk-profile-dependent progression of health conditions. Methodology: We apply an ambiguous partially observable Markov decision process (APOMDP) approach where dynamic optimization with respect to a “cloud” of possible models allows us to make decisions that are robust to potential misspecifications of risks. Results: We first provide various structural results that facilitate characterizing the optimal medication policies. Utilizing a clinical data set, we then compare the performance of the optimal medication policies obtained from our APOMDP model with the policies currently used in the medical practice. We observe that, in one year after transplant, our proposed policies can improve the life expectancy of each patient up to 4.58%, while reducing the medical expenditures up to 11.57%. Managerial implications: Balancing the risks of organ rejection and diabetes complications and considering factors such as physicians’ attitudes toward ambiguous outcomes, partial observability of medical tests, and patient-specific risk factors are shown to result in more cost-effective strategies for management of post-transplant medications compared with the current medical practice. Finally, simultaneous management of medications can facilitate the care coordination process between transplantation/nephrology and endocrinology departments of a hospital that are typically in charge of administering such medications.

Abstract Objectives This article aims to evaluate adult type 1 diabetes mellitus (T1DM) self-management behaviors (SMBs) related to exercise and alcohol on a survey versus a smartphone app to compare self-reported and self-tracked SMBs, and examine inter- and intrapatient variability. Methods Adults with T1DM on insulin pump therapy were surveyed about their alcohol, meal, and exercise SMBs. For 4 weeks, participants self-tracked their alcohol, meal, and exercise events, and their SMBs corresponding with these events via an investigator-developed app. Descriptive statistics and generalized linear mixed-effect models were used to analyze the data Results Thirty-five participants self-tracked over 5,000 interactions using the app. Variability in how participants perceived the effects of exercise and alcohol on their blood glucose was observed. The congruity between SMBs self-reported on the survey and those self-tracked with the app was measured as mean (SD). The lowest congruity was for alcohol and exercise with 61.9% (22.7) and 66.4% (20.2), respectively. Congruity was higher for meals with 80.9% (21.0). There was significant daily intra- and interpatient variability in SMBs related to preprandial bolusing: recommended bolus, p < 0.05; own bolus choice, p < 0.01; and recommended basal adjustment, p < 0.01. Conclusion This study highlights the variability in intra- and interpatient SMBs obtained through the use of a survey and app. The outcomes of this study indicate that clinicians could use both one-time and every-day assessment tools to assess SMBs related to meals. For alcohol and exercise, further research is needed to understand the best assessment method for SMBs. Given this degree of patient variability, there is a need for an educational intervention that goes beyond the traditional “one-size-fits-all” approach of diabetes management to target individualized treatment barriers.

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.

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.