Hypoglycemia Incidence and Factors Associated in a Cohort of Patients With Type 2 Diabetes Hospitalized in General Ward Treated With Basal Bolus Insulin Regimen Assessed by Continuous Glucose Monitoring

Machine Learning and Deep Learning Techniques Applied to Diabetes Research: A Bibliometric Analysis

BACKGROUND

The use of machine learning and deep learning techniques in the research on diabetes has garnered attention in recent times. Nonetheless, few studies offer a thorough picture of the knowledge generation landscape in this field. To address this, a bibliometric analysis of scientific articles published from 2000 to 2022 was conducted to discover global research trends and networks and to emphasize the most prominent countries, institutions, journals, articles, and key topics in this domain.

METHODS

The Scopus database was used to identify and retrieve high-quality scientific documents. The results were classified into categories of detection (covering diagnosis, screening, identification, segmentation, among others), prediction (prognosis, forecasting, estimation), and management (treatment, control, monitoring, education, telemedicine integration). Biblioshiny and RStudio were used to analyze the data.

RESULTS

A total of 1773 articles were collected and analyzed. The number of publications and citations increased substantially since 2012, with a notable increase in the last 3 years. Of the 3 categories considered, detection was the most dominant, followed by prediction and management. Around 53.2% of the total journals started disseminating articles on this subject in 2020. China, India, and the United States were the most productive countries. Although no evidence of outstanding leadership by specific authors was found, the University of California emerged as the most influential institution for the development of scientific production.

CONCLUSION

This is an evolving field that has experienced a rapid increase in productivity, especially over the last years with exponential growth. This trend is expected to continue in the coming years.

Feasibility of Real-Time Continuous Glucose Monitoring Telemetry System in an Inpatient Diabetes Unit: A Pilot Study

BACKGROUND

Hospitalization of persons with diabetes in an inpatient diabetes unit is challenging, notably for patients having different profiles. We aimed to evaluate the feasibility and the benefit of a continuous glucose monitoring (CGM) telemetry system to control glucose excursions in hospitalized patients with diabetes, according to their diabetes type and the reasons for their hospitalization.

METHOD

A prospective pilot study was conducted in 53 insulin-requiring diabetes patients hospitalized in the general ward. Glucose was monitored using Guardian Connect (GC, Medtronic) to adopt insulin therapy. The time in range (TIR, target 70-180 mg/dL), the time below range (TBR), and the time above range (TAR) were recorded by GC between the start of hospitalization (SH) and end of hospitalization (EH), and analyzed according to the diabetes type (type 1 diabetes n = 28, type 2 diabetes n = 25) and the reasons for hospitalization (acute complications n = 35, therapeutic education n = 18). Patient and caregiver satisfaction was also assessed.

RESULTS

In patients with type 2 diabetes and those hospitalized for acute complications, TIR significantly increased between the SH and EH, from 75.7% (95%CI 48.5-84.6) to 82.2% (95%CI 63.2-91.8) P = 0.043 and from 58.3% (95%CI 46.3-69.7) to 66.4% (95%CI 55.6-75.5) P = 0.031, respectively, and TAR significantly decreased, with no change in TBR. In patients with diabetes hospitalized for therapeutic education, TBR significantly decreased from 3.4% (95%CI 0-9.4) to 0% (95%CI 0-3.8) P = 0.037. Finally, 94% of patients and caregivers deemed the GC system useful.

CONCLUSIONS

CGM telemetry system use is feasible and well accepted in patients hospitalized in diabetes care unit and could be useful to improve therapeutic education and metabolic control, especially for specific homogenous populations with diabetes.

Early Post-Renal Transplant Hyperglycemia

CONTEXT

Though posttransplant diabetes mellitus (PTDM, occurring > 45 days after transplantation) and its complications are well described, early post-renal transplant hyperglycemia (EPTH) (< 45 days) similarly puts kidney transplant recipients at risk of infections, rehospitalizations, and graft failure and is not emphasized much in the literature. Proactive screening and management of EPTH is required given these consequences.

OBJECTIVE

The aim of this article is to promote recognition of early post-renal transplant hyperglycemia, and to summarize available information on its pathophysiology, adverse effects, and management.

METHODS

A PubMed search was conducted for "early post-renal transplant hyperglycemia," "immediate posttransplant hyperglycemia," "post-renal transplant diabetes," "renal transplant," "diabetes," and combinations of these terms. EPTH is associated with significant complications including acute graft failure, rehospitalizations, cardiovascular events, PTDM, and infections.

CONCLUSION

Patients with diabetes experience better glycemic control in end-stage renal disease (ESRD), with resurgence of hyperglycemia after kidney transplant. Patients with and without known diabetes are at risk of EPTH. Risk factors include elevated pretransplant fasting glucose, diabetes, glucocorticoids, chronic infections, and posttransplant infections. We find that EPTH increases risk of re-hospitalizations from infections (cytomegalovirus, possibly COVID-19), acute graft rejections, cardiovascular events, and PTDM. It is essential, therefore, to provide diabetes education to patients before discharge. Insulin remains the standard of care while inpatient. Close follow-up after discharge is recommended for insulin adjustment. Some agents like dipeptidyl peptidase-4 inhibitors and glucagon-like peptide-1 receptor agonists have shown promise. The tenuous kidney function in the early posttransplant period and lack of data limit the use of sodium-glucose cotransporter 2 inhibitors. There is a need for studies assessing noninsulin agents for EPTH to decrease risk of hypoglycemia associated with insulin and long-term complications of EPTH.

Management of diabetes in elderly patients during the COVID-19 pandemic: current and future perspectives

Introduction: The COVID-19 pandemic has affected the entire population with the most deleterious effects in elders. Elders, especially those with diabetes, are at the highest risk of COVID-19 related adverse outcomes and mortality. This is usually linked to the comorbidities that accumulate with age, diabetes-related chronic inflammation, and the pandemic's psychosocial effects.Areas covered: We present some approaches to manage these complicated elderly patients with diabetes during the COVID-19 pandemic. In the inpatient setting, we suggest similar (pre-pandemic) glycemic targets and emphasize the importance of using IV insulin and possible use of continuous glucose monitoring to reduce exposure and PPE utilization. Outside the hospital, we recommend optimal glycemic control within the limits imposed by considerations of safety. We also describe the advantages and challenges of using various technological platforms in clinical care.Expert opinion: The COVID-19 pandemic has lifted the veil off serious deficiencies in the infrastructures for care at both the individual level and the population level and also highlighted some of the strengths, all of which affect individuals with diabetes and COVID-19. We anticipate that things will not return to 'normal' after the COVID-19 pandemic has run its course, but rather they will be superseded by 'New Normal.'

Implementation of Continuous Glucose Monitoring in the Hospital: Emergent Considerations for Remote Glucose Monitoring During the COVID-19 Pandemic

Continuous glucose monitoring (CGM) has become a widely used tool in the ambulatory setting for monitoring glucose levels, as well as detecting uncontrolled hyperglycemia, hypoglycemia, and glycemic variability. The accuracy of some CGM systems has recently improved to the point of manufacture with factory calibration and Food and Drug Administration clearance for nonadjunctive use to dose insulin. In this commentary, we analyze the answers to six questions about what is needed to bring CGM into the hospital as a reliable, safe, and effective tool. The evidence to date indicates that CGM offers promise as an effective tool for monitoring hospitalized patients. During the current coronavirus disease 2019 crisis, we hope to provide guidance to healthcare professionals, who are seeking to reduce exposure to SARS-Cov-2, as well as preserve invaluable personal protective equipment. In this commentary, we address who, what, where, when, why, and how CGM can be adopted for inpatient use.

Accuracy and stability of an arterial sensor for glucose monitoring in a porcine model using glucose clamp technique

Intravascular glucose sensors have the potential to improve and facilitate glycemic control in critically ill patients and might overcome measurement delay and accuracy issues. This study investigated the accuracy and stability of a biosensor for arterial glucose monitoring tested in a hypo- and hyperglycemic clamp experiment in pigs. 12 sensors were tested over 5 consecutive days in 6 different pigs. Samples of sensor and reference measurement pairs were obtained every 15 minutes. 1337 pairs of glucose values (range 37–458 mg/dl) were available for analysis. The systems met ISO 15197:2013 criteria in 99.2% in total, 100% for glucose <100 mg/dl (n = 414) and 98.8% for glucose ≥100 mg/dl (n = 923). The mean absolute relative difference (MARD) during the entire glycemic range of all sensors was 4.3%. The MARDs within the hypoglycemic (<70 mg/dl), euglycemic (≥70–180 mg/dl) and hyperglycemic glucose ranges (≥180 mg/dl) were 6.1%, 3.6% and 4.7%, respectively. Sensors indicated comparable performance on all days investigated (day 1, 3 and 5). None of the systems showed premature failures. In a porcine model, the performance of the biosensor revealed a promising performance. The transfer of these results into a human setting is the logical next step.

Linagliptin plus insulin for hyperglycemia immediately after renal transplantation: A comparative study

AIMS

Post-renal-transplanted patients frequently present hyperglycemia immediately after the procedure. The goal of this work was to evaluate the effect of linagliptin + insulin in post-renal-transplanted patients with hyperglycemia.

METHODS

Retrospective comparative study in post-renal transplanted patients with hyperglycemia after transplantation who were treated with linagliptin 5 mg daily plus insulin vs insulin alone for 5 days after renal transplantation with hyperglycemia. Main outcomes were glucose levels, insulin dose and severity of hypoglycemia.

RESULTS

There were 14 patients treated with linagliptin + insulin and 14 patients treated only with insulin. Glucose levels and insulin doses were lower in the linagliptin + insulin group in comparison with the insulin alone group, 131.0 ± 15.1 vs 191.1 ± 22.5 mg/dl (7.27 ± 0.84 vs 10.61 ± 1.25 mmol/l) and 37.5 ± 6.3 vs 24.2 ± 6.6 U, respectively (p < 0.05). Hypoglycemia was less severe in the linagliptin + insulin group, 65.1 ± 2.2 vs 54.2 ± 3.3 mg/dl (3.61 ± 0.12 vs 3.00 ± 3.3 ± 0.18 mmol/l), p 0.036.

CONCLUSIONS

The combination of linagliptin + insulin provided better glycemic control with a lower insulin dose and less severe hypoglycemia in comparison to insulin alone in patients with hyperglycemia immediately after renal transplantation.