Improving glycemic control in critically ill patients: personalized care to mimic the endocrine pancreas

Virtual patient framework for the testing of mechanical ventilation airway pressure and flow settings protocol

BACKGROUND AND OBJECTIVE

Model-based and personalised decision support systems are emerging to guide mechanical ventilation (MV) treatment for respiratory failure patients. However, model-based treatments require resource-intensive clinical trials prior to implementation. This research presents a framework for generating virtual patients for testing model-based decision support, and direct use in MV treatment.

METHODS

The virtual MV patient framework consists of 3 stages: 1) Virtual patient generation, 2) Patient-level validation, and 3) Virtual clinical trials. The virtual patients are generated from retrospective MV patient data using a clinically validated respiratory mechanics model whose respiratory parameters (respiratory elastance and resistance) capture patient-specific pulmonary conditions and responses to MV care over time. Patient-level validation compares the predicted responses from the virtual patient to their retrospective results for clinically implemented MV settings and changes to care. Patient-level validated virtual patients create a platform to conduct virtual trials, where the safety of closed-loop model-based protocols can be evaluated.

RESULTS

This research creates and presents a virtual patient platform of 100 virtual patients generated from retrospective data. Patient-level validation reported median errors of 3.26% for volume-control and 6.80% for pressure-control ventilation mode. A virtual trial on a model-based protocol demonstrates the potential efficacy of using virtual patients for prospective evaluation and testing of the protocol.

CONCLUSION

The virtual patient framework shows the potential to safely and rapidly design, develop, and optimise new model-based MV decision support systems and protocols using clinically validated models and computer simulation, which could ultimately improve patient care and outcomes in MV.

Estimating Enhanced Endogenous Glucose Production in Intensive Care Unit Patients with Severe Insulin Resistance

BACKGROUND

Critically ill ICU patients frequently experience acute insulin resistance and increased endogenous glucose production, manifesting as stress-induced hyperglycemia and hyperinsulinemia. STAR (Stochastic TARgeted) is a glycemic control protocol, which directly manages inter- and intra- patient variability using model-based insulin sensitivity (SI). The model behind STAR assumes a population constant for endogenous glucose production (EGP), which is not otherwise identifiable.

OBJECTIVE

This study analyses the effect of estimating EGP for ICU patients with very low SI (severe insulin resistance) and its impact on identified, model-based insulin sensitivity identification, modeling accuracy, and model-based glycemic clinical control.

METHODS

Using clinical data from 717 STAR patients in 3 independent cohorts (Hungary, New Zealand, and Malaysia), insulin sensitivity, time of insulin resistance, and EGP values are analyzed. A method is presented to estimate EGP in the presence of non-physiologically low SI. Performance is assessed via model accuracy.

RESULTS

Results show 22%-62% of patients experience 1+ episodes of severe insulin resistance, representing 0.87%-9.00% of hours. Episodes primarily occur in the first 24 h, matching clinical expectations. The Malaysian cohort is most affected. In this subset of hours, constant model-based EGP values can bias identified SI and increase blood glucose (BG) fitting error. Using the EGP estimation method presented in these constrained hours significantly reduced BG fitting errors.

CONCLUSIONS

Patients early in ICU stay may have significantly increased EGP. Increasing modeled EGP in model-based glycemic control can improve control accuracy in these hours. The results provide new insight into the frequency and level of significantly increased EGP in critical illness.

Practice of hyperglycaemia control in intensive care units of the Military Hospital, Sudan—Needs of a protocol

Hyperglycaemia is a major risk factor in critically ill patients leading to adverse outcomes and mortality in diabetic and non-diabetic patients. The target blood glucose remained controversial; this study aimed to contribute in assessing the practice of hyperglycaemia control in intensive care units of the Military Hospital. Furthermore, the study proposed a protocol for hyperglycaemia control based on findings. A hospital-based cross-sectional study assessed the awareness and practice towards hyperglycaemia management in a sample 83 healthcare staff selected through stratified random sampling technique. In addition, 55 patients were enrolled, through quota sampling, after excluding those with diabetic ketoacidosis, hyperosmolar-hyperglycaemic state and patients < 18 years. A self-administrated questionnaire enabled to collect data from health staff and patient data were extracted from the medical records. SPSS-23 was used to analyze the collected data. Chi-square and ANOVA tests assessed the association among variables, these tests were considered statistically significant when p ≤ 0.05. The training on hyperglycaemia control differed (p = 0.017) between doctors and nurses. The target glycaemic level (140–180 mg/dl) was known by 11.1% of the study participants. Neither the knowledge nor the practice of hyperglycaemia control methods differed among staff (p> 0.05). The use of sliding scale was prevalent (79.3%) across the ICUs (p = 0.002). 31.5% of the patients had received different glycaemic control methods, 11.8% were in the targeted blood glucose level. Sliding scale was the method used by doctors and nurses (71.4% and 81.6% respectively). Lack of awareness about hyperglycaemia management methods was prevalent among ICU healthcare staff. Use of obsolete methods was the common practice in the ICUS of the Military Hospital. Target blood glucose for patients were unmet. Development of a local protocol for glycaemic control in all ICUs is needed along with sustained training programs on hyperglycaemia control for ICU healthcare staff.

The Effect of a Liberal Approach to Glucose Control in Critically Ill Patients with Type 2 Diabetes: A multicenter, parallel-group, open-label, randomized clinical trial

Rationale Blood glucose concentrations affect outcomes in critically ill patients but the optimal target blood glucose range in those with type 2 diabetes is unknown. Objective To evaluate the effects of a 'liberal' approach to targeted blood glucose range during intensive care unit (ICU) admission. Methods This mutlicenter, parallel-group, open-label, randomized clinical trial included 419 adult patients with type 2 diabetes expected to be in the ICU on at least three consecutive days. In the intervention group intravenous insulin was commenced at a blood glucose >252 mg/dL and titrated to a target range of 180 to 252 mg/dL. In the comparator group insulin was commenced at a blood glucose >180 mg/dL and titrated to a target range of 108 to 180 mg/dL. The primary outcome was incident hypoglycemia (<72 mg/dL). Secondary outcomes included glucose metrics and clinical outcomes. Main Results At least one episode of hypoglycemia occurred in 10 of 210 (5%) patients assigned the intervention and 38 of 209 (18%) patients assigned the comparator (incident rate ratio: 0.21 (95% CI, 0.09 to 0.49); P<0.001). Those assigned the intervention had greater blood glucose concentrations (daily mean, minimum, maximum), less glucose variability and less relative hypoglycaemia (P<0.001 for all comparisons). By day 90, 62 of 210 (29.5%) in the intervention and 52 of 209 (24.9%) in the comparator group had died (absolute difference 4.6 percentage points (95%CI, -3.9 to 13.2%); P=0.29). Conclusions A liberal approach to blood glucose targets reduced incident hypoglycemia but did not improve patient-centered outcomes. Clinical trial registration available at www.anzctr.org.au, ID: ACTRN12616001135404.

Bolus versus continuous insulin infusion in immediate postoperative blood glucose control in liver transplantation: pragmatic clinical trial

Objective:

To analyze the effectiveness and safety of two insulin therapy techniques (continuous and intermittent infusion) in the blood glucose control of people who have undergone liver transplantation, in the immediate postoperative period.

Methods:

The study was a prospective, open, pragmatic clinical trial with 42 participants, divided into two groups of 21 patients each, in the immediate postoperative period following liver transplantation. Participants in the Experimental Group and Control Group received continuous infusion and bolus insulin, respectively, starting at capillary blood glucose ≥150mg/dL.

Results:

There were no statistically significant differences in the blood glucose reduction time to reach the target range between the Experimental Group and Control Group in the transplanted patients (p=0.919). No statistically significant differences regarding the presence of low blood glucose (p=0.500) and in the initial blood glucose value (p=0.345) were found. The study identified the final blood glucose value in postoperative intensive care unit lower and statistically significant in the continuous infusion pump group in relation to the Bolus Group (p<0.001). Additionally, the variation of blood glucose reduction was higher and statistically significant in the continuous method group (p<0.05).

Conclusion:

The continuous infusion method was more effective in the blood glucose control of patients in the postoperative period following liver transplantation.

Brazilian Registry of Clinical Trials:

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Association between dysglycemia and mortality by diabetes status and risk factors of dysglycemia in critically ill patients: a retrospective study

AIMS

Dysglycemia, including the three domains hyperglycemia, hypoglycemia, and increased glycemic variability (GV), is associated with high mortality among critically ill patients. However, this association differs by diabetes status, and reports in this regard are limited. This study aimed to evaluate the associations between the three dysglycemia domains and mortality in critically ill patients by diabetes status and determined the contributing factors for dysglycemia.

METHODS

This retrospective study included 958 critically ill patients (admitted to the ICU) with or without DM. Dysglycemia was defined as abnormality of any of the three dimensions. We evaluated the effects of the three domains of glucose control on mortality using binary logistic regression and then adjusted for confounders. The associations between dysglycemia and other variables were investigated using cumulative logistic regression analysis.

RESULT

GV independently and similarly affected mortality in both groups after adjustment for confounders (DM: odds ratio [OR], 1.05; 95% confidence interval [CI]: 1.03-1.08; p <0.001; non-DM: OR, 1.07; 95% CI, 1.03-1.11; p = 0.002). Hypoglycemia was strongly associated with ICU mortality among patients without DM (3.12; 1.76-5.53; p <0.001) and less so among those with DM (1.18; 0.49-2.83; p = 0.72). Hyperglycemia was non-significantly associated with mortality in both groups. However, the effects of dysglycemia seemed cumulative. The factors contributing to dysglycemia included disease severity, insulin treatment, glucocorticoid use, serum albumin level, total parenteral nutrition, duration of diabetes, elevated procalcitonin level, and need for mechanical ventilation and renal replacement therapy.

CONCLUSION

The association between the three dimensions of dysglycemia and mortality varied by diabetes status. Dysglycemia in critical patients is associated with excess mortality; however, glucose management in patients should be specific to the patient's need considering the diabetes status and broader dimensions. The identified factors for dysglycemia could be used for risk assessment in glucose management requirement in critically ill patients, which may improve clinical outcomes.

Two Decades of Research in Artificial Pancreas: Insights from a Bibliometric Analysis

BACKGROUND

Artificial pancreas is a well-known research topic devoted to achieving better glycemic outcomes that has been attracting increasing attention over the years. However, there is a lack of systematic, chronological, and synthesizing studies that show the background of the knowledge generation in this field. This study implements a bibliometric analysis to recognize the main documents, type of publications, research categories, countries, keywords, organizations, and authors related to this topic.

METHODS

Web of Science core collection database was accessed from 2000 to 2020 in order to select high-quality scientific documents based on a specific search query. Bibexcel, MS Excel, Power BI, R-Studio, VOSviewer, and CorText software were used for a descriptive and network analysis based on the local database obtained. Bibliometric parameters as the h-index, frequencies, co-authorship and co-ocurrences were computed.

RESULTS

A total of 756 documents were included that show a growing scientific production on this topic with an increasing contribution from engineering. Outstanding authors, organizations, and countries were identified. An analysis of trends in research was conducted according to the scientific categories of the Web of Science database to identify the main research interests of the last 2 decades and the emerging areas with greater prominence in the coming years. A keyword network analysis allowed to identify the main stages in the development of the AP research over time.

CONCLUSIONS

Results reveal a comprehensive background of the knowledge generation for the AP topic during the last 2 decades, which has been strengthened with international collaborations and a remarkable interdisciplinarity between endocrinology and engineering, giving rise to a growing number of research areas over time, where computer science and medical informatics stand out as the main emerging research areas.

Preadmission Insulin-Treated Type 2 Diabetes Mellitus Patients Had Increased Mortality in Intensive Care Units

AIM

To explore the clinical outcomes among preadmission insulin-treated type 2 diabetes mellitus (T2DM) in intensive care units (ICU).

PATIENTS AND METHODS

In this retrospective observational study, 578 T2DM patients admitted to ICU were recruited from March 2011 to February 2021, which were composed of 528 patients treated with insulin after ICU admission (including 300 preadmission non-insulin-treated and 228 preadmission insulin-treated patients) and 50 patients treated without insulin before and after ICU admission. Clinical outcomes were compared between the groups. Variables of age (± 10 years), gender, blood glucose >10 mmol/l on ICU admission, and original comorbidities were used for matching to get the 1:1 matched cohort. The Kaplan-Meier survival curves were graphed to describe the survival trend and Cox regression analysis was performed to get adjusted hazard ratio (HR).

RESULTS

Compared with the preadmission non-insulin-treated T2DM patients, preadmission insulin-treated T2DM patients had higher incidence of hypoglycemia [14.5% (33/228) vs 8.7% (26/300); p = 0.036]. In the 1:1 matched cohort, the preadmission insulin-treated T2DM patients had significantly increased mortality rate [30.0% (45/150) vs (16.0% (24/150)); adjusted HR, 1.68 (1.01-2.80)] than preadmission non-insulin-treated T2DM patients. Compared with T2DM patients treated without insulin before and after ICU admission, preadmission insulin-treated T2DM patients had higher mortality and longer length of ICU stay (all p < 0.05).

CONCLUSION

Preadmission insulin treatment was associated with increased mortality rate and longer length of ICU stay among T2DM patients in ICU. Preadmission insulin-treated T2DM patients might have worse clinical outcomes when they are critically ill.

The goldilocks problem: Nutrition and its impact on glycaemic control

BACKGROUND

Glucose intolerance and insulin resistance manifest as hyperglycaemia in intensive care, which is associated with mortality and morbidities. Glycaemic control (GC) may improve outcomes, though safe and effective control has proven elusive. Nutritional glucose intake affects blood glucose (BG) outcomes, but few protocols actively control it. This study aims to examine BG outcomes in the context of nutritional management during GC.

METHODS

Retrospective cohort analysis of 5 glycaemic control cohorts spanning 4 years (n = 273) from Christchurch Hospital Intensive Care Unit (ICU). GC is delivered using a single model-based protocol (STAR), with default 4.4-8.0 mmol/L target range via. modulation of insulin and nutrition. Clinical adaptations/cohorts include variations on upper target (UL-9 with 9.0 mmol/L, reducing workload and nutrition responsiveness), and insulin only (IO) with clinically set nutrition at 3 glucose concentrations (71 g/L vs. 120 and 180 g/L in the TARGET study).

RESULTS

Percent of BG hours in the 4.4-8.0 mmol/L range highest under standard STAR conditions (78%), and was lower at 64% under UL-9, likely due to reduced time-responsiveness of nutrition-insulin changes. By comparison, IO only resulted in 64-69% BG in range across different nutrition types. A subset of patients receiving high glucose nutrition under IO were persistently hyperglycaemic, indicating patient-specific glucose tolerance.

CONCLUSION

STAR GC is most effective when nutrition and insulin are modulated together with timely responsiveness to persistent hyperglycaemia. Results imply modulation of nutrition alongside insulin improves GC, particularly in patients with persistent hyperglycaemia/low glucose tolerance.

Virtual patients for mechanical ventilation in the intensive care unit

BACKGROUND

Mechanical ventilation (MV) is a core intensive care unit (ICU) therapy. Significant inter- and intra- patient variability in lung mechanics and condition makes managing MV difficult. Accurate prediction of patient-specific response to changes in MV settings would enable optimised, personalised, and more productive care, improving outcomes and reducing cost. This study develops a generalised digital clone model, or in-silico virtual patient, to accurately predict lung mechanics in response to changes in MV.

METHODS

An identifiable, nonlinear hysteresis loop model (HLM) captures patient-specific lung dynamics identified from measured ventilator data. Identification and creation of the virtual patient model is fully automated using the hysteresis loop analysis (HLA) method to identify lung elastances from clinical data. Performance is evaluated using clinical data from 18 volume-control (VC) and 14 pressure-control (PC) ventilated patients who underwent step-wise recruitment maneuvers.

RESULTS

Patient-specific virtual patient models accurately predict lung response for changes in PEEP up to 12 cmH₂O for both volume and pressure control cohorts. R² values for predicting peak inspiration pressure (PIP) and additional retained lung volume, V_frc in VC, are R²=0.86 and R²=0.90 for 106 predictions over 18 patients. For 14 PC patients and 84 predictions, predicting peak inspiratory volume (PIV) and V_frc yield R²=0.86 and R²=0.83. Absolute PIP, PIV and V_frc errors are relatively small.

CONCLUSIONS

Overall results validate the accuracy and versatility of the virtual patient model for capturing and predicting nonlinear changes in patient-specific lung mechanics. Accurate response prediction enables mechanically and physiologically relevant virtual patients to guide personalised and optimised MV therapy.

Insulin sensitivity in critically ill patients: are women more insulin resistant?

Background

Glycaemic control (GC) in intensive care unit is challenging due to significant inter- and intra-patient variability, leading to increased risk of hypoglycaemia. Recent work showed higher insulin resistance in female preterm neonates. This study aims to determine if there are differences in inter- and intra-patient metabolic variability between sexes in adults, to gain in insight into any differences in metabolic response to injury. Any significant difference would suggest GC and randomised trial design should consider sex differences to personalise care.

Methods

Insulin sensitivity (SI) levels and variability are identified from retrospective clinical data for men and women. Data are divided using 6-h blocks to capture metabolic evolution over time. In total, 91 male and 54 female patient GC episodes of minimum 24 h are analysed. Hypothesis testing is used to determine whether differences are significant (P < 0.05), and equivalence testing is used to assess whether these differences can be considered equivalent at a clinical level. Data are assessed for the raw cohort and in 100 Monte Carlo simulations analyses where the number of men and women are equal.

Results

Demographic data between females and males were all similar, including GC outcomes (safety from hypoglycaemia and high (> 50%) time in target band). Females had consistently significantly lower SI levels than males, and this difference was not clinically equivalent. However, metabolic variability between sexes was never significantly different and always clinically equivalent. Thus, inter-patient variability was significantly different between males and females, but intra-patient variability was equivalent.

Conclusion

Given equivalent intra-patient variability and significantly greater insulin resistance, females can receive the same benefit from safe, effective GC as males, but may require higher insulin doses to achieve the same glycaemia. Clinical trials should consider sex differences in protocol design and outcome analyses.

Insulin sensitivity in critically ill patients: are women more insulin resistant?

BACKGROUND

Glycaemic control (GC) in intensive care unit is challenging due to significant inter- and intra-patient variability, leading to increased risk of hypoglycaemia. Recent work showed higher insulin resistance in female preterm neonates. This study aims to determine if there are differences in inter- and intra-patient metabolic variability between sexes in adults, to gain in insight into any differences in metabolic response to injury. Any significant difference would suggest GC and randomised trial design should consider sex differences to personalise care.

METHODS

Insulin sensitivity (SI) levels and variability are identified from retrospective clinical data for men and women. Data are divided using 6-h blocks to capture metabolic evolution over time. In total, 91 male and 54 female patient GC episodes of minimum 24 h are analysed. Hypothesis testing is used to determine whether differences are significant (P < 0.05), and equivalence testing is used to assess whether these differences can be considered equivalent at a clinical level. Data are assessed for the raw cohort and in 100 Monte Carlo simulations analyses where the number of men and women are equal.

RESULTS

Demographic data between females and males were all similar, including GC outcomes (safety from hypoglycaemia and high (> 50%) time in target band). Females had consistently significantly lower SI levels than males, and this difference was not clinically equivalent. However, metabolic variability between sexes was never significantly different and always clinically equivalent. Thus, inter-patient variability was significantly different between males and females, but intra-patient variability was equivalent.

CONCLUSION

Given equivalent intra-patient variability and significantly greater insulin resistance, females can receive the same benefit from safe, effective GC as males, but may require higher insulin doses to achieve the same glycaemia. Clinical trials should consider sex differences in protocol design and outcome analyses.

Stochastic Modelling of Respiratory System Elastance for Mechanically Ventilated Respiratory Failure Patients

While lung protective mechanical ventilation (MV) guidelines have been developed to avoid ventilator-induced lung injury (VILI), a one-size-fits-all approach cannot benefit every individual patient. Hence, there is significant need for the ability to provide patient-specific MV settings to ensure safety, and optimise patient care. Model-based approaches enable patient-specific care by identifying time-varying patient-specific parameters, such as respiratory elastance, E_rs, to capture inter- and intra-patient variability. However, patient-specific parameters evolve with time, as a function of disease progression and patient condition, making predicting their future values crucial for recommending patient-specific MV settings. This study employs stochastic modelling to predict future E_rs values using retrospective patient data to develop and validate a model indicating future intra-patient variability of E_rs. Cross validation results show stochastic modelling can predict future elastance ranges with 92.59 and 68.56% of predicted values within the 5-95% and the 25-75% range, respectively. This range can be used to ensure patients receive adequate minute ventilation should elastance rise and minimise the risk of VILI should elastance fall. The results show the potential for model-based protocols using stochastic model prediction of future E_rs values to provide safe and patient-specific MV. These results warrant further investigation to validate its clinical utility.

Mean amplitude of glycemic excursions in septic patients and its association with outcomes: A prospective observational study using continuous glucose monitoring

PURPOSE

To apply continuous glucose monitoring (CGM) and determine the mean amplitude of glycemic excursions (MAGE) in septic patients and to assess the associations of MAGE with outcomes and oxidative stress.

MATERIALS AND METHODS

This study was conducted in adult septic patients expected to require intensive care for >48 h. We continuously measured blood glucose level for the first 48 h in the ICU using FreeStyle Libre®. MAGE was calculated using glycemic information obtained by CGM during the study period of 48 h. The primary outcome was 90-day all-cause mortality. The secondary outcomes were 90-day ICU-free days and the concentration of urinary 8-isoprostaglandinF2α measured 48 h after commencement of the study as a surrogate of oxidative stress.

RESULTS

Forty patients were included in this study. Median of MAGE was higher in non-survivors than in survivors: 68.8 (IQR;39.7-97.2) vs. 39.3 (IQR;19.9-53.3), p = 0.02. In multivariate analysis, MAGE was independently associated with 90-day all-cause mortality rate (p = 0.02), urinary 8-isoprostaglandinF2α level (p = 0.03) and 90-day ICU-free survival days (p = 0.03).

CONCLUSIONS

In the current study, MAGE for the first 48 h of treatment that was obtained by using CGM was associated with 90-day all-cause mortality, 90-day ICU-free days and urinary 8-isoprostaglandinF2α level in septic patients.

Practice of Hyperglycaemia Control in Intensive Care Units of the Military Hospital, Sudan – Needs of a Protocol.. [DOI: 10.1101/2020.08.17.20176453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Hyperglycaemia is a major risk factor in critically ill patients as it leads to adverse outcomes and mortality in diabetic and non-diabetic patients. The target blood glucose remained controversial; this study aimed to contribute in assessing the practice of hyperglycaemia control in intensive care units of Khartoum Military Hospital. Furthermore, it proposed a protocol for hyperglycaemia control based on findings. A hospital-based cross-sectional study assessed the awareness and practice towards hyperglycaemia management in a sample of 83 healthcare staff selected through stratified random sampling technique. In addition, 55 patients were enrolled, through quota sampling, after excluding those with diabetic ketoacidosis, hyperosmolar-hyperglycaemic state and patients < 18 years. A self-administrated questionnaire enabled to collect data from healthcare staff, patients data were extracted from medical records. SPSS 23 was used to analyse the collected data. Chi-square and ANOVA tests assessed the association among variables. All statistical tests were considered statistically significant when p < 0.05. The training on hyperglycaemia control differed statistically (p = 0.017) among healthcare staff. The target glycaemic level (140-180 mg/dl) was knew by 11.1% of the study participants. Neither the knowledge nor the practice of hyperglycaemia control methods differed among staff (p> 0.05). The use of sliding scale was 79.3% across the ICUs with a statistically significant difference (p = 0.002). 31.5% of patients had received glycaemic control based on different methods and 11.8% were in the targeted blood glucose level. Sliding scale was the prevalent method used by doctors (71.4%) and nurses (81.6%). A patient benefited from insulin infusion method, which achieved the NICE-SUGAR target. The poor knowledge and lack of awareness towards hyperglycaemia monitoring led to inappropriate implementation of glycaemia control methods across the Military Hospital ICUs. Sustained training programs on hyperglycaemia control to ICU staff and the availability of a protocol on glycaemia control are highly required.

Issues for the management of people with diabetes and COVID-19 in ICU

In the pandemic “Corona Virus Disease 2019” (COVID-19) people with diabetes have a high risk to require ICU admission. The management of diabetes in Intensive Care Unit is always challenging, however, when diabetes is present in COVID-19 the situation seems even more complicated. An optimal glycemic control, avoiding acute hyperglycemia, hypoglycemia and glycemic variability may significantly improve the outcome. In this case, intravenous insulin infusion with continuous glucose monitoring should be the choice. No evidence suggests stopping angiotensin-converting-enzyme inhibitors, angiotensin-renin-blockers or statins, even it has been suggested that they may increase the expression of Angiotensin-Converting-Enzyme-2 (ACE2) receptor, which is used by “Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to penetrate into the cells. A real issue is the usefulness of several biomarkers, which have been suggested to be measured during the COVID-19. N-Terminal-pro-Brain Natriuretic-Peptide, D-dimer and hs-Troponin are often increased in diabetes. Their meaning in the case of diabetes and COVID-19 should be therefore very carefully evaluated. Even though we understand that in such a critical situation some of these requests are not so easy to implement, we believe that the best possible action to prevent a worse outcome is essential in any medical act.

Assessment of Glycemic Control Protocol (STAR) Through Compliance Analysis Amongst Malaysian ICU Patients

Purpose

This paper presents an assessment of an automated and personalized stochastic targeted (STAR) glycemic control protocol compliance in Malaysian intensive care unit (ICU) patients to ensure an optimized usage.

Patients and Methods

STAR proposes 1–3 hours treatment based on individual insulin sensitivity variation and history of blood glucose, insulin, and nutrition. A total of 136 patients recorded data from STAR pilot trial in Malaysia (2017–quarter of 2019*) were used in the study to identify the gap between chosen administered insulin and nutrition intervention as recommended by STAR, and the real intervention performed.

Results

The results show the percentage of insulin compliance increased from 2017 to first quarter of 2019* and fluctuated in feed administrations. Overall compliance amounted to 98.8% and 97.7% for administered insulin and feed, respectively. There was higher average of 17 blood glucose measurements per day than in other centres that have been using STAR, but longer intervals were selected when recommended. Control safety and performance were similar for all periods showing no obvious correlation to compliance.

Conclusion

The results indicate that STAR, an automated model-based protocol is positively accepted among the Malaysian ICU clinicians to automate glycemic control and the usage can be extended to other hospitals already. Performance could be improved with several propositions.

Issues of Cardiovascular Risk Management in People With Diabetes in the COVID-19 Era

People with diabetes compared with people without exhibit worse prognosis if affected by coronavirus disease 2019 (COVID-19) induced by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), particularly when compromising metabolic control and concomitant cardiovascular disorders are present. This Perspective seeks to explore newly occurring cardio-renal-pulmonary organ damage induced or aggravated by the disease process of COVID-19 and its implications for the cardiovascular risk management of people with diabetes, especially taking into account potential interactions with mechanisms of cellular intrusion of SARS-CoV-2. Severe infection with SARS-CoV-2 can precipitate myocardial infarction, myocarditis, heart failure, and arrhythmias as well as an acute respiratory distress syndrome and renal failure. They may evolve along with multiorgan failure directly due to SARS-CoV-2-infected endothelial cells and resulting endotheliitis. This complex pathology may bear challenges for the use of most diabetes medications in terms of emerging contraindications that need close monitoring of all people with diabetes diagnosed with SARS-CoV-2 infection. Whenever possible, continuous glucose monitoring should be implemented to ensure stable metabolic compensation. Patients in the intensive care unit requiring therapy for glycemic control should be handled solely by intravenous insulin using exact dosing with a perfusion device. Although not only ACE inhibitors and angiotensin 2 receptor blockers but also SGLT2 inhibitors, GLP-1 receptor agonists, pioglitazone, and probably insulin seem to increase the number of ACE2 receptors on the cells utilized by SARS-CoV-2 for penetration, no evidence presently exists that shows this might be harmful in terms of acquiring or worsening COVID-19. In conclusion, COVID-19 and related cardio-renal-pulmonary damage can profoundly affect cardiovascular risk management of people with diabetes.

Accuracy of Blood Glucose Measurement and Blood Glucose Targets

BACKGROUND

To summarize new evidence regarding the methodological aspects of blood glucose control in the intensive care unit (ICU).

METHODS

We reviewed the literature on blood glucose control in the ICU up to August 2019 through Ovid Medline and Pubmed.

RESULTS

Since the publication of the Leuven studies, the benefits of glycemic control have been recognized. However, the methodology of blood glucose control, notably the blood glucose measurement accuracy and the insulin titration protocol, plays an important but underestimated role. This may partially explain the negative results of the large, pragmatic multicenter trials and made everyone realize that tight glycemic control with less-frequent glucose measurements on less accurate blood glucose meters is neither feasible nor advisable in daily practice. Blood gas analyzers remain the gold standard. New generation point-of-care blood glucose meters may be an alternative when using whole blood of critically ill patients in combination with a clinically validated insulin dosing algorithm.

CONCLUSION

When implementing blood glucose management in an ICU one needs to take into account the interaction between aimed glycemic target and blood glucose measurement methodology.

Risk and reward: extending stochastic glycaemic control intervals to reduce workload

Background

STAR is a model-based, personalised, risk-based dosing approach for glycaemic control (GC) in critically ill patients. STAR provides safe, effective control to nearly all patients, using 1–3 hourly measurement and intervention intervals. However, the average 11–12 measurements per day required can be a clinical burden in many intensive care units. This study aims to significantly reduce workload by extending STAR 1–3 hourly intervals to 1 to 4-, 5-, and 6-hourly intervals, and evaluate the impact of these longer intervals on GC safety and efficacy, using validated in silico virtual patients and trials methods. A Standard STAR approach was used which allowed more hyperglycaemia over extended intervals, and a STAR Upper Limit Controlled approach limited nutrition to mitigate hyperglycaemia over longer intervention intervals.

Results

Extending STAR from 1–3 hourly to 1–6 hourly provided high safety and efficacy for nearly all patients in both approaches. For STAR Standard, virtual trial results showed lower % blood glucose (BG) in the safe 4.4–8.0 mmol/L target band (from 83 to 80%) as treatment intervals increased. Longer intervals resulted in increased risks of hyper- (15% to 18% BG > 8.0 mmol/L) and hypo- (2.1% to 2.8% of patients with min. BG < 2.2 mmol/L) glycaemia. These results were achieved with slightly reduced insulin (3.2 [2.0 5.0] to 2.5 [1.5 3.0] U/h) and nutrition (100 [85 100] to 90 [75 100] % goal feed) rates, but most importantly, with significantly reduced workload (12 to 8 measurements per day). The STAR Upper Limit Controlled approach mitigated hyperglycaemia and had lower insulin and significantly lower nutrition administration rates.

Conclusions

The modest increased risk of hyper- and hypo-glycaemia, and the reduction in nutrition delivery associated with longer treatment intervals represent a significant risk and reward trade-off in GC. However, STAR still provided highly safe, effective control for nearly all patients regardless of treatment intervals and approach, showing this unique risk-based dosing approach, modulating both insulin and nutrition, to be robust in its design. Clinical pilot trials using STAR with different measurement timeframes should be undertaken to confirm these results clinically.

STAR-Liège Clinical Trial Interim Results: Safe and Effective Glycemic Control for All

While the benefits of glycemic control for critically ill patients are increasingly demonstrated, the ability to deliver safe, effective control to intermediate target ranges is widely debated due to the increased risk of hypoglycemia. This study analyzes interim clinical trial results of the fully computerized model-based Stochastic TARgeted (STAR) glycemic control framework at the University Hospital of Liège, Belgium. Patients with dysglycemia were randomly assigned to the full version of STAR, modulating both insulin and nutrition inputs, or STAR-IO, an insulin only version of STAR. Both arms target the normoglycemic 80-145 mg/dL (4.4-8.0 mmol/L) band. Results are further compared to retrospective data from 20 patients under the standard unit protocol targeting a higher 100-150 mg/dL (5.6-8.3 mmol/L) band. Much higher time in target band is provided under the full version of STAR, with similar safety and significantly lower incidence of mild hyperglycemia (blood glucose > 145 mg/dL or 8.0 mmol/L) and severe hyperglycemia (blood glucose > 180 mg/dL or 10.0 mmol/L). As a result, lower median blood glucose levels are safely and consistently achieved with lower glycemic variability, suggesting STAR's potential to improve clinical outcomes. These interim results show the possibility to achieve safe, effective control for all patients using STAR, and suggest glycemic control to lower targets could be beneficial.

Association Between Blood Glucose Within 24 Hours After Intensive Care Unit Admission and Prognosis: A Retrospective Cohort Study

PURPOSE

The aim of this study was to evaluate the association between blood glucose within 24 hours after intensive care unit (ICU) admission and prognosis.

PATIENTS AND METHODS

A retrospective cohort study was conducted using data from a large critical care database. Patients who had a length of ICU stay ≥24 hours and at least two blood glucose records within 24 hours after ICU admission were included and hospital mortality was chosen as the primary outcome. The average, minimum, and maximum blood glucose within 24 hours after ICU admission were a priori selected as exposures and associations between each exposure and outcomes were assessed after adjusted for potential confounders.

RESULTS

A total of 14,237 patients were included finally with an average age of 62.9±17.7 years and a mean SAPS II on admission of 34 (26-44). Among the study population, 20.2% (2872/14,237) had uncomplicated diabetes, and 6.7% (953/14,237) had complicated diabetes. Lowest hospital mortality rate was observed in the stratum with an average blood glucose ranged 110-140 mg/dL, a minimum blood glucose ranged 80-110 mg/dL, and a maximum blood glucose ranged 110-140 mg/dL. After adjusted for confounders including age, sex, disease severity scores and comorbidities, an average blood glucose ranged 110-140 mg/dL, a minimum blood glucose ranged 80-110 mg/dL, and a maximum blood glucose ranged 110-140 mg/dL were associated with the lowest risk of hospital mortality. Consistent results were found among patients without diabetes in the subgroup analyses stratified by diabetes.

CONCLUSION

A range of 110-140 mg/dL for average and maximum blood glucose and a range of 80-110 mg/dL for minimum blood glucose within 24 hours after ICU admission predicted better prognosis especially among patients without diabetes.

A Century of Diabetes Technology: Signals, Models, and Artificial Pancreas Control

Arguably, diabetes mellitus is one of the best-quantified human conditions: elaborate in silico models describe the action of the human metabolic system; real-time signals such as continuous glucose monitoring are readily available; insulin delivery is being automated; and control algorithms are capable of optimizing blood glucose fluctuation in patients' natural environments. The transition of the artificial pancreas (AP) to everyday clinical use is happening now, and is contingent upon seamless concerted work of devices encompassing the patient in a digital treatment ecosystem. This review recounts briefly the story of diabetes technology, which began a century ago with the discovery of insulin, progressed through glucose monitoring and subcutaneous insulin delivery, and is now rapidly advancing towards fully automated clinically viable AP systems.

Glucose control in the ICU

PURPOSE OF REVIEW

Critically ill patients usually develop hyperglycemia, which is associated with adverse outcome. Controversy exists whether the relationship is causal or not. This review summarizes recent evidence regarding glucose control in the ICU.

RECENT FINDINGS

Despite promising effects of tight glucose control in pioneer randomized controlled trials, the benefit has not been confirmed in subsequent multicenter studies and one trial found potential harm. This discrepancy could be explained by methodological differences between the trials rather than by a different case mix. Strategies to improve the efficacy and safety of tight glucose control have been developed, including the use of computerized treatment algorithms.

SUMMARY

The ideal blood glucose target remains unclear and may depend on the context. As compared with tolerating severe hyperglycemia, tight glucose control is well tolerated and effective in patients receiving early parenteral nutrition when provided with a protocol that includes frequent, accurate glucose measurements and avoids large glucose fluctuations. All patient subgroups potentially benefit, with the possible exception of patients with poorly controlled diabetes, who may need less aggressive glucose control. It remains unclear whether tight glucose control is beneficial or not in the absence of early parenteral nutrition.