Space GlucoseControl system for blood glucose control in intensive care patients--a European multicentre observational study

The Quality and Utility of Artificial Intelligence in Patient Care

BACKGROUND

Artificial intelligence (AI) is increasingly being used in patient care. In the future, physicians will need to understand not only the basic functioning of AI applications, but also their quality, utility, and risks.

METHODS

This article is based on a selective review of the literature on the principles, quality, limitations, and benefits AI applications in patient care, along with examples of individual applications.

RESULTS

The number of AI applications in patient care is rising, with more than 500 approvals in the United States to date. Their quality and utility are based on a number of interdependent factors, including the real-life setting, the type and amount of data collected, the choice of variables used by the application, the algorithms used, and the goal and implementation of each application. Bias (which may be hidden) and errors can arise at all these levels. Any evaluation of the quality and utility of an AI application must, therefore, be conducted according to the scientific principles of evidence-based medicine-a requirement that is often hampered by a lack of transparency.

CONCLUSION

AI has the potential to improve patient care while meeting the challenge of dealing with an ever-increasing surfeit of information and data in medicine with limited human resources. The limitations and risks of AI applications require critical and responsible consideration. This can best be achieved through a combination of scientific.

Glucometrics in the first week of critical illness and its association with mortality

OBJECTIVE

Evaluation of glucometrics in the first week of ICU stay and its association with outcomes.

DESIGN

Prospective observational study.

SETTING

Mixed ICU of teaching hospital.

PATIENTS

Adults initiated on insulin infusion for 2 consecutive blood glucose (BG) readings ≥180mg/dL.

MAIN VARIABLES OF INTEREST

Glucometrics calculated from the BG of first week of admission: hyperglycemia (BG>180mg/dL) and hypoglycemia (BG<70mg/dL) episodes; median, standard deviation (SD) and coefficient of variation (CV) of BG, glycemic lability index (GLI), time in target BG range (TIR). Factors influencing glucometrics and the association of glucometrics to patient outcomes analyzed.

RESULTS

A total of 5762 BG measurements in 100 patients of median age 55 years included. Glucometrics: hyperglycemia: 2253 (39%), hypoglycemia: 28 (0.48%), median BG: 169mg/dL (162-178.75), SD 31mg/dL (26-38.75), CV 18.6% (17.1-22.5), GLI: 718.5 [(mg/dL)²/h]/week (540.5-1131.5) and TIR 57% (50-67). Diabetes and higher APACHE II score were associated with higher SD and CV, and lower TIR. On multivariate regression, diabetes (p=0.009) and APACHE II score (p=0.016) were independently associated with higher SD. Higher SD and CV were associated with less vasopressor-free days; lower TIR with more blood-stream infections (BSI). Patients with higher SD, CV and GLI had a higher 28-day mortality. On multivariate analysis, GLI alone was associated with a higher mortality (OR 2.99, p=0.04).

CONCLUSIONS

Glycemic lability in the first week in ICU patients receiving insulin infusion is associated with higher mortality. Lower TIR is associated with more blood stream infections.

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.

Effect of Treatment and Nutrition on Glycemic Variability in Critically Ill Patients

Nondiabetic hyperglycemia is a dangerous metabolic phenomenon in the intensive care unit. Inattentive treatment of glycemic disorders is a serious health hazard promoting negative outcomes. The aim of our study was to assess glycemic variability and its basic determinants, and to verify its relationship with mortality in patients hospitalized in a mixed ICU (intensive care unit). The medical records of 37 patients hospitalized 13 January−29 February 2020 were analyzed prospectively. The BG (blood glucose) variability during the stay was assessed using two definitions, i.e., the value of standard deviation (SD) from all the measurements performed and the coefficient of variation (CV). A correlation between the BG variability and insulin dose was observed (SD: R = 0.559; p < 0.01; CV: R = 0.621; p < 0.01). There was also a correlation between the BG variability and the total energy daily dose (SD: R = 0.373; p = 0.02; CV: R = 0.364; p = 0.03). Glycemic variability was higher among patients to whom treatment with adrenalin (p = 0.0218) or steroid (p = 0.0292) was applied. The BG variability, expressed using SD, was associated with ICU mortality (ROC = 0.806; 95% CI: 0.643−0.917; p = 0.0014). The BG variability in the ICU setting arises from the loss of balance between the supplied energy and the applied insulin dose and may be associated with a worse prognosis.

Glycemic targets in critically ill adults: A mini-review

Illness-induced hyperglycemia impairs neutrophil function, increases pro-inflammatory cytokines, inhibits fibrinolysis, and promotes cellular damage. In turn, these mechanisms lead to pneumonia and surgical site infections, prolonged mechanical ventilation, prolonged hospitalization, and increased mortality. For optimal glucose control, blood glucose measurements need to be done accurately, frequently, and promptly. When choosing glycemic targets, one should keep the glycemic variability < 4 mmol/L and avoid targeting a lower limit of blood glucose < 4.4 mmol/L. The upper limit of blood glucose should be set according to casemix and the quality of glucose control. A lower glycemic target range (i.e., blood glucose 4.5-7.8 mmol/L) would be favored for patients without diabetes mellitus, with traumatic brain injury, or who are at risk of surgical site infection. To avoid harm from hypoglycemia, strict adherence to glycemic control protocols and timely glucose measurements are required. In contrast, a higher glycemic target range (i.e., blood glucose 7.8-10 mmol/L) would be favored as a default choice for medical-surgical patients and patients with diabetes mellitus. These targets may be modified if technical advances for blood glucose measurement and control can be achieved.

The development of a glucose prediction model in critically ill patients

PURPOSE

The aim of the current study is to develop a prediction model for glucose levels applicable for all patients admitted to the ICU with an expected ICU stay of at least 24 h. This model will be incorporated in a closed-loop glucose system to continuously and automatically control glucose values.

METHODS

Data from a previous single-center randomized controlled study was used. All patients received a FreeStyle Navigator II subcutaneous CGM system from Abbott during their ICU stay. The total dataset was randomly divided into a training set and a validation set. A glucose prediction model was developed based on historical glucose data. Accuracy of the prediction model was determined using the Mean Squared Difference (MSD), the Mean Absolute Difference (MAD) and a Clarke Error Grid (CEG).

RESULTS

The dataset included 94 ICU patients with a total of 134,673 glucose measurements points that were used for modelling. MSD was 0.410 ± 0.495 for the model, the MAD was 5.19 ± 2.63 and in the CEG 99.8% of the data points were in the clinically acceptable regions.

CONCLUSION

In this study a glucose prediction model for ICU patients is developed. This study shows that it is possible to accurately predict a patient's glucose 30 min ahead based on historical glucose data. This is the first step in the development of a closed-loop glucose system.

Effectiveness and safety of the Space GlucoseControl system for glycaemia control in caring for postoperative cardiac surgical patients

BACKGROUND

Hyperglycaemia is a very common complication in post-cardiac surgical patients, and as such, it must be properly managed. For this purpose, the enhanced Model Predictive Control algorithm for glycaemia control has been implemented into a nurse-led device called Space GlucoseControl (SGC) that aims to achieve a safe and effective blood glucose control in a better way than the traditional "paper-based" protocols.

PURPOSE

The aim of the study was to know the effectiveness and safety of the SGC in glycaemia control in cardiosurgical adult patients in the immediate postoperative period in the intensive care unit.

METHODS

A prospective before-and-after intervention study was conducted. One hundred sixty cardiosurgical adult patients with hyperglycaemia were selected: 80 in the control group from May to November 2018 and 80 in the intervention group (use of the SGC device) from January to December 2019. The primary outcome was the percentage of time within the target range (140-180 mg/dL in the control group and 100-160 mg/dL in the intervention group).

RESULTS

The percentage of time within the target range was significantly higher in the SGC group than in the control group (70.5% [58.25-80] vs 54.83% [36.09-75], p < 0.001). The range was also achieved earlier with the SGC (5 [3-6.875] hours vs 7 [4-11] hours; p < 0.05). The first blood glucose value after reaching the target range was higher in the control group, with statistical significance (p < 0.05). There were no hypoglycaemia episodes in the control group. However, during SGC treatment, six episodes of hypoglycaemia occurred, and all of them were nonsevere (mean value = 61 mg/dL).

CONCLUSION

The SGC is useful to achieve a faster tight glycaemic control, with a higher percentage of time within the target range, although episodes of nonsevere hypoglycaemia could be observed.

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.

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.

Usability study of a new tool for nutritional and glycemic management in adult intensive care: Glucosafe 2

The new decision support tool Glucosafe 2 (GS2) is based on a mathematical model of glucose and insulin dynamics, designed to assist caregivers in blood glucose control and nutrition. This study aims to assess end-user acceptance and usability of this bedside decision support tool in an adult intensive care setting. Caregivers were first trained and then invited to trial GS2 prototype on bedside computers. Data for qualitative analysis were collected through semi-structured interviews from twenty users after minimum three trial days. Most caregivers (70%) rated GS2 as convenient and believed it would help improving adherence to current guidelines (85%). Moreover, most nurses (80%) believed that GS2 would be timesaving. Nurses' risk perceptions and manual data entry emerged as central barriers to use GS2 in routine practice. Issues emerged from the caregivers were compiled into a list of 12 modifications of the GS2 prototype to increase end-user acceptance and usability. This usability study showed that GS2 was considered by ICU caregivers as helpful in daily clinical practice, allowing time-saving and better standardization of ICU patient's care. Important issues were raised by the users with implications for the development and deployment of GS2. Integrating the technology into existing IT infrastructure may facilitate caregivers' acceptance. Further clinical studies of the performance and potential health outcomes are warranted.

Factors influencing harmonized health data collection, sharing and linkage in Denmark and Switzerland: A systematic review

INTRODUCTION

The digitalization of medicine has led to a considerable growth of heterogeneous health datasets, which could improve healthcare research if integrated into the clinical life cycle. This process requires, amongst other things, the harmonization of these datasets, which is a prerequisite to improve their quality, re-usability and interoperability. However, there is a wide range of factors that either hinder or favor the harmonized collection, sharing and linkage of health data.

OBJECTIVE

This systematic review aims to identify barriers and facilitators to health data harmonization-including data sharing and linkage-by a comparative analysis of studies from Denmark and Switzerland.

METHODS

Publications from PubMed, Web of Science, EMBASE and CINAHL involving cross-institutional or cross-border collection, sharing or linkage of health data from Denmark or Switzerland were searched to identify the reported barriers and facilitators to data harmonization.

RESULTS

Of the 345 projects included, 240 were single-country and 105 were multinational studies. Regarding national projects, a Swiss study reported on average more barriers and facilitators than a Danish study. Barriers and facilitators of a technical nature were most frequently reported.

CONCLUSION

This systematic review gathered evidence from Denmark and Switzerland on barriers and facilitators concerning data harmonization, sharing and linkage. Barriers and facilitators were strictly interrelated with the national context where projects were carried out. Structural changes, such as legislation implemented at the national level, were mirrored in the projects. This underlines the impact of national strategies in the field of health data. Our findings also suggest that more openness and clarity in the reporting of both barriers and facilitators to data harmonization constitute a key element to promote the successful management of new projects using health data and the implementation of proper policies in this field. Our study findings are thus meaningful beyond these two countries.

Management of Central Nervous System Infections, Vientiane, Laos, 2003-2011

During 2003–2011, we recruited 1,065 patients of all ages admitted to Mahosot Hospital (Vientiane, Laos) with suspected central nervous system (CNS) infection. Etiologies were laboratory confirmed for 42.3% of patients, who mostly had infections with emerging pathogens: viruses in 16.2% (mainly Japanese encephalitis virus [8.8%]); bacteria in 16.4% (including Orientia tsutsugamushi [2.9%], Leptospira spp. [2.3%], and Rickettsia spp. [2.3%]); and Cryptococcus spp. fungi in 6.6%. We observed no significant differences in distribution of clinical encephalitis and meningitis by bacterial or viral etiology. However, patients with bacterial CNS infection were more likely to have a history of diabetes than others. Death (26.3%) was associated with low Glasgow Coma Scale score, and the mortality rate was higher for patients with bacterial than viral infections. No clinical or laboratory variables could guide antibiotic selection. We conclude that high-dependency units and first-line treatment with ceftriaxone and doxycycline for suspected CNS infections could improve patient survival in Laos.

Feasibility of Blood Glucose Management Using Intra-Arterial Glucose Monitoring in Combination with an Automated Insulin Titration Algorithm in Critically Ill Patients

Background: This two-center pilot study combined for the first time an intra-arterial glucose sensor with a decision support system for insulin dosing (SGCplus system) in critically ill patients with hyperglycemia. Methods: Twenty-two patients who were equipped with an arterial line and required iv insulin therapy were managed by the SGCplus system during their medical treatment at the intensive care unit. Results: Time to target was 111 ± 195 min (80-150 mg/dL) and 135 ± 267 min (100-160 mg/dL) in the lower and higher glucose target group. Mean blood glucose (BG) was 142 ± 32 mg/dL with seven BG values <70 mg/dL. Mean daily insulin dose was 62 ± 38 U and mean daily carbohydrate intake 148 ± 50 g/day (enteral nutrition) and 102 ± 58 g/day (parenteral nutrition). Acceptance of SGCplus suggestions was high (93%). Conclusions: The SGCplus system can be safely applied in critically ill patients with hyperglycemia and enables good glycemic control.

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

There is considerable physiological and clinical evidence of harm and increased risk of death associated with dysglycemia in critical care. However, glycemic control (GC) currently leads to increased hypoglycemia, independently associated with a greater risk of death. Indeed, recent evidence suggests GC is difficult to safely and effectively achieve for all patients. In this review, leading experts in the field discuss this evidence and relevant data in diabetology, including the artificial pancreas, and suggest how safe, effective GC can be achieved in critically ill patients in ways seeking to mimic normal islet cell function. The review is structured around the specific clinical hurdles of: understanding the patient’s metabolic state; designing GC to fit clinical practice, safety, efficacy, and workload; and the need for standardized metrics. These aspects are addressed by reviewing relevant recent advances in science and technology. Finally, we provide a set of concise recommendations to advance the safety, quality, consistency, and clinical uptake of GC in critical care. This review thus presents a roadmap toward better, more personalized metabolic care and improved patient outcomes.

Next-generation, personalised, model-based critical care medicine: a state-of-the art review of in silico virtual patient models, methods, and cohorts, and how to validation them

Critical care, like many healthcare areas, is under a dual assault from significantly increasing demographic and economic pressures. Intensive care unit (ICU) patients are highly variable in response to treatment, and increasingly aging populations mean ICUs are under increasing demand and their cohorts are increasingly ill. Equally, patient expectations are growing, while the economic ability to deliver care to all is declining. Better, more productive care is thus the big challenge. One means to that end is personalised care designed to manage the significant inter- and intra-patient variability that makes the ICU patient difficult. Thus, moving from current "one size fits all" protocolised care to adaptive, model-based "one method fits all" personalised care could deliver the required step change in the quality, and simultaneously the productivity and cost, of care. Computer models of human physiology are a unique tool to personalise care, as they can couple clinical data with mathematical methods to create subject-specific models and virtual patients to design new, personalised and more optimal protocols, as well as to guide care in real-time. They rely on identifying time varying patient-specific parameters in the model that capture inter- and intra-patient variability, the difference between patients and the evolution of patient condition. Properly validated, virtual patients represent the real patients, and can be used in silico to test different protocols or interventions, or in real-time to guide care. Hence, the underlying models and methods create the foundation for next generation care, as well as a tool for safely and rapidly developing personalised treatment protocols over large virtual cohorts using virtual trials. This review examines the models and methods used to create virtual patients. Specifically, it presents the models types and structures used and the data required. It then covers how to validate the resulting virtual patients and trials, and how these virtual trials can help design and optimise clinical trial. Links between these models and higher order, more complex physiome models are also discussed. In each section, it explores the progress reported up to date, especially on core ICU therapies in glycemic, circulatory and mechanical ventilation management, where high cost and frequency of occurrence provide a significant opportunity for model-based methods to have measurable clinical and economic impact. The outcomes are readily generalised to other areas of medical care.

Impact of a Paper-Based Dynamic Insulin Infusion Protocol on Glycemic Variability, Time in Target, and Hypoglycemic Risk: A Stepped Wedge Trial in Medical Intensive Care Unit Patients

BACKGROUND

Stress-induced hyperglycemia is a common feature of intensive care unit (ICU) patients. Besides mean blood glucose (BG) level, glucose variability and hypoglycemia have been highlighted as independent predictors of ICU and hospital mortality. Recent ICU recommendations suggest using insulin infusion protocols that can minimize glucose variability and hypoglycemic risk. Our aim was to assess the efficacy, safety, and acceptance by nurses of a paper-based simple dynamic insulin protocol compared with those by nurses of a paper-based static protocol.

METHODS

This is a 1 year stepped-wedge study that compared a static sliding scale protocol (SP - static protocol) with a validated dynamic paper-based intravenous insulin infusion protocol (DP - dynamic protocol) in medical ICU patients of a single university hospital. Patients with stress-induced hyperglycemia >9.9 mmol/L and ≥48 h intravenous insulin infusion were included in this trial.

RESULTS

One hundred thirty-one patients were included and received continuous intravenous insulin infusion managed with SP (n = 65) or DP (n = 66). Glucose variability was significantly higher in the SP group than in the DP group (mean average glucose excursion index: 0.90 [0.00-1.91] mmol/L vs. 0.00 [0.00-0.90] mmol/L, respectively; P = 0.001). The percentage of time spent in the target range (7.7-9.9 mmol/L) was lower in the SP group than in the DP group (42.5% [28.8%-54.2%] vs. 47.5% [36.6%-57.1%]; P = 0.037). Low BG (<4.4 mmol/L) and hypoglycemia (<3.3 mmol/L) were more frequent in the SP group than in the DP group. According to a satisfaction survey, this protocol was well accepted by nurses.

CONCLUSIONS

Our simple and feasible paper-based, dynamic insulin infusion protocol reduced glycemic variability and hypoglycemic risk in a medical ICU.