Abstract Intensive insulin therapy (IIT) and tight glycaemic control (TGC), particularly in intensive care unit (ICU), are the subjects of increasing and controversial debate in recent years. ...Model-based TGC has shown potential in delivering safe and tight glycaemic management, all the while limiting hypoglycaemia. A comprehensive, more physiologically relevant Intensive Control Insulin-Nutrition-Glucose (ICING) model is presented and validated using data from critically ill patients. Two existing glucose–insulin models are reviewed and formed the basis for the ICING model. Model limitations are discussed with respect to relevant physiology, pharmacodynamics and TGC practicality. Model identifiability issues are carefully considered for clinical settings. This article also contains significant reference to relevant physiology and clinical literature, as well as some references to the modeling efforts in this field. Identification of critical constant population parameters was performed in two stages, thus addressing model identifiability issues. Model predictive performance is the primary factor for optimizing population parameter values. The use of population values are necessary due to the limited clinical data available at the bedside in the clinical control scenario. Insulin sensitivity, S I , the only dynamic, time-varying parameter, is identified hourly for each individual. All population parameters are justified physiologically and with respect to values reported in the clinical literature. A parameter sensitivity study confirms the validity of limiting time-varying parameters to S I only, as well as the choices for the population parameters. The ICING model achieves median fitting error of <1% over data from 173 patients ( N = 42,941 h in total) who received insulin while in the ICU and stayed for ≥72 h. Most importantly, the median per-patient 1-h ahead prediction error is a very low 2.80% IQR 1.18, 6.41%. It is significant that the 75th percentile prediction error is within the lower bound of typical glucometer measurement errors of 7–12%. These results confirm that the ICING model is suitable for developing model-based insulin therapies, and capable of delivering real-time model-based TGC with a very tight prediction error range. Finally, the detailed examination and discussion of issues surrounding model-based TGC and existing glucose–insulin models render this article a mini-review of the state of model-based TGC in critical care.
Abstract Sepsis occurs frequently in the intensive care unit (ICU) and is a leading cause of admission, mortality, and cost. Treatment guidelines recommend early intervention, however positive blood ...culture results may take up to 48 h. Insulin sensitivity ( S I ) is known to decrease with worsening condition and could thus be used to aid diagnosis. Some glycemic control protocols are able to accurately identify insulin sensitivity in real-time. Hourly model-based insulin sensitivity S I values were calculated from glycemic control data of 36 patients with sepsis. The hourly S I is compared to the hourly sepsis score (ss) for these patients (ss = 0–4 for increasing severity). A multivariate clinical biomarker was also developed to maximize the discrimination between different ss groups. Receiver operator characteristic (ROC) curves for severe sepsis (ss ≥ 2) are created for both S I and the multivariate clinical biomarker. Insulin sensitivity as a sepsis biomarker for diagnosis of severe sepsis achieves a 50% sensitivity, 76% specificity, 4.8% positive predictive value (PPV), and 98.3% negative predictive value (NPV) at an S I cut-off value of 0.00013 L/mU/min. Multivariate clinical biomarker combining S I , temperature, heart rate, respiratory rate, blood pressure, and their respective hourly rates of change achieves 73% sensitivity, 80% specificity, 8.4% PPV, and 99.2% NPV. Thus, the multivariate clinical biomarker provides an effective real-time negative predictive diagnostic for severe sepsis. Examination of both inter- and intra-patient statistical distribution of this biomarker and sepsis score shows potential avenues to improve the positive predictive value.
•Investigated the relationship between model-based insulin sensitivity and sepsis score.•The modified hourly sepsis score shows better relation with insulin sensitivity.•Insulin sensitivity is more ...significant when comparing hourly sepsis score at a very distinguish level.•Rising insulin sensitivity is a marker of improving condition from sepsis and vice versa.
Sepsis is highly correlated with mortality and morbidity. Sepsis is a clinical condition demarcated as the existence of infection and systemic inflammatory response syndrome, SIRS. Confirmation of infection requires a blood culture test, which requires incubation, and thus results take at least 48h for a syndrome that requires early direct treatment. Since sepsis has a strong inflammatory component, it is hypothesized that metabolic markers affected by inflammation, such as insulin sensitivity, might provide a metric for more rapid, real-time diagnosis. This study uses clinical data from 30 sepsis patients (7624h in ICU) of whom 60% are male. Median age and median Apache II score are 63 years and 19, respectively. Model-identified insulin sensitivity (SI) profiles were obtained for each patient, and insulin sensitivity and its hourly changes were correlated with modified hourly sepsis scores (SSH1). SI profiles and values were similar across the cohort. The sepsis score is highly variable and changes rapidly. The modified hourly sepsis score, SSH1, shows a better relation with insulin sensitivity due to less fluctuation in the SIRS element. Median SI and median ΔSI of the cohort is 0.4193e-3 and 0.004253e-3L/mU.min, respectively. Additionally, median SI are 4.392×10−4L/mUmin (SSH1=0), 4.153×10−4L/mUmin (SSH1=1), 3.752×10−4L/mUmin (SSH1=2) and 2.353×10−4L/mUmin (SSH1=3). Significant relationship between insulin sensitivity across different SSH1 groups was observed (p<0.05) even when corrected for multiple comparisons. CDF of SI indicates that insulin sensitivity is more significant when comparing an hourly sepsis score at a very distinguished level.
The robustness of a model-based control protocol as a less intensive TGC protocol using insulin Glargine for provision of basal insulin is simulated in this study. To quantify the performance and ...robustness of the protocol to errors, namely physiological variability and sensor errors, an in-silico Monte Carlo analysis is performed. Actual patient data from Christchurch Hospital, New Zealand were used as virtual trial patients.
Insulin resistance and sensitivity variabilities exacerbated diabetes mellitus (DM) and non-diabetes mellitus (NDM) patients’ conditions in the intensive care unit (ICU). This problem has been ...affiliated with glycaemic control performance and external errors, thus, influencing the blood glucose (BG) monitoring in those patients. A model-based glycaemic control was proposed as it offers a non-invasive observation of DM patients’ insulin sensitivity (SI) in the ICU. This model-based glycaemic control used the Intensive Care Insulin Nutrition Glucose (ICING) model that combines stochastic targeted (STAR) protocol which was developed in Christchurch enabling the estimation of SI. However, lower SI in Malaysian cohorts has led to ICING model enhancement, giving better SI estimation to represent each critically ill DM and NDM patient's metabolic parameter. To identify the enhanced ICING model robustness, BG sensitivity error was added with 5% ±1 of noise error then simulated 100 times with Monte Carlo simulations. A total of 131 patients (170 DM and 101 NDM episodes) from the STAR trial in a general ICU was simulated producing 17000 and 10100 Monte Carlo simulations. The Monte Carlo analysis results showed with model enhancement, the model-based glycaemic control for Malaysian DM and NDM is robust and most importantly safe to be used with less than 0.1% of mild and severe hypoglycaemias. The median BG level, the % BG 6.0 – 10.0 mmol/L with and without Monte Carlo for DM and NDM cohort were in the target. In conclusion, through this validation, the enhanced ICING model is robust, optimised and safe to be used for glycaemic control within the DM and NDM in Malaysian ICUs.
Many critically ill patients are benefiting from extensive research done in tight glucose control (TGC) within the ICU. But moderate to high levels of hyperglycaemia are still tolerated within high ...dependency (HDU) and surgical units. The use and benefits of insulin protocols within these units have not yet been addressed in the literature. The management of tight glycaemic control still remains under the influence of ineffective standards characterized by tolerance for hyperglycaemia and a reluctance to use insulin intensively.
A validated Glargine and intravenous insulin-glucose pharmacodynamic model are presented. Virtual trial results on 16 stable ICU patients showed that Glargine can provide effective blood glucose management for these long term recovering patients. An initial intravenous injection and higher Glargine dosing is required for the first day to quickly lower elevated blood glucose levels. However, once patient's blood glucose levels are within a desirable range, Glargine alone can provide effective glycaemic management, thus reducing nursing effort. Median blood glucose for the entire cohort when simulated with the combination of Glargine and an intravenous insulin injection is 6.5 with interquartile range of 5.6, 7.5. The 90% confidence interval is 4.6, 9.7 with no occurrence of hypoglycaemia. This in silico study provides a first virtual trial analysis of the in-hospital transition between intravenous and subcutaneous insulin for TGC.
Sepsis and hyperglycemia are highly associated with increases in mortality rates, particularly in the critically ill patients. Sepsis diagnosis has been proven challenging due to delay in getting the ...blood culture results. Thus, often clinical experiences overrule the protocol to prevent the worsening outcome of the patients. In some cases, the erroneous clinical judgement cause antibiotic resistance and even adverse clinical outcomes. This paper investigates the correlation between two parameters; insulin sensitivity and blood glucose level among sepsis patients. The blood glucose level is measured at the bedside during the patient's stay, whereas insulin sensitivity is obtained using the validated glucose-insulin model. Thus, the insulin sensitivity is a specific parameter of the patient, unregimented of the protocol given to the patient. The same parameters, blood glucose and insulin sensitivity, are also compared to the non-sepsis patients to establish a relationship that can be used for sepsis diagnosis. Given the availability of these two parameters that can be captured rapidly and instantly, a significant relationship can, therefore, help clinicians to identify sepsis at an early stage without second-guessing.
Tight glycaemic control is now benefiting medical and surgical intensive care patients by reducing complications associated with hyperglycaemia. Once patients leave this intensive care environment, ...less acute wards do not continue to provide the same level of glycaemic control. Main reason is that these less acute wards do not have the high levels of nursing resources to provide the same level of glycaemic control. Therefore developments in protocols that are less labour intensive are necessary. This study examines the use of insulin glargine for basal supplement in recovering critically ill patients. These patients represent a group who may benefit from such basal support therapy. In silico study results showed the potential in reducing nursing effort with the use of glargine. However, a protocol using only glargine for glucose control did not show to be effective in the simulated patients. This may be an indication that a protocol using only glargine is more suitable after discharge from critical care.
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