Insulin Sensitivity and Sepsis Score: A Correlation between Model-based Metric and Sepsis Scoring System in Critically Ill Patients

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 48 h 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 (7624 h 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 SI of the cohort is 0.4193e-3 and 0.004253e-3 L/mU.min, respectively. Additionally, median SI are 4.392 × 10−4 L/mU min (SSH1 = 0), 4.153 × 10−4 L/mU min (SSH1 = 1), 3.752 × 10−4 L/mU min (SSH1 = 2) and 2.353 × 10−4 L/mU min (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

Efficacy and safety of SPRINT and STAR protocol on Malaysian critically-ill patients

Abstract—Intensive care unit patients may have a better glycaemic management with the right control protocol. Results of virtual trial performance on Malaysian critically-ill patients adopting a model-derived and model-based control protocol known as SPRINT and STAR are presented in this paper. These ICU patients have been treated by intensive sliding-scale insulin infusion. The effectiveness and safety of glycaemic control are then analysed. Results showed that patient safety improved by 83% with SPRINT and STAR protocol as the number of hypoglycaemic patients significantly reduced (BG<2.2 mmol/L). Percentage of time within desired bands and median BG improves in both SPRINT and STAR. However the improvements are associated with higher number of BG measurements (workload)

Validation of a model-based virtual trials method for tight glycemic control in intensive care

peer reviewedBACKGROUND: In-silico virtual patients and trials offer significant advantages in cost, time and safety for designing effective tight glycemic control (TGC) protocols. However, no such method has fully validated the independence of virtual patients (or resulting clinical trial predictions) from the data used to create them. This study uses matched cohorts from a TGC clinical trial to validate virtual patients and in-silico virtual trial models and methods. METHODS: Data from a 211 patient subset of the Glucontrol trial in Liege, Belgium. Glucontrol-A (N = 142) targeted 4.4-6.1 mmol/L and Glucontrol-B (N = 69) targeted 7.8-10.0 mmol/L. Cohorts were matched by APACHE II score, initial BG, age, weight, BMI and sex (p > 0.25). Virtual patients are created by fitting a clinically validated model to clinical data, yielding time varying insulin sensitivity profiles (SI(t)) that drives in-silico patients.Model fit and intra-patient (forward) prediction errors are used to validate individual in-silico virtual patients. Self-validation (tests A protocol on Group-A virtual patients; and B protocol on B virtual patients) and cross-validation (tests A protocol on Group-B virtual patients; and B protocol on A virtual patients) are used in comparison to clinical data to assess ability to predict clinical trial results. RESULTS: Model fit errors were small (<0.25%) for all patients, indicating model fitness. Median forward prediction errors were: 4.3, 2.8 and 3.5% for Group-A, Group-B and Overall (A+B), indicating individual virtual patients were accurate representations of real patients. SI and its variability were similar between cohorts indicating they were metabolically similar.Self and cross validation results were within 1-10% of the clinical data for both Group-A and Group-B. Self-validation indicated clinically insignificant errors due to model and/or clinical compliance. Cross-validation clearly showed that virtual patients enabled by identified patient-specific SI(t) profiles can accurately predict the performance of independent and different TGC protocols. CONCLUSIONS: This study fully validates these virtual patients and in silico virtual trial methods, and clearly shows they can accurately simulate, in advance, the clinical results of a TGC protocol, enabling rapid in silico protocol design and optimization. These outcomes provide the first rigorous validation of a virtual in-silico patient and virtual trials methodology

Organ failure and tight glycemic control in the SPRINT study

INTRODUCTION: Intensive care unit mortality is strongly associated with organ failure rate and severity. The sequential organ failure assessment (SOFA) score is used to evaluate the impact of a successful tight glycemic control (TGC) intervention (SPRINT) on organ failure, morbidity, and thus mortality. METHODS: A retrospective analysis of 371 patients (3,356 days) on SPRINT (August 2005 - April 2007) and 413 retrospective patients (3,211 days) from two years prior, matched by Acute Physiology and Chronic Health Evaluation (APACHE) III. SOFA is calculated daily for each patient. The effect of the SPRINT TGC intervention is assessed by comparing the percentage of patients with SOFA 2) are also compared. Cumulative time in 4.0 to 7.0 mmol/L band (cTIB) was evaluated daily to link tightness and consistency of TGC (cTIB >/=0.5) to SOFA /=0.5 (37% Pre-SPRINT) reaching 100% by Day 7 (50% Pre-SPRINT). Conditional and joint probabilities indicate tighter, more consistent TGC under SPRINT (cTIB >/=0.5) increased the likelihood SOFA /=0.5 metric provides a first benchmark linking TGC quality to organ failure. These results support other physiological and clinical results indicating the role tight, consistent TGC can play in reducing organ failure, morbidity and mortality, and should be validated on data from randomised trials

Performance of STAR virtual trials for diabetic and non-diabetic in HTAA Intensive Care Unit

Critically ill patients are commonly linked to stress-induced hyperglycaemia which relates to insulin resistance and the risk of per-diagnosed with diabetes and other metabolic illnesses. Thus, it is essential to choose the best practice of blood glucose management in order to reduce morbidity and mortality rates in intensive care unit. This study is focusing on clinical data of 210 critically ill patients in Hospital Tengku Ampuan Afzan (HTAA), Kuantan who underwent Intensive Insulin Therapy which utilized a sliding scale method. Patients were identified in two main groups of diabetic (123) and non-diabetic (87) where stochastic model is generated to observe 90% confidence interval of insulin sensitivity. Blood glucose levels comparison between these two cohorts is conducted to observe the percentage of blood glucose levels within targeted band of 4.4 – 10.0 mmol/L. It is found that 82% of BG levels are within targated band for non-diabetes cohort under stochastic targeted (STAR) glycaemic control protocol. However, only 59.6% and 70.6% BG levels are within targeted band for diabetes cohort for insulin infusion therapy used in HTAA and STAR protocols. Thus, further investigation on blood glucose control protocol for diabetes patients is required to increase the reliability and efficacy of current practice despite of patient safety

Pilot proof of concept clinical trials of Stochastic Targeted (STAR) glycemic control

ABSTRACT: INTRODUCTION: Tight glycemic control (TGC) has shown benefits but has been difficult to achieve consistently. STAR (Stochastic TARgeted) is a flexible, model-based TGC approach directly accounting for intra- and inter- patient variability with a stochastically derived maximum 5% risk of blood glucose (BG) /=3 days. Written informed consent was obtained for all patients, and approval was granted by the NZ Upper South A Regional Ethics Committee. RESULTS: A total of 402 measurements were taken over 660 hours (~14/day), because nurses showed a preference for 2-hourly measurements. Median [interquartile range, (IQR)] cohort BG was 5.9 mmol/L [5.2-6.8]. Overall, 63.2%, 75.9%, and 89.8% of measurements were in the 4.0-6.5, 4.0-7.0, and 4.0-8.0 mmol/L bands. There were no hypoglycemic events (BG < 2.2 mmol/L), and the minimum BG was 3.5 mmol/L with 4.5% < 4.4 mmol/L. Per patient, the median [IQR] hours of TGC was 92 h [29-113] using 53 [19-62] measurements (median, ~13/day). Median [IQR] results: BG, 5.9 mmol/L [5.8-6.3]; carbohydrate nutrition, 6.8 g/h [5.5-8.7] (~70% goal feed median); insulin, 2.5 U/h [0.1-5.1]. All patients achieved BG < 6.1 mmol/L. These results match or exceed SPRINT and clinical workload is reduced more than 20%. CONCLUSIONS: STAR TGC modulating insulin and nutrition inputs provided very tight control with minimal variability by managing intra- and inter- patient variability. Performance and safety exceed that of SPRINT, which reduced mortality and cost in the Christchurch ICU. The use of glucometers did not appear to impact the quality of TGC. Finally, clinical workload was self-managed and reduced 20% compared with SPRINT

Insulin Sensitivity, Its Variability and Glycemic Outcome: A model-based analysis of the difficulty in achieving tight glycemic control in critical care

peer reviewedEffective tight glycemic control (TGC) can improve outcomes in intensive care unit (ICU) patients, but is difficult to achieve consistently. Glycemic level and variability, particularly early in a patient’s stay, are a function of variability in insulin sensitivity/resistance resulting from the level and evolution of stress response, and are independently associated with mortality. This study examines the daily evolution of variability of insulin sensitivity in ICU patients using patient data (N = 394 patients, 54019 hours) from the SPRINT TGC study. Model-based insulin sensitivity (SI) was identified each hour and hour-to-hour percent changes in SI were assessed for Days 1-3 individually and Day 4 Onward, as well as over all days. Cumulative distribution functions (CDFs), median values, and inter-quartile points (25th and 75th percentiles) are used to assess differences between groups and their evolution over time. Compared to the overall (all days) distributions, ICU patients are more variable on Days 1 and 2 (p < 0.0001), and less variable on Days 4 Onward (p < 0.0001). Day 3 is similar to the overall cohort (p = 0.74). Absolute values of SI start lower and rise for Days 1 and 2, compared to the overall cohort (all days), (p < 0.0001), are similar on Day 3 (p = .72) and are higher on Days 4 Onward (p < 0.0001). ICU patients have lower insulin sensitivity (greater insulin resistance) and it is more variable on Days 1 and 2, compared to an overall cohort on all days. This is the first such model-based analysis of its kind. Greater variability with lower SI early in a patient’s stay greatly increases the difficulty in achieving and safely maintaining glycemic control, reducing potential positive outcomes. Clinically, the results imply that TGC patients will require greater measurement frequency, reduced reliance on insulin, and more explicit specification of carbohydrate nutrition in Days 1-3 to safely minimise glycemic variability for best outcome

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

Author Correction: Nuclear-mitochondrial DNA segments resemble paternally inherited mitochondrial DNA in humans

An amendment to this paper has been published and can be accessed via a link at the top of the paper